rafmacalaba/data-use-mentions-extended huggingface dataset:
using cross-country and within-country data, although we cannot rule out non-causal explanations for this.

This analysis contributes to three literatures. The first examines the role of earnings in attracting quality candidates to the public sector. Ferraz and Finan (2009) document that higher wages for politicians improve political performance in Brazil. In addition, Dal Bó, Finan, and Rossi (2013) find that when salaries increased, more able applicants applied for public-sector jobs. With respect to teachers, combining cross-country data on teacher salary and student performance on international assessments in OECD countries, Dolton et al. (2011) suggest that “the relative wage of teachers is a very good proxy for their average quality.” Recently, Hanushek et al. (2019) show that in OECD countries, teacher salary premiums are associated with teacher cognitive skills and student performance. Our results are consistent with these studies and contribute to the literature by adding evidence from African countries.

The second examines the role of teacher pay changes on student learning. When Indonesia substantially increased teacher pay, teachers’ job satisfaction rose but student academic achievement did not (de Ree et al., 2018).<sup>5</sup> Several studies examine the effect of providing hardship allowance to teachers working in remote areas in developing countries on student performance, and most of them find little to no effect (Cabrera and Webbink, 2020; Bau and Das, 2020; Pugatch and Schroeder, 2018; da Silva Filho and de Xavier Pinto, 2014); recent work from Peru is an exception (Bobba et al. 2021). By contrast, we find a positive association between teacher earnings premia across countries and student performance. These findings might not be contradictory to each other if we consider the potential mechanisms for how student learning might be affected. Most of the literature on changes in teacher pay measures the short-term effects of such policies, where the primary mechanism to improve student learning in the time frame studied is through the effort of teachers in the current system
. The household level model remains moderately more efficient after the rescaling, however, as the average mean squared error and average CV are each about 20 percent lower.

Finally, table 11 compares the precision of the unit level model with the direct survey estimates for super-areas, which are districts in Sri Lanka and regions in Tanzania. The super-areas are levels for which the household survey is considered representative and survey-based monetary poverty rates are routinely published. In Sri Lanka, where there are 331 target areas, the mean squared errors for the subdistrict estimates are about sixty percent larger than the direct survey estimates for districts. In Tanzania, meanwhile, there are only 159 areas and the same number as super-areas as Sri Lanka. In this case, the average mean squared error of the small area district estimates is slightly lower than the direct estimates for regions, while the mean CV is modestly higher. This type of comparisons is useful to inform decisions by national statistics offices of whether small area estimates are sufficiently precise to publish.

_Table 11: Precision of household level EB model compared with direct estimates for super-areas_

||Sri La|nka|Tan|zania|
|---|---|---|---|---|
||Mean MSE|Mean CV|Mean MSE|Mean CV|
|Super-area estimates from direct sample<br>estimate(Horvitz-Thompson approximation)|1.4|16.8|11.4|15.8|
|Area estimates from Household EB model|4.6|32.0|11.1|17.5|

_Notes: Top row shows the unweighted mean squared error (MSE) times 10,000 and coefficient of variation (CV) for nonmonetary poverty estimates calculated from the national household budget survey across 25 Districts in Sri Lanka and 25 Regions in Tanzania. Bottom row shows the unweighted MSE times 10,000 and mean CV for non-monetary poverty estimates that incorporating geospatial data across 331 subdistricts in Sri Lanka and 159
(annex table 15). This is consistent with HEs selling mostly to households, rather than other businesses.

Start-up capital is a major problem for HEs. When asked in household surveys to report the most important problem they faced in starting their business, the most popular response was lack of capital (annex table 18). 87 percent of HE owners used their own or family capital to start their business. Even in countries such as Ghana, where household access to financial services is high, most start-up capital comes from home savings. This is not surprising, as with any small business banks need to see evidence of an ability to save and a willingness to invest own funds before taking the risk of making a loan. In addition, banks may wish to have key assets such as land, house, or business equipment pledged as collateral, which may deter formal borrowing. Microfinance approaches could solve some of these market gaps, but so far they are providing start-up capital for only 1.3 percent of all HEs. The use of formal credit (either from bank or micro finance institutions) for start-up is slightly higher in urban areas (3.2%) compared to rural areas (1.7%) and more common for those with completed secondary or above (5.5%) while there is no substantial difference across genders on average (annex tables 13-16).

Consistent with the 42 percent of HE owners reporting it as a secondary activity, only about half report that their business is open more than six months a year. The majority of urban HEs are operated year round, but the seasonality of rural HEs varies by country, with full time HE activity more common in the richer countries, except for Cameroon (annex table 4).

Although unlikely to be registered in national data bases, when required HEs do register with local authorities. In many countries, including Ghana and Tanzania, registration is optional, as it is legal to do business as a HE in one‟s own
action in near real time<sup>13</sup> and to assess channels for observed effects _ex post_ . Second, by providing an independent source of information, they can allay concerns about bias in statistical data that may result from misreporting to statistical agencies (e.g., to avoid taxes); partial coverage such as the omission of nonregistered or household farms in the case of Ukraine; or other types of measurement error.<sup>14</sup> To address these issues within the confines of this paper,<sup>15</sup> we complement yield analysis with an aggregate analysis where the means/sums of four key vegetation indices over relevant crops’ entire vegetation period is used as outcome variables in regression equation (1). These indices, widely used in the literature and practical work are the enhanced vegetation index (EVI), the leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR), and the Land Surface Water Index (LSWI).

The EVI is based on the normalized difference vegetation index (NDVI) that is widely used in the literature and agronomy but minimizes canopy-soil variations and improves sensitivity in dense vegetations. The

> 13 While several firms provide such services to large farms in Ukraine, they generally use satellite imagery to identify areas at risk and complement it with drone imagery or manual scouting.

> 14 In our case, inability to match all the village councils is a relevant concern in this respect.

> 15 While detailed discussion of temporal variation in these indices together with detailed climatic data would be of great interest to provide insights on the mechanisms through which crop rotation and other factors affect yields, it is well beyond the scope of this paper.

12
Canberra Group. 2011. _Handbook on Household Income Statistics (2nd edn.)_ . New York and Geneva: United Nations.

- Choe, Chung, and Philippe Van Kerm. 2018. “Foreign Workers and the Wage Distribution: What Does the Influence Function Reveal?” _Econometrics_ 6(3): 41.

- Chopra, Deepta, and Meenakshi Krishnan. 2019. _Linking Family-Friendly Policies to Women’s Economic Empowerment: An Evidence Brief_ . UNICEF Early Childhood Development, New York NY. <u>www.unicef.org/sites/default/files/2019-07/UNICEF-Gender-Family-FriendlyPolicies-2019.pdf.</u>

- Cowell, Frank A., and Maria-Pia Victoria-Feser. 1996. “Robustness Properties of Inequality Measures.” _Econometrica_ 64 (1): 77-101.

- Dao, N.T., Dávila, J. & Greulich, A.  2021 “The education gender gap and the demographic transition in developing countries. Journal of Population Economics 34, 431–474.

- Eika, Lasse, Magne Mogstad, and Basit Zafar. 2019. ÎEducational Assortative Mating and Household Income Inequality.” _Journal of Political Economy_ 127 (6): 2795-2835.

- Elveren, Adem Y. 2014. ÎWomenÑs Labour Force Participation and Pay Inequality: Evidence from Panel Cointegration.” _Applied Economics Letters_ 21 (12): 862-65.

- Esping-Andersen, Gøsta. 2007. “Sociological Explanations of Changing Income Distributions.” _American_

22
2|22,490|14,232|99|1.1%|
|Tanzania|2,909|2.8|1,025|1,747|19|2.1%|
|Uganda|2,216|2.2|1,024|1,310|21|1.9%|
|Uruguay|41,111|3.1|13,445|7,629|66|1.7%|
|United<br>States|279,700|3.2|86,586|45,655|163|0.0%|
|Vietnam|4,606|3.0|1,543|1,471|22|0.0%|
|South Africa|41,674|2.5|16,476|12,979|80|0.9%|
|Zimbabwe|793|1.1|751|726|26|0.0%|

_Note_ : All initial conditions correspond to averages from 2008‐2012, when available. _Sources_ : For methods and sources for variables in columns 4‐6, see Table 1. For columns 1‐3:

- World Development Indicators (WDI), 1960‐2012

- Penn World Tables  (1950‐2011)

- International Labor Organization (1990‐2012)

37
16/140||||

Note: - indicates up to date microdata have not been collected.

# 3.6 Accessibility of Microdata Internationally

We have not (yet) considered non-NSO websites and/or repositories from which a country’s data could be available. We excluded these on purpose in table 3 as data users –most of whom would be nationals, should be able to access data for their country from their national NSO (or other national agencies: health surveys, for instance, are at times collected and published by Ministries of Health). Yet there are instances where microdata sets are available in international repositories, even while they are unavailable locally. For example, the National Statistics Office of Malta makes microdata from some surveys available to Eurostat who then makes it available to data users upon successful registration and application for the data – these data may not be available on the website of Malta’s National Statistical Office.<sup>17</sup> To complete the picture of microdata accessibility for each country, we explore what is available in international microdata libraries. We do so by visiting the WB microdata library, the web-portals of the MICS and DHS surveys as well as the microdata libraries maintained by the International Household Survey Network (IHSN), IPUMS and Eurostat.<sup>18</sup> The results from this exercise are summarized in table 4.

> 17 For example, Malta National Statistics Office sends microdata from its <mark>European Statistics on Income and Living Conditions Survey (EU-SILC), Household Budgetary Survey as well as Labor Cost Survey – Enterprise survey- to Eurostat where it can be requested by data users.</mark>

> 18 See annex table A2 for the links to these microdata libraries.

12
(2010), and Round 4 (2012). Figure I presents an illustration of the study design, and a more detailed description of the experiment can be found in (Baird, McIntosh and Özler 2011).<sup>11</sup> Girls receiving UCTs simply had to show up at a local distribution point each month to pick up their transfers. Monthly school attendance for all girls in the CCT arm was checked and payment for the following month was withheld for any student whose attendance was below 80% of the number of days school was in session for the previous month. However, participants were never removed from the program for failing to meet the monthly 80% attendance rate, meaning that if they subsequently had satisfactory attendance, their payments would resume. Other design aspects of the program were kept identical so as to be able to isolate the marginal effect of imposing a schooling conditionality on outcomes of interest among _baseline schoolgirls_ .<sup>12</sup>

# **2.3 Data Sources and Outcomes**

The focus of this paper is data collected in Round 4, which took place in 2012, more than two years after the end of the intervention. However, to provide context to these results, we also present impacts on the same outcomes, when available, for data collected during Rounds 2 and 3. Focusing on the core respondent, the data sources include household surveys (all rounds), biomarker data collection on HIV (Round 2-4) and Anemia (Round 4), and competencies (Round 4). In Round 4, data collection also included anthropometric data (children under 60 months of

> program. Towards the end of the first year, upon successfully obtaining additional funding, we circulated new offer letters informing the beneficiaries that the program would be continued for one more year, but not more. This message was repeated regularly at the cash distribution points by the program staff during the second and final year of the intervention.

> 11 The size of
Several studies directly examine whether an investment in green/clean technologies increase firms’ productivity or production costs (Hamamoto, 2006; Lanoie et al., 2008; Böhringer et al., 2012; Horvathova, 2012; Rubashkina et al., 2015; Tugcu and Tiwari, 2016; Palmer and Truong, 2017; Rath et al., 2019; Leoncini et al., 2019; Stucki, 2019; Sohag et al., 2021). Table 1 summarizes the methods used and the main findings and arguments of selected studies. Based on Japanese manufacturing industries, Hamamoto (2006) finds that the pollution control expenditures have a positive relationship with R&D expenditures and have a negative relationship with the average age of capital stock. It also shows that increases in R&D investment stimulated by the regulatory stringency have a significant positive effect on the TFP growth. Lanoie et al. (2008) find that investments in pollution-control technologies, as a proxy of stringent environmental regulations, resulted in modest, long-term gains in TFP in a sample of 17 Quebec manufacturing sectors. They show that this effect is more important in industries that are more exposed to international competition. Using a panel dataset of German manufacturing sectors for the 19962002 period, Böhringer et al. (2012) examine the relationship between environmental policies, including green/clean investment, and firms’ productivity growth. They do not find a positive impact of environmental investment on production growth. They, however, conclude that environmental policies or regulations should stimulate environmental investment to realize firms' productivity growth. Analyzing 79 global firms between 2007 and 2012, Palmer and Truong (2017) find a positive relationship between new green technologies use and firms’ profitability.

Using data from 25 OECD countries, Sohag et al. (2021) examine if the investment in renewable energy technologies promotes TFP. They find that a higher share of renewable energy in the production process significantly promotes TFP in the long run. This is not necessarily the case in the short run. The
Figure A.2. Women’s Marriage Before Age 18 Post the Gujarat Riots of 2002 <!-- Start of picture text --> Marriage Year<br>Data Source: NFHS-4<br>.2<br>.1<br>0<br>Diff-in-diff Coefficients<br>-.1<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV. The outcome variable is an indicator of women’s marriage before age 18. The control states only include the bordering states of Gujarat, which are Maharashtra, Rajasthan and Madhya Pradesh. Standard errors were clustered at the state-year level 32
**Are You Being Asked? Impacts of Respondent Selection on Measuring Employment**

Talip Kilic<sup>‡</sup> , Goedele Van den Broeck<sup>ǂ</sup> , Gayatri Koolwal<sup>§</sup> , Heather Moylan*<sup>1</sup>

**Keywords:** Gender, Labor, Respondent Selection, Household Surveys, Malawi, Sub-Saharan Africa.

> 1 ‡Senior Economist, Living Standards Measurement Study (LSMS), Data Production and Methods Unit, Development Data Group (DECDG), The World Bank, Rome, Italy; tkilic@worldbank.org.<sup>ǂ</sup> Assistant Professor, Earth and Life Institute, UC Louvain, Louvain-la-Neuve, Belgium, <u>goedele.vandenbroeck@uclouvain.be</u> .<sup>§</sup> Consultant, LSMS, Data Production and Methods Unit, DECDG, The World Bank; <u>koolwalg@devscience.org.</u><sup>*</sup> Survey Specialist, LSMS, Data Production and Methods Unit, DECDG, The World Bank, Rome, Italy; hmoylan@worldbank.org. The authors thank Kathleen Beegle for her comments on the earlier draft of this paper.
However, that has not been the case in the past; Chen and Ravallion (2010) show important changes when moving away from extrapolated 1993 PPP factors toward 2005 PPPs. To overcome this limitation, Jolliffe and Prydz (2015) base their estimates on more up-to-date national poverty lines (i.e., on average from 2008). However, their results are obtained using an estimation of the _implicit_ national poverty lines from combining official poverty headcounts at national poverty lines, as reported by the World Development Indicators (WDI), with the corresponding consumption and income distributions from the World Bank’s PovcalNet database. One caveat of this exercise is that the combination of official poverty rates from the WDI with _unofficial_ harmonized micro-data sets from PovcalNet may result in poverty lines that deviate from the official ones. The poverty lines we use in this paper as inputs for updating comparable regional poverty lines allow for the replication of countries’ official poverty estimates in LAC and is one of the main contributions of this paper. In addition, by using more up-to-date information, our estimates are less sensitive to errors in the CPI than the ones that arise from updating the $2.5 and $4 per person per day poverty lines at 2005 PPPs using national CPIs.

# **3 Data**

We use three different data sets in this paper: (i) country-specific official extreme and moderate poverty lines from NSOs in the region, (ii) household per capita income distribution from harmonized household surveys, and (iii) the most up-to-date PPPs from the 2011 ICP round.

# _3.1 Country-specific official poverty lines_

In order to control for geographical discrepancies in the cost of living within countries when computing the regional poverty lines, we make use of the most up-to-date data on _subnational_ official poverty lines obtained from NSOs or governmental agencies from 18 countries in LAC.<sup>13</sup> The countries considered in this paper include: Argentina, Bolivia, Brazil,<sup>14</sup> Chile,
observations with fathers aged over 100 years or missing, or sons aged over 65 years are excluded from the samples used in the empirical analysis.

In each data set, we observe the education level and an indicator of whether the main occupation is agriculture or nonfarm activity for both the father and the son. Our main analysis of educational mobility is based on years of schooling as the measure of educational attainment. Father’s schooling is used as the indicator of parental education to avoid complications from many missing observations on mother’s schooling. In our data sets, the maximum of parental education coincides with the education level of the father in most of the cases. We define the parental occupation dummy _Di_<sup>_np_</sup> = 0 when the father of child _i_ reports agriculture as the main occupation (corresponding to _Oi_<sup>_p_=</sup><sup>_f_inthe</sup> theoretical model), _Di_<sup>_np_</sup> = 1 otherwise. This means that the households who are primarily engaged in farming with some nonagricultural sources of income are classified as agricultural occupation.

Online appendix Table A.1 shows the descriptive statistics of our main data samples from the REDS and the CFPS. In the REDS sample, we have 6887 observations, and the children’s age is 29 years on average in the survey year 1999. Fathers are 60 on average. About half of the children’s main occupation is agriculture, while 60% of the fathers also reported agriculture as their main occupation. The children attain significantly higher levels of education than the fathers, when comparing their average years of schooling (6.26 vs. 4.13.) In CFPS sample, a similar pattern is observed. We have 3,305 father-son pairs, and children’s age is about 40 years in the survey year, 2010 (29 years in 1999, same as that for India in 1999 REDS data). Fathers are aged 68 years on average
Policy Research Working Paper 9224

# **Abstract**

This paper contributes to better understanding firms’ discriminatory behavior in the presence of gender-based legal discrimination and its linkages with labor market outcomes for women in a developing country setting. Using data collected through the World Bank Enterprise Surveys in the Democratic Republic of Congo, the paper documents the existence of nonnegligible employer discrimination and limitations in women’s autonomy in the presence of a discriminatory environment. Interestingly, these are more pervasive outside the capital city, Kinshasa, which suggests that cultural norms or differences in regulation enforcement may be at play. The paper also finds that firms’ discriminatory behavior harms women’s labor market outcomes, in their representation among the upper echelons of management and participation in the overall workforce. The negative relationship between restrictions from discriminatory behaviors and female employment is particularly strong in the manufacturing sector.

This paper is a product of the Global Indicators Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at smuzi@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 Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team