rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8512

# **Abstract**

Caste is a persistent driver of inequality in India, and it is generally analyzed with government-defined broad categories, such as Scheduled Caste and Scheduled Tribe. In everyday life, however, caste is lived and experienced as _jati_ , which is a local system of stratification. Little is known about economic inequality at the _jati_ level. This paper uses data from poor rural districts in Bihar to explore expenditure inequality at the _jati_ level. Inequality decompositions show much more variation between _jatis_ than between broad caste categories. The analysis finds that even within generally disadvantaged Scheduled Castes and Scheduled Tribes, some _jatis_ are significantly worse off than others. Consistent with previous work, the paper also finds that inequality is largely driven by inequality within _jatis_ . This finding has implications for the implementation of largescale poverty alleviation programs: the benefits of programs intended for disadvantaged castes are concentrated among specific _jatis_ .

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at vrao@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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# The Behaviour of Russian irms In 1992 Evidence from a Survey

# By Simon Commander, Leonid Liberman, Cecilia Ugaz and Ruslan Yemtsov

# Table of Contents

|I.|Introduction.1................|...<br>I|
|---|---|---|
|1|The Survey .....................|1|
|2|Output Chang. .<br>-----..-..--.--.|2|
|_3._|Financial Performance<br>.............|_3_|
|_3.1_|Market Power ....................|10|
|3.2|Inter-enterprise Arrearm. .|12|
|_3.3_|Decapitalization .15||
|4|Employment Changes .*-|15|
|4.1|Labour Hoarding............<br>.|24<br>|
|_5_|WageSetting ...............<br>.|25|
|5.1|Relative Wages .................|27|
|_5.2_|Bargaining and Wage Targets .|29|
|6|Firm-Provided Benefits .30||
|6.1|Costs of Beneits .34||
|6.2|Housing ..|37|
|7|Condusion............-.<br>-.|41|
|Bibli|ography<br>.<br>.43||
|<br>App|<br>endix Table1<br>.|44|
|App|endix Table 2 ................|45|
|Appe|ndix Table 3 .<br>--..---.-|46|
examining _changes_ in outcomes of interest (Δy), which removes individual (and household) time-invariant effects.

The second source of endogeneity is choice of residence. The event of a death among household members is a reflection of household composition, which can be endogenous. Two pieces of evidence support this idea in the KHDS data. First, there is a high degree of change in household composition over time. Second, within the first 4 rounds (1991-1994) of the KHDS, about one-third of all adult deaths in the data are among people who move into the household within six months of dying. To the extent that individuals chose to live in households in response to illness or recent death, the event of death in their household is endogenous. In part, we rely on the length of the panel to address this concern. First, AIDS deaths (as well as other deaths caused by major illnesses in this region) are preceded by serious morbidities. In the KHDS baseline, on average, illness began 12 months before the event of a death. For the vast majority of the deaths in our sample, both the baseline and 2004 data are more than 1 year from the symptom onset and the death. So, the majority of these deaths had a preceding illness whose onset with respect to observed morbidities was after baseline. In that case, we do not expect that either the surviving older adult or the ill household member would have anticipated the future death with respect to 1991 living arrangements. Second, while there is evidence that adult children move back in with their parents when very ill and shortly before they pass, this type of death is largely eliminated from our sample or combined with deaths of other children regardless of co-residing with the parent at time of death (our third death indicator).

# **3.2. Attrition bias**

As mentioned earlier, there is a great deal of attrition in the sample over the 13 years of the panel, as Table 1 shows. This attrition is almost wholly due to death (i.e., deaths of elderly individuals). Of the 1,060 potential elderly individuals in the first round of the survey (those individuals who were surveyed who would have been over the age of 50 in 2004), 403 (38%) had died by 2004, and only 44 (4%) were not located. Most of the deaths, <mark>85%,</mark> were reported to be due to illness, rather than injury or accident. Among the 61% of deaths, the specific causes range from blood pressure, malaria, cancer, AIDS, 14
related to the probability of being a Nini for men, but not for women. Several of these findings are also corroborated using data from Argentina and Brazil, in particular, the importance of previous decisions on time use and their relevance for current decisions.

The most important lessons from this paper are: (i) once youth aged 15 to 18 leave school it is very unlikely that they will return; (ii) being a Nini is a highly persistent condition; (iii) marriage of young women (perhaps as a result of teen pregnancy) increases the probability of girls leaving school and raising children by themselves, which increases their likelihood of becoming Ninis and also the probability of their male children being Ninis in the future, potentially creating an intergenerational transmission of Nininess.

These results seem to validate the predictions of the theoretical model, although in this paper we do not interpret them in a causal way. However, they are indicative of strong relationships between youth decisions and the variables of interest. In all analyses presented, moreover, we have seen the inadequacy of the definition of _Nini_ to capture non-labor market work (our definition of Nini refers only to work performed in the labor market), in particular for women. The analyses should thus be interpreted with this in mind.

The patterns shown in this paper for different socioeconomic groups suggest a place for policies that target education access programs particularly at young boys between 15 and 18 years of age, as well as including girls in anti-dropout programs and facilitating schooling opportunities for young women with children. The first dropout is crucial for the future of Ninis and as such is important that policy responses target youth at risk of dropping out for the first time. In addition, the high concentration of female Ninis who have started a household is an intuitively different phenomenon to that of male Ninis. The former could potentially reflect a rational decision to partake in domestic work and reducing the number of Ninis in this category, if deemed desirable, would likely involve intrinsically different policies probably oriented more towards child care.

32
smoking and alcohol consumption. The paper does not explicitly discuss the identification conditions, but seems to rely on identification by the functional form. Such an approach could be problematic due to the “weak instruments bias” (Staiger and Stock 1997). The instrumental variables in Van Ours (2002) include whether the respondent has a partner and whether the respondent indicated the ‘early start’ of smoking. Auld (2005) includes religion and the price of tobacco as instrumental variables. Heineck and Schwarze (2003) use such variables as ‘strong religious conviction’ and martial status as instruments in their 2SLS estimation. In our view, it is hard to justify the validity of these instruments for identification of the effect of smoking on wages. Empirical research has consistently shown that married men have substantially higher wages, on average, than otherwise similar unmarried men, the so-called “marriage wage premium” phenomenon (e.g., Cornwell and Rupert 1997, Daniel 1991, Gray 1997). ‘Early start’ of smoking may be an invalid instrument if length of smoking exposure directly affects wages through health; long-term adverse effects of teenage smoking on health are well-documented in the medical literature (e.g. CDC 1994). Using the price of tobacco as an instrument could also be problematic because of the addictive nature of smoking (see footnote 3). Finally, it is difficult to argue that religious beliefs, while affecting the individual’s probability to smoke, have no effect on earnings given the extensive literature that demonstrates the opposite (e.g., Nigel 1984). Indeed, Van Ours (2002) concludes that “... _it is difficult to find good instrumental variables_ ” and Heineck and Schwarze (2003) state that their “ _...identifying instruments ... perform poor[ly] in the first stage_ ” of 2SLS regression.<sup>5</sup> Our identification strategy relies on two variables about smoking behavior of the respondent’s parents. We had an opportunity to introduce these variables in the ALSMS questionnaire specifically for the purpose of using them as instruments in the current analysis. We argue that the parental smoking history is correlated with the probability to smoke and has no direct impact on individual’s earnings<sup>6</sup> . The effect of parental smoking on offspring tobacco use is well established in the literature (e.g., Andrews _et al._ 1993; Pedersen and Lavik 1991). Smoking parents may present a model of smoking behavior to

> 5 See Van Hours (2002), page 11; Heineck and Schwarze (2003), page 8.

> 6 MacDonald and Shields (2001) use similar instruments in the analysis of the impact of alcohol use on occupational attainments in England.  Here we discuss these instruments in the context of probability of being a current smoker with the caveat that these instruments are arguably stronger for identification of non-smokers who never having smoked as opposed to non-smokers who had previously smoked and quit.

8
**Table 3:** Randomization check

|||Total|Wa|ge bonus|Wa|ge bonus|Diference|
|---|---|---|---|---|---|---|---|
||||trea|tment (1)|co|ntrol (2)||
|Variable|N|Mean/SE|N|Mean/SE|N|Mean/SE|(1)-(2)|
|Female|1924|0.501<br>[0.011]|970|0.499<br>[0.016]|954|0.503<br>[0.016]|-0.004|
|Age|1924|17.81|970|17.78|954|17.84|-0.060*|
|||[0.017]||[0.023]||[0.026]||
|Married|1924|0.022<br>[0.003]|970|0.026<br>[0.005]|954|0.019<br>[0.004]|0.007|
|Caregiver|1924|0.221|970|0.223|954|0.220|0.003|
|||[0.009]||[0.013]||[0.013]||
|Home internet|1924|0.484<br>[0.011]|970|0.476<br>[0.016]|954|0.493<br>[0.016]|-0.016|
|Number bathrooms (HH)|1923|1.147<br>[0.013]|970|1.140<br>[0.018]|953|1.153<br>[0.019]|-0.013|
|Number bedrooms (HH)|1924|3.145<br>[0.024]|970|3.121<br>[0.034]|954|3.170<br>[0.034]|-0.049|
|Number cars (HH)|1924|1.031<br>[0.021]|970|1.044<br>[0.031]|954|1.018<br>[0.028]|0.027|
|HH SES well-being score|1924|139.6<br>[0.932]|970|139<br>[1.316]|954|140.2<br>[1.321]|-1.173|
|Number of earners (HH)|1914|1.990<br>[0.024]|964|1.971<br>[0.034]|950|2.008<br>[0.033]|-0.037|
|Grit score|1924|3.536<br>[0.012]|970|3.574<br>[0.017]|954|3.498<br>[0.016]|0.076***|
|Vocational school|1924|0.800<br>[0.009]|970|0.801<br>[0.013]|954|0.799<br>[0.013]|0.002|
|Commute time city|1924|0.759<br>[0.010]|970|0.748<br>[0.014]|954|0.771<br>[0.015]|-0.023|
|Plan to work|1924|0.557<br>[0.011]|970|0.553<br>[0.016]|954|0.561<br>[0.016]|-0.008|
|Plan to study|1924|0.435<br>[0.011]|970|0.438<br>[0.016]|954|0.432<br>[0.016]|0.006|
|Has a job at baseline|1924|0.372<br>[0.011]|970|0.371<br>[0.016]|954|0.372<br>[0.016]|-0.001|
|Has held a formal job|1924|0.182<br>[0.009]|970|0.177<br>[0.012]|954|0.188<br>[0.013]|-0.010|
|Reservation wage|1924|4679<br>[74.8]|970|4635<br>[99.9]|954|4723<br>[111.6]|-87.88|
|F-test joint signifcance (F-stat)|||||||1_._203|
|F-test joint signifcance (p-value)|||||||0_._216|

_Notes:_ Source: Baseline survey. The last column reports differences in the means across the groups. *, **, *** indicate t-test significance at the 10, 5, and 1 percent level. Standard errors are robust. Household vulnerability score is based on the national Mexican AMAI measure which considers the number of bathrooms, bedrooms, cars, and employed persons in the household, as well as internet access and head of household education level.

16
functioning judicial systems and legal enforcement (e.g. La Porta, Lopez-de-Silanies, Shleifer, and Vishny, 1997), and weak property rights protection (e.g. Claessens and Laeven, 2003).<sup>20</sup> Our findings on SMEs are broadly consistent with the OECD evidence in Haltiwanger, Scarpetta, and Schweiger’s (2010) where they study net employment and find that small firms account for a higher pace of job creation and destruction. Our findings are also consistent with the results in Beck, Demirguc-Kunt, and Levine (2005) who find a large SME contribution to employment across 54 (mostly developed) countries and a strong association between the SME sector and GDP/capita growth but no evidence of causality.

On age, we also find that the conditional on size, youngest firms (five years old or less) have higher employment growth, sales growth, and productivity growth. Our results pertain to continuing firms, so it is important to bear in mind that the youngest firm class is most subject to survivorship bias in our data. In addition, our surveys are not stratified by age and we do not have growth rates in the year of the birth. So we are cautious in not emphasizing our results on firm age.

We also find that while small firms are important for employment and job creation, the large firms have the highest productivity growth in our sample. This is consistent with the evidence in other work such as Banejee and Duflo (2005), Maksimovic and Phillips (2002), and Bartelsman, Haltiwanger, and Scarpetta (2009) who find that larger firms are more productive. Other studies such as Beck, Demirguc-Kunt, and Maksimovic (2006) also suggest that there is a positive relationship between firm size and the development of financial and legal institutions in a country.

> 20 Note however that Rauch (2010) shows that in less developed countries institutional reforms that disproportionately benefit small businesses may have adverse consequences such as interfering with the impact of trade reform since SMEs tend not to be export oriented and  produce low quality output.

20
|Population at decade of birth (cohort<br>weighted)|0.0207<sup>*</sup><br>(0.0110)|0.0235<sup>**</sup><br>(0.0092)|0.0096<br>(0.0115)|0.0160<sup>**</sup><br>(0.0076)|
|---|---|---|---|---|
|Population at decade of birth squared|-0.0001<sup>**</sup>|-0.0001<sup>***</sup>|-0.0000|-0.0000|
|(cohort weighted)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|
|Infant mortality rate at decade of birth|-0.0015|-0.0015|-0.0025|0.0046<sup>**</sup>|
|(cohort weighted)|(0.0028)|(0.0025)|(0.0029)|(0.0020)|
|_R_<sup>2</sup>|0.988|0.988|0.988|0.990|
|Number of observations|1244|1244|1244|1244|
|Numberofcountries|68|68|68|68|

_<mark>Source:</mark>_ <mark>All specifications include country and year fixed effects. Standard errors (in parentheses) are clustered at the country level.</mark>

> *** <mark>significant at the 1% level,</mark> ** <mark>significant at the 5% level,</mark> * <mark>significant at the 10% level.</mark>

28
Rajan and Zingales 2003b; Acemoglu and Johnson 2005), poor institutions, weak government and powerful elites should severely hinder India’s long-run economic growth.<sup>33</sup> However, our aggregate and disaggregate evidence shows that corruption has not prevented a high rate of growth for India’s firms, in particular, firms in the SME sector, where legal protection is perhaps weaker and problems of corruption worse compared to firms in other sectors. Perhaps one of the most effective solutions for corruption for firms in this sector is the common goal of sharing high prospective profits. This common goal can align interests of the investors and government officials with entrepreneurs and managers to overcome numerous obstacles. Under this common goal in a multi-period setting, implicit contractual agreements and reputation can act as enforcement mechanisms to ensure that all parties, including government officials, fulfill their roles to make the firm successful. Another potential effective solution for corruption is _competition_ among local governments/bureaucrats from different regions within the same country. Entrepreneurs can move from region to region to find the most supportive government officials for their private firms, which in turn motivates officials to lend “helping hands” rather than “grabbing hands,” or else there will be an outflow of profitable private businesses from the region. This remedy should be typically available in a big country with multiple regions like India.

Finally, in our surveys we find that the majority of entrepreneurs resolve disputes outside of courts, similar to survey results from other emerging countries (McMillan and Woodruff (1999a) survey of Vietnamese firms and AQQ (2005) survey of Chinese firms). However, we do find that some Indian entrepreneurs and their business partners also rely to an extent on the legal system (e.g., courts) to resolve disputes and enforce contracts. It is possible that, going forward, the legal system will plays a more important role in supporting the development of stock markets and attracting more foreign capital inflows. In order for this to happen, we must consider the costs of improving the

> 33 In addition, LLSV (1999) find that governments in countries with French or socialist origins have lower quality (in terms of supporting economic growth) than those with English common laws and richer countries.

38
<!-- Start of picture text --> =]<br>850000 I<br>| AFR<br>40000 oH EAP<br>30000 cca<br>— I Bi<br>10000 | Meat,<br>elise sos<br>0 2500 5000 7500 10000<br>distance to nearest 2003 crop pixel(m)<br><!-- End of picture text -->
# **Figure 2 OECD index of regulatory restrictions in telecommunications services**

<!-- Start of picture text --> Turkey<br>Tunisia<br>Luxembourg<br>Morocco<br>Iceland<br>Norway<br>Slovak Republic<br>Switzerland<br>Belgium<br>France<br>Australia<br>Sweden<br>Greece<br>Japan<br>Austria<br>Germany<br>Ireland<br>Portugal<br>Hungary<br>Mexico<br>Spain<br>Korea<br>0 1 2 3 4 5 6<br>Restrictiveness index<br><!-- End of picture text --> Data source: Based on OECD (2005b).

24
**Table VI. Regression Analysis: Actual vs. Predicted Bilateral Current Account**

||Basic Specification<br>|<br>|BS + La<br>|gged Vars.<br>||
|---|---|---|---|---|---|
||(BS)|BS + Time Trend|PBCA|L.PBCA|Vars. in Diff.|
|Basic VAR|0.004 ***|0.001 ***|0.001 **|-0.001 **|0.001 **|
|+ RER in Levels|0.003 ***|0.001 ***|0.001 *|-0.001 *|0.001 *|
|+ RER in Differences|0.004 ***|0.001 ***|0.001 *|-0.001 *|0.001 *|
|+ Infl., NER in Levels|0.003 ***|0.001 ***|0.001 **|-0.001 *|0.001 *|
|+ Infl., NER in Differences|0.004 ***|0.001 ***|0.001 **|-0.001 **|0.001 **|

This table shows the results of four different regression specifications: a basic specification that regresses the actual bilateral current account on the predicted bilateral current account (PBCA), the basic specification with a time trend, the basic specification including lagged values of both the dependent and independent variables, and a fourth specification with both variables in differences. The results are reported for different series of model-predicted portfolio shares: our five VAR specifications are considered to calculate expected asset returns. The regressions use quarterly data. Actual data is scaled by U.S. GNP and the predicted data is scaled by model-based U.S. wealth. A relative risk aversion coefficient of 10 has been assumed. * means significant at 10%, ** at 5%, and *** at 1% levels.

52
- 5 -

However, in 2001 and 2002, membership stagnated and even declined a little, as result of the lack of official support to the reform. The 1998/2002 Fidesz administration did not support the 1997 pension reform and reverted some of the transition rules – the contribution rate was not increased from 6 to 8 percent as planned, membership of new entrants became voluntary and workers were allowed to switch back to the pure PAYG system. The original rules were restored with the return of the Socialist Government in mid-2002, leading to a slow but steady increase in membership since that time, in line with the original reform plans.<sup>5</sup> 16. In 2004, second pillar assets amounted to about 4 percent of GDP, as previously shown in Table 1. Although the second pillar has surpassed the third pillar in size, it is still small by international comparison. As shown in Table 5, the ratio of second pillar assets to GDP in Hungary is lower than other countries in Latin America and Central Europe with the same period of implementation, and even lower than younger pension systems. Second pillar assets in Hungary have grown at a modest rate of about 0.5 percent of GDP per year, as shown in Table 6, a slower pace of growth than the one projected at the time of the reform.

**Figure 1: Growth of Second Pillar, 1998 – 2004**

<!-- Start of picture text --> 1000 3000<br>900<br>2500<br>800<br>700<br>2000<br>600<br>500 1500<br>400<br>1000<br>300<br>200<br>500<br>100<br>0 0<br>Dec- Dec- Dec- Dec- Mar- Jun- Sep- Dec- Mar- Jun- Sep- Dec- Mar- Jun- Sep- Dec- Mar- Jun- Sep- Dec-<br>97 98 99 00 01 01 01 01 02 02 02 02 03 03 03 03 04 04 04 04<br>Assets (HUF bln) Contributions (HUF bln) Members (thousand)<br><!-- End of picture text --> Source: HFSA 17. In order to gain further insights into the causes of the slow growth of assets, the changes in the ratio of pension assets of GDP were tracked by estimating each of its four components: the flow of contributions, asset returns, fees charged, and the growth of GDP. The first two components increase the ratio by increasing the stock of assets, while the third and fourth components decrease the ratio by decreasing the stock of assets and increasing the denominator, respectively. Appendix A describes the methodology used for estimating these components in the absence of flow of funds data.

> 5 Reform reversals also affected the PAYG component of the pension system.  The rate of contribution to the PAYG is 4.5 percentage points lower than the levels envisaged by the reform and may be lowered further by 2 percentage points in 2007 and 2009.  The government also granted a 13<sup>th</sup> monthly pension to retirees to compensate for the Swiss indexation introduced with the reform (Orbán and Palotai, 2005).