rafmacalaba/data-use-mentions-extended huggingface dataset:
**<u>Table 9: Comparison of Performance Indices – Jalanidhi & KWA-BM Schemes</u>**

|**Performance Indices**|**Jalanidhi**<br>|**Schemes**<br>|**KWA Built**<br>**Sche**<br>|**& Managed**<br>**mes**<br>|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|Availability & Reliability Index|0.14|0.99|‐0.57|0.85|
|Household Satisfaction Index|0.16|0.97|‐0.63|0.88|
|Household Cost of Service Index|‐0.17|0.70|0.66|1.61|
|Operation & Maintenance Index|0.05|0.97|‐0.18|1.12|
|Overall PerformanceIndex|0.07|0.96|‐0.27|1.11|

**<u>Table 10: Comparison of Performance Indices – Jalanidhi & Other Community Schemes</u>**

|**Performance Indices**|**Jalanidhi**|**Schemes**<br>|**Other Co**<br>**Managed**|**mmunity**<br>**Schemes**<br>|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|Availability & Reliability Index|‐0.01|1.
**Table A.12:** Effects on Self-Reported Bureaucratic Knowledge

||Training|Village Law|Knowledge index|
|---|---|---|---|
||(1)|(2)|(3)|
|New village head|-0.089|0.019|0.088|
||(0.117)|(0.107)|(0.119)|
|Observations|1067|1065|1065|
|Control mean|0.61|0.76|0.12|
|Robust p-value|0.313|0.886|0.391|
|<br>MSE-opt. bandwidth|19.1|17.8|28.4|
|Effective obs.|500|476|662|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in all columns. The dependent variable is: in column 1, a dummy equal to 1 if the bureaucrat received any training in the past 12 months; in column 2, a dummy equal to 1 if the bureaucrat reports being informed about Village Law regulations; in column 3, a standardized index of self-reported knowledge across 5 topics: development management & accountability, financial management, village regulations, drafting development plans, and the Village Law. See Section 4 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

**Table A.13:** Effects on Village Transfers and Budgets

|||**Adminis**|**trative data**||**Village head survey**|
|---|---|---|---
Policy Research Working Paper 9842

# **Abstract**

Spatial difference-in-differences analysis is used to study the impacts of a large-scale development intervention aimed at improving energy efficiency in Malawi. The estimation strategy takes advantage of the geographical variation in the implementation of different project components and is based on a combination of remote-sensing (satellite) data and national household survey data. The results suggest that a combination of demand-side and supply-side interventions was associated with a statistically significant increase in electricity access, a decrease in the frequency of blackouts, and a switch from traditional fuels to electricity as the main source of energy for lighting (but not for cooking). At the same time, there is no evidence that the intervention caused households to pay more for electricity. The results are consistent with an emerging view in the literature that there are synergies between energy efficiency and energy access, especially in places where the bottleneck to wider electricity access is limited electricity generation capacity rather than the cost of connecting more clients to the grid.

This paper is a product of the Independent Evaluation 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://www.worldbank.org/prwp. The authors may be contacted at rnarayanan1@ifc.org, dnaeher@worldbank.org, and vziulu@ifc.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
WPS3851

# The Return to Firm Investment in Human Capital

Rita Almeida* The World Bank

## Pedro Carneiro

University College London, Institute for Fiscal Studies and Center for Microdata Methods and Practice

#### Abstract

In this paper we estimate the rate of return to firm investments in human capital in the form of formal job training. We use a panel of large firms with unusually detailed information on the duration of training, the direct costs of training, and several firm characteristics such as their output, workforce characteristics and capital stock. Our estimates of the return to training vary substantially across firms. On average it is -7% for firms not providing training and 24% for those providing training. Formal job training is a good investment for many firms and the economy, possibly yielding higher returns than either investments in physical capital or investments in schooling. In spite of this, observed amounts of formal training are very small.

Keywords: On-the-Job Training, Panel Data, Production Function, Rate of Return JEL Classification codes: C23, D24, J31

### World Bank Policy Research Working Paper 3851, February 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We thank conference participants at the European Association of Labor Economists (Lisbon, 2004), Meeting of the European Economic Association (Madrid, 2004), the IZA/SOLE Meetings (Munich, 2004), ZEW Conference on Education and Training (Mannheim, 2005),
inequality.

We also use the National Health and Family Surveys (NFHS) to obtain estimates of changes in consumer durable assets and access to public services, such as electricity, water and toilet on household premises. We follow Somanchi (2021) and use the publicly released state-level aggregates of 14 states from the NFHS’ 2019 round to validate our reweighting strategy (see section 3).

Finally, we use changes in real rural wages reported in Kundu (2019) to validate estimated changes in the consumption distribution for rural India observed after 2011.

_Non-official surveys_ : We rely on two private survey data sources to further our understanding of household consumption since 2014. The first is the India Human Development Survey (IHDS) subsample round, comprising of a sample of 4,828 households from three states of Rajasthan, Bihar, Uttarakhand and fielded during February to July 2017. The first two rounds of IHDS are nationally representative household panels with waves conducted in 2004 and 2011. Households interviewed in the third subsample round of 2017 are part of IHDS’ original panel (Desai, 2020). Consumption aggregates from IHDS are based on a basket of 52 items. Average national consumption growth between 2004 and 2011 based on IHDS is 3.8 percent compared to compared to 3.5 percent growth reported in NSS. Historically, the mean consumption growth from the two surveys have closely tracked each other.

We also use publicly reported quarterly growth estimates of fast-moving consumer goods (FMCG) from Nielsen to track consumption trends. These estimates are based on Nielsen’s extensive network tracking sales, stock and prices of FMCG goods across brick-and-mortar shops and online channels in rural as well as urban centers.

_National accounts and remote sensing data._ We use growth in private final consumption expenditure (PFCE) per capita based on national accounts and night-time lights data from 2014 to 2020 from Beyer, et al. (2021) to validate our main results. Nighttime light data are aggregated to the district level and measured in Nanowatts/cm2/steradian.
larger impact for longer periods, but the economy still rebounds quickly after the lockdown ends. Rigid labor markets policies without balance sheet support result in more protracted recoveries.<sup>2</sup> It is possible that the temporary shutdown shock can lead to lasting changes in people’s and firms’ preferences and behaviors. The idea is that the pandemic renders some goods and services less appealing (e.g., cruises) and others more appealing (e.g., teleconferencing and online shopping, including food and grocery delivery) to consumers in a permanent way, and some firms adapt better to new practices. We model this as a permanent reallocation shock that reshuffles productivity across firms without affecting their average productivity.<sup>3</sup> We

> 1Nonessential contact-intensive industries represent 25% of final demand and 35% of employment in the US (Leibovici et al., 2020). GDP in these industries fell by 40% to 54% between the end of 2019 and the second quarter of 2020. Consumption of transportation services, recreation services, and food services and accommodations fell by 41, 54, and 40 percent, respectively, between 2019Q4 and 2020Q2.

> 2This result extends to small open economies with tighter financial frictions.

> 3Barrero et al. (2020) report evidence of expected reallocation shocks using data of expected sales and

1
2020 and the second wave of data (Round 2) was collected between November and December 2020. The CIMS database sampled households through three different sample frames: the Sierra Leone Integrated Household Survey (SLIHS, 2018), a nation-wide random sampling and a random sample of beneficiaries from the ECT. The intent of including the beneficiaries of the ECT in the sampling frame was to allow an analysis of how the ECT recipients fared throughout the pandemic. Using this feature of the CIMS survey, as a secondary step, we matched the CIMS survey data to administrative data with information on the 32,499 individuals enrolled in the ECT. This step allowed to retrieve the LPMT scores of beneficiaries of the ECT, which are not available in the CIMS data.

This paper analyzes two types of socioeconomic outcomes that are likely to be affected by the pandemic and its induced policy responses: (1). Objective economic security outcomes which pertain to the labor market, food security and children’s human capital and include: individual employment and hours worked, household income change, inability to buy staple foods (rice, dried fish and palm oil), ability to support children’s return to school, child identified as malnourished and child given vitamin A supplementation; (2). Subjective indicators of psychological wellbeing and satisfaction with government measures (concern about children being out of school, food shortages, price increases, being sick with COVID-19, quarantine, lack of other health care and satisfaction with government response to COVID19).

Although the CIMS panel data has 7,369 respondents in Round 1 and 5,685 respondents in Round 2, our analysis only includes a small share of this data for the following reasons. First, given our aim to analyze the ECT transfer, we restrict the analysis to the districts in which the transfer was provided. Second, 9
Europe and the Middle East. We use the 2018 average bond spreads for each region to calculate the change of spreads in basis points. We take the coefficient of the World Bank data transparency index of 3.112 in Table 5 [1] and the interaction with external debt (while holding constant for the level of institutional quality). Our estimates suggest that if there is, for instance, a 10 percent improvement in data transparency, and we account for the average of external public debt in SSA (average of 22.42 percent of GDP from 2009 to 2018), the sovereign bond spreads in the region could be reduced by 0.7 basis points (see Figure 5.a). This drop in sovereign spreads is slightly larger than that of Latin America and Asia. Figure 5.b shows changes in sovereign spreads across Sub-Saharan African countries. An increase of 10 percent in data transparency leads to the largest declines in sovereign spreads in Nigeria, Zambia and Cameroon whereas the cost of external borrowing increases in Mozambique. This finding implies that countries with lower stock of external debt tend to have largest drop in spreads in the event of improved data transparency. In the case of Mozambique, a high and unsustainable external debt position more than offsets any progress in data transparency.

Figure 6 shows the potential gains from greater data transparency across Sub-Saharan African countries measured in terms of lower external public debt burden expressed in million dollars and as a percentage of GDP. We calculate the gains from improving data transparency to the level of two benchmarks: the top quartile of the middle-income countries and the top quartile of the uppermiddle-income countries. We compute the likely reduction in their amount of external debt using the outstanding Euro bonds (expressed in millions of US dollars and as a percentage of GDP). For example, from Table 8, if Angola improves its level of data transparency to the top quartile of the 13
Growth, based on nationally representative cross-sectional data gathered between 1980 and 1992, covers 87% of the total population under age five in developing countries (de Onis _et al._ 1993). It reveals that the prevalence of stunting (low height-for-age) among Guatemalan children below age three in 1987 (57.9%) was the highest in Latin America. Moreover, among all 79 countries for which there is reliable information available, only Bangladesh, E]thiopia, and India have a higher prevalence of stunting: 64.6%, 64.2%, and 62.1% respectively. In addition, the prevalence of underweight (low weight-for-age) children in Guatemala was 33.5%, the second highest in Latin America after Haiti. The prevalence of wasting (low weight-for-height), however, was very low (1.4%).

The paper is structured as follows. Section 2 outlines the issues involved in using anthropometric outcomes as a measure of child health status. Section 3 describes the main features of the _Encuesta Guatemalteca de Salud Familiar_ (EGSF), which provided the data for our empirical work. In Section 4, we present the results of a descriptive analysis of the EGSF data. Section 5 presents the conceptual framework, the analytical strategy and the statistical methods that we have adopted to model the process 1
of the individual consulting services. Their results show that both approaches lead to improvements in management practices of a similar magnitude (8-10 percentage points), so that the new group-based approach dominates on a cost-benefit basis.<sup>7</sup> This points to the potential of group-based approaches as a pathway to scaling up management improvements.

_Finally_ , given that management can be improved, our analysis of the survey data already points to some of the shortcomings in the incentive system for firms that inhibit adoption of structured management practices. For example, results suggest that management practices are negatively correlated with firm age, and especially so in lowtech manufacturing and non-knowledge intensive services. Moreover, the magnitude of this negative association is larger in Slavonia. This finding is surprising when compared with the United States or Mexico, where firms tend to improve their management practices as they age. The hypothesis is that, as firms grow, their production processes become more complex and, in order to stay in the market, they have to upgrade their management capabilities. However, in Croatia, firms continue to survive without the need to do so. This indicates the possibility that in Croatia learning and market selection are not operating perhaps due to the lack of pro-competitive forces.<sup>8</sup> Likewise, the finding that exporting firms are better managed confirms that the selection and learning mechanism actually works in Croatia, if firms compete in international markets. More precisely, the management score of a manufacturing exporter in Croatia is likely to be higher by 8 percent than for an average firm. The fact that this result is mainly driven by high-tech manufacturing exporters in the other regions of Croatia, where the management score is likely

> 7 Their results suggest that group-based intervention led to increases in firm size over the next 1.5 years, including a statistically significant increase in employment, while the impacts on firm outcomes are smaller and statistically insignificant for the individual consulting.

> 8 In the United States, it is well-known that “up-or-out
**Figures 13(a)-(d):  Sub-Saharan Africa in the New Multi-Polar Growth World**

<!-- Start of picture text --> (b) Share of World Merchandise Exports<br>(a) Annual GDP Growth (%)<br>8 (%)<br>5.0%<br>Sub-Saharan<br>Africa 4.5%<br>6<br>World<br>4.0%<br>3.5%<br>4<br>3.0%<br>2 2.5%<br>2.0%<br>0<br>1.5%<br>1.0%<br>-2<br>-4<br>(c) Share of Merchandise Exports That Go to (d) FDI (net inflows) as a Share of GDP<br>Developing Countries in East Asia & Pacific (%) (%)<br>6 5<br>4.5<br>5<br>4<br>3.5<br>4<br>3<br>3 2.5<br>2<br>2<br>1.5<br>1 1<br>0.5<br>0<br>0<br>1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009<br><!-- End of picture text --> Source: World Development Indicators Technology is also part of the story. From both theory and practice, we expect developing countries to lag the high income countries in technology
municipalities, with only Ethiopia having 91 towns. _Third_ , they need to tabulate product price data that is essential for calculating technical efficiency. _Fourth_ , they need to be accessible. The only advanced country with price data we were able to get access to was Belgium which did not meet the second criterion. In the end, however, we have substantial representativeness across regions (Africa-Ethiopia; Asia-Indonesia; Latin America- Colombia and Chile) as well as level of development ranging from Ethiopia with per capita income $US (2020) 2,422, Indonesia 12,068, Colombia 14,570, and Chile 25,068 (World Bank). After matching to geographical covariates, plant count varies from 3,465 over 9 years in Ethiopia, to 34,366 in over 15 years in Indonesia. The surveys contain the geographical location of each plant which is then matched to local population and geospatial controls. Annexes A.1 and A.2 provide detailed data description for each of the countries, including spatial variables.

# Measurement and Estimation Challenges

The common use of the nominal wage elasticity arises partly because wages capture all agglomeration-related sources of productivity growth – ease of capital accumulation, better job matching, knowledge spillovers and so on – but also because the absence of location specific production data and local prices often dictates a reliance on labor force data.<sup>6</sup> In the present work, we use plant-level average wages (total wages/number of workers) directly observed from the production data which can be directly compared to other plant-level productivity measures. Since we do not have geographically disaggregated data on local consumption baskets, we do not calculate a measure of real purchasing power.

Our second principal measure, revenue-based TFP, is the Hicks-neutral measure of plant’s efficiency, commonly used in the literature to capture the residual output variation after controlling for tangible inputs of production. This strips out agglomeration effects arising from availability of capital or other inputs and thereby focuses on externalities arising from matching and
>www.met.reading.ac.uk/~tamsat/cgi-bin/data/rfe.cgi (accessed 5 July 2015).</u>

> 13 Available at: <u>beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4 (accessed 23 August 2015).</u>

> 14 For example, Elvidge et al. (2009) use night light data to construct a global poverty map, while Elvidge et al. (2012) develop a ‘night light development index’ to measure human development and track the distribution of wealth and income across countries. Other studies have used light data to measure economic growth (Henderson et al. 2012), income per capita (Alesina et al. 2015; Chen and Nordhaus 2011; Gennaioli et al. 2014; Hodler and Raschky 2014; Michalopoulos and Papaioannou 2013), and more recently regional income inequality (Mveyange 2015).

15 Our constructed data set contains over 40 mineral types. Using the standard international trade classification (SITC), we group these mineral types into 25 major types, as shown in Table 1 in Appendix 1.

7
EPZ program in Bangladesh appears, after 10‐12 years, to have moved to a much faster growth path. This is driven by the increasing competitiveness of Bangladesh in the global

> 16 Unfortunately, we have insufficient time series data on Senegal and Nigeria to include them in the analysis, and the Tanzania program has too little history to be useful for this analysis. It is worth noting that based on what we know anecdotally about the Nigeria and Senegal programs, had their data been available it would strongly support the conclusions we make here.

> 17 Note that we have data on Lesotho only from 2000

18
mand generated in other sectors as a result of exports, and thus calculate the overall exposure index, we incorporate the 2011 input-output (I-O) GTAP tables. We chose 2011 I-O table instead of a more recent year available (2016) because it reflects the economy at the beginning of the studied period, which guarantees that all the shocks computed are ex-ante. An important underlying assumption with this decision is that there isn’t any significant changes in the sectoral structure. Admittedly, this is a major limit of the paper that we have to accept due to data constraint.<sup>1</sup> We begin by computing the input-output coefficients from the GTAP I-O tables, which capture the interdependencies between sectors in an economy. We match these coefficients with trade data the UNCOMTRADE data to compute the total export value for each sector, accounting for indirect changes in export demand through input-output linkages.

The next step is to link these total export data with the LFSs. We utilize concordance tables available from UNSD that translate International Standard classification (ISIC) rev 3.1. codes into HS codes. By leveraging this concordance, we merge the micro-data on labor force variables at the industry and area level in Viet Nam with total export data. Once the integrated labor and trade data is prepared, we are able to calculate the total trade exposure index based on provinces, as previously explained. The starting point for the analysis is the idea that the impact of a trade shock differs across regions, depending on each province-industry composition.

A fundamental principle for this approach is the existence of segmented labor markets. Existing labor mobility barriers or rigidities (such as commuting costs or lack of transport infrastructure) allow us to observe variations in local labor market outcomes and, as a result, to estimate the effects of differentiated exposure to trade. One heuristic method for assessing labor-market integration involves examining the standard deviation of wages across regions and over time. This heuristic measure is used because various factors can prevent wage equalization across regions. To