rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Bloom and Van Reenen’s (2007) work measuring management practices in large firms around the world has been both path breaking and eye opening. They and their collaborators show that better managed firms perform better, and that the quality of management practices is strongly correlated with per capita income at the country level. But the vast majority of the non-agricultural labor force in most low- and middle-income countries is employed not in the large firms this blossoming literature studies, but in firms with fewer than 10 employees.<sup>1</sup> Bloom et al (2012) justify the focus on larger firms by noting that “...the formal management practices we consider will not be so important for smaller firms...where more formal management practices may not be necessary.” In this paper, we test the assertion that management practices are less important for smaller firms. We examine the relationship between management practices and firm outcomes using several samples of micro- and small enterprises in seven countries. In each of these samples, firm owners were asked 26 questions related to business practices using a common survey instrument first used in de Mel et al (2014). Management in the small-firm context is less focused on human relations than is the case in larger firms. Our diagnostic instrument reflects this, with questions covering the areas of marketing, record keeping, financial planning, and stock control. We refer to these as “business practices” rather than management practices to reflect the fact that HR management is less important in our context.

Our samples come from two South Asian, three African and two Latin American countries. The samples were all drawn for purposes other than testing our business practices instrument, and some were selected to reflect very specific sub-populations of interest for particular studies. They range from female-owned subsistence enterprises to a sample of highly-educated owners applying to a business plan competition. But while the samples were not formally designed to be representative of micro- and small-scale enterprises in each country, collectively they reflect the ranges of enterprises in low- and middle-income countries quite well.

Our measures of business practices are designed to be collected in closed-end surveys, and we code each of the 26 as either carried out or not. For most of the analysis, we focus on a simple

> 1 For example, Hsieh and Olken (2014, p.93) note that “About 90 percent of firms in Mexico employ less than 10 workers. In India and Indonesia, the fraction of firms with less than 10 workers is almost visually indistinguishable from 100 percent”, while McKenzie (2015) reports 99.6 percent of firms in Nigeria have fewer than 10 workers.

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UQR, the household response is much less elastic with a value of -0.30. Similarly for caloric intake, the average wheat-price elasticity (as estimated by OLS) is -0.18, but when estimated at the point on the distribution corresponding to a daily intake of 2,100 calories, this estimate shrinks in magnitude to -0.12. If the policymaker is focused on how the food poor, or calorie deficient, are affected by wheat price increase, these results suggest that OLS will overestimate this effect by about 30% in the case of consumption and 38% in the case of calories. This finding is consistent with a story of market exposure, as well as the story described above that richer households have more to give in terms of the value of their food consumption and their caloric intake. We expect households living close to subsistence levels to have a lower level of market participation (e.g., more likely to be subsistence farmers) and thus be less affected by price shocks.

For dietary diversity the difference between the average elasticity and the elasticity estimated at the cutoff for a borderline diet (food consumption score = 42) go in the opposite direction. In this case, the OLS estimate is -0.19, but the UQR estimate for households with borderline diet is -0.26, indicating a greater decline in diversity from an increase in wheat prices. The substantial decreases in dietary quality indicated by these results suggest that the micronutrient intake of some or all individuals in these households may have declined in a significant way. These findings provide useful information for policymakers as they seek to better understand the behavior of the most vulnerable households during times of high food price inflation.

# **Extensions**

Equation (1) models changes in food security for all households at the national level; however we may expect these changes to differ based on whether a household cultivates wheat. Deaton (1989, 2000) draws a theoretical distinction between households that produce (and sell) their own food and those that do not. Net buyers depend on the market to meet their food needs, while net sellers produce enough food to consume and to sell on the market. Increases in food prices hurt net buyers, but benefit net sellers whose revenues increase. The NRVA data do not allow us to distinguish directly between these households, thus we use an indicator for households that report owning or operating agricultural land as a broad proxy for this distinction. While not all households that have access to agricultural 18
existing methodologies to comparably differentiate small-scale, autonomous establishments from larger employers which constitute the organized sector, as generally defined. Following Ghani, Kerr, and O’Connell (2013), we assign establishments with less than five workers and/or listed as an “own-account enterprise” (OAE) to the unorganized sector. OAE enterprises are firms that do not employ any hired worker on a regular basis. The choice of five employees as the size cutoff recognizes that average establishment size in services is significantly smaller than in manufacturing. The results that we show below are robust to varying the demarcation point for services.

The organized manufacturing sector is surveyed by the Central Statistical Organisation every year through the Annual Survey of Industries (ASI), while unorganized manufacturing and services establishments are separately surveyed by the National Sample Survey Organisation (NSSO) at approximately five-year intervals. These surveys are the foundation for many published reports on the state of Indian businesses and government agency monitoring of the Indian economy. The typical survey collects data from over 150,000 Indian establishments. In this respect, the surveys are comparable to the Annual Survey of Manufacturing conducted in the United States, with the Indian sampling frame being about three times larger.

Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. The survey provides sample weights that we use to construct populationlevel estimates of total establishments and employment spatially and by industry. We use state and industry variation in our descriptive analyses, and our empirical regressions consider districtlevel variation. Districts are administrative subdivisions of Indian states or territories that provide meaningful local economic conditions. The average district size is around 5,500 square kilometers—roughly twice the size of a U.S. county—and there is substantial variability in district size (standard deviation of ~5,500). Indian districts can be effectively considered as selfcontained labor markets.

We consider a sample of 20 states that are a subset of all 35 states/union territories. The 15 exclusions were due to three potential factors: 1) the state was not sampled across all of our surveys, 2) the small sample size for the state raised data quality concerns, or 3) persistent conflict and political turmoil existed in the region. Across our state sample, there are 514 districts. Due to changes in district definitions (e.g., bifurcations, combinations), we build a 7
the ‘informal economy’ focuses on the nature of employment in addition to the characteristics of enterprises.” (Chen 2006, p. 76).

# **2.2 Measurement and Trends**

With so many different conceptualizations of informality and formality, it is perhaps not surprising that getting nationally comparable estimates of informality is fraught with difficulties. Even if the basic concept is agreed--for example, registered enterprises--the detailed specification may vary across countries, since the requirement for registration will vary. Estimates produced by national authorities may differ from those produced by international agencies because of differing definitions and data sources.

Despite concerns about data and definitions, there is an agreement that the informal sector is large in most developing countries, and it is the overwhelming employer of labor in the non-agricultural sector. For developing countries as a group, more than half of all jobs—over 900 million workers—are in the informal sector (Jutting and de Laglesia, 2009). The sector is large not just in terms of employment, but also by the number of enterprises. Many more new establishments are created in the informal sector than in the formal sector.

Figure 1 plots the share of informal employment in total non-agricultural employment for 50 developing countries against their per capita income, using data from ILO KLIM, OECD, and World Development Indicators. For these 50 developing countries as a group, nearly half of the total nonagricultural jobs is generated in the informal sector. In India, it is much higher, at more than 80 percent of total non-agricultural jobs generated in the informal sector. It is also high in Sub Saharan Africa. The share of the informal sector is slightly lower in Latin America, with Brazil at 42% and Mexico at more than 50%. As a region, the lowest share of informal employment is in the Central & South-Eastern Europe, with the share of informal employment in Poland and Russia in single digits.

**Figure 1: Jobs are concentrated in the Informal Sectors in developing countries**

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Figure 5. Total Factor Productivity Growth A. Average TFP growth <!-- Start of picture text --> B. Share of economies and GDP with TFP<br>growth below its longer-term average<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent Share of countries<br>3 100 Share of GDP<br>1998-2007 average 80<br>2 60<br>40<br>1 20<br>0<br>0<br>World AEs EMDEs World AEs EMDEs<br>Sources: Penn World Tables, World Bank.<br>Note: TFP growth stands for total factor productivity growth. This figure refers to potential TFP<br>growth.<br>2003-07 2013-17 2018-27 2003-07 2013-17 2018-27 2003-07 2013-17 2018-27<br>2003-07 2013-17 2018-27 2003-07 2013-17 2018-27 2003-07 2013-17 2018-27 1998-2002 1998-2002 1998-2002<br><!-- End of picture text --> A. GDP-weighted average of total factor productivity growth. Includes 50 EMDEs and 30 advanced economies.

B. Number of economies among 30 advanced economies and 50 EMDEs in which potential total factor productivity growth is lower than its longer-term average (1998-2017).

# Figure 6. Investment Growth A. Investment growth

<!-- Start of picture text --> B. Share of economies with investment<br>growth below its long-term average<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent Share of countries<br>16 100 Share of GDP<br>2003-07 average<br>1998-2017 average 80<br>12<br>60<br>40<br>8<br>20<br>4 0<br>0<br>World AEs EMDEs World AEs EMDEs<br>2003-07 2013-17 2003-07 2013-17 2003-07 2013-17<br>2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 1998-2002 1998-2002 1998-2002<br><!-- End of picture text --> Sources: Penn World Tables, World Bank. A. Investment-weighted averages. For a sample of 37 advanced economies and 145 EMDEs. B. Share of 139 countries in which investment growth is below the longer-term average (1998-2017).

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# **Table 7. Joining,** **<u>expectations, and past play</u>**

||(1)<br>Burning|(2)<br>Stealing|(3)<br>Giving|
|---|---|---|---|
|Expected burning/stealing/receiving|**-0.268****|0.029|-0.066|
||(-2.417)|(0.148)|(-0.679)|
|Kenya x expected burning/stealing/receiving|**0.440****|0.168|**0.219***|
||(2.536)|(0.836)|(1.898)|
|Uganda x expected burning/stealing/receiving|**0.278***|0.060|0.109|
||(1.986)|(0.272)|(0.853)|
|Own past burning/stealing/giving|**-0.290****|-0.021|0.298|
||(-2.243)|(-0.173)|(0.755)|
|Kenya x own past burning/stealing/giving|**0.581****|0.193|0.081|
||(2.589)|(1.349)|(0.188)|
|Uganda x own past burning/stealing/giving|**0.539*****|0.307|-0.020|
||(2.913)|(1.675)|(-0.048)|
|Other regressors (*)|YES|YES|YES|
|Observations|487|829|830|
|R-squared|0.214|0.098|0.083|

Source: Authors analysis based on data described in the text. Note: (*) Other regressors included in the regressions are as in Table 6: Kenya dummy, Uganda dummy, gain from joining, Kenya dummy x gain from joining, Uganda dummy x gain from joining, initial endowment, initial endowment x Kenya dummy, initial endowment x Uganda dummy, γ. The dependent variable is 1 if subject _i_ joins the group, 0 otherwise. Each regression is estimated using a linear probability model. This ensures that each coefficient can be interpreted as the marginal effect of the regressor on the probability of joining. Gain from joining = (p-1)*e_i. Standard errors are clustered at the session level. _t_ -statistics appear in parentheses. *** p<0.01, ** p<0.05, * p<0.1
# **Appendix 2: Descriptive Statistics of the Variables in Occupation Probit Regression**

|**Category**|**Description**|||**1988**|||**2002**||
|---|---|---|---|---|---|---|---|---|
|||**Mean**|**Min**|**Max**|**Std. dev.**|**Mean**|**Min**<br>**Max**|**Std. dev.**|
|Dependent variable|Dummy, 1 if the adult child participate in non‐farming|0.19|0|1|0.39|0.50|0<br>1|0.49|
|Parents' occupation|Dummy, 1=if either parents is in non‐farming|0.22|0|1|0.41|0.32|0<br>1|0.46|
||Age|21.77|18|50|3.51|23.56|18<br>52|4.61|
|Individual characteristics|Age squared|486|324|2500|177|576|324<br>2704|249|
||Years of schooling|6.44|2|14|2.59|8.54|0<br>16|2.17|
||Mother's age|48.3|35|81|6.23|49.56|35<br>82|6.21|
||Mother's years of schooling|2.18|0|14|3.04|4.79|0<br>15|2.83|
||Father's age|50.81|35|80|6.49|51.91|35<br>87|6.59|
|Parents' characteristics|Father's years of schooling|4.87|0|14|3.12|6.73|0<br>15|2.56|
||Dummy, 1=if either parents is ethnic minority|0.08|0|1|0.26|0.15|0<br>1|0.35|
||Dummy, 1 if either parents is Communist Party member|0.19|0|1|0.39|0.21|0<br>1|0.41|
||Dummy, 1 if either parents is cadre|0.05|0|1|0.23|0.33|0<br>1|0.47|
||Dummy, 1= if female household head|0.03|0|1|0.16|0.03|0<br>1|0.15|
||Number of adults per mu of land|0.63|0.01|13.3|0.66|1.01|0.02<br>62.5|2.06|
||Proportion of female adults to the number of total adults|0.5|0|1|0.16|0.49|0<br>1|0.16|
||Ratio of young children (younger than 7 years) to total adult women|0.16|0|3|0.31|0.11|0<br>2|0.11|
||Ratio of children (between 8 and 17 years) to the household size|0.17|0|0.625|0.14|0.09|0<br>0.57|0.13|
||Proportion of student in the household|0.02|0|0.57|0.06|0.08|0<br>0.5|0.12|
|Household characteristics|Share of disabled or retired in household|0.002|0|0.667|0.03|0.01|0<br>0.66|0.05|
||Dummy, 1=if irrigation available to the household|0.6|0|1|0.48|0.3|0<br>1|0.46|
||Land, unit is_mu_|14.31|0.3|959|42.41|5.99|0<br>180|7.14|
||House value, unit is current_yuan_|5584|3|80000|6034|25477|450<br>300000|28425|
||Dummy, 1=access to electricity|0.86|0|1|0.34|0.99|0<br>1|0.06|
||Productive assets (current_yuan_)|1448|20|35250|1925|5244|0<br>793460|17872|
||Savings, unit is current_yuan_|202|0|11450|788|2088|0<br>180000|8378|
||Debt, unit is current_yuan_|243|0|36000|1014|872|0<br>200000|5200|
||Dummy,1 if topography is terrain (base is flat)|0.3|0|1|0.46|0.33|0<br>1|0.47|
|Village characteristics|Dummy, 1 if topography is mountain (base is flat)|0.18|0|1|0.38|0.21|0<br>1|0.41|
||Dummy, 1 if old revolutionary base|0.14|0|1|0.35|0.21|0<br>1|0.41|

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FUNDING AND FINANCING INFRASTRUCTURE: THE JOINT-USE OF PUBLIC AND PRIVATE FINANCE<sup>1</sup> Marianne Fay<sup>_∗_</sup> , David Martimort<sup>_†_</sup> and St ́ephane Straub<sup>_‡_</sup>

# 1. INTRODUCTION

The debate around the role of private partners in infrastructure ventures has dominated the infrastructure policy agenda at least since the revival of public-private partnerships (PPPs) in the 1980s. Currently, with many developing and emerging countries struggling with tight fiscal resources and mounting debts, and concessional funds from multilateral and bilateral agencies being limited, the question of whether and how to attract private resources to infrastructure projects once again is on the agenda.

Mobilizing resources to deliver infrastructure investments involves two related questions:

1. How infrastructure is funded, i.e., who eventually pays for the full cost of delivering the service. The range of possibilities goes from full cost recovery through users’ fees or other innovative mechanisms such as land value recovery, to full tax funding. Some of it comes down to the characteristics of the sector (lower levels of cost recovery are more likely in rural roads or sanitation than in electricity for example), and some to political and regulatory decisions about how to price services.

> 1We thank the _World Bank_ for providing financial support for this work, and Fernanda Ruiz Nunez, Deblina Saha, and Danett Song for help with the data. Participants in seminars and conferences at the World Bank, Meridiam - Paris, and the Paris-Berkeley Organizational Economics workshop are gratefully acknowledged. All errors remain ours.

> _∗_ World Bank. mfay@worldbank.org.

> _†_ Paris School of Economics-EHESS. david.martimort@parisschoolofeconomics.eu.

> _‡_ Toulouse School of Economics, University of Toulouse Capitole (IDEI and IAST). stephane.straub@tse-fr.eu.
country-invariant,<sup>8</sup> while the Enterprise Survey indicators are city-industry–level indicators that capture more local BE variations. Second, in contrast to the objective nature of the _Doing Business_ indicators, some Enterprise Survey indicators are subjective and are based on whether the entrepreneurs consider certain aspects of the business environment to be a relatively severe obstacle. Third, the Enterprise Survey indicators capture the real actions that firms take and the real conditions firms face. For example, while the Global Competitiveness indicators measure the level of internet infrastructure by proxying a country’s broadband quality , the Enterprise Survey indicators measure the same aspect by proxying the share of firms using websites to conduct business; compared with measuring the ease of getting credit in _Doing Business_ , the Enterprise Survey indicators directly measure the share of firms having an overdraft facility.

Table 4 reports how the Enterprise Survey indicators are related to the Global Competitiveness and _Doing Business_ indicators, In particular, we run a simple regression in which the dependent variable is an Enterprise Survey indicator and the key explanatory variable is either a Global Competitiveness or a _Doing Business_ indicator. The regression is at the cityindustry level, the level that an Enterprise Survey indicator is measured uniquely.<sup>9</sup> In the regressions, we control for basic country-level characteristics (that is, log GDP per capita, log population, and log population density), along with the industry fixed effects. We cluster the standard errors at the country level because some of the key variables are at this level.

The Enterprise Survey indicators and those country-level indicators are significantly correlated, with some exceptions. The following pairs are reasonably correlated: (1) the Enterprise Survey indicator on Corruption and the Global Competitiveness indicator on Ethics; (2) the Enterprise Survey indicator on Security Costs and the Global Competitiveness indicator score on Police System; (3) the Enterprise Survey indicator on Web intensity and the Global Competitiveness indicator on Broadband; (4) the Enterprise Survey indicator on Power Outage and the Global Competitiveness indicator on Electricity Quality (or the _Doing Business_ indicator on Getting Electricity); (5) the Enterprise Survey indicator on access to bank finance (Overdraft) and Global Competitiveness indicator on Financial Development.

In contrast, there is no significant correlation at the conventional level (i.e., 5 percent) between (1) the Enterprise Survey indicator on skill constraints (Skill Labor Obstacle) and the Global Competitiveness indicator on Quality of Education System (and the World Bank’s

> 8 The variables are time-varying, but when being merged into the WBES data, which are largely cross-sectional, the _Doing Business_ and _the Global Competitiveness_ indicators are in effect time-invariant. 9 In table 4, all BE indicators are standardized with their sample mean and standard deviation so that they have means of zero and a standard deviation of one.

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