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What’s the latest in development economics research? Microsummaries of 150+ papers from NEUDC 2018

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  • The saturation rate of communities with planting material for highly nutritious crop technologies (vitamin-A-rich orange sweet potato and high-iron biofortified beans) substantially increases the average probability of adopting the crops and increased spillovers to neighboring households by 16-19 percent. A treatment in which opinion leaders were invited to promote the technologies did not lead to no more diffusion of either technology. ( Baird, Gilligan, and McNiven ) #RCT
  • How much are women willing to sacrifice to avoid sexual harassment? In India (Delhi), “women  are  willing  to  choose  a  college  in  the  bottom  half  of  the  quality  distribution over  a  college  in  the  top  quintile  in  order  to  travel  by  a  route  that  is  perceived  to  be  one  standard deviation safer.” ( Borker )
  • Getting married one year later in India results in “a significant decline in physical violence, although it has no impact on sexual or emotional violence.” ( Dhamija & Roychowdhury )
  • The opening of all women police stations in India “increased reported crime against women by 22 percent. This is due to increases in reports of female kidnappings and domestic violence.” ( Amaral, Bhalotra, & Prakash )
  • A multi-year intervention that “engaged adolescents in classroom discussions about gender equality” improved gender attitudes and reported gender-equitable behavior (e.g., “boys report helping out more with household chores”). ( Dhar, Jain, & Jayachandran ) #RCT
  • Does your daughter think she’s bad at math? It’s probably because of her classmates’ parents. Evidence from China. ( Eble & Hu )
  • In recent years in Bangladesh, researchers find “a pro-female bias in enrollment decision but a pro-male bias in the decisions on the conditional expenditure and core share in education expenditure.” A program providing stipends to females helped with enrollment but didn’t overcome gender bias overall. ( Xu, Shonchoy, & Fujii )
  • Cash transfers in Kenya reduced physical violence against wives regardless of whether the husband or wife received them, but they reduced sexual violence against wives only when the wives received them. ( Haushofer et al. )
  • Participants in a lab experiment in Ethiopia “are ten percent less likely to follow the same advice from a female leader than an otherwise identical male leader, and female-led subjects perform .33 standard deviations worse as a result.” ( Ayalew, Manian, & Sheth )
  • Across 42 countries, what are the differences in infant feeding patterns by wealth, parental education, and community infrastructure? ( Choudhury, Headey, & Masters )
  • Teaching mothers how to improve children’s diets improved children’s diets in Ethiopia, but providing vouchers for them to afford more food did not. Combining the two was the most effective. ( Park, Han, & Kim ) #RCT
  • Give a multi-layered child nutrition program or its equivalent in cash? Mixed bag in Rwanda. But giving a lot more cash makes a real difference. ( McIntosh & Zeitlin ) #RCT
  • “Mothers who received free meals during primary school are less likely to have stunted children compared to mothers who did not receive free meals” in India. ( Chakrabarti et al. )
  • “Women exposed to cow slaughter bans” in India “in their year of birth have lower levels of hemoglobin (Hb) and are up to 10% more likely to be anemic in their prime reproductive ages between 15 and 35, particularly those who have not completed primary schooling or who come from poorer families.” ( Dasgupta, Majid, & Orman )
  • What’s the optimal level of health insurance subsidy? In Ghana, a one-time partial subsidy affects long-term health care service use more than a one-time full subsidy. This seems to be due to selection – sicker people opted into the partial subsidy program. ( Asuming, Kim, & Sim ) #RCT
  • Public anti-malaria investments in Senegal did not crowd out household investments in health. ( Rossi & Villar )
  • “Peers are… more effective than health workers in bringing in new suspects for testing” for tuberculosis in India. “Low-cost incentives of about $3.00 per referral considerably increase the probability that current patients make referrals.” ( Goldberg, Macis, & Chintagunta ) #RCT
  • The cost of low effort among clinicians in Nigeria is about US$350 million annually. Peer monitoring increases effort. ( Okeke )
  • A soda tax in Mexico increased gastrointestinal disease because of low-quality drinking water. ( Gutierrez & Rubli )
  • “Piped water at home reduces childhood” obesity in Morocco and the Philippines. ( Ritter )
  • Providing double-fortified salt to primary school children in India reduced anemia but didn’t affect test scores on average. However, for the kids who complied best with treatment, test scores did rise. ( Krämer, Kumar, & Vollmer )
  • In China, regulating salt to make sure it contained iodine resulted in higher test scores for girls but not for boys, reducing the math ability gap. ( Deng & Lindeboom )
  • Games in Kenya show that spouses don’t totally trust each other. Letting them communicate increase trust a bit. ( Castilla, Masuda, & Zhang ) #LabInField
  • When a carpet manufacturer offered jobs to women in India, their likelihood of taking the job was unchanged whether they received the offer directly or their husbands got the information to share with their wives. When couples discussed the opportunity together, women were less likely to take the job. ( Lowe & McKelway ) #RCT
  • Households differ in who makes decisions but also in why that person makes the decisions. Among farmers in Senegal, “households achieve greater milk production, higher hemoglobin levels among children, and more satisfaction with decisions when the most informed member or members of the household make the relevant decision.” ( Bernard et al. )
  • In Bangladesh, “women, children, and the elderly face significant probabilities of living in poverty even in households with per-capita expenditure above the poverty threshold.” ( Brown, Calvi, & Penglase )
  • “A drop of 1 percentage point in the earnings gap” between husbands and wives in Mexico led to “an increase in the divorce rate of 2 percent.” ( Davila )
  • Christian missionaries settled in healthier, safer and more developed locations in 43 sub-Saharan African countries (early 20 th century) and in Ghana (18 th -20 th century) – this endogeneity led to an overly optimistic account of the importance of colonial missions for long-term development. ( Jedwab, Meier zu Selhausen, and Moradi ) #RDD
  • Greater suitability for opium cultivation in India under British Rule is associated with lower present-day literacy outcomes and a lower rate of public good provision. In opium-growing districts, the Colonial administration spent less on education and health, while spending more on police forces. ( Lehne ) #RDD
  • The United Fruit Company (UFCo), active in Costa Rica from 1889 to 1984, had positive, large and persistent effects even after it stopped production: households in former UFCo areas have better housing, sanitation, education, and consumption capacity. UFCo invested in physical and human capital, such as sanitary and health programs, housing for its employees, and vocational training. ( Méndez-Chacón and Van Patten ) #RDD
  • In locations where plantation estates were ruled by private, foreign enterprises during the Dutch colonial period in Java (Indonesia) weaker economic outcomes and institutions persist to this day. ( Fetzer and Mukherjee )
  • A novel index of ethnic segregation – taking into account both ethnic and spatial distances between individuals and computed for 159 countries - reveals that countries where ethnically diverse individuals lived far apart, have higher-quality government, higher incomes and higher levels of trust. ( Hodler, Valsecchi, and Vesperoni )
  • Information can break the political resource curse: Giving information - related to a recent discovery of natural gas in Mozambique - only to community leaders increases elite capture and rent-seeking, while information targeted at the general population increases mobilization, trust, demand for political accountability and decreases conflict.  ( Armand et al.)  
  • Group size of minorities has no relation with its representation in national government under proportional electoral systems, while it shows an inverted-U shaped relationship in majoritarian electoral systems (i.e., if “too small” or “too large” they suffer a disadvantage against the majority group) based on 421 ethno-country minority groups across 92 democracies spanning the period 1946–2013. ( Chaturvedi and Das )
  • The majority of citizens in Bangladesh prefer taking common decisions via democratic and inclusive institutions, and these positive evaluations of participatory governance are reinforced by the exposure to a Community-Driven Development program. ( Cocciolo ) #LabInField
  • Rewarding politicians by making their political effort more visible to citizens - either through public recognition or by increasing their access to public funds - improves citizens’ wellbeing in south Indian state Tamil Nadu.  ( Mansuri et al. )
  • Caste quotas lead to political candidates with lower wealth, lower criminal records, but similar education levels. Quotas also increase women’s representation in politics. There is no difference in the level of public goods between quota-bound and non-quota-bound areas. ( Jogani ) #RDD
  • Presence of political opposition in the city council improves mayors’ performance in Brazil: it increases legislative oversight, reduces corruption, increases the probability that a physician will be present at the local health clinic, and decreases the infant mortality rate by 3.4 per 1000 births for uneducated mothers. ( Poulsen and Varjão ) #RDD
  • Registered citizens in Tanzania are more likely to work in the formal economic sector, have higher education, bank accounts, and pay taxes. ( Bowles ) #IV
  • A land certification program in Zambia improved perceptions of tenure security, but it had no impact on investment. ( Huntington and Shenoy ) #RCT
  • Improved schools increased satisfaction with government’s education policy, voter registration and vote share for incumbent representatives in Liberia. Electoral gains were concentrated in places where test score gains were largest, suggesting that voters perceive and reward school quality. ( Romero, Sandefur, and Sandholtz )
  • An alcohol ban led to an increase in crime in the Indian State of Bihar. Since state capacity and supply of police is fixed, diverting law enforcement resources towards implementing the alcohol ban effectively reduces capacity to prevent crimes. ( Dar and Sahay )
  • But wait! Alcohol regulation policies in the Indian State of Bihar led to a 0.21 standard deviation reduction in the incidence of violent crimes but had no significant impact on non-violent crimes. ( Chaudhuri et al. )
  • Brazil’s 2007 voter re-registration reform, intended to curb voter-buying, increased political competition and healthcare expenditures, which in turn led to better health outcomes: a 6.6 percent increase in prenatal visits, a 15 percent decrease in the incidence of low birthweight, and 5.3 percent reduction in the infant mortality rate.  ( Karim ) #RDD
  • Electing “parachuters” (those who have hereditary/dynastic background) leads to 0.2 percentage point lower GDP growth per year compared to constituencies where “climbers” are elected (those who have made their way up on their own). Impact is likely driven by misallocation of bureaucratic resources. ( Dar ) #RDD
  • Workers will privately accept jobs at a wage below the prevailing norm in India, but not when other workers can observe them making the choice. “Workers give up 38% of average weekly earnings in order to avoid being seen as breaking the social norm.” ( Breza, Kaur, & Krishnaswamy )
  • A youth training intervention subsidizing skills training and employment placement services in Nepal showed increased non-farm employment, hours worked and earnings one year after the program. The effects are mainly driven by women, who engage in non-farm self-employment activities carried out inside (but not outside) the house.  ( Chakravarty et al .) #RDD
  • The decline in Mexican net migration from 2006 to 2012 reduced employment for lower educated men and increased wages for higher education men and women. Informality does not change, and women switch from unpaid to salaried jobs (likely because of reduced remittances). ( Conover, Khamis, and Pearlman ) #IV
  • Fear of sexual assault reduces women’s labor market participation in India: a one standard deviation increase in sexual assault reports within one’s own district reduced women’s employment probability by 0.36 percentage points, especially among highly educated married urban women. There is no effect of lagged physical assault reports on employment outside home. ( Siddique )
  • Tax rate changes do not increase formal employment in Ghana, Rwanda, Tanzania and Uganda. ( McKay, Pirttilä, and Schimanski )
  • Risk averse children in Kenya are more likely to make an independent decision to work, as opposed to being sent by or negotiating with parents over the decision. This suggests a strategic decision by risk averse children who face a risky outside option in semi-nomadic pastoralism. ( Walker and Bartlett ) #LabInField
  • Effects of local labor demand shocks can differ significantly by gender. In 1991-2010 Brazil, male labor demand shocks, relative to equivalent female shocks, lead to larger increases in population (migration), own-gender wages, and the gender economic gap, particularly for those without high school education. ( Chauvin )
  • In the short run, job application workshops and transport subsidies increase the probability of finding employment for young job seekers in Addis Ababa, Ethiopia. The workshop also helped young people access stable jobs with an open-ended contract. Four years later, the workshop has a large and significant impact on earnings, while the effects of the subsidy have dissipated. ( Abebe et al. ) #RCT
  • Decision-making responsibilities shift towards women during the seasonal migration period in Bangladesh. Seasonal migration brings clear changes in some beliefs with respect to gender and income inequality, but no accompanying behavioral change. ( Mobarak, Reimão, and Shenoy ) #RCT
  • Migration generates bilateral cultural convergence even if migrants are excluded from the pool of respondents (hence eliminating social mixing). International migration appears as a stronger and more robust driver of cultural convergence than trade. ( Rapoport, Sardoschau, and Silve )
  • In the agrarian sector in the Philippines, self-selection effects accounts for 60% of the productivity difference between fixed wage and individual piece rate contracts. Social norms significantly alter the decision-making by workers: Guilt aversion and kinship taxation discourage workers to choose the remunerative option, whereas enviousness facilitates them to opt for it. ( Goto et al. )
  • Employment Protection Laws decreased employment of the disabled by 9 percentage points, particularly for women and employees. Employers reduce their demand for disabled labor to avoid the cost of workplace accommodations for disabled workers. ( Palmer and Williams )
  • What happens when a single sector is hit with a negative shock? The EU ban on black tiger shrimp pushed some workers out of the industry in Thailand, increasing incomes of those who stayed. But those who were pushed out also received a benefit in their children’s education. ( Banternghansa & Giannone )
  • “Countries’ dollar-denominated net external debt (dollar debt) helps explain the large differences in risk premia across currencies and how U.S. monetary policy affects the global economy.” ( Wiriadinata )
  • With novel data from Mexico, “larger firms (in terms of sales and employees) tend to use more interfirm trade credit relative to bank credit… These firms use interfirm trade credit as a mechanism to smooth variations in their prices. All else equal, firms with a higher trade-to-bank credit ratio tend to lower prices.” ( Shapiro et al. )
  • In China, the road network veers towards the birthplaces of top officials who were in power when it was built. ( Alder & Kondo )
  • When the poorest households in a cash transfer program in Kenya experience monetary penalties failing to comply with conditions, consumption drops significantly. Less poor households are better able to avoid getting fined in the future. ( Heinrich & Knowles )
  • A new model suggests the existence of a network-level poverty trap. “Transfer programs can be made more cost-effective by targeting communities at the threshold of the aggregate poverty trap.” Based on data in Bangladesh. ( Advani )
  • Not all marginalized groups in India are catching up! Mobility in India has remained the same overall since before the early 1990s, but in fact it has risen among some groups (the traditionally lowest castes) and fallen among others (Muslims). ( Asher, Novosad, & Rafkin )
  • Cash transfers in Indonesia decreased suicides by 18%. ( Christian, Hensel, & Roth )
  • How to incorporate ordinal measures (e.g., ranked positions rather than levels of income) into multidimensional poverty measurement. ( Seth & Yalonetsky )
  • Introducing formal insurance can crowd-out private redistributive transfers in Ethiopia’s rural communities. To donors, new information based on insurance decisions allows them to place recipients of private funds in a different light, and reduce their support. ( Anderberg and Morsink )
  • Increases in the generosity of in-kind food subsidies led to lower labor supply and higher wages, mostly in the low-skilled casual labor market in India. ( Shrinivas, Baylis, and Crost )
  • Insurance is an important factor in explaining effort supply and fertilizer use. Going from no sharing to full insurance, effort supply decreases by more than six times and fertilizer use drops by almost 50 percent in rural India. ( Pietrobon )
  • A large-scale HIV prevention program in public secondary schools in Malawi provided free circumcision and transport subsidies to clinics. Demand for circumcision increased in addition to positive peer effects among untreated students. In the long run, the preventive effect of circumcision is mitigated through risk compensation behavior in the group that got circumcised due to the intervention, but not for those induced by peer effects. ( Kim et al. ) #RCT
  • Access to a new financial product, offering guaranteed credit access after a shock, improves household welfare in Bangladesh through two channels: an ex-ante insurance effect where households increase investment in risky production and an ex-post effect where households are better able to maintain consumption and asset levels after a shock.  ( Lane ) #RCT
  • Without financial incentives such as discounts or rebates, farmers in Bangladesh do not use insurance to manage production risk during the monsoon season, even at actuarially-favorable prices. Purchasing insurance yields both ex ante risk management effects as well as ex post income effects on production practices ( Hill et al. ) #RCT
  • In India, the association between yield losses and rainfall index losses are stronger for large deviations. Therefore, demand for commercially priced rainfall insurance is more likely to be positive when coverage is restricted to extreme losses. ( Negi and Rawasmani )
  • A large-scale environmental disaster in 2016, when toxic industrial waste contaminated the marine ecosystem of Vietnam’s central coast, reduced fishing activities by 23 percent and fishermen’s income by 45 percent. ( Hoang et al. )
  • Do you want practical advice for your farm? Go to your local church or mosque! In Kenya, “shared attendance of two peers at” a religious institution “increases the likelihood of seeking out and receiving advice from their peer by 33 percentage points.” ( Murphy, Lee, & Nourani )
  • Based on a field experiment in Ethiopia, “conventional job referrals through social networks can reinforce labour market inequalities and prevent less socially connected individuals from getting access to jobs. However, when given referral opportunities, individuals can manage to escape exclusion.” ( Witte ) #RCT
  • Matching employers and employees using social networks can lead to bad matches, particularly among “less productive, poorer workers and firms” in Ethiopia. ( Matsuda & Nomura )
  • Business training for micro-entrepreneurs in Uganda rewires social networks, as entrepreneurs who don’t receive the training seek to network with trained peers. ( Stein )
  • When the social network is not completely informative, any self-report which is not supported by a third party must be discarded. ( Bloch and Olckers )
  • In Filipino villages with high social fragmentation, workers earn higher wages and occupations are disproportionately less likely to be dominated by a single social group. ( Caria and Labonne )
  • Households which experience climate shocks tend to invest more in family-caste (formal and informal) and vertical network relationships. Those networks bring benefits which are key to mitigating the impact of negative climate shocks. ( Ramsawak )
  • Income shocks facilitate altruistic giving that better targets the least well off within one’s network in Ghana. ( Barrett et al. )
  • What does major bridge construction do for economic activity (in Bangladesh)? In the formerly disconnected area, workers move from agriculture to services, population grows, and agricultural productivity rises. ( Blankespoor et al. )
  • Big, surprising oil and gas discoveries lead to lots of additional foreign direct investment (FDI). In Mozambique, each FDI job leads to between 4.4 and 6.5 additional other jobs. ( Toews & Vézina )
  • India’s Freight Equalization Scheme “contributed to the decline of industry in eastern India” but it took time. But repealing it reversed the decline, at least in some states. ( Firth & Liu )
  • In Brazil, trade with China reduced unemployment for areas exporting stuff and increased unemployment for areas importing stuff. ( Brummond & Connolly )
  • In Indonesia, “each percentage point of additional agriculture-driven poverty reduction also corresponds to around three percent of district area in forest loss since 2000.” ( Edwards )
  • New state-built formal housing on the outskirts of Addis Ababa, Ethiopia, is popular among slum dwellers, despite longer, and slightly more expensive commutes. Moving to formal housing on the outskirts of the city has no effects on labor supply and earnings. ( Franklin )
  • Public housing projects in South Africa decrease close-by formal residential home prices by 16 percent. While there is greater access to services and improved home quality within project areas, surrounding neighborhoods experience substantial growth in informal housing which exacerbates congestions and generate declines in formal home prices.  ( Bradlow, Polloni, and Violette ) #DiD
  • Air pollution substantially lowers productivity among industries with labor intensive technology while industries that rely less on labor inputs are less affected. ( Hansen-Lewis ) #IV
  • The TransJakarta Bus Rapid Transit (BRT) system, a public transport initiative designed to improve mobility for the greater Jakarta metropolitan area, did not increase transit ridership and exacerbated congestion on the routes it served, leading to increased travel times for other modes. Motorcycle vehicle ownership increased substantially. ( Gaduh, Gračner, and Rothenberg )
  • Two light-touch psychological interventions – one that helped women improve planning and another that helped reduce impatience – both had impacts on sanitation behavior and health outcomes after ten weeks in Kenya. ( Haushofer, John, & Orkin ) #RCT
  • After training street food vendors in India on food safety, vendors knew a lot more but didn’t change what they actually did. ( Daniele, Mookerjee, & Tommasi ) #RCT
  • Providing subsidies lead more households to purchase latrines in India. “A household becomes more likely to invest if a larger fraction of its community are also offered a subsidy.” ( Guiteras, Levinsohn, & Mobarak ) #RCT
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    Digital economy, green dual innovation and carbon emissions, 1. introduction, 2. theoretical framework and hypotheses, 2.1. digital economy and carbon emissions, 2.2. the mediating role of green dual innovation, 2.3. the moderating role of social concerns, 3. empirical analysis of the impact of the digital economy on carbon emissions, 3.1. model construction, 3.2. variable description and data source, 3.2.1. variable description, 3.2.2. data source and description, 3.3. regression analysis of benchmark model, 3.4. robustness test, 3.5. endogenous analysis, 4. mechanisms of the digital economy’s role in carbon emissions, 4.1. model construction for mechanism testing, 4.2. analysis of mediating effects based on green dual innovation, 4.3. panel threshold analysis based on green dual innovation, 4.4. analysis of moderating effects based on social concerns, 5. further analysis, 5.1. heterogeneity analysis based on foreign investment, 5.2. heterogeneity analysis based on technology transactions, 5.3. heterogeneity analysis based on geographic location, 6. conclusions and policy recommendations, author contributions, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

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    Click here to enlarge figure

    Primary IndexSecondary IndexConcrete Measure Index (Attribute)Measurement Method
    Digital Economy Growth Leveldigital industrializationNumber of Internet-related employees (+)Percentage of employees in computer services and software
    Internet-related output (+)Total telecommunications services per capita
    digital financeInclusive growth of digital finance (+)The Peking University Digital Financial Inclusion Index of China (PKU-DFIIC)
    digital infrastructureNumber of mobile Internet users (+)Number of mobile phone users per 100 people
    Internet penetration (+)Density of Internet access terminals
    Variable TypeVariable NameMeasurement Method
    Dependent variableCarbon emissions (ce)Calculate the natural logarithm of carbon dioxide emissions
    Key explanatory variableDigital economy growth level (Dige)Digital economy comprehensive growth index
    Intermediary variableDisruptive green technology innovation (gti-o)Number of green invention patents granted (tens of thousands)
    Progressive green technology innovation (gti-p)Number of green utility model patents granted (tens of thousands)
    Moderator variableGovernment attention
    (att-g)    		The natural logarithm of number of local environmental regulations
    Public attention (att-s)The natural logarithm of Baidu haze search index
    Control variablesDegree of marketization (mark)Marketization index
    Level of foreign investment (fdi)The natural logarithm of FDI actually utilized
    Level of industrialization (indus)Ratio of industrial value added to GDP
    Financial growth level (fin)The ratio of total deposits and loans of financial institutions to the GDP
    Level of economic growth (el)Natural logarithm of gross domestic product per capita
    YearThe value of a virtual variable is 1 in a certain year, otherwise 0
    ProvinceThe value of a virtual variable that belongs to a certain province is 1, otherwise it is 0
    VariableNMeanp50SDMinMax
    ce33010.33510.3700.7788.35312.217
    Dige3300.2170.1720.1540.0070.960
    gti-o3300.1120.0480.16601.001
    gti-p3300.4000.1860.61404.579
    att-g3304.7264.7320.7772.4856.652
    att-s3300.1950.1530.1710.0011.118
    fdi3300.0190.0170.0150.0000.080
    mark3306.7156.7052.3690.01012.480
    fin3303.2763.0511.1501.5188.131
    indus3300.3510.3630.0880.0970.530
    el33010.86610.8330.4598.53112.122
    Variable(1)(2)(3)
    Reference RegressionLag Phase 1Lag Phase 2
    Dige−0.904 ***
    (0.249)
    L.Dige −0.863 ***
    (0.231)
    L2.Dige −1.094 ***
    (0.245)
    fdi0.8040.4080.254
    (0.756)(0.709)(0.646)
    fin−0.014−0.021−0.012
    (0.023)(0.021)(0.017)
    indus0.965 ***1.094 ***1.076 ***
    (0.218)(0.198)(0.170)
    el0.0140.0200.025
    (0.041)(0.035)(0.029)
    mark−0.013−0.018−0.025 *
    (0.014)(0.014)(0.013)
    _cons9.845 ***9.846 ***9.885 ***
    (0.456)(0.402)(0.343)
    FE-YEARYESYESYES
    FE-PROVINCEYESYESYES
    N330300270
    r20.2240.2410.310
    Variable(1)(2)(3)(4)
    Replace the Key Explanatory VariableReplace the Dependent VariableExcluding Special YearsExcluding Municipalities Directly under the Central Government
    DFI−0.004 ***
    (0.001)
    Dige −3.431 **−0.794 ***−0.790 **
    (1.467)(0.289)(0.316)
    fdi0.262−9.030 **0.927−0.155
    (0.703)(4.450)(0.696)(0.943)
    fin−0.015−0.070−0.037−0.029
    (0.021)(0.134)(0.031)(0.026)
    indus0.857 ***−3.039 **0.693 ***0.861 ***
    (0.203)(1.285)(0.206)(0.233)
    el−0.010−0.288−0.291 ***−0.059
    (0.039)(0.239)(0.100)(0.041)
    mark0.015−0.061−0.003−0.008
    (0.013)(0.084)(0.015)(0.016)
    _cons11.971 ***10.303 ***13.963 ***11.364 ***
    (0.502)(0.508)(1.172)(0.513)
    FE-YEARYESYESYESYES
    FE-PROVINCEYESYESYESYES
    N330330270286
    r20.3270.2240.2330.334
    Variable2sls Instrumental Variable MethodSYS-GMM Model
    Phase 1Phase 2
    Digecece
    IV0.049 ***
    (0.010)
    Dige −1.858 **−0.220 *
    (0.805)(0.123)
    ControlsYESYESYES
    FE-YEARYESYESYES
    FE-PROVINCEYESYESYES
    N330330300
    Underidentification testLM statistic = 25.748; p = 0.0000
    Weak identification testCragg–Donald Wald F statistic = 26.664
    10% maximal IV size = 16.38
    AR (1) 0.001
    AR (2) 0.665
    Hansen 0.764
    VariableReference RegressionDisruptive Green Technology InnovationProgressive Green Technology Innovation
    (1)(2)(3)(4)(5)
    cegti-ocegti-pce
    Dige−0.904 ***0.863 ***−0.496 **1.850 **−0.780 ***
    (0.249)(0.165)(0.248)(0.810)(0.246)
    gti-o −0.474 ***
    (0.085)
    gti-p −0.067 ***
    (0.018)
    fdi0.8040.7301.1492.3460.962
    (0.756)(0.500)(0.722)(2.455)(0.740)
    fin−0.0140.014−0.0070.136 *−0.005
    (0.023)(0.015)(0.022)(0.074)(0.022)
    indus0.965 ***0.266 *1.091 ***0.4060.993 ***
    (0.218)(0.144)(0.209)(0.709)(0.213)
    el0.0140.072 ***0.0490.389 ***0.041
    (0.041)(0.027)(0.039)(0.132)(0.040)
    mark−0.0130.035 ***0.0030.148 ***−0.003
    (0.014)(0.009)(0.014)(0.046)(0.014)
    _cons9.845 ***−1.140 ***9.305 ***−5.491 ***9.474 ***
    (0.456)(0.301)(0.444)(1.480)(0.456)
    FE-YEARYESYESYESYESYES
    FE-PROVINCEYESYESYESYESYES
    N330330330330330
    r20.2240.4700.3000.4320.261
    VariableThreshold NumberFstatProbConfidence IntervalThreshold Value
    10%5%1%
    gti-oSingle 28.490.046724.638829.683038.57100.1520
    Double17.860.153320.167127.313740.4712
    Triple21.690.330035.127139.596554.1837
    VariableThreshold NumberFstatProbConfidence IntervalThreshold Value
    10%5%1%
    gti-pSingle 24.270.070022.039426.778934.07660.3357
    Double14.370.150017.461220.846726.0678
    Triple6.140.743319.492023.039030.4939
    Variable(1)(2)
    Disruptive Green Technology InnovationProgressive Green Technology Innovation
    0_c−0.643 ***−0.709 ***
    (0.246)(0.244)
    1_c−1.254 ***−1.007 ***
    (0.228)(0.242)
    fdi0.8770.842
    (0.729)(0.730)
    fin−0.013−0.014
    (0.022)(0.022)
    indus0.982 ***0.957 ***
    (0.210)(0.211)
    el0.0470.040
    (0.040)(0.040)
    mark−0.0050.011
    (0.014)(0.015)
    _cons9.429 ***9.433 ***
    (0.448)(0.449)
    FE-YEARYESYES
    FE-PROVINCEYESYES
    N330330
    r20.2820.279
    VariableReference RegressionGovernment AttentionPublic Attention
    (1)(2)(3)(4)(5)
    Dige−0.904 ***−1.052 ***−1.168 ***−0.906 ***−0.860 ***
    (0.249)(0.252)(0.245)(0.249)(0.247)
    att-g 0.032 *0.030 *
    (0.017)(0.017)
    Dige*att-g −0.143 **
    (0.061)
    att-s 0.0240.004
    (0.029)(0.029)
    Dige*att-s −0.146 ***
    (0.053)
    fdi0.8040.7800.9270.7890.740
    (0.756)(0.755)(0.760)(0.756)(0.748)
    fin−0.014−0.021−0.020−0.014−0.007
    (0.023)(0.023)(0.023)(0.023)(0.023)
    indus0.965 ***0.965 ***0.974 ***0.982 ***1.008 ***
    (0.218)(0.218)(0.218)(0.219)(0.217)
    el0.0140.0150.0280.0130.044
    (0.041)(0.041)(0.041)(0.041)(0.042)
    mark−0.013−0.013−0.014−0.012−0.011
    (0.014)(0.014)(0.014)(0.014)(0.014)
    _cons9.845 ***9.769 ***9.638 ***9.753 ***9.496 ***
    (0.456)(0.459)(0.466)(0.468)(0.472)
    FE-YEARYESYESYESYESYES
    FE-PROVINCEYESYESYESYESYES
    N330330330330330
    r20.2240.2280.2350.2260.246
    Variable(1)(2)
    Low Level of Foreign InvestmentHigh Level of Foreign Investment
    Dige−0.710−0.974 ***
    (0.543)(0.184)
    fdi1.895−0.516
    (1.640)(0.676)
    mark0.064 *0.041 ***
    (0.034)(0.013)
    fin−0.0410.072 ***
    (0.039)(0.027)
    indus0.815 **0.662 ***
    (0.347)(0.189)
    el−0.0460.280 ***
    (0.050)(0.093)
    _cons10.310 ***6.698 ***
    (0.579)(1.077)
    FE-YEARYESYES
    FE-PROVINCEYESYES
    N165165
    r20.3920.324
    Variable(1)(2)
    Low Tech-Trading ActivityHigh Tech-Trading Activity
    Dige−1.173 **−0.321 *
    (0.474)(0.182)
    fdi0.768−0.175
    (1.551)(0.596)
    mark0.0260.036 ***
    (0.022)(0.013)
    fin0.120 **0.076 ***
    (0.055)(0.026)
    indus0.727 **0.451 ***
    (0.341)(0.163)
    el−0.093 *0.251 ***
    (0.051)(0.093)
    _cons10.784 ***6.956 ***
    (0.620)(1.055)
    FE-YEARYESYES
    FE-PROVINCEYESYES
    N165165
    r20.5290.375
    Variable(1)(2)(3)(4)
    EasternCentralWesternNortheast
    Dige−1.135 ***−0.094−1.526 **−1.677
    (0.223)(1.160)(0.659)(1.501)
    indus−0.1420.7661.811 ***0.048
    (0.292)(0.573)(0.466)(0.343)
    fin−0.0240.010−0.022−0.375 ***
    (0.032)(0.124)(0.061)(0.122)
    fdi0.6941.1697.816 **−1.595
    (0.824)(3.342)(3.852)(0.912)
    el0.117−0.321−0.079−1.258 **
    (0.129)(0.217)(0.055)(0.455)
    mark0.023−0.0480.031−0.014
    (0.018)(0.035)(0.038)(0.037)
    _cons9.240 ***13.884 ***10.035 ***24.986 ***
    (1.478)(2.193)(0.633)(4.997)
    FE-YEARYESYESYESYES
    FE-PROVINCEYESYESYESYES
    N121779933
    r20.5260.6200.4240.862
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    Share and Cite

    Zhang, Y.; Liu, X.; Yang, J. Digital Economy, Green Dual Innovation and Carbon Emissions. Sustainability 2024 , 16 , 7291. https://doi.org/10.3390/su16177291

    Zhang Y, Liu X, Yang J. Digital Economy, Green Dual Innovation and Carbon Emissions. Sustainability . 2024; 16(17):7291. https://doi.org/10.3390/su16177291

    Zhang, Yu, Xiaomeng Liu, and Jiaoping Yang. 2024. "Digital Economy, Green Dual Innovation and Carbon Emissions" Sustainability 16, no. 17: 7291. https://doi.org/10.3390/su16177291

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    Economics JIW - Tips for Choosing a Topic: Home

    Choosing a topic.

    Choosing a topic that can answer an economic research question is challenging.  Some tips:

    •  Ripped from the headlines rarely makes a good economic paper.  You will be using data to determine causation or correlation.  Sometimes a similar event can be used.  Topics such as artificial intelligence may make a good policy paper but not a good economic one due to lack of data.
    • Literature Review: Your JIW should use primarily scholarly sources.  Start with Econlit (the database of the American Economic Association).  Econlit indexes major journals, working papers, conference proceedings, dissertations, and chapters in critical books. It takes a long time for scholarly literature to appear.   Preprints are called working papers in economics and major ones are indexed in Econlit.  Y ou are your own research team and have limited time.  Many articles are written over a couple of years and involve many people gathering and cleaning the data. Some starting places: see https://libguides.princeton.edu/econliterature/gettingstarted
    • Outside of finance and some macroeconomic data, most data will not have many points in time.  Data determines the methods used .   While a linear regression can be great for time series data, it is likely not what you will use for survey data.
    • Longitudinal or panel study :  same group of individuals is interviewed at intervals over a period of time.  This can be very useful to observe changes over time. Keep in mind when using a long running longitudinal dataset that the panel generally is not adding new participants so may not reflect today’s demographics.
    • Cross-sectional study :  data from particular subjects are obtained only once.  While you are studying different individuals each time, you are looking at individuals with similar demographic characteristics.  Demography is typically rebalanced to reflect the population.
    • Summary statistics : aggregated counts of survey or administrative data.
    • Typically around a 2 year time lag from the time the survey data is collected to the time of release.  The Economic Census and Census of Agriculture take about 4 years for all data to be released.  Many surveys never release the microdata.
    • Very little subnational data is available and is often restricted when available.   State level macro data for the United States is more prevalent.  City level data is often a case study or only available for very large cities.
    • Many micro-level datasets are restricted. It is not uncommon to wait a year before getting permission or denial to use the data.  Each organization has its own rules.
    • Historical data in electronic format prior to 1950 is rare. Most governmental links provide current data only.
    • What is measured changes over time .  Do not assume modern concepts were tracked in the past.  Definitions of indicators often change over time.
    • Data cannot be made more frequent.  Many items are collected annually or even once a decade.  Major macroeconomic indicators such as GDP tend to be quarterly but some countries may only estimate annually. 
    • What exists for one country may not exist for another country. Data is generally inconsistent across borders .
    • Documentation is typically in the native language .
    • Always look at the methodology. The methodology section is one of the most important parts of the paper. Someone should be able to replicate your work. Describe the dataset and its population. Describe how the data was subset, any filters used, and any adjustment methods. While you are likely not trying to publish in American Economic Review  or Journal of Finance , these are the gold standards.  See how they layout the articles and in particular the methodology and data sections.
    • The basic question to ask when looking for economic data is " who cares about what i am studying ?"  Unfortunately, the answer may be no one. Ideally, look for an organization that is concerned with your research as part of its mission. Examples include the International Labor Organization or the Bureau of Labor Statistics focusing on labor research; the International Monetary Fund or the Board of Governors of the Federal Reserve System focusing on monetary and fiscal concerns; the World Bank focusing on development; and the World Health Organization focusing on health. This does not mean these organizations collect data on all topics related to that field.
    • Find a topic for which there is literature and data but allows room to add a contribution.  Topics such as sports and music are popular due to personal interests but may not make good research topics due to lack of data and overuse.

       More tips:

    • Data is typically not adjusted for inflation.  It is usually presented in current (nominal) currency.  This means the numbers as they originally appeared.  When data has been adjusted for inflation (constant or real), a base year such as 2020 or 1990 will be shown.  If a base year is not provided, then data is current and therefore not adjusted for inflation.  If given a choice, choose current dollars.  Data is often derived from different datasets and many will use different base years.  Adjust everything at the end.  It is easier than doing reverse math!
    • While most datasets are consistent within the dataset for currency used such as all in US Dollars or Euro or Japanese Yen or each item in local currency, some will mix and match.  LCU is a common abbreviation meaning local currency units. Consider looking at percent changes rather than actual values.  If adjusting use the exchange rate for each period of time, not the latest one.
    • Economic indicators may be either seasonally adjusted or not seasonally adjusted.  This is very common for employment and retail sales.   Unless something says it is seasonally adjusted, it is not.  Be consistent and note in methodology.

    Librarians are here to help!  Librarians can help to devise a feasible topic, assist with the literature search, and choose appropriate data.  Your data may fall into multiple categories.  Think of the primary aspect of your topic in terms of first contact.  Do not email librarians individually.  If unsure who to contact either put all that apply on same email or email just one.  If that person is not the best, they will refer you.  

    Bobray Bordelon Economics, Finance, & Data Librarian   [email protected]

    Charissa Jefferson

    Labor Librarian [email protected]

    Mary Carter Finance and Operations Research Librarian [email protected]

    Data workshops

    • Environmental and energy data  (Bordelon), 9/23/2024  - 7:30-8:50 pm
    • Health, Crime and other Socioeconomic Data  (Bordelon), 9/23/2024 and 10/02/2024 - 3-4:20 pm 
    • Macroeconomics and trade data  (Bordelon), 9/25/2024 and 9/30/2024 - 3-4:20 pm
    • Finance data  (Carter), 9/23/2024 and 9/25/2024 - 3-4:20 pm
    • Labor and education data  (Jefferson), 9/23/2024 and 9/25/2024 - 3-4:20 pm

    Workshops listed twice have the same content and are done as an opportunity to fit your schedule.  While you must attend at least one data workshop, it is wise to attend more than one.  If in a certificate program, with the exception of political economy which has to be incorporated into your JIW, other programs have different requirements which are typically for your senior year.  As an example, if in finance, if you choose not to explore a finance topic this year you will still need to incorporate in your senior theses so try and attend a finance workshop in addition to your topical workshop for your JIW since these are intended to help you for your time at Princeton and both the JIW but also the senior thesis.

    • Last Updated: Aug 28, 2024 9:32 AM
    • URL: https://libguides.princeton.edu/ECOJIWTopics

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