rafmacalaba/data-use-mentions-extended huggingface dataset:
effects models.<sup>12</sup> Below, as a robustness check, we show that giving equal weight to each municipality worsens the predictions.

Finally, we ignore heteroscedasticity in the dependent variable, which is the average AGEB labor market outcome taken from the sample. A sample of 40 individuals was taken from each AGEB, except for the few cases where AGEBs contain fewer than 40 working aged persons, which implies that heteroscedasticity is not a first order concern when estimating labor force participation rates. Different rates of labor force participation across sample AGEBs will affect the number of labor force participants used to calculate unemployment rates from the sample, but we do not adjust for this source of heteroscedasticity.<sup>13</sup> Population counts for each AGEB taken from the 2020 census were used as population weights when aggregating AGEBs to municipal estimates. We used a modified version 2.0 of the EMDI package in R, modified to incorporate sample and population weights, to estimate the model (Kreutzmann et al., 2018).<sup>14</sup>

# **e. Evaluating the estimates**

To evaluate the estimates, we compare each estimate to the population values from the 2020 census. We calculate the following statistics to compare these four separate estimation methods:

- Estimated mean squared error. For the direct estimates, this is assumed to be equal to the estimated variance of the mean, estimated using the Horwitz-Thompson approximation.<sup>15</sup> For the small area estimates, MSE estimates were generated using the parametric bootstrap procedure.

- The median estimated relative standard error across municipalities, where the relative standard error for each municipality is defined as the square root of the estimated mean squared error divided by the point estimate.

- Coverage rate: This is the share of municipalities for which the actual census value falls within the 95% confidence interval generated by the estimate.

> 12 See Rabe-Hesketh and Skondral (2006). In conditional random effect models,
exceeds the number of enclosed test papers, the testing agency is directed to select the longertenure students by examining the school enrollment records for the last three months (again, ADU reports that this is a rare event, as changes in enrollment usually occur at the start of the academic year and the QATs are administered well into the academic year). The testing agency also checks the enrollment and attendance records over the last three months for fictitious students and impersonation cases.

QAT procedures have been developed to discourage schools from strategically concentrating on some grades for test preparation and to limit the risk of test leakage and its consequences. In primary schools, the QAT tests two grades; in middle and secondary schools, it tests three grades. Which specific grades are selected to be tested in a given school are kept strictly confidential by the ADU team—both the testing agency and the program school learn which grades will be tested only when the sealed test packets are opened by the testing agency at the school in the presence of the school administrator(s) just before testing time. The testing agency also only brings in the sealed test packets to be used at that school on the day of the test. In addition, for each grade, eight different question papers are prepared by the ADU. These questions papers are randomized for each grade. Table 2 shows the practical results of implementing these procedures: it presents the top-four combinations of tested grades and the associated shares of program schools by QAT round.

A number of additional, more standard procedures are applied to prevent cheating. Teachers and school administers are not permitted to enter the testing area during the test. Test invigilation is typically carried out by a team of at least three persons. Children are not allowed to bring any materials or stationery (other than pens) into the testing area. The grades selected for the QAT are tested simultaneously in the same testing area and the seating for the students is mixed. All test materials provided to the test takers are collected back. All
(although this depends in part on the choice of technology). In fact, it is often argued that an important bene fi t of deregulation is increased employment: workers forego job security for higher employment. However, employment effects can be quantitatively small.<sup>15</sup> In fact, when labor regulation became more restrictive in Brazil there were only small effects on employment, as reported by Barros and Corseuil (2001). In the presence of reduced enforcement, if all fi rms substitute formal by informal workers then there will be no change in employment. Still, there could exist large changes in fi rm performance, especially if the substitution of formal by informal workers leads to a reduction in the fi rm’s labor costs.

Net wages may increase if the fi rm is more productive, but gross labor costs will probably fall since the fi rm is paying less to social security. Net average wages may also increase if informal workers earn more than formal workers (keeping worker characteristics fi xed). This will be the case if fi rms share with informal workers the savings in social security payments they get by hiring them.<sup>16</sup> In the empirical work that follows we examine how enforcement affects the fi rm’s employment of informal workers, and how it subsequently affects total employment, wages, productivity and investment.

# 3. Data

Descriptive statistics of the main variables that we use in the analysis are reported in appendix table A1. The source of fi rm level data is the Investment Climate survey collected by the World Bank. The survey is representative of a set of manufacturing sectors that together account for 75% of the manufacturing value added and employment in 2002 (details of the survey are given in the data appendix). In the collection of this dataset the World Bank worked with a private survey fi rm. Given the detail and the sensitive nature of some

> 15For example, if fi rms change their technology then the demand for labor
by about 0.044 s.d–an impact bigger than ours.<sup>40</sup> Since our sample children are very young, our cash transfer simulation is comparable to cash transfers for households with newborn or very young children, common in numerous European countries and Canada (OECD, 2011). Gonz ́alez (2013) considers the effect of a large and unanticipated lump-sum universal child cash transfer to mothers with newborn babies. She finds the policy led to a substantially lower use of formal day care services and a temporary reduction in mother’s labor supply, with an increase in maternal care time. Schirle (2015) studies the Canadian Universal Child Care Benefit, which distributes a monthly amount ($100 in 2012) per child under six years of age. She finds substantial reductions in labor supply for mothers with high and low education levels, with stronger impacts for the latter. Milligan and Stabile (2009) studies a transfer expansion to households with children ages zero to five in the Canadian state of Manitoba. They find large differences in labor supply responses by mother’s education and estimate a negative impact only for households with low education levels. In our cash transfer simulation, we also find different effects on labor force participation and child development by mother’s education, with larger effects for college mothers. Since education is correlated with income, these heterogenous effects by mother’s education are consistent with the heterogenous effects by income estimated in the literature (see Løken et al. (2012) using Norwegian data.)

> 40In our simulations, the subsidy and voucher are $40 per day, generating a subsidy of $10,000 a year if the subsidy is used five days a week for about 11 months a year. As shown in Table F.1, the effect of the most effective interventions for married mothers is at most 0.13 s.d. The highest effect is for single mothers under quality-based vouchers (0.16 s.d
to correct for within-household correlation over time.

The dynamic panel data specification captures causal impacts if the livelihood diversification index is not correlated with the error term. We address issues of endogeneity which might arise from (1) simultaneity or reverse causality between the independent and dependent variables, (2) omitted variable bias, or (3) non-classical measurement error. With regard to the first issue, it is possible that households with better welfare outcomes have more resources, enabling them to diversify their livelihoods. In this case, a simultaneity problem arises as the dependent and independent variables are co-determined. To account for simultaneity, we use lagged independent variables to ensure temporal precedence of livelihood diversification relative to observed household food insecurity, thus isolating the relationship between past diversification and subsequent outcomes.<sup>9</sup> Our panel data, with a pre-COVID baseline helps us avoid omitted variable bias. By including household

> 8We pre-specified using past income level and focused on diversification as the coping strategy, but our results are robust to alternative proxies, such changes in income level, and use of other coping strategies and alternative estimators. See Online Appendix B for additional details and results.

> 9The use of lagged variables has the potential to bias coefficients downward, which Arellano and Bond (1991) and others address through instrumental variables. In Online Appendix B we implement the Arellano-Blover/BlundellBond (Arellano and Bover, 1995; Blundell and Bond, 1998) GMM estimator and show that the size of the downward bias is small and does not affect the interpretation of our results.

11
Conscientiousness|3|0.278|0.044|
|Openness|3|0.246|0.053|
|Emotional Stability|3|0.197|0.036|
|Agreeableness|3|0.437|0.114|
|Big Five|15|0.576|0.042|
|Grit|3|0.280|0.067|
|Decision Making|4|0.591|0.120|
|Hostile Bias|2|0.216|0.070|
|Non Cognitive|24|0.680|0.042|

# **9.3 District Level Administrative Data**

_Uganda Certificate of Education (UCE) scores_ : we use data for 2011-2013 on average total scores and division allocation of students at the end of lower secondary school. Students take exams in six mandatory subjects and may choose four additional ones . Note that 67
priority weights because their vulnerability to human encroachment differs greatly. Third, species’ distributions may be far from uniform in the spaces enclosed by their maps. If a species’ spatial distribution density is known, its counting weight for each cell can be made proportional to its likelihood The scientific literature features plentiful examples of weighted species counting (e.g., Guo et al. 2022; genetic and environmental data, as well as expert analysis of their roles in the assignment of counting weights. These are inevitably time-consuming and intensive in technical resources that remain in short to 30% global protection by 2030. Global biodiversity could be assessed with expert-mapped, wellresearched species only, but this would ignore the huge population of other species with recorded GBIF occurrences. Unfortunately, policy makers cannot automatically assume that well-documented species adequately represent the larger population.

For more detailed information, see ~~hitps://www.livingplanetindex.org/latest_results~~ https://www.gbif.org/analytics/global
wave 3, leading to the loss of 139 households from the sample. Furthermore, some households dropped from the sample because they refused to be interviewed, they were untraceable, or all members had died (NBS 2014, 2016). Since we are conducting analysis based on households that were part of the GHS wave 3, our sample does not in and of itself have conflict-induced attrition. However, we remain wary of the fact that this conflict-induced attrition could have biased our sample selection in such a way that we are not capturing the households that were most severely affected by conflict in Borno and Yobe. This attrition would therefore induce our victimization figures to be downward biased.

Table A.3 lists the sample sizes in each wave, showing that a vast majority of the households had been visited in all three waves. In the telephone survey, a total of 1,030 households from the GHS wave 3, visit 2 were attempted to be reached. Most of the nonresponses came from nonfunctioning phone numbers, as only 2.7 percent refused to answer. The survey first attempted to reach only 742 households, of which 529 could be reached and interviewed. In order to increase the sample size to a level that was considered adequate for the survey, an v
covered the same households after the project had been introduced between August 2000 and February 2001. The third phase was completed in March–July 2005. Out of this sample of 28 villages, 10 served as the control, while the remaining 18 received the project at different times (either between 1997 and 2001 or between 2001 and 2005).

About 65 percent of the households sampled (833 out of 1,284 households) received the project sometime between 1997 and 2005; 62 percent of this group (which we refer to as project 1 households) received access to the project between 1997 and 2001, while the remaining 38 percent (project 2 households) received the project between 2001 and 2005.

The data are used here to calculate economic returns to road improvement and the resulting impact on poverty, overcoming some of the pitfalls of earlier road evaluations that have relied mostly on cross-sectional household survey data. The data sets have a true before-and-after and with-and-without structure and are reasonably large, allowing for a study of household-level effects, especially with reference to households above and below the poverty line. These cover not only standard road project outputs, such as transportation costs and trip duration, but also key outcomes (e.g., household consumption and schooling) and a broad range of market interactions.

Tables 1a and b provide, for project and control areas, summary statistics on the outcomes and explanatory variables of interest for each survey round between 1997 and 2005. T-tests are also presented for whether the difference across project and control areas in each round is statistically significant. The household outcomes we are interested in cover a broad range of household socioeconomic characteristics, including household consumption (total, food, and nonfood), asset ownership, landholdings, wage and self employment activities across farm and non-farm sectors, and primary and secondary enrollment of school-age children. We also examine market prices paid by the household for transport, credit, and basic commodities (fish, rice, pulses, and different types of fertilizer for agricultural activities).
26 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity from the Emissions Database for Global Atmospheric Research (EDGAR)<sup>7</sup> of the JRC. Finally, we used OpenStreetMap data to account for a number of urban amenities like hospitals, universities, among others. ( © OpenStreetMap contributors<sup>8</sup> ). Table 5 presents a complete list of all control variables.

# **5 Empirical results**

# _5.1 Strategy 1: Lagged model_

Table 6 presents the correlations between the seven variables that we use to describe the three dimensions of urban form, and Table 7 presents a series of 10 alternative specifications for equation (2).<sup>9</sup> The main equation is estimated using OLS and assuming intra-group correlation; i.e., that the residuals are correlated within geographical clusters, but uncorrelated between clusters. We cluster at the country level and have a total of 32 countries in the sample.

Even though the three indexes used to describe the shape of the urban extent seek to measure different aspects of urban form (roundness, smoothness and fullness), the correlations in Table 6 show that there is very high positive correlation between _exchange_ index_ and _perimeter_ index_ (0.74). We also found high correlation between the variables _intersection_ density_ and _street_ density_ . Both variables measure the level of connectivity within a city and therefore have a very high correlation of 0.95. For this reason, we test alternative specifications, as shown in Table 7 where columns 1 to 5 use _intersection_ density_ and columns 6 to 10 use _street_ density_ . Finally, there is a high and negative correlation between _fullness_ and _sprawl_ (-0.71); for this reason we use them separately.

> 7 http://edgar.jrc.ec.europa.eu/gallery.php?release=v42&substance=
# 3.3 Asset ownership among men and women in different occupations

Across men and women in these occupation categories, Table 4 presents the ownership distribution of different types of assets covered in the Cambodia LSMS+, including across exclusive versus joint ownership.<sup>24</sup> Ownership of assets in general varies widely, by class, and women were in general less likely than men to own assets like mobile phones, financial accounts, and motorized vehicles. Mobile phones were owned by 91 percent of men and 77 percent of women, for example, with the widest gender gaps among those not working in any sector. Within these groups, a slightly higher share (47 percent of men and 40 percent of women) owned mobile phones exclusively, although joint ownership of these devices was also substantial. Motorized vehicles were owned by 68 percent of men and 53 percent of women, and only 14 and 12 percent of men and women, respectively, owned a financial account.

Table 4 also shows that respondents who own an NFE are more likely than other groups to have a motorized vehicle, financial accounts and a mobile phone, although women continue to be less likely than men to own these assets. On the other hand, women were significantly more likely than men to own livestock, residential land, as well as (among off-farm workers, and in particular, in wage work) non-residential land. For all assets except for mobile phones, gender differences in ownership tend to be driven by differences in exclusive, as opposed to joint, ownership.

Figure 3 presents, by age and gender, respondents’ self-reported valuation of these assets in Cambodian Riel. For all assets, other than financial accounts, respondents were asked “If you were to sell <THIS ASSET> today, how much would you receive for it?” For financial accounts, respondents were asked about the current value of the account. For assets that were jointly owned, the value of the asset owned by the respondent was calculated by dividing
15. World Bank Global Payment Systems Survey, 2010.

16. For more on documentation requirements and safeguards against money laundering, see Yikona and others (2011) and FATF (2011).

17. This figure is consistent with the findings of Beck, Demirguc-Kunt, and Martinez Peria (2008). Surveying an international sample of banks, they show that in many Sub-Saharan African economies annual fees and minimum balances required to open and maintain accounts exceed those elsewhere, constituting significant barriers to access.

18. In the core Gallup World Poll questionnaire respondents are asked to rate their trust in banks, and again respondents in Europe and Central Asia—account holders and non-account-holders— report the least amount of trust (Demirguc-Kunt, Klapper, and Zingales 2012).

19. Hallward-Driemeier and Hasan forthcoming.

20. The institutional barriers to financial inclusion are further analyzed in Allen and others (2012).

21. For more information, see Mas and Kumar (2008).

22. Klapper and Love 2011.

23. World Bank, World Development Indicators database.

24. Ratha 2006.

25. Collins and others 2009.

26. Gallup World Poll, 2011.

27. Jack and Suri 2011.

28. CGAP 2010.

**29**

MEASURING FINANCIAL INCLUSION
collection for such studies can be costly. World Bank support in collecting product-level diffusion data in developing countries would be very useful to the study of green technology diffusion in low-income countries.

Other sources of technology diffusion data include international trade data, which are available at the product level. For example, Sawhney and Kahn (2011) use the harmonized commodity description and coding system of the World Customs Organization to assemble data on U.S. imports of wind and solar energy technology. They find a growing role for emerging economies such as China and India, which they interpret as evidence of increased technology adoption in those countries. Similarly, World Bank (2008a) uses trade data to show that removing trade barriers on alternative energy technologies would increase trade volumes, and hence technology transfer.

# **III. Green R&D in Developing Countries**

As shown in Figure 2, most of the world’s R&D occurs in a few high income countries. In 2007, global R&D expenditures were an estimated $1.107 trillion, with OECD nations accounting for 80 percent, and the United States and Japan together accounting for 46 percent. Among non-OECD countries, China performs 9 percent of global R&D. Estimates of R&D from India and Brazil also place them among the top 15 R&D performers worldwide (National Science Board, 2010).

The dominance of high-income countries among the top R&D performers holds true for environmental innovation as well. Using patent data from the US, Japan, Germany, and 14 lowand middle-income countries, Lanjouw and Mody (1996) study technological change for a variety of environmentally-friendly technologies. They find that such innovation increases as 10
about 80 cities in 60 countries. From the enterprise-specific EOS survey database (2003), we construct city-specific averages for 12 governance indicators, as well as 5 service access/performance variables, covering 271 cities in 101 countries (see Table 1 below for details). For instance, two governance-related variables for which we constructed city-wide averages, were frequency of bribery in utility, and the extent of state capture (from firms’ reports on the distortionary impact on competition of illegal payments made by certain firms) to influence government policies, laws or regulations. From the Taylor database, we downloaded the number of offices of major advertising, accounting and financial firms in each city and we constructed a “global city” variable by adding up the three variables, covering a total of 261 cities in 114 countries.

14
lowest RMSE overall for 2005 as testing, is the baseline, linear model. For 2011 testing the best performing model is the parsimonious model.

### **Regional Results**

**Table 5.3: Results of excluding regions**

||Baseline|Model 1|Model 2|Model3|
|---|---|---|---|---|
|RMSEtestHRE|0.0514|0.0546|0.0595|0.0602|
|RMSEtestHDR|0.0302|0.0297|0.0519|0.0544|
|RMSEtestLOE|0.0042|0.0060|0.0055|0.0055|
|RMSEtestLOR|0.0602|0.0080|0.0034|0.0013|

Notes: cv=cross validation. R2=r‐squared; AIC=Aikike information Criterion; BIC= ; boot=bootstrap; RMSE=root mean squared error. HRE=Highlands, Reliable Region; HDR=Highland, drought‐prone Region; LOE=Lowlands Enset growing region; LOR=Lowlands reliable region.

For the regional validation we follow the example of Todd and Wolpin (2007), by non‐randomly holding out one region at a time to use as a testing dataset. Using the Highlands non‐drought as the testing region (row 1), the baseline performs best. Using the highlands drought as the testing region, the parsimonious model (1) is the best performer. Using the Enset lowlands as testing region, the baseline performs best. Using the lowlands‐reliable as testing, the full model performs best.

For the validation using an external dataset we attempted to use the 2012 Ethiopian Rural Sociological Survey (ERSS). This survey was also implemented by the Central Statistical Agency (CSA) of Ethiopia. However the consumption (and some other) indicators were deemed to be incomparable for the purpose of the exercise. The survey was conducted over