rafmacalaba/datause-encoder-data huggingface dataset:
firms. We first examine firms’ ownership structure. Table 6-A compares the ownership structure of Indian firms to those from the LLS (1999) sample of over 1,000 firms from 27 countries (India _not_ included), from the Claessens et al., (2000) sample of Asian firms (excluding Japan) and the AQQ (2005) sample of over 1,100 firms from China. In 80% of the 854 listed firms in India, the largest shareholder (controlling shareholder) is the founder’s family or a different family or individual.<sup>28</sup> Among 15% of the 854 firms, the largest shareholder is another corporation (or organization), and thus cross-holding is also a prevalent ownership pattern in India. Since we do not have detailed information on the ownership patterns of the corporate owners, we do not know whether the corporate owners themselves are widely held or not. However, given the fact that family (individual) ownership is prevalent among listed firms, it is reasonable to assume that these corporate owners are not widely held. Only 1.76% of the 854 firms are widely held (i.e., no shareholder owns more than 10% of stocks).

Our findings on ownership structure of India’s listed firms are similar to those of other Asian countries (e.g., Claessens, Djankov and Lang 2000; Claessens, Djankov, Fan and Lang 2002; and AQQ 2005). The main result of LLS (1999) is that countries that protect minority shareholders poorly (strongly) tend to have more concentrated (dispersed) ownership. Our evidence on India contradicts this hypothesis, in that despite India’s strong investor protection (by law) its ownership structure is close to other Asian countries (with family ownership) and countries with weak investor protection. However, if we take into account India’s weak law enforcement and institutions (e.g., using the anti self-dealing index in DLLS 2005 and the revised creditor rights score in DMS 2005), then observed ownership structure is by and large consistent with the prediction of DLLS (2005).

> 28 Since we do not have detailed information on the identities of all the largest shareholders of these firms (e.g., whether they belong to the same family or a _group_ of a few unrelated blockholders), our figure (80%) may be biased.  However, we are certain that the largest block of equity of these firms is _not_ held by organizations, the government, or a large number of disperse shareholders.

25
Table 1: Payments for 1<sup>st</sup> Prenatal Visit

|Time Period|D|ates|Payment for 1<sup>s</sup>|<sup>t</sup>Prenatal Visit|
|---|---|---|---|---|
||Begin|End|Before Week<br>13 of<br>pregnancy|At week 13 of<br>pregnancy or<br>after|
|Pre‐Intervention|January 2009|April 2010|$ 40 ARS|$ 40 ARS|
|Intervention|May 2010|December 2010|$ 120 ARS|$ 40 ARS|
|Post Intervention|January 2011|December 2012|$ 40 ARS|$ 40 ARS|

Source: National Ministry of Health, Argentina (2010b) Table 2: Clinic Assignment and Compliance Status

|Assigned to|Actually Treated|Total|
|---|---|---|
|Treatment|Yes<br>No||
|Yes|14<br>4|18|
|No|1<br>18|19|
|Total|15<br>22|37|

Source: Authors’ elaboration.

36
# **IV.  Establishment Size and Age in India**

# **_A. Firm Life Cycle in the Formal Manufacturing Sector_**

To examine the relationship between firm size and age, we pool repeated cross-section data from the Indian census. We begin with some descriptive statistics of our data, following Hsieh and Klenow (2013). For each census year separately, we first compute the mean employment in each age bin reported by that census. We then compute an index for each year of data where the index takes the value 1 for establishments less than five years old. For all other age bins, the index is the ratio of the mean employment in that age bin to the mean employment for firms under five years of age reported in the same census. Figure 7 plots the mean index for each age bin using data for five repeated cross-sections – 1983/84, 1989/90, 1994/95, 2000/01, and 2004/05. The figure shows that the average 40-year-old plant is 3 to 5 times the size of the plants under five years of age. Figure 8 presents data using weighted employment and shows that for the average 40+ year old plant, the ratio varies from 4.8 times in 1983 to 2.2 in 1994. We also see that in recent years, the average 30-year-old plant is not that much larger than plants younger than five years old but after that, older plants are larger than the young plants. To mitigate concerns about sampling issues, we present data only using the census sector in Figure 9 and find that even 20-year-old plants in most years are twice as large as plants younger than five years of age. Figure 9 also shows that over time the size-age gap for the 40+ year old plant has shrunk starting from 4.91 in 1983/84 to 2.62 in 2004/05. In unreported figures we find similar results when we use a weighted average across all industries in each age bin with the weights being the value-added shares of each industry.

The graphs show a much faster growth rate for Indian plants than found by Hsieh and Klenow (2013) who document a flat plant life cycle in Indian manufacturing, including in just 25
# **Figure 7a. Average Change in Log Real Wages for Stayers in Tradeables and NonTradables, 2007-2009**

<!-- Start of picture text --> Average Change in Log Real Wages<br>(workers who have not changed firms)<br>qtr<br>tradeable (stayers) non-tradeable (stayers)<br>2009<br>Average Change in Log Real Wages<br>(all workers)<br>qtr<br>tradeable non-tradeable<br>.04<br>.02<br>0<br>change in log wage<br>-.02<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br>.02<br>.01<br>0<br>change in log wage -.01<br>-.02<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text -->

**Figure 7b. Average Change in Log Real Wages in Tradeables and Non-Tradables, 20072009**

40
of the 12-month SPEI observed over the period 2000-2009. The national average SPEI ranges around -0.5; when we consider the extreme negative values, at least five periods of severe drought have taken place. To better characterize our empirical setting, we considered SPEIs at the provincial level, and the associated classification of drought intensity is reported in Table A1. Accordingly, we classified as treated provinces in which the SPEI value assumed a value lower than -2 s.d. over the period 2000-2009; this threshold corresponds to “severe” drought events. Provinces that never experienced severe droughts constituted our control group. The distributions across years of these groups are reported in Table 1. Finally, Table 2 shows the summary statistics, for the treated and untreated provinces, for all the socioeconomic and climatic variables described above.

# **3 Empirical Strategy**

Our goal was to estimate the causal effect of a drought shock on various labor market outcomes at the provincial level in Morocco. Since drought occurs at different points in time across provinces, we have framed the analysis in a staggered difference-in-difference (DiD) setting that exploits two sources of variation: the cross-sectional variation in the probability of experiencing an SPEI lower than -2; and the variation in timing of the observed years when the drought hits a province. In a standard dynamic two-way fixed effect (TWFE), the following specification is estimated: where yp,t is one of the three outcomes considered, i.e., the employment rate in the agriculture sector, the employment rate in other sectors and the unemployment rate in province _p_ and year _t_ , αt are year fixed effects, γp are province fixed effects and εp,t is an idiosyncratic error term. Dp.te is a distance-to-event indicator being _e_ periods away from the 10
Despite the relevance and strong interest in the issue, analyses of the impacts of wealthier middle classes face constraints in measuring income directly, and most use GDP per capita as a proxy for income. Only a handful of cross country datasets report headcount indexes for income thresholds other than poverty, and the ones that exist span too short time periods to exploit both cross country and time series variations. As a result, current analyses fail to investigate directly whether, as postulated by the literature, “critical masses” of people overcoming certain income thresholds can affect institutional outcomes.

This paper makes two contributions. First, it presents a new panel dataset that contains information about households’ mean income and expenditures, inequality, and headcount indexes for several income and expenditures thresholds, which complements existing large cross-country datasets reporting poverty and inequality measures. The dataset spans 672 yearly observations across 128 countries. To build the dataset, we draw both from a collection of nationally representative household surveys, and from parameterized distributions of income and expenditures using parameters from the World Bank’s _PovCal_ database. Second, we use this new dataset to test the extent to which policy and institutional outcomes are affected by an expansion of the middle class, which in the paper is measured as the proportion of the population who have achieved income above 10 US dollars a day.

The joint endogeneity of the size of the middle class (and of the income distribution in general) is a concern. So is the likely presence of unobserved country-specific effects. We use an econometric methodology that, by taking advantage of the panel nature of the data, attempts to gauge the causal effect of expansions in the middle class while controlling for country-specific effects. Specifically, the estimation draws from the generalized method of moments (GMM) estimator for panel data developed by Arellano and Bond (1991) and Arellano and Bover (1995). We estimate the impact of the middle class on policies and institutions in three broad categories: social policy regarding public expenditures on health and education, market-oriented economic structure on international trade and finance, and quality of governance regarding democratic participation and absence of official corruption. Our analysis thus also relates to the literature investigating the association between poverty, institutions and growth (Dollar and Kraay, 2002; Lopez and Serven, 2009).

We find that when the size of the middle class increases, social policy on health and education becomes more active, and the quality of governance regarding democratic participation and official corruption improves. This does not occur at the expense of economic freedom, as an expansion of 3
_These outcomes do not represent the ideal level of knowledge; for example, they do not account for co-morbidities._

# _To quantify whether providers are_ **_available to provide medical service_**

**_when patients seek care_** _, we followed a standard procedure in which enumerators visited each facility twice. During the first visit, information on all staff working at the facility, by professional cadre, was collected. (See Section S5 in the appendix for details on the selection of staff,_ **_drugs, and equipment_** _for surveys.) A few days after the first visit, enumerators made a second unannounced visit to the facility and assessed the presence of a randomly pre-selected list of providers (or all providers, if less than 10 providers were available). Only staff on duty that day were included in the absenteeism measure. Country-level averages for each assessment were calculated using country-specific sampling weights._

_Combining data from the clinical vignettes (to measure knowledge) with data from the unannounced visit (to measure the extent to which providers are present in the facility at regular opening hours), and visual inspections (to quantify the availability of drugs and equipment), we estimate the probability that a facility is ready to provide care that meets minimum quality standards.”_

- Bias 9 Describe any efforts to address potential sources of bias

_In order to minimize bias, the study includes nationally representative samples for all but one country in the sample (Nigeria). In Nigeria, the sample excluded some states due to security concerns but was representative at the state level for 12 of 37 states. In all countries, the team used a “multistage cluster-sampling design to ensure data were representative of rural and urban areas, private and public facilities, and of each facility type (e.g., front-line clinics versus hospitals).”_ Study size 10 Explain how the study size was arrived at _See the relevant text: “In total, the sample includes information from 89,826 vignettes from 13,754 providers and absenteeism observations from 22,747 providers across 8,061 facilities in 10 countries. In each country, the sample size was selected in order to provide representative data for the country and for sub-groups (e.g., rural versus urban areas).”_

- Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why

_See the relevant text: “We present averages and 95% confidence intervals by first averaging all observations across facilities (or health workers) in each country (using country-specific facility weights to calculate a representative country average), and then averaging over the country means to calculate an overall average for the whole sample. (This is a suitable approach if we treat the country as the core unit of observation; i.e., we want to know how countries compare and how they perform as a group.)”_ 39
-23 -

because of the political implications of the presence of private providers with a direct mandate to invest the funds in the best possible way.

# c) Sources of non-marketing costs

In this section we investigate sources of cost by disaggregating between different AFPs. In the following table, effective covered workers are defined as registered persons that had contributed at least once in the last 12 months. This latter number was corrected by an estimate of the number of contributors whose processing had taken more than a month, estimated to be 6.2% from the industry figure for March 1992.14 Total net charges by each AFP are defined as commission income minus payments for invalidity and survivorship insurance, both of which are taken from audited income and loss statements for 1991. The non-marketing charge subtracts reported marke:ing costs from the previous figure.

Table 6 _Charnt ner covered workers by AFP. 1991 (1992 US dollars per effecfive covered worker)_

|Name of AFP<br>c|Number of<br>Effective<br>overed workers<br>(March<br>1992)|Average<br>Net Charge<br>(US dollars<br>/year)|Non-marketing<br>Average<br>average<br>Wage of Contributors<br>Charge<br>(US doll. per<br>(US dol./year)<br>month, Mar. 92)|
|---|---|---|---|
|Concordia|108,103|35.3|26.8<br>169.8|
|Provida|964,130|37 4|28.6<br>250.1|
|Santa Maria|676.195<br>|45.4<br>|35.5<br>256.9<br>|
||-<br>-- average|for the sy|stem -|
|Habitat|599,840|_56.0_|47.9<br>322.0|
|Summa|264,654|57.0|46.8<br>370.7|
|Planvital|69.613|57.8|40.1<br>250.7|
|El Libertador|65,646|63.3|45.3<br>351.7|
|Magister|57,976|63.9|48.8<br>265.7|
|Invierta|92.490|67.5|55.4<br>236.8|
|Uni6n|245,555|70.3|55.7<br>291.0|
|Cuprum|93,515|95.1|61.7<br>673.3|
|Proteccidn|36,012|131.2|85.4<br>601.1|
|Futuro|7,103|148.5|119.6<br>656.6|
|TOTAL|3,280,832|51.6|40.4<br>299.2|

Source: Last column, Boletin N<sup>0</sup> 1109 April 1992, page 64.

14 More precisely, the estimate was produced with the formula: Eff. Cov. Workers = A - B(10.062x(A/C)). where A = total registered persons: B= registered persons that did not contribute in the last 12 months; C = registered persons that did not contribute in the current month.
higher mandate, gasoline price, and tax exemption<sup>7</sup> for hydrous ethanol results in higher ethanol market (i.e., wholesale) prices, but a lower gasoline tax and a higher tax exemption on anhydrous ethanol results in lower ethanol prices.

**Figure 4: U.S. and Brazil Gasoline Prices Compared**

<!-- Start of picture text -->
1.80<br>NY Harbor São Paulo<br>1.60<br>1.40<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Source: Brazil ANP, U.S. EIA<br>liter<br>R$/<br><!-- End of picture text -->

The primary objectives of this paper are to (a) develop a general economic model of the trade-off between ethanol and sugar production in Brazil that occurs in a sugarcane processing flex-plant that produces both sugar and ethanol, and where the world prices of each product are determined endogenously; and (b) determine the market effects of Brazil’s ethanol policies. To achieve these objectives, we incorporate unique features of Brazil’s market and policy into the economic model; especially, we model the two fuel demands and the anhydrous ethanol mandate, the changing parity gap between E100 and fuel prices, ethanol produced from molasses, the by-product of sugar production, and bagasse for electricity production. We also use our model to explain the dramatic change in market conditions from 2010/11 to 2011/12 where ethanol and sugar prices soared, fuel consumption increased, sugarcane production fell and the

> 7 Intuitively, a higher tax exemption for hydrous ethanol incentivizes its consumption which necessitates more production, hence the wholesale price increase.

6
- **Love, Inessa** , “Financial Development and Financing Constraints: International Evidence from the Structural Investment Model,” _Rev. Financ. Stud._ , 2003, _16_ (3), 765–791.

**Neumeyer, P.A. and F. Perri** , “Business cycles in emerging economies: the role of interest rates,” _Journal of Monetary Economics_ , 2005, _52_ (2), 345–380.

**Oviedo, P.** , “Intermediation of Capital Inflows: The Macroeconomics Implications of Neoclassical Banks and Working Capital,” _manuscript, Iowa State University, January_ , 2004.

**Tybout, James R.** , “Credit Rationing and Investment Behavior in a Developing Country,” _The Review of Economics and Statistics_ , nov 1983, _65_ (4), 598–607.

**Whited, Toni M.** , “Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel Data,” _The Journal of Finance_ , sep 1992, _47_ (4), 1425–1460.

# **Appendix**

## **Contrast With the Standard Model of Financial Constraints**

### **Standard model of constraints**

The standard model utilizes the Jorgenson model of investment with financial constraints (see Bond and Meghir (1994) for review). Capital takes one period to install and the firm chooses current labor inputs and the next period’s capital. The implicit assumption is that there is no delay in the receipt of revenue and the model is given as: There are four characteristics of the standard model that contrasts with a model with working capital: 1) Labor is always at optimal regardless of financial constraints. This is an equilibrium condition as the marginal product of labor is immediately received to not only cover the cost of labor but also to fund capital.<sup>46</sup> Take for instance that a firm does not have enough cash to purchase steady state capital (where _E_ [ _MPK_ ] = _p_<sup>_k_</sup> ( _r_ + _δ_ ) the rental cost of capital). The firm at optimal labor where _MPL_ = _w_ , chooses to decrease a dollar’s worth of labor and put it towards capital. Although

> 46This implies that there must be a slight timing difference between when labor decisions are made and when capital decisions are made.

22
than 200 employees). All BE variables and indicators of competition are interacted with these four size indicators. The results are shown in table 9.

_Government Protection_ . Containing corruption is more positively associated with firm employment growth for smaller firms. Using both the Enterprise Survey Corruption Obstacle and Global Competitiveness Corporate Ethics measures, small firms, but especially micro firms, benefit the most from a relatively noncorrupt business environment. Reducing the Enterprise Survey Corruption Obstacle by one standard deviation, for instance, is associated with an increase in the firm employment growth rate for micro firms of 0.9 percentage point and for small firms of 0.4 percentage point, but a decrease for large firms of 0.4 percentage point. Reducing corruption thus enables the performance of both micro and small firms.

The positive association between indicators of physical safety and firm productivity tends to be stronger for large firms. Physical safety is associated with higher productivity for medium and large firms, using both the Enterprise Survey’s Security Costs and the Global Competitiveness Police System measures. On the other hand, the relationship between physical safety and firm growth is not robust depending on the source of data: a higher score on the Global Competitiveness Police System mesure is positive (and marginally significant) for firm growth only for large firms, but a lower score on the Enterprise Survey’s Security Costs measure (and therefore better physical safety) is associated with higher employment growth only for small firms. Given that the developing countries are dominated by micro firms (Hsieh and Olken 2014), we conclude that physical safety is relatively important for firm performance for non-micro firms.

_Infrastructure_ . Availability of modern infrastructure is clearly enabling to improved business performance of smaller firms. The Enterprise Survey Web measure is positively and significantly associated with firm growth only for micro firms. Moreover, both the Enterprise Survey Web and Global Competitiveness Broadband mesures are associated with higher productivity, with the association being stronger and increasing from medium to small and then to micro firms. Increasing the Enterprise Survey Web measure by one standard deviation is associated with higher productivity by 6, 12, and 17 percentage points for medium, small, and micro firms, respectively.

Good basic infrastructure, as characterized by electricity provision, tends to be enabling for relatively small firms. The _Doing Business_ Getting Electricity measure has a positive and stronger association with productivity for relatively smaller firms. The Global Competitiveness Electricity Quality measure has a positive and significant association with productivity only 22