rafmacalaba/data-use-mentions-extended huggingface dataset:
mixed results regarding the effect of the minimum wage on the employment of young workers. In Poland, the period 2005‐2010 saw a relatively rapid rise in the minimum wage in parallel with a significant decline in the employment of 15 to 24 year‐olds. The increase was so high that the ratio between the minimum wage and the average wage went from 0.34 to 0.41 in less than five years. Since the increase was uniform throughout the country, it was estimated that people in the poorest regions were most adversely affected (Majchrowska and Zołkiewski (2012). The negative impact on young workers was strongest in the less advanced regions and where the ratio between the minimum wage and the average wage was relatively high (Broniatowska et al., 2015).

For the United States between 1954 and 1979, Brown et al. (1982) noted that a 10 percent increase in the minimum wage generated a 1 to 3 percent reduction in the employment of young workers. Similarly, a more recent study by Neumark (2014) observed an overall reduction in employment following an increase in the minimum wage and young workers were affected the most. Studies on Brazil by Fajnzylber (2001) and Lemos (2004) also found greater job losses among young workers than for the workforce in general.

The evidence from some countries indicates that when faced with minimum wage increases, businesses tend to replace marginally productive low‐skilled workers whose performance is below minimum wage level with more skilled workers. In Portugal, for example, Pereira (2003) analyzed the impact of a minimum wage equalization policy for young workers compared with the minimum wage level for workers over 19 years old. Based on panel data from a number of businesses, this study found that 10 percent increases in the minimum wage reduced employment for workers 18 and 19 years old by 2 and 4 percent, respectively, whereas they actually increased employment for workers 20 to 25 years old. This study also found a significant reduction in hours worked or cutbacks from full
sup> percentile of the log of the true consumption distribution by 100.

The results from Table 2 and Table A2 collectively present a clear argument for the consideration of the proposed PMT model for targeting Samurdhi. A significantly higher share of the poor can be reached, and inclusion errors from the program can be simultaneously minimized relative to the program’s current targeting method. Models 2 and 3 perform a few percentage points worse than Model 1 in undercoverage and leakage at all cutoffs (Table 3). The advantage of Model 3 is its use of fewer predictors, making the model simpler and cheaper to implement, but at the cost of slightly higher undercoverage and leakage.

# _Choice of Target Population_

Sri Lanka’s official poverty headcount, based on the HIES 2016 data, was 4.1 percent. That survey reveals 18.8 percent of the population to be Samurdhi beneficiaries, but the true proportion is believed to be 20‐ 25 percent due to underreporting among survey respondents. For this reason, we recommend setting the target population for Samurdhi as the bottom 25 percent.

Table 4 presents the shares of beneficiaries identified by PMT, by (actual) consumption deciles under different cutoff rates and PMT models. The eligibility rates for all three models are more or less similar, with overall coverage of 17.5, 23.7 and 36.6 percent for target populations of 25, 30, and 40 percent 11
Table 11: Intervention impact on household borrowing from MFIs (amount borrowed)

||(1)<br>Any MFI|(2)<br>Partner MFI|(3)<br>Other MFIs|
|---|---|---|---|
|SL|5180.6|3187.3|1993.3|
||(4157.4)|(3125.3)|(2422.5)|
|Cluster-robust p-value|[0.2128]|[0.3079]|[0.4107]|
|Romano-Wolf p-value|[0.3986]|[0.4885]|[0.4885]|
|Strata FE|Yes|Yes|Yes|
|Interviewer FE|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|
|Ratio sample clients/GP size|Yes|Yes|Yes|
|Control mean|85276.0|50289.4|34986.6|
|N|2496|2496|2496|

_Notes_ : Credit bureau information available for 2,496 households. SL equals sanitation loan treatment arm. Standard errors clustered at the village level are shown in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p-values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. Amounts are in Indian Rupees (1 USD = Rs. 67.5). Data source: household survey. Dependent variables come from credit bureau data.

treatment arm (see Online Appendix E). Having established that there was no switching away from MFI borrowing, we can further check whether households might have switched between loans
with an extreme “efficient bargaining” model.  Both Saba Arbache (2002) and MenezesFilho et al (2002) find wage effects, and the latter work finds positive employment effects of unions as well.  Menezes also finds a concave relationships where some union concentration raises both wages and salaries, consistent with efficient bargaining, but after a point leads to deleterious employment effects.  In Uruguay, Cassoni, Labadie and Fachola (2002) also find that unionization promotes both higher wages and higher levels of employment.<sup>34</sup> Though suggesting that unions may increase employment in their host firm, the firm level analyses cannot capture  economy wide effects.  Some labor codes, for instance, dictate that negotiated contracts cover all firms regardless of size, and are widely seen as imposing inflexibility and hardship on smaller firms.

**_More importantly, unions may pose a barrier to technological adoption although the empirical work to date is only suggestive._** The theoretical literature does not unambiguous conclude how unions impact innovation. Hjalmaarson (1990) suggests that Swedish unions understand the link between technological upgrading and wages increases and aggressively encourage firms to adopt new techniques.  Parente and Prescott (2000) argue that more commonly unions focus on the short term goal of protecting jobs at a great cost in foregone productivity gains.  US studies find consistently negative impacts of unions on R&D but this is not so clear for European countries, which seem to bargain more on employment rather than wages. To date, there is little evidence for Latin America on which to report.  One of the few examples is Reis and Crespo (2004) who, using information for 1600 firms in Brazil, find evidence that labor legislations restrict firms to hire relatively more skilled workers which may, in fact, push toward more technological adoption. On the other hand, a case study of the aviation sector by Garcia Verdu (2004) suggests negative impact of

> 33 See Background paper by Bosch and Siga 2004

> 34 They argue, somewhat counter-intuitively,
42. Harrington, L. J. & Otto, F. E. L. Reconciling theory with the reality of African heatwaves. _Nat. Clim. Change_ **10** , 796–798 (2020).

43. Editorial. Ending hunger: science must stop neglecting smallholder farmers. _Nature_ **586** , 336–336 (2020).

44. Derbile, E. K., Chirawurah, D. & Naab, F. X. Vulnerability of smallholder agriculture to environmental change in North-Western Ghana and implications for development planning. _Clim. Dev._ **14** , 39–51 (2022).

45. Chandra, A., McNamara, K. E., Dargusch, P., Caspe, A. M. & Dalabajan, D. Gendered vulnerabilities of smallholder farmers to climate change in conflict-prone areas: A case study from Mindanao, Philippines. _J. Rural Stud._ **50** , 45–59 (2017).

46. Harvey, C. A. _et al._ Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. _Philos. Trans. R. Soc. B Biol. Sci._ **369** , 20130089 (2014).

47. Zeleke, T., Beyene, F., Deressa, T., Yousuf, J. & Kebede, T. Vulnerability of smallholder farmers to climate change-induced shocks in East Hararghe Zone, Ethiopia. _Sustainability_ **13** , 2162 (2021).

48. Marulanda, M. C., Cardona, O. D. & Barbat, A. H. Revealing the socioeconomic impact of small disasters in Colombia using the DesInventar database. _Disasters_ **34** , 552–570 (2010).

49. Gourlay, S., Kilic, T., Martuscelli, A., Wollburg, P. & Zezza, A. Viewpoint: High-frequency phone surveys on COVID-19: Good
measures. As stated above, the original motivation to introduce Landsat imagery was due to the expected difficulty of nightlights to capture very small but highly productive units. This section lends evidence to this by performing additional analysis of the prediction power of Landsat and nightlights by dividing the data into subsamples.

At first, the sample is divided by the share of formal employment. Since the data only measures economic activity of formal enterprises, there is concern that the share of informal employment will be correlated with the satellite data and thus will introduce a non-classical measurement error. To check this, the paper follows McCaig and Pavcnik (2015) and calculates the share of informal workers at the district level using the 2009 Population and Housing Census.<sup>11</sup> The sample is then split into deciles of formal employment and run separate regressions for log employment at the commune level for each sub-sample.<sup>12</sup> Figure 3 shows the resulting R-squared measures for three specifications: 1) Sum of Lights, 2) a specification with only the two Landsat spectral indices (NDBI and NDVI), 3) the first two moments of all Landsat spectral bands. In the upper panel of Figure 3 one can see that, as expected, the Landsat measures perform extraordinarily well in communes with a high share of formal employment. These communes tend to be cities with smaller area size. The flexible Landsat specification dominates the nightlight approach at all deciles, while the simple indices are superior to the SOL measure only for communes with a high share of formal employment.

Next, the sample is split up into deciles of commune size and again regress individual models for each decile. The lower panel of Figure 3 shows the resulting R-squared measures according to commune size. As expected, the sum of lights approach does extremely poorly at predicting economic activity of very small communes. The prediction power then increases sharply for the next decile and then decays almost monotonously with size. In contrast, both

> 11McCaig and Pavcnik (2015
**Table 1.2 Uganda 2006, special funds in infrastructure**

|**Fund**|**Administrating**<br>**authority**|**Funding sources**|**Objectives**|
|---|---|---|---|
|Rural Electrification Fund|Rural<br>Electrification<br>Agency|5% levy of transmission bulk<br>purchases by generation<br>companies; World Bank;<br>government|Capital subsidies to private rural<br>generation companies; tariff subsidies<br>to rural distribution companies.|
|Credit Support Facility||World Bank, government|Refinancing facility for long-term<br>private lenders to rural electrification<br>projects; partial riskguarantees.|
|Tariff Stabilisation Fund|UECL<br>(transmission<br>company)|Tariff levy on cost of generation|Smoothing of electricity tariff increase<br>until Bujugali comes on stream.|
|Rural Communications Fund|Uganda<br>Communications<br>Commission|1% levy on gross revenue from<br>telecommunications and postal<br>services providers (universal<br>service levy)|Support for rural communication;<br>provision of at least one public<br>telephone per 5,000 people at the<br>subcounty level; ensure Internet<br>access at every district headquarters.|
|_Source:_World Bank 2006b.||||

# Collecting readily available relevant data

## **Private participation in infrastructure (template 3)**

Indicative levels and trends of private participation in infrastructure according to various private participation modalities and arrangements can be gleaned from the World Bank‘s private participation in infrastructure database (http://ppi.worldbank.org/). One template should be completed for each country.

Template 4 contains a list of transactions in infrastructure services involving private participation. The source
joined _Mais Educação_ in 2010 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
6,111|
|R-squared|0.491|0.461|0.241|0.485|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for regression (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Survey length_ is the time length of the interview in minutes. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

42
# _Overall results_

To provide more detail on legal and regulatory barriers to investment, the GIC Survey 2019 asked respondents about the degree to which various legal and regulatory issues presented obstacles to their operations. Such issues covered issues ranging from cumbersome investment approval processes to restrictions on expatriate staff.

Cumbersome investment approval processes and operational restrictions are the most often cited regulatory barriers for FDI in surveyed MICs (Figure 2). Fifty-six percent of respondents list cumbersome investment approval processes as moderate or major obstacles to operations, and 44 percent cite price, technology, or product restrictions as moderate or major obstacles. The salience of these top two concerns holds across most host countries and sectors. In addition, these findings are consistent with prior work that also finds approval processes and restrictions on prices, technology, or products to be a significant obstacle for foreign affiliates (Mistura and Roulet 2019; UNCTAD 2019).

# _Heterogeneity across investor types_

Compared with manufacturing affiliates, services affiliates perceive joint venture requirements to be more significant obstacles on average, as evidenced by the positive and statistically significant (at the p<0.10 level) coefficient on the dummy of services in our preferred specification (Table 16, Column 6). Affiliates in the services sector also perceive expatriate restrictions to be more significant obstacles on average (see the positive and significant—at the p<0.10 level—coefficient in Table 18, Column 6).

**Figure 2. Salience of legal and regulatory obstacles (% of respondents)**

**Question:** To what degree are the following factors an obstacle for your company to operate in this country?

<!-- Start of picture text --> Investment approvals 26% 30%<br>Price, tech, or product restrictions 16% 28%<br>Foreign investment limits 16% 22%<br>Expat restrictions 13% 24%<br>JV requirements 15% 20%<br>Local sourcing requirements 12% 21%<br>R
in life (WHO & UNICEF, 2021). Similarly, data on child consumption of sweet beverages and sentinel unhealthy foods<sup>2</sup> in the previous day/time interval are now included as unhealthy eating indicators in largescale surveys (i.e. MICS and DHS) based on evidence showing increased overweight/obesity risk among children consuming unhealthy snack foods and sugar-sweetened beverages (SSB) (Pries, Filteau, et al. 2019; Pries, Rehman, et al. 2019; Rousham et al. 2022).

There are no validated metrics for unhealthy diets in children 2-5 and 6-10 years. In LMIC, consumption of **unhealthy foods and SSBs** , as well as **insufficient intake of fruits and/or vegetables among adolescents** are monitored as health risk behaviors through Global School-based Student Health Surveys administered to school-going children 12-17 years of age (Beal et al. 2019). Eating fruit (Parra et al. 2021) or vegetables is associated with lower risk of overweight/obesity in children 12-17 years, while fast food and soft drinks consumption are associated with higher risk of overweight/obesity (Mahumud et al. 2021).

There are various **other characteristics of diet quality that are not assessed by the measures above** and are considered out of the scope of our work. These include several nutrition and health promoting components such as micronutrients added to fortified foods, dietary supplements, higher levels of nutrients in biofortified food products, and non-nutritive bioactive compounds (e.g. probiotics, polyphenols) present in maternal milk (Andreas et al. 2015; Badillo-Suárez et al. 2017) unprocessed or functional foods (Konstantinidi and Koutelidakis 2019). Similarly, food safety, including food borne contaminants, is beyond the scope of our work.

> 2 “Sentinel unhealthy foods” are foods that are likely to be consumed by infants and young children and are high in sugar, salt and/or unhealthy fats. These include four categories of unhealthy
5.10 The multi-dimensional poverty proxy used for illustrative purposes in this study is based on a weighting of five variables demonstrated to have a strong correlation with poverty, namely: overcrowding; inappropriate housing; unsanitary conditions; presence of infants; and head of household education (Table 7). This proxy is used to estimate per capita household income and hence to predict whether a particular household is above or below the poverty (or extreme poverty) line.

5.11 The results of using this device indicate that errors of exclusion for the poverty proxy weighting all five indicators are substantially lower than the errors of exclusion associated with any one of the five indicators individually. Nevertheless, the poverty proxy performs substantially better at predicting poverty that at predicting extreme poverty, illustrating that this smaller second group is much more difficult to identify.

**Table 8: Targeting** **<u>performance of the multi-dimensional poverty proxy</u>**

|%|Overcrowding|<sup>Inappropriate</sup><br>housing|Unsanitary<br>conditions|Presence<br>infants|of<br>Head<br>household<br>education|of<br> <br>Multi-<br>dimensional<br>proxy|
|---|---|---|---|---|---|---|
|**Poverty**|||||||
|Error of inclusion|19.6|27.1|10.5|32.7|39.8|39.7|
|Error of exclusion|90.7|96.1|91.1|67.2|85.1|36.3|
|**Extreme poverty**|||||||
|Error of inclusion|42.2|77.0|42.1|73.3|65.7|36.
complement these papers by documenting the main extensive and intensive margins of transport consumption that matter for predicting the increase in carbon emissions in low- and middleincome countries and simulate changes in carbon footprints as well as within-country carbon inequality for 18 low- and middle-income countries.

The remainder of this paper is organized as follows: Section 2 presents data sources. Section 3 presents a series of stylized facts between income and the margins of transport consumption. Section 4 presents findings on income as a main determinant of transport consumption. Section 5 presents the results of the simulations of rising incomes and changes in transport carbon footprint and inequality. Section 6 concludes.

# **2 Data**

This paper relies on times series of country-level data to provide stylized facts across countries as well as micro-level data from household surveys to provide in depth analysis of the determinants of transport demand (Table 1).

Table 1: Summary of data sources and main uses

|**Source**|**Coverage**|**Aggregation**|**Main use**|
|---|---|---|---|
||Household|surveydata||
|Global<br>consumption<br>database|105 countries between<br>2000 and 2018|Averages at the quin-<br>tile/urban levels|Global stylized facts|
|LSMS|18 countries after 2010|Raw data - Household<br>level|Regressions for trans-<br>port expenditures and<br>vehicle ownership|
|World Bank SSAPOV|Sub-Saharan Africa|Countrylevel averages|Global stylized facts|
||Nation|al data||
|International Road Fed-<br>eration|Unbalanced panel for<br>194 countries|Country level|Global<br>stylized<br>facts<br>and Simulations|
|World Bank WDI GDP|Panel for all countries|Country level|Simulations|
|CEPII GDPprojections|Panel for all countries|Countrylevel|
Policy Research Working Paper 5883

# **Abstract**

Despite their explicit focus on reaching the poor, many community driven development (CDD) projects have been found to be only mildly pro-poor in their funding allocations. This paper presents evidence of an explanation that has been overlooked in the CDD literature to date: the requirement that beneficiaries must apply for projects in order to receive support. The authors first examine data on the universe of project applications and funding under Tanzania’s flagship CDD program, Tanzania’s Social Action Fund, and then use a census of 100 program villages to examine the determinants of both program awareness and program participation at the household level. The data paint a consistent picture at both levels: wealth, access to information, and political capital are important correlates of the ability to navigate the application process successfully. The centrally dictated features of this decentralized program appear to be the most effective mechanisms in directing funds to the poor. The results suggest that unless demand-driven projects can develop ways of soliciting engagement from a broader cross-section of the population, they are unlikely to achieve truly progressive targeting.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at bozler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World
0.193|0.000***|
|Access to nonlabor income|0.470|0.451|0.529|0.000***|
|Share of nonfarm labor income (%)|0.248|0.297|0.103|0.000***|
|Share of nonlabor income (%)|0.174|0.144|0.262|0.000***|
|Number of unpaid ag family workers|0.179|0.220|0.059|0.000***|
|Agricultural inputs (Rs./month)|4,812|5,449|2,923|0.000***|
|Tractor (=1 if tractor(s), =0 otherwise)|0.104|0.122|0.048|0.000***|
|Incidence of natural calamity|0.114|0.109|0.132|0.071*|
|Access to finance|0.090|0.098|0.066|0.000***|
|Access to ICT|0.880|0.882|0.874|0.267|
|Incidence of crop subsidy|0.337|0.389|0.180|0.000***|
|Value of crop subsidy (Rs./month)|898|935|658|0.150|
|Export Orientation Index|0.216|0.177|0.339|0.000***|
|Simpson Index of Diversification|0.102|0.115|0.060|0.000***|

Source: authors’ calculation using HIES 2016. Notes:<sup>a</sup> Self-employed farmers are defined as those who had income from at least one crop activity. Those