rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 4: Regressions Including the Interaction of Competition and Concentration with Financial Development**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI)_ and _Concentration 3_ are measures of concentration _. Log firm size_ is the logarithm the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). Results in column (b) are weighted by the inverse of the standard deviation of the Lerner Index. *** p<0.01, ** p<0.05, * p<0.1

|Variables||Ac|cess to finance||
|---|---|---|---|---|
||(a)|(b)|(c)|(d)|
|Lerner index|-1.354***|-1.386***|||
||[0.368]|[0.429]|||
|Concentration 3|||-0.521***||
||||[0.166]||
|Herfindahl index (HI)||||-0
future profitability; and (ii) the cost of switching. But that does not allow us to separate out the future relative profitability from the responsiveness. Having, then, a panel of such ‘pull’ estimates and the costs, we can put them together in Stage 2 with wages to see how much the ‘pull’ is affected by changes in wage differentials. This allows us to separate out the ‘responsiveness’ factor<sup>_<u>β</u>_</sup> _ν_<sup>andcompletetheestimation.Essentially,ifgrossflowsdonot</sup> respond very much to future wage differentials, a low value of<sup>_<u>β</u>_</sup> _ν_<sup>willbeindicated,otherwise</sup> a high value.

A qualification that should be noted is that in principle equation (5) can be used to estimate _β_ as well as _ν_ , but in practice it turns out to be difficult to do so. The reason is that significant changes in _β_ induce only small changes in equilibrium aggregates. As a result, we impose a value of _β_ that seems reasonable based on the literature, and examine how robust results are to changes in its value.

# **2 Data and Regression Analysis**

## **2.1 Sector and Occupation Categories.**

We use the March Current Population Surveys (CPS) from 1975 to 2000 of the US Census to estimate the model. Dimensionality issues force us to aggregate sectors and occupations, with the result that we consider 4 sectors and 5 occupations. The sectors are: 1. Agriculture and Construction; 2. Manufacturing; 3. Non-traded Service; and 4. Traded Service.<sup>3</sup> Similarly, we need to aggregate occupations. We follow the Census categories to do this, with one small exception. The occupations we use are:

1. _White collar_ (the Census category of “Managerial and Professional Specialty
Indicator. Formally, the left panel illustrates _T F PT F P{{τ_ 0 _i_<sup>_E_</sup> _<u>,γ</u>_<sup>_,γ_</sup> _<u>i</u>_<sup>_i_</sup> _<u>}</u>_<sup>_}−_</sup> _T F PT F P_ <u>�</u> _τi_<sup>_E,DBI_</sup> _<u>{</u>_ 0 _<u>,γi},γi</u>_ <u>�</u> in the vertical axis, against the log of GDP per capita in the horizontal one. GDP per capita is drawn from Penn World Table version 9.0, for the year 2014. The right panel in turn computes the differential _TFP_ gain from removing each type of entry barrier assuming no underlying misallocation (i.e., ).

48
and government officials were interviewed in all 40 original evaluation LGAs (both in the first and second rounds) and in 12 newly added LGAs (only in the second round). The 40 LGAs consisted of 18 project LGAs and 22 comparator LGAs. The process of selecting comparator LGAs, drawing on a mix of propensity score matching and local expert surveys, is detailed in Appendix A. Since the main goal of this paper is to assess changes in outcomes between the first and second rounds of surveys, for the remainder of the text we will only refer to the 40 LGAs originally present in the first round survey, ignoring the additional LGAs added at the second round.

The Household panel dataset includes 5,996 observations across the two waves - 2,998 households were interviewed in each wave. The sampling of households in the first survey round was

> 10This timing could present a problem if all the gains in the program took place at the very beginning, before most activities were implemented, and/or at the very end. According to the APAs, indicators kept growing over subsequent APAs which suggests that not all gains were achieved at the early stages and indicators do not present large jumps towards the end of the period, i.e. between 5th APA and 6th APA.

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|0.61|0.36|-0.23|
|Post-natal care, 0-5|0.45|0.26|0.19|0.35|0.17|0.19|0.00|
|Prenatal care, 0-4|0.97|0.75|0.22|0.91|0.51|0.39|-0.17|
|Immunization vaccines, 0-4|0.98|0.84|0.14|0.94|0.87|0.07|0.06|
|Non-wasting, 0-4|0.89|0.75|0.14|0.93|0.87|0.06|0.08|
|Non-stunting, 2-17|0.81|0.68|0.13|0.88|0.70|0.18|-0.05|
|Non-underweight,0-17|0.94|0.86|0.08|0.94|0.84|0.10|-0.02|
|_Aggregate HOIs_||||||||
|IOHa9-15-Housing Services|0.98|0.66|0.32|0.97|0.24|0.73|-0.41|
|IOHa0-4-Housing Services|0.99|0.81|0.18|0.99|0.4|0.59|-0.41|
|IOHa0-4-Nutrition & ECD|0.54|0.04|0.50|0.44|0.08|0.36|0.14|

_Source_ : HIECS 2000 and 2009 surveys, EDHS 2000 and 2008, and authors’ calculations.

_Substantial Opportunity Gaps between Children in Favorable and Unfavorable._ Opportunity gaps between children in favorable and unfavorable circumstances in the year 2009 are considerable if we consider that the for largest opportunity gaps range between 29
The next section of the paper describes the data used in our empirical analysis. Section 3 lays out the context, describing the structure of villages, the nature of female seclusion norms, and the salience of caste, following which, in sections 4, 5, and 6 we present, respectively, our empirical framework, results, and policy experiment. We conclude in section 7.

# 2 Data

Our analysis combines representative household survey data with information from a village and school census undertaken in the two most populous provinces of Pakistan, Punjab and Sindh. The household data come from the second round of the Pakistan Rural Household Survey (PRHS-II) conducted in 2004-05, in which 3519 households were randomly drawn from 165 villages. The school census collects detailed information on all schools inside each of these villages as well as schools lying within a 2 km walk of the perimeter of each settlement/habitation of the village.<sup>2</sup> GPS coordinates are available for households and schools, so the distance between each can be calculated. The hamlet in which households and schools are located is also known. The PRHS-II village census provides land ownership data and the caste/clan (zaat/biradari) affiliation of every household in each of the 165 villages.

The school census identifies 1326 schools of which 1112 (84%) have classes at the elementary level (up to grade 5). Of these, 63% are exclusively elementary schools, while the rest had an elementary school attached to a middle or high school. Since three-fourths of elementary schools are public (90% in Sindh province), government school allocation is central in determining access to education (Appendix Table A.1, Panel A). Among elementary schools, 51% are coeducational, 28% are exclusively for boys and 21% exclusively for girls. Public elementary schools are far more likely to be single-sex (62%) compared to private ones (13%).

We consider all children who were age 9-15 in the survey
# _2.3. Employment outcomes across sources_

Given the data disparities across countries and over time, it is not surprising that employment outcomes depend very much on the data source and do not necessarily coincide with those reported by the ILO. Figure 2 compares observations in our dataset with both national estimates and model-based estimates publicly available from the ILO. The focus is on employment rates, defined as the ratio of total employment to the working-age population, defined as all individuals aged 15 and above.

In Bangladesh, employment rates from the HIES are much lower than those from the LFS. This is in part because the HIES does not fully account for unpaid work while the LFS does. Another difference between the LFS and the HIES that may explain the disparity is the procedure for employment data collection. While the HIES asks about employment directly, in the LFS the employment variable is constructed from activity descriptions. There are also differences with the ILO data. The national estimate reported by ILO shows a decline of the employment rate from 2010 to 2013 but an increase from 2013 to 2016. The LFS, on the other hand, shows a steady decrease. For the last observation, the national estimate reported by the ILO is 3 percentage points above ours.

In India, the census delivers much lower employment rates than the LFS, a disparity that has been extensively discussed (e.g. Hirway 2012, Bardhan 2013, Shaw 2013, Papola 2014, Kasturi 2015). A first explanation for the gap is that the census classifies the population binarily into workers and non-workers while in the NSS a person’s ‘activity status’ can be either employed, unemployed, or not being in the labor force. Second, the NSS underestimates the total population compared to the Census. Third, there are differences in the definition of economic activity. And fourth, there are differences in the minimum time a person has to spend working before being considered employed.

In Sri Lanka, Nepal and Pakistan, the ILO model-based estimates are generally above
in a linear model when compared to legal origin and religion, we constructed a five point scale for the variables, based on their quantiles, to ensure the same number of categories as legal origin. In this section we provide further evidence of the non-linearities by adding the square and cube of each of the institutional variables to see if it makes a difference to the adjusted R-squares of the model. In Table 7 we present results using the raw data as well as higher order terms and compare it to the contributions from the rescaled variables. The results are shown for the full sample of 80 countries as well as smaller sub-samples of non-transition economies and former colonies. The first column of Table 7 presents results using raw data (when ethnic fractionalization and latitude are not rescaled) for the full sample of countries. We see that Legal Origin explains the most variation (3.89%) in firms’ perceptions of property rights protection. In the second column we include higher order terms of the institutional variables in the regression model. Note that the higher order terms are included only for the continuous variables and not for country dummies, legal origin dummies or religion dummies. The ethnic diversity and endowment theories increase in explanatory power by as much as 411% and 94% respectively when we use higher order terms of these variables but Legal Origin still dominates. The third column repeats the results from Table 3 where we used rescaled values for latitude and ethnic fractionalization. The third column also shows that there is a substantial increase in explanatory power of the ethnic fractionalization (by a factor of 7) and latitude variable (by a factor of 2.2) on rescaling. The table suggests that the results in current literature may be biased by ignoring non-linearities present in several of the institutional theories.

# **Insert Table 7**

We repeat the above analysis for a sample of 56 non-transition countries and a sample of 40 former colonies. In both cases we observe substantial increase in explanatory power when we use higher order terms of
clients active at the time of the intervention launch. Our sampling strategy focused on including clients from the same lending centre (kendra), in order to allow us to collect information on joint liability groups. Though it is not a random sample of clients, our high sampling rate ensures that the obtained sample is mostly representative of clients active in February 2015.<sup>19</sup> The household survey, asked of the household head, collected detailed information on household demographics, labour supply, and borrowing from formal and informal sources. In addition, a separate client survey elicited information on a number of different dimensions of her joint liability group, and interactions with the micro-finance provider. Detailed information on sanitation investment was also collected, including that on type of toilet, construction date and costs, and toilet usage. The information on the construction date allows us to obtain a retrospective measure of toilet ownership before the start of the

> 17For a sub-sample of these households, we have baseline data collected before the intervention began. We use this data to verify sample balance pre-intervention as discussed in detail in the project’s baseline report (available on request).

> 18Around 6 percent of sampled households, balanced across treated and control GPs, could not be interviewed because of refusals or lack of availability, and were replaced with back-up respondents.

> 19Simple t-tests comparing the characteristics of the obtained sample with the population of active clients reveal that the samples are similar on most observed characteristics other than small differences in the proportion of clients from backward castes, and age of the client. In particular, the sample includes fewer clients from backward castes and younger clients than the population of active clients. These results are available on request.

11
The main data sources are the World Bank Enterprise Surveys (ES) in 709 cities of 128 countries. The ES data are collected by the World Bank to benchmark the business climate in (mostly) developing countries and to understand the determinants of firm performance. The annual GDP per capita of sample countries ranges from around US$200 (low-income countries) to US$46,000 (upper-middle-income countries). In each country the survey is based on the universe of eligible firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the nonagricultural private economy in the country.<sup>2</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 and 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade.<sup>3</sup> Each survey is largely a cross-sectional data set. However, firms are asked about their sales and employment for both the survey year and three years prior to it, which allows us to construct annualized growth rates.

We include data collected after 2006, although some Enterprise Surveys were conducted earlier. Before 2006, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology, and the samples were not generally representative. A complete list of variables and data sources is shown in table 1 and table 2; the summary statistics for our key variables are presented in table 3. Our final sample consists of approximately 80,000 firms covering 709 cities in 128 countries.<sup>4</sup>

# **Measurements of the business environment**

We first discuss the logic of the _Doing Business_ (DB) and the Global Competitiveness (GC) data collection and the pros and cons of these data sets. The World Bank _Doing Business_ project provides objective measures of business
mind and deemed the HDI a success after noting the attention the index attracted and the subsequent debates it fostered.<sup>9</sup> The European Skills Index (ESI) was first released in 2016 under the title “Making Skills Work Index” and recently updated in 2020. The ESI developed by CEDEFOP measures the performance of EU skills systems. The ESI consists of three pillars - _skills development; skills activation; skills matching_ - each of which measures a different aspect of a skills system. The ESI is based on 15 individual indicators and then averaged to arrive at a composite score. The scores range from 0 (lowest) to 100 (highest). A score of 75, for instance, indicates that a country has reached 75% of the ideal performance leaving a 25% margin for improvement.

Our objective in this paper is to develop a skills index for developing countries in Asia. Consequently, our focus is on developing an “Asian Skills Index” (ASI) roughly modeled on the ESI. An Asian Skills Index is, however, substantially more complicated to develop. Most obviously, consistent, comparable data series for many Asian countries is not as widely available as OECD data. In addition, several structural and institutional features of labor markets in Asian countries such as part-time employment, vulnerable employment, and unemployment among the highly educated means that the ASI cannot simply replicate the ESI. Recent work by Angrist et al (2021, p.403) point out that _“For decades, studies used schooling as a proxy for human capital. This applies even to the most prominent index of human capital to date, the United Nation’s human development index (HDI). However, using schooling as a proxy for human capital assumes that being in school translates to learning. Evidence suggests that this is often not the case”._ In

> 9 Most recently, the Economist (June 3-9, 2021, page. 84-85) developed a “Normalcy Index” based on indicators
combined: 1990:3-1991:3, 1991:1-1992:1, 19 9 1:2 -199 2 :2 .5 Another panel was created spanning 1991:1-1992:1 that terminates in the more detailed Micro-Enterprises Survey (MES). This survey was constructed by identifying 11,000 owners of micro-enterprises, defined as firms of fewer than six individuals, from the 1990:4 NUES and reinterviewing them in 1991: 1 in more depth about issues of capital structures, costs, and employment patterns. It also specifically asks why they left their previous job and why they started the present business which offer a partial alternative to naive corrections for selectivity bias.<sup>6</sup> Thus, for a reduced sample, we have far more information on both earnings differentials, and motivations for moving. The panels in the combined NUES sample above were chosen to include and span either side of the MES panel.

Two popular definitions of informality are employed. The first focuses specifically on the issue of "protectedness" and comprises owners or workers in firms of fewer than 16 employees who ; The cohort beginning in the fourth quarter of 1990 was not used because it would incorporate the end of the year bonuses, the aguinaldo. This normally amounts to a 13th monthly payment but may vary by year and firm imparting an undetermined upward bias to the monthly wage reports. Since we are concerned with expected differentials in income, the wages of workers who reported nonnally receiving the aguinaldo were increased by 13/12.

6Heckman (1979) is insistent that the standard two stage methods of correctingfor selectivity bias depend on having confidence in the underlying model of how workers choose among sectors. A bad first stage selection specification may induce bias rather than correct for that existing. This is likely to be the case as the premise of this work is that we have little knowledge of the role each sector serves. Further, since a principle argument of this work is that, in the absence of knowledge of the value of
informal sector where wages are higher and where women are more likely to have access to health, retirement, and other benefits.

Active Labor Market Programs are often adopted by countries as a way of ameliorating the effects of economic shocks. These programs are not usually targeted at women, although women often seek out the employment and training services offered by these programs. The Latin American programs surveyed in this paper were implemented in Argentina, Mexico, and Peru. The majority were found to increase women‟s employment rates and to increase their exit rate out of unemployment. Oftentimes, the program impacts for women exceeded those for men. Most of the programs did not, however, lead to substantial wage increases, with the exception of 57
implies that the probability of an average project surviving until the end of its contract period is 0.92 (figure 5, panel b.).<sup>10</sup>

# **Figure 4 Probability of public-private partnership project terminating early by share of contract period remaining**

<!-- Start of picture text --> 0.0015<br>0.0010<br>0.0005<br>0.0000<br>0 20 40 60 80 100<br>Percent of contract period elapsed<br>Hazard rate<br><!-- End of picture text --> _Source_ : Authors’ calculations using PPI database. _Note_ : Figure shows smoothed hazard rate estimate using Gaussian kernel and optimal bandwidth.

# **Figure 5 Survival and cumulative hazard estimates of public-private partnership projects**

- a. Nelson-Aalen cumulative hazard curve b. Kaplan-Meier survival curve

<!-- Start of picture text --> 0.10 1.00<br>0.08 0.98<br>0.06 0.96<br>0.04 0.94<br>0.02 0.92<br>0.00 0.90<br>0 20 40 60 80 10 0 20 40 60 80 10<br>Percent of contract period elapsed Percent of contract period elapsed<br>Cumulative hazard rate<br>Cumulative survival probability<br><!-- End of picture text --> _Source_ : Authors’ calculations using PPI database.

10 Equations A.3 and A.4 in appendix A present the relationships among hazard, cumulative hazard, and cumulative survival probability presented in figures 5, 6, and 7.

12
**Figure 6.** Concentration coefficient by public program. Uruguay, 2017.

<!-- Start of picture text --> Food transfers -0.770<br>Plan de equidad -0.689<br>Non-contributive pensions -0.541<br>Child care -0.457<br>Primary education -0.445<br>Preschool education -0.412<br>Direct transfers -0.407<br>Low secondary education -0.405<br>Education -0.231<br>Upper secondary education -0.165<br>Other direct transfers -0.123<br>Health 0.010<br>Tertiary education 0.316<br>Adjusted income 0.380<br>Disposable income 0.426<br>Pre-fiscal income 0.461<br>Net market income 0.466<br>-1.000 -0.800 -0.600 -0.400 -0.200 0.000 0.200 0.400 0.600<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology. Note: the concentration coefficient is calculated after ordering population according Pre-fiscal income.

# **D. Comparing findings from 2009 and 2017**

This is not the first study perform an incidence analysis in Uruguay. In fact, Bucheli et al. (2013) benefit from the availability of data in 2009 and look into several policies. Specifically, they consider direct taxes and transfers, in-kind education and health, and indirect taxes. The authors conclude that direct taxes are progressive, indirect taxes are neutral, and the rest of the policies are progressive (except for spending in tertiary education). We revisit the 2009 data with the updated CEQ methodology in an effort to produce estimates for income concepts that are as comparable as possible and to
skilled-labor-intensive goods would translate into a rise in the relative wages of unskilled labor, narrowing the wage gap between skilled and unskilled workers. However, the validity of the Heckscher-Ohlin-Samuelson (H-O-S) theory has been challenged since, contrary to the prediction of the theory, many developing countries experienced an _increase_ rather than a 2 The average for the period 1986-2011 (the World Development Indicators (WDI), the World Bank).

2