rafmacalaba/data-use-mentions-extended huggingface dataset:
could be more suited to successful implementation of aid projects, thanks to higher levels of social and human capital. This would be consistent with recent evidence that more-developed areas receive more aid (Briggs, 2017; Nunnenkamp, Öhler, & Andrés, 2017).

The second part of the article (Section 3) puts this hypothesis to an empirical test, employing two different strategies. The first compares the performance of projects implemented in the vicinity of missions with those further away, using the ratings by the World Bank’s Independent Evaluation Group (IEG) as a proxy for project performance. There is no evidence of a correlation between proximity to mission stations and project ratings, but the estimates are rather imprecise and thus do not allow definitive conclusions to be drawn. The second strategy exploits information on start and end dates of the aid projects to implement a triple-differences design. In particular, areas that received aid at different points in time are compared to test whether aid arrival affects the level of development and, more importantly, whether the effect is higher in the vicinity of mission stations. Proxies of development are constructed with georeferenced survey data from the Demographic and Health Survey (DHS) and include measures of wealth and access to public utilities. Under the identifying assumption that development trends (not levels) between areas with and without missions as of 1903 are comparable in the period 1995–2014, there is no evidence that mission presence matters for aid effectiveness. As with the first strategy, it is not possible to precisely estimate the absence of an effect, and so the conclusion drawn from this exercise is suggestive rather than definitive.

The final part of the article (Section 4) investigates whether favoritism can explain the correlation between aid and missions. First, it examines the role of _political_ favoritism. There is evidence from Africa that funds from the World Bank and the African Development Bank have been diverted to politically important areas—competitive electoral districts (Masaki, 2018), strongholds of the incumbent regime (Briggs, 2014;
reduction are more likely to increase their innovative capacities. I exclude firms that belong to industries that had the highest benefit from these reforms as a robustness test. The results with sub-samples are presented in Table 6. In the first column I exclude 4-digit industries that had more than half of the industry liberalized in FDI by 1995. In the second column I exclude the top quartile of industries that had the highest drops in output tariff rates between 1989 and 1995. In the third column I exclude top quartile of industries that had the highest drops in input tariffs for the same time period. In all three columns licensing reform significantly increases innovation performances of firms which provide further support that the relationship between licensing reforms and innovation is not affected by these other reforms.

In his survey on technology diffusion, Keller (2004) summarizes theoretical and empirical literature on how imports provide knowledge and technology transfer in a macroeconomic perspective. Using micro-level data from developing countries Almeida and Fernandes (2008) and Seker (2011) provide evidence on importing and exporting firms being more innovative. Criscuolo et al. (2010) reach a similar conclusion on the contribution of exporting on innovation for British firms. In the fourth and fifth columns of Table 6, I exclude export oriented and import oriented industries respectively. The coefficient of licensing reform indicator continues to be significant. Another test with sub-samples is with employee income levels. Industries with high wage payments are likely to employ more skilled workers and produce more knowledge embodied products. This could facilitate the innovation activities in these industries. In the last column I exclude the top quartile of the industries with the highest wage payments.<sup>13</sup> Some of the industries went through multiple reforms and the sub-sample used in each column of the table could include overlapping industries which could affect the results. Although there are industries that have been included in multiple groups, their numbers are not large with a possible exception of industries with highest input and output tariff drops. 50 percent of firms that belong to
tasks carried out at work. Our final sample for all three data sets includes employed individuals ages 16 to 64 years.

> 4  Garrote Sanchez, Gomez Parra, Ozden, & Rijkers (2020) consider the role of essential workers in their assessment of WFH measures in the European Union.

> 5 We exclude China since the data are only representative of Yunnan province. There is no STEP survey for El Salvador, thereby we use instead a skills survey that includes a similar questionnaire.

5
, urban policies (distribution in urban environments), and especially decarbonization (“green logistics”).</mark><sup>4</sup> <mark>The World Bank introduced the Logistics Performance Index (LPI) in 2007 as a set of country indicators to inform policy makers and practitioners.</mark> What is the World Bank Logistics Performance Index (LPI)?

The World Bank’s Logistics Performance Index is a comprehensive index that has been covering the entire supply chain for between 139 and 160 countries in the 2007 to 2023 editions. It is based on a survey of nearly 1,000 logistics professionals worldwide and is useful for comparing performance across countries and identifying and prioritizing broad reform areas for interventions within countries. The index is based on numerical ratings of 1 (weakest) to 5 (strongest).

The Logistics Performance Index is a weighted average of six components:

1. Efficiency of the clearance process

2. Quality of trade- and transport-related infrastructure 3. Ease of arranging competitively priced international shipments 4. Competence and quality of logistics 5. Ability to track and trace consignments

> 2 Banomyong et al. 2022.

> 3 Arvis et al. 2007; World Bank 2010.

> 4 McKinnon 2015.

1
effects of shocks, including those that are climate related (Premand & Stoeffler, 2020; Bossuroy, et al., 2022). Yet adaptive safety net systems need to know how to weight efforts to address chronic poverty versus unpredictable shocks versus seasonality and what the best modalities are for providing households with assistance.

This paper uses new household survey data to examine Sahelian households’ susceptibility in more detail. In particular, the paper draws on the _‘Enquête Harmonisée sur les Conditions de Vie des Ménages’_ (EHCVM) _,_ which collected harmonized data from Burkina Faso, Mali, Niger, Senegal, and Chad during two distinct ‘waves’ throughout 2018 and 2019. These waves correspond approximately to the lean and nonlean seasons in Burkina Faso, Mali, Niger, and Senegal, so comparing them reveals the impact of seasonality in those countries. In Chad the timing of data collection was different, so those sections of the paper making cross-season comparisons will not include Chad.<sup>4</sup> The 2018 rainy season, which would determine the extent of the 2019 lean season, was regarded as ‘above average’ in terms of the timing and reliability of rainfall, making it possible to isolate the additional impact of ‘regular’ seasonality over and above the effects of unpredictable weather shocks.<sup>5</sup> Descriptive statistics from the EHCVM data immediately hint at Sahelian households’ exposure to seasonality. The majority of households rely primarily on agriculture or pastoralism, and their income

> 2 Further details on seasonal variation in temperature, rainfall, livelihoods, and food prices can be found in Annex 2. 3 Using the 2018 poverty numbers and the 2020 population numbers reported in the World Bank’s Poverty and Inequality platform, 27.5 percent of people in Burkina Faso, Niger, Mali, Senegal, and Chad, live below the international poverty line of 1.90 USD 2011 PPP per person per day.

> 4 Chad is retained for the descriptive statistics on strategies
male|-0.029|0.033|-0.027|0.039|
|Beta max - male|-1.03e+30|1.64e+31|-1.67e+30|1.15e+33|
|R-squared|0.045|0.123|0.031|0.127|
|Observations|1510|1510|1510|1510|

Note: Robust standard errors are reported in parentheses. All regressions include village fixed effects and the controls listed in Table 1. Self-esteem measures are corrected for acquiescence bias. Significance levels are denoted as follows: * p<0.10, ** p<0.05, *** p<0.01.

# **6 Additional Results**

To speak to welfare impacts and examine how our detected pattern varies across specifications, we re-run our main result with the value of earned income, rather than income-earning. That is, we explore whether the "battle of the sexes" results hold when analyzing the effects of selfesteem on spouse’s crop income rather than just the likelihood of earning crop income.

This alternative outcome is not our preferred option due to the imprecision in its measurement in our setting, as discussed in Section 2. Nevertheless, Tables 13 and 14 show results for male and female incomes separately. Consistent with the literature, we find a strong positive correlation between male self-esteem and their own income. A one standard deviation increase in self-esteem corresponds to a US$ 477 _._ 3 increase (19 _._ 5 percent) in total income. Most of the increase is attributable to export-oriented crop income (Column 3 in Table 13). The positive association partly holds for women, whose self-esteem significantly increases both export crop income and off-farm income (through self-employment) but not total income. Given substantially lower baseline levels, the magnitude of the effect for export-oriented crop income is four times larger for women than it is for men. A
|1.3|13.9|13.3|15|11.5|66|90.4|
|Zambia|1.2|10.9|7.9|49.1|65.5|65|59.1|95|74.1|
|Zimbabwe|8.3|31.7|33.1|51.2|51.2|56.1|76.1|94|91.8|
|Seychelles|||||||10.7||55.6|
|Madagascar|||||||9.3||72.1|
|Intra-SADC|1.6|4.7|5.1|9.9|10.2||12.3|90|80.3<sup>a</sup>|
|excl. Angola,|DRC,Ma|dagascar|and Seyc|helles||10.6|12.3||78.8|

Source: Development Network Africa (2007) and Chauvin, S. and G. Gaulier (2002) updated to 2008 using HS6-digit data obtained from UNComtrade. Note: Intra-SACU trade is excluded. 2002 values used for Zimbabwe in 2003.

a. 80.3 percent of SADC (excluding SACU countries) imports from the region are sourced from SACU.

# **Table 2:** **<u>Share of SADC trade in SADC country exports</u>**

|||||||||Propo<br>on<br>SACU|rti<br>to<br> <br>Proportio<br>n<br>to<br>SACU|
|---|---|---|---|---|---|---|---|---|---
*|-0.0447**|-0.0470**|-0.0506***|
|2008 survey|-0.0145|-0.0119|-0.0072|-0.0087|-0.0075|-0.0059|-0.0144|
|Full immunization (district level)||-0.0167|-0.0218|-0.0155|-0.0219|-0.0222|0.0347|
|Birth in clinic|||0.0218**|0.0268**|0.0269**|0.0258**|0.0233**|
|Antenatal tetanus protection|||0.0078|0.0077|0.0081|0.0077|0.0094|
|Antenatal antimalarial drugs|||-0.0107|-0.0104|-0.0103|-0.0112|-0.0085|
|Antenatal iron supplement|||-0.0033|-0.0035|-0.0047|-0.0049|-0.0087|
|Unimproved water source (not surface)||||-0.0168|-0.0166|-0.0177|-0.0199|
|Improved water source||||-0.0066|-0.0061|-0.0095|-0.0091|
|Unimproved sanitation||||-0.0134|-0.0168|-0.0218|-0.0200|
|Improved sanitation||||-0.0382**|-0.0487**|-0.0553**|-0.0555**|
|Mother has primary education|||||0.0193|0.0189|0.0086|
|Mother has secondary education|||||-
|_1.34%_<sup>_∗∗∗_</sup>|_0.82%_<sup>_∗_</sup>|_1.74%_<sup>_∗∗∗_</sup>|
|Convergence rate (PPP, annual)|_3.71%_<sup>_∗∗∗_</sup>|_5.45%_<sup>_∗∗∗_</sup>|_2.71%_<sup>_∗∗∗_</sup>|_-0.88%_|_3.48%_<sup>_∗∗∗_</sup>|
|Observations (All)|98|98|98|98|392|
|Observations (EMDE)|69|69|69|69|276|
|Observations (AE)|29|29|29|29|116|
|_Note:_|||_∗_p_<_|0.1; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Decade dummies are used in the panel specification but country fixed effects are not. Initial productivity is the average of log productivity over the 10-years in the preceding decade, measured in US dollars at 2010 prices and exchange rates. Productivity growth is calculated as the change in average log-productivity between the two decades (10-year average growth). Productivity is assumed to grow at its average rate between 2010-8 for the final year of the decade, 2019.

27
that the treatments have equivalent effects. To both improve patient survival and contain the epidemic, it is particularly important that infected patients report. We find large increases in the average number of confirmed cases reporting in treated sections: for every confirmed case in control, we count five confirmed cases in treated sections.

**Table 3:** Reported Ebola Cases

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Total|0.281|0.173|0.204|0.148|0.055|5,440|
||(0.727)|(0.084)<sup>_∗∗_</sup>|(0.117)<sup>_∗_</sup>|(0.099)|(0.133)||
|Confrmed|0.011|0.059|0.086|0.039|0.047|5,440|
||(0.129)|(0.024)<sup>_∗∗_</sup>|(0.038)<sup>_∗∗_</sup>|(0.025)|(0.041)||
|Negative|0.238<br>(0.648)|0.1<br>(0.061)|0.079<br>(0.077)|0.115<br>(0.075)|-0.036<br>(0.093)|5,440|
|IHS(Ebola Cases)|||||||
|Total|1.015|0.057|0.065|0.05
[0.17]|
|Constant|0.29***|4.24***|4.07***|0.27***|3.23***|3.49***|
||[0.07]|[0.99]|[0.51]|[0.05]|[0.85]|[0.35]|
|Observations|15506|6820|7162|21211|9180|9670|
|Adjusted R-squared|0.23|0.17|0.18|0.22|0.16|0.17|
|Mean Female|0.48|9.52|4.26|0.47|9.42|4.27|
|Mean Male|0.42|6.17|2.93|0.42|6.20|2.94|
|PersBelief+Fem(P-value)|0.80|0.26|0.16|0.00|0.00|0.00|
|ComNorms+Fem(P-value)|0.17|0.00|0.66|0.07|0.00|0.00|
|Mean VIF|3.65|4.79|4.62|2.60|3.80|3.65|
|Sample|ALL|ALL|ALL|ALL|ALL|ALL|
|CountryFE|YES|YES|YES|YES|YES|YES|
from 3,954 randomly sampled feeders within JVVNL’s network. The data set separates technical losses from losses due to commercial and billing inefficiencies. The former component leads to an overall reductional in society’s welfare, while the latter represents a welfare transfer from households that are billed for their consumption, to household that consume electricity without a bill. We use technical losses at the feeder level to estimate the quadratic relation. Using calculations for each 15-minute period and the parameter estimates of fitted quadratic equation, we estimate marginal distribution DD ii losses. The average marginal distribution losses in our sample is 18.4% with a standard deviation of QQ 3%. Figure 15 shows the frequency distribution of marginal transmission and distribution losses based on these calculations.<sup>24</sup>

# • **Marginal cost of emissions**

The final component of SMC is the external cost associated with emissions from generating plants that increase their output in response to an increase in demand. Ideally, the cost of emissions should be calculated separately for all pollutants such as carbon dioxide, nitrogen oxide, sulfur dioxide and particulate matter. However, this paper calculates only the cost carbon dioxide emissions, as generator-wise emissions data for other pollutants is unavailable in India. Moreover, to precisely estimate the marginal cost of carbon, emissions data at 15-minute or hourly intervals for each generator are required. Such detailed data sets are unavailable in the Indian context.

Facing these limitations, the marginal cost of emissions is proxied by the average carbon dioxide emissions per unit of energy produced by each generator. A data set<sup>25</sup> that contains information on annual carbon dioxide emissions for FY2016-17 for each power generating company in India is used for this purpose. The data set uses plant-level information on the type of fuel used, the calorific value of the fuel mix and its emission and oxidation factors to calculate the total carbon dioxide emitted by the plant in a year. The annual tons of carbon dioxide divided by the total
nonentrepreneurial MFI compared with other loans). These results make sense since entrepreneurial MFI borrowers are more likely to invest in durable goods than nonentrepreneurial borrowers.

Looking at the temptation goods results, the results are mixed. Thus, comparing MFI borrowers to those without any loans, we still find large and significant expenditures on temptation goods in both groups (columns 1 and 2). However, comparing MFI borrowers to those with other loans, the temptation goods expenditures are close to zero and not significant in the entrepreneurial group (3% column 3), but about ten times as large and significant in the nonentrepreneurial group (39% column 4). The mixed results could be due to the volatile nature of temptation goods data, but, at a minimum, they show that relative to those with other loans, MFI does not increase temptation goods expenditures for entrepreneurial borrowers. Despite small sizes of entrepreneurial borrowers, these results are at least suggestive of the possibility that temptation and commitment problems are more present among the nonentrepreneurial borrowers. While these results do not represent conclusive evidence, such sample splits demonstrate that the PSM method has an edge in unpacking the heterogeneous impacts for various groups of borrowers, which is not possible with the intention to treat RCT designs that estimate the average for the treated versus nontreated areas.

# V. CONCLUSIONS

We employ the PSM method to evaluate the impact of microfinance on various expenditure categories. While we use the data from a recent RCT experiment (Banerjee et al. 2015b), our approach is able to answer a set of interesting and important questions unaddressed by the RCT design.

We contribute to existing literature on evaluation of the impact of microfinance in several important ways. First, we provide evidence on the impact of microfinance on an individual level, which is not possible using RCT designs that can only produce either ITT or LATE estimates (see footnote 3). While such parameters can be of interest in answering some policy questions (such as estimating the impact of introduction of microfinance on the total expenditures
use macro (sector-level) time series data to conduct growth-accounting exercises to compare China and India’s productivity growth patterns. Hsieh and Klenow (2009) use firm-level data to quantify resource misallocation in China and India and find substantial gains from correcting resource misallocation in both countries, with gains in India substantially larger. Our paper differs in that we focus on policy and environmental factors that may explain India’s disadvantage in performance and find that labor regulations and infrastructure seem to be particularly relevant.

The second related literature links Indian economic performance with its labor market flexibility. Besley and Burgess (2004) use state-level panel data from India to investigate the impact of labor regulation on state economic performance and find that pro-labor regulations hinder economic performance. Amin (2009a, b) find that cumbersome labor regulations in India lead to smaller firm sizes, more informality, and inefficient adoption of labor-saving computer technology. Aghion et al (2008) link the elimination of the system of industrial regulation with state-industrylevel performance over time in India. They find the growth effects of de-licensing to be significantly stronger in states with pro-employer than in pro-employee labor regulations. World Bank (2010) argues that labor regulations in India encourage informality and dualism in India. We add firm-level evidence on the complementarity between labor flexibility, infrastructure and access to finance in India.

The third strand of related literature analyzes the impact of measurable business climate on firm performance. Since Stern (2002) argues for improving business climate and overcoming government failure as a strategy for development, the World Bank has conducted numerous investment climate surveys in developing countries.<sup>3</sup> The existing studies have tended to assume identical effects of business climate on firm performance across countries. Our results suggest that, consistent with Hausman et al. (2005) and Kremer (1993), it is critical to allow for heterogeneous policy effects, to recognize local variations in policies and policy implementations within the same country, to consider interactions among various elements of the business climate, and to identify
extra year of primary schooling is associated with larger learning gains in Viet Nam compared to other developing countries.<sup>2</sup> More broadly, large increases in educational enrollment in many developing countries have been associated with stagnant or even declining learning achievements by level of education (Pritchett, 2013; Le Nestour et al., 2023), highlighting the challenges brought by rapid educational expansions. Several studies argue that when quality differences are incorporated in the measurement of human capital, its contribution in accounting for cross-country income gaps can be substantially larger (see for example Schoellman, 2012; Angrist et al., 2021b).

Figure III: Average Learning Score and Development <!-- Start of picture text --> (a) Primary (b) Secondary<br>600 600<br>SGP<br>500400300 BDICODMWINERLBRMOZ MDGBFAETHSLEHTITGOTCDUGAMLIRWAGINGMBTZAYEMNPLZWECOMSENBEN ZMB TJKCMRLSOKHMKEN BGD SDNMRTCIVPAKCOGMMRKGZHNDNICGHANGABLZLAOPHL MDA SLVMAR GTM AGOJAMSWZIDNJORIRQNAM GUY ECUPERPRYEGYGEOZAFIRNCOLMKDAZEBRADOMBWAGABCRIBGRMEXMUSURYARG ROU HRVCHLKAZSYCLVAPANHUNRUSTURGRCLTUPOLSVK CYP PRTTTOSVNCZEMLTOMNESPITANZLISRFRAFINTWNCANISLDNKDEUBHRAUSAUTSWEHKGNLDSAUKWTUSA NORAREIRLLUX MACQAT 550500450400 VENINDVNM MDA IDNJORTUNALBPERDZAGEOCHNCOLMKDAZEBRADOMLBNSRBTHACRIBGRMNEMEX MUS URYARG ROU HRVCHLMYSKAZLVAPANHUN RUS TURGRC LTU POLSVKESTPRTTTOSVNCZEMLT ESP ITANZLISRKORJPNFRAFINGBRBELTWNCANISLDNKDEUAUSAUTSWEHKGNLD USA NOR CHEARESGPIRLLUX MACQAT<br>200 350 KGZ<br>7 8 9 10 11 12 8 9 10 11 12<br>Log GDP per worker in PPP (2015) Log GDP per worker in PPP (2015)<br>Average Learning Score Average Learning Score<br><!-- End of picture text --> _Notes_ : The figure plots the average learning score for primary (left panel) and secondary school (right panel) students from the Harmonized Learning Outcomes Database (Angrist et al., 2021a) against log GDP per worker (in PPP terms) in 2015. The learning score is averaged across all available subjects and years. GDP per worker is taken from version 9.0 of the Penn World Tables (Feenstra et al., 2015).

**These results underline the need for effective investments making schools in poor countries well-equipped to deliver learning against the backdrop of a rapidly growing student population** . The existing literature highlights a number
of employment (%), 1987-2018<br>aggregate productivity in 2007<br>Log of ratio of sectoral productivity to<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM Note: Sectors depicted are agriculture, business services, construction, manufacturing, trade services, transport services, and other services.

**Figure 15: Relative productivity and share of employment by sector, 2018**

<!-- Start of picture text --> 2.5 30<br>2.0 25<br>20<br>1.5<br>15<br>1.0<br>10<br>0.5 5<br>0.0 0<br>Relative productivity Share of employment (RHS)<br>productivity Share of employment (%)<br>Ratio of sector productivity to overall Construction Other Agriculture Trade Manufacturing Transport Business<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM 16