rafmacalaba/data-use-mentions-extended huggingface dataset:
no evidence that the internal validity of the study is jeopardized by attrition bias (Dumville et al., 2006).

Annex Table A1 also provides a description of the short-term and medium-term samples. Overall, these samples consist of highly educated and typically unmarried individuals. Females represent about a quarter of them.<sup>28</sup> More than four in ten report belonging to socially and economically disadvantaged castes. Individuals in the sample hold mixed attitudes towards gender norms and VAW. For example, while a large majority believes that women should be able to wear clothing of their choice, almost a quarter thinks that women should be banned from the kitchen/shrine during menstruation or justifies VAW in cases of unfaithfulness.

# **4.2 Take-up rates and objective compliance**

Panel B of Table 1 shows viewership statistics of the media campaigns for the full baseline sample. The data suggests that the drama (treatment 1) and placebo movie experienced higher viewership rates compared to the documentary (treatment 2), potentially due to their higher entertainment content. While 65% of treatment 1 viewers self-reported watching half or more clips, only 47% of treatment 2 individuals reported doing so. The objective metrics, as measured by click data, confirmed higher take-up rates for treatment 1, compared to treatment 2, and provided overall insights into intervention compliance. These show that around 25% of people in any arm over-reported watching more than half of the videoclips (35% vs. 60%), though over-reporting was more-or-less similar across treatment arms.<sup>29</sup> Panels C and D of Table 1 provide information on compliance with the intervention for the two follow-up samples, using click data from the video “play” events. These data will also be used to estimate the Treatment-on-the-Treated effects, which thus will account for any differences in viewership across experimental arms. About 78% and 66% of respondents in the short-term and medium-term samples played half or more of the assigned series, respectively.
With our panel of constructed ENLACE data, we merge information on program receipt at the individual level derived from administrative data. We define an individual’s beneficiary status based on the receipt of a PROSPERA scholarship in the sixth grade. Because enrollment in primary school is near universal, we assume that if administrative data shows that a sixth grader was not receiving a PROSPERA grant, that sixth grader lives in a household that does not receive PROSPERA. Our definition thus of beneficiary versus non-beneficiary reflects status in sixth grade and the identification strategy compares sixth grade PROSPERA recipients with sixth grade non-recipients in all the years we study.

One might expect PROSPERA recipients to show higher poverty rates than non-recipients and this would imply lower rates of school enrollment between the groups in the absence of the program. We are fortunate to be able to also merge information on children and their households using Context Questionnaires that were applied to children at the time of the ENLACE application to sixth graders in 2007-2008 and this information provides valuable variables for controlling for differences in characteristics between PROSPERA recipients and non-recipients. The context questionnaires were applied only to a random subsample of schools, so we restrict our analysis to this subsample.

We also have data on characteristics of the schools that are collected using “911” forms, which are administrative forms that are filled out at the level of the school at the beginning and the end of each school year, which we also merge to our data. These data allow us to control for measures of quality and to potentially study if PROSPERA impacts are significantly different when children have access to different quality schools. We also use these variables to control for impacts of available school quality on achievement. The particular variables available varies by school year. We use those variables available at the beginning of our panel, for the school year 2007-2008 to construct the following school level indicators: student/teacher ratio, whether school is “multi-grado” meaning a teacher has more than 1 grade in his/her classroom, teacher education levels (measured by proportion
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_Notes:_ * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. Estimated via OLS. Standard errors are clustered at the country level. The omitted categories for income group and region are LMIC and SSA. _Source:_ Authors’ analysis based on the variables and data sources described in Section III, Table 1.

Of course, the negative relationship between levels of WBG engagement and median DEA scores observed in Figure 10 only represents a simple association which is unconditional of other factors. To test whether this relationship also holds when controlling for other factors, Table 4 reports the results of regressing WBG engagement on the DEA efficiency scores and other potentially relevant variables. Colum (1) reports the results when technological readiness, natural resources, and control of corruption are included as regressors along with the DEA scores. In column (2), countries’ income group and geographical region are added. Columns (3) to (5) further include tax policy, informal sector activity, and social trust. According to the results in Table 4, WBG engagement elated to DRM indeed tends to be stronger in countries that feature lower DRM efficiency (the coefficient is statistically significant in most specifications in Table 4).<sup>22</sup> This finding is consistent with the result in Figure 10 that the median DEA scores tend to decrease with higher levels of WBG engagement (as well as with the result that there are nevertheless many countries with large untapped revenue potential that feature only little WBG engagement).

In addition, Table 4 shows that WBG engagement in DRM tends to be stronger in countries that feature lower levels of technological readiness and better control of corruption. Moreover, there is some evidence that WBG engagement in DRM plays a smaller role in UMICs than in LICs and LMICs (column 2). For regions, the results indicate that WBG engagement tends to be stronger in SSA than in EAP and SA,
overall degree of connectedness of the company, our baseline specification does not impose any constraint on the original issuance of the loan. However, our results are broadly robust if we restrict the analysis to loans that are issued within shorter horizons (ten years, seven, five, two, or even one year before the interview). Second, we employ information on loan applications in the last year ( _Loan application_ ) to provide preliminary evidence on whether the heterogeneous availability of funds is due to firms’ credit demand or is, instead, driven by a differential probability of banks’ acceptance. Our extensive set of controls include information on firms’ belonging sector ( _Manufacturing, Retail, Other services_ ) and form of proprietorship ( _Listed company, LLC, Sole proprietorship, Partnership, Ltd Partnership_ ), structural characteristics ( _Size, Age_ ), exporting status ( _Export_ ), realized past performance ( _Sales growth_ ), and number of competitors ( _N competitors_ ). All variables are defined in Tables B1 and B2 of the Data Appendix.

**Selection and attrition** Since we rely on the longitudinal dimension of the WBES dataset, it is worth discussing possible selection issues affecting our estimating sample. This has clearly to do with the nonrandom probability of response and the self-selection of companies that kept answering the survey in following waves, vis-`a-vis firms that dropped out of the sample. Indeed, if such selection is somewhat simultaneously correlated with our main regressors of interest and dependent variables, it may create a bias driving our conclusions. In Table A1 of the Online Appendix, we tackle this issue by focusing on the full set of respondents in the original 2013-waves and testing the correlation between firms’ likelihood of being interviewed a second time and the variables employed in the analysis. Our estimates assuage concerns about systematic biases by showing no significance between firms’ probability of belonging to the panel and our main variables of interest (Constrained by informal, Loan availability, Loan application, and Investment).<sup>6<
# **1 Introduction**

Value-addition to raw commodity exports is an important (aspirational) industrial policy strategy for commodity exporters in the Global South. This is because, aside from the increase in export revenues, adding value to commodities prior to export, as opposed to exporting them in their raw form, can stimulate job creation and growth of the local economy (Wang, 2013; Rodrik, 2022). The local economic effects of industrial (trade) policy measures have thus gained significant attention in the literature (Busso et al., 2013; Wang, 2013; Goldberg and Pavcnik, 2016; Grogan, 2023). However, many of these studies focus on the "first-order" impacts of industrial (trade) policies, such as employment (Glick and Roubaud, 2006), wages (Cling et al., 2005; Glick and Roubaud, 2006) and income (Picarelli, 2016). Little is known about the "second-order" effects on outcomes such as health and education (Atkin, 2016). Meanwhile, these "secondorder" effects play a key role in determining the long-run impact of these policies.

In this paper, we explore how agro-industrialization policies affect human capital accumulation in low-income environments using administrative data from Rwanda. Theoretically, the effect of agro-industries or any local economic shock on human capital accumulation is ambiguous. On the one hand, local economic shocks boost household income, which enables them to invest in the schooling and healthcare of their children. On the other hand, standard economic models suggest that an increase in wages and employment opportunities for lowskilled workers, resulting from a boom in the local economy, increases the opportunity cost of schooling and reduces the marginal returns to higher education, thereby reducing educational attainment, particularly in environments where regulations on compulsory schooling for children are loosely enforced (Atkin, 2016; Mensah et al., 2019; Carrillo, 2020). Thus, the net effect of these competing factors determines the extent to which local economic shocks influence human capital accumulation
# **1 INTRODUCTION**

More rapid green growth is inconceivable without innovation. Frontier innovations shift out production possibilities, allowing the production of more output and newer, more environmentally-friendly outputs with fewer or different inputs. Innovations thereby help to decouple growth from natural capital depletion and environmental pollution, for example towards more resource-efficient and cleaner technologies. Some innovations can directly increase resilience to environmental shocks. Catch-up innovations, that make the use of existing technologies more widespread by adapting them to local contexts, are even more important for all countries. They typically reduce production costs and increase enterprise competitiveness, and are lower risk than frontier innovations. The introduction of new products, processes, business models and other organizational methods, and marketing techniques, whether through frontier or catch-up innovation, in principle contribute to the expansion of existing markets and the creation of new markets, in the process increasing the job content and poverty alleviation of growth.

This paper examines existing patterns of green innovation, to what extent innovation policies should be designed differently to address the green growth agenda, and what policy modifications can best help yield short-run or at least medium-term impact. The paper discusses the implications of the inherent ̳double externality‘ of knowledge-related market failures compounding the traditional environmental externalities. It motivates appropriate policy action in the absence of global agreements, answering the question of why developing countries should undertake green innovation policies, and what types of policies should be pursued depending on existing technological capabilities.

In contrast to most recent empirical analyses that use older patent data to 2005 or at most to 2008 to characterize international patterns of green frontier innovation (OECD 2011a, Dechezleprêtre et al. 2011, Aghion et al. 2011), this paper uses patent data to end-2010 and explores patterns across developing countries in greater detail. This matters since there have been significant increases in green patenting over the 2006-2010 period. The paper also examines different frontier innovation patterns by level of technological sophistication of countries, and by extent of cross-country collaboration. As a proxy for patterns of broader green catch-up
suggests that particularly struggling households are well-represented in the sample with approximately one-third of the sample being displaced and over one-third receiving food assistance.

> 22See Appendix 6 for a map presenting the number of respondents by district over the entire course of the WFP survey, where there are only two districts out of 333 that do not have any respondents in any of the surveys. Alternatively, the vast majority of districts are represented in any single month of the survey. For example, in November 2017, the survey reached respondents in 264 of the 333 districts in the country- nearly 80 percent of the total. It is important to note that the districts for which there are zero respondents tend to be less populated and in the east of the country. Only 9.2 percent of the country’s 2017 population resided in the 69 districts for which there are zero respondents in the November 2017 survey; and the districts overwhelmingly came from the governorates of Hadramaut and Al Mahrah, where only 39 and 33 percent of districts, respectively, had at least one respondent. Importantly, these governorates are less affected by the conflict and have less displacement than the rest of the country (TFPM 2016) and are not the primary focus of the analysis.

> 23See Appendix 7. The WFP survey began recording the number of working mobile phones owned by each household in March 2016. For each survey, the 95 percent confidence interval for the average number of mobile phones owned by the household is presented and compared to the 95 percent confidence interval prior to the conflict (based on data from the 2014 Household Budget Survey). Despite the large shock to household incomes and energy sources needed to charge mobile phones, the confidence intervals overlap in approximately 78 percent of the governorate-survey observations. In most instances where the two do not overlap, cell phone ownership is lower in the WFP surveys than in the pre-conflict estimate, which is expected given the negative shocks that have occurred since then.

> 24See Appendix 8 for estimates of the timing of the displacement
Figure 8: Pre-trends in proxies for state capacity <!-- Start of picture text --> ULGSP<br>Control<br>2007 2008 2009 2010 2011<br>Year<br>(a) Own revenue collection<br>.01<br>.008<br>.006<br>Mean own per capita revenue .004<br>.002<br><!-- End of picture text --> _Note:_ This figure presents trends in own revenue per capita for ULGSP and comparison districts, between 2007 and 2011.

Table 1: Bivariate correlations in household survey

||Urban|Fiduciary|Infrastructure|Accountability|Taxation|
|---|---|---|---|---|---|
|Urban|1|||||
|Fiduciary|0.238***|1||||
|Infrastructure|0.228***|0.237***|1|||
|Accountability|0.130**|0.108*|0.333***|1||
|Taxation|-0.0334|-0.0208|0.296***|0.0157|1|

_Note:_ This table presents bivariate correlations between indices created using the inverse-covariance weights for individual items (Anderson, 2008). All indices are created such that higher values correspond to more positive outcomes (e.g. less corruption or better responses of government performance).

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whether project LGAs improved on the specific indicators that the project targeted. The survey data play three key additional roles. First, many of the survey indicators provide an independent check on data gathered via the APAs.<sup>8</sup> Second, the surveys with government officials complement the APA data with broader measures of governance quality. Third, the surveys with households in the LGAs provide a further measure of governance improvements, which is whether the public observes improved local governance.

A fundamental question for the impact evaluation is whether local governments that did not receive the project, and thus access to the incentives and training under the project, made similar improvements in the areas measured under the APAs. One obvious way to measure these outcomes in non-project local governments would be to engage the same firms that are scoring 8The survey data are independent in the sense that there are no rewards associated with the survey data, unlike the APA data which constitute the basis to allocate project funds to LGAs.

8
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- Tariffs do not have to be exceptionally low. Where consumption at the subsistence level is a very small share of monthly household cash expenditures, consideration may be given to raising tariffs. In the sampled countries, Angola and Nigeria have exceptionally low unit tariffs in absolute terms and relative even to the income of the poor.

- Volume‐differentiated tariffs at the high end may be considered. Several countries have volume‐ differentiated tariffs between the first and the second block, followed by increasing block tariffs thereafter. But if households are individually metered so that high monthly consumption signals a household with many appliances, their ability to pay can be exploited more to cross‐subsidize the low end of monthly consumption by switching the order: increasing block tariffs between the first and the second block, followed by volume‐differentiated tariffs.

- Reallocation of cross‐subsidies can help make electricity affordable in most countries. Targeted subsidies needed to make 30 kWh a month of grid electricity affordable to all urban residents, are not large in most countries. While recognizing that perfect targeting is not possible, the subsidies calculated in this study point to the potential for achieving universal access in urban and peri‐urban areas.

- Eliminating operational inefficiencies may not eliminate underpricing, but can make subsidies to the poor more financially viable to utilities.

- Many Latin American countries treat assets associated with new connection no differently from all other assets—high‐ and medium‐voltage transmission lines, for example—and capture them in the regulatory assets for tariff setting. Such an approach spreads the costs of new connection and helps reduce multiple connections by making the initial connection much more affordable in countries where connection fees are currently high.

Lastly, this paper found areas where questions in household surveys delivered ambiguous or questionable answers. A standardized approach to data analysis would also be helpful. Some examples follow.

- Quantitative information on kWh consumed seems difficult to obtain. Enumerators should be better trained to spot implausible answers if this question is asked. Without information on kWh consumed
Figure 1: Average Size and Development <!-- Start of picture text --> Kenya<br>FranceFinland<br>HungaryChileMalaysiaBelgium<br>Romania<br>Peru<br>Ethiopia SalvadorIndiaBulgariaColombiaLatvia SpainItaly<br>Portugal<br>Ghana<br>6 7 8 9 10 11<br>log GDPpc<br>200<br>150<br>Average Size 100<br>50<br><!-- End of picture text --> Note: The vertical axis measures the average firm size, conditional on 10-plus workers, applying the US’ distribution of firms across two-digit industries to all countries. The horizontal axis measures the natural logarithm of GDP per capita, for the year, 2014, as reported in the Penn World Table version 9.0. The list of countries in the sample is reported in Table 2 of Appendix **??** .

revenue products of firms within an industry. The property of the resource misallocation that matters for the purpose of understanding changes in average firm size is given by the elasticity between TFPR and TFPQ. We estimate this elasticity by running the following regression for each country in the sample<sup>27</sup> : where _TFPRs_ and _TFPQs_ stand for the average revenue and physical productivity in industry _s_ .

Unlike previous estimates of the distortion elasticity, such as Bento and Restuccia [2017] and Buera and Fattal-Jaef [2018], we pursue an employment weighted least squares (WLS) estimation. The goal of the weighting is to account for the possibility that the most productive firms, which account for a

> 27Given that the US, our efficient benchmark, also exhibits correlated distortions, we subtract the US elasticity from the estimated one for each country. We read the US distortion elasticity from Hsieh and Klenow [2007], which reports a value of 0.18 for the year 1998.

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ge growth<br>rent US d<br><sup>1997-99</sup>|rate<br>ollars)<br><sup>2000-03</sup>|Change<br>Index, 1996<br>1991=100|in share in e<br>Index 1999 <br>1997=100|xports<br> Index 2003 <br>2000=100|Change i<br> Index, 1996<br>1991=100|n share in E<br>Index 1999<br>1997=100|U imports<br>Index 2003<br>2000=100|
|---|---|---|---|---|---|---|---|---|---|
|Natural Resource|11.3|-1.1|7.5|78|98|89|143|106|114|
|Unskilled Labor|21.4|7.3|14.1|120|124|113|199|113|129|
|Capital Intensive|20.3|-8.1|10.1|114|78|98|176|65|136|
|SkilledLabor|20.2|-5.1|8.7|114|86|93|197|71|100|
|All abovegoods|17.1|-0.3|10.5|N/a|N/a|N/a|168|91|126|

Source: Based on EU Data from UN COMTRADE Statistics.

Notwithstanding differences in dynamics, Bulgaria’s specialization profile, as captured by export specialization (ES) indices,<sup>17</sup> has remained unchanged during transition. Bulgaria has had “revealed comparative advantage” in unskilled labor and natural resource intensive products in EU markets. Except for
Egypt, Arab Rep.|0.581|0.008|11306|Senegal|0.584|0.016|2157|
|Jordan|0.468|0.012|7316|Sierra Leone|0.517|0.026|1029|
|||||South Africa|0.383|0.050|343|
|South Asia||||Tanzania|0.563|0.018|1952|
|Afghanistan|0.523|0.015|2727|Togo|0.570|0.039|430|
|Bangladesh|0.614|0.010|5228|Uganda|0.666|0.017|1489|
|India|0.631|0.003|53132|Zambia|0.681|0.018|1325|
|Nepal|0.618|0.016|2171|Zimbabwe|0.609|0.021|1481|
|Pakistan|0.633|0.004|27858|||||

_Notes_ : This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1980s birth cohort sample.

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