rafmacalaba/datause-encoder-data huggingface dataset:
-22-

The second adjustment concerns the unit of observation. Data was collected for the household, but information was also available on household size (i.e., number of members in each household). Since household size is not the same across income levels, a measurement based on household income is an inaccurate reflection of individual poverty (cf. Anand, 1983, pp. 63-7). Moreover, since lower income households are larger (see table 3 below), 28 estimates based on household incomes underestimate poverty . To correct for this phenomenon, I rank households on the basis of per capita household income, obtained by dividing the income level of each household by household size, and measure poverty at the level of the individual.

One final remark. Although the IES has very detailed coverage of expenditures, with special emphasis on food, it provides no information on 2 9 asset ownership or ethnic characteristics. Thus, while the information is very valuable, it is insufficient to make direct connections between income and expenditure patterns, on the one hand, and asset ownership and productive activity, on the other. In particular, it is important to know whether agricultural workers' income levels are correlated with type of crop (cereals vs. other; within cereals corn vs. wheat, etc.), or with type of land tenure (ejido vs. private land). This information is essential to test hypotheses on the relationship between poverty, crop pattern and land te-ure status.

# IV._.B. The Lines of Moderate and Extreme-Poverty.

My departure point for constructing x and z is the Coplamar (1983) study on basic needs, where the annual cost of a basket of necessities for an average family of 4.9 members, made up of 2.7 adults (older than 15 years of age), 1.66 children (between 3 and 14 years of age) and 0.47 babies is calculated. Coplamar followed a three part procedure to construct this 28Consider households A and B with, say, household incomes 2 and 4 (pesos) and household size 1 and 3, respectively. Ranked by household income household A is poorer, but ranked by per capita household income household B is poorer. As table 3 below shows, there are substantial differences in household size across income levels in Mexico, so that reference to the 'average family' may for some purposes be quite misleading. 29A potentially important issue of poverty in Mexico concerns the fact that some poor families belong to indigenous communities; for some of them language and other dimensions may be barriers to income growth.
an approach similar to Carroll and Samwick (1997), Meghir and Pistaferri (2004), and Storesletten, Telmer, and Yaron (2004). More specifically, we focus on the variance of (unpredictable) changes of individual income as a measure of income risk, and carefully distinguish between transitory and persistent income shocks. The distinction between transitory and persistent income shock is important since workers can effectively self-insure against transitory shocks through own savings, which implies that the effect of these types of shocks on workers’ consumption and welfare are quite small (Aiyagari (1994) Heaton and Lucas (1996), Levine and Zame (2002)). In contrast, highly persistent or permanent income shocks have a substantial effect on the present value of future earnings, and therefore lead to significant changes in consumption even if workers have own savings. Thus, from a welfare point of view, persistent income shocks matter the most, and we therefore focus on the relationship between trade policy and the persistent component of income risk. More specifically, after obtaining the estimates of the persistent component of income risk for each industry and each year, we use these estimates in conjunction with tariff data (as a proxy for trade policy) to study empirically the effect of trade policy on income risk.

Our previous discussion highlights the need for longitudinal information on incomes at a disaggregated level (individual or household)<sup>7</sup> in countries that have undergone discernable (and, ideally substantial) changes in their external regime. Unfortunately, countries that maintain detailed longitudinal records on individual incomes have rarely undertaken major trade reforms and countries that have undertaken extensive trade policy reforms have rarely collected data on individuals of requisite scope and quality. In this paper, however, we focus on one country that satisfies both criteria, namely Mexico. As is well known, the Mexican economy experienced substantial changes in trade policy in the late 1980s and in the later half of the 1990s.<sup>8</sup> Moreover, as we discuss in detail later in this paper, the Mexican government, since the mid1980’s, has conducted quarterly longitudinal income surveys that comprehensively − surveyed workers in all manufacturing sectors of the economy providing the unique data source that we use in our study.

> 7It should be clear that our need for longitudinal data follows from our desire to study how trade policy impacts the magnitude and frequency of individual income shocks (changes). This is a quite distinct task from that of measuring the impact of trade policy on the distribution of income levels. 8In an early wave of trade reforms in the late 1980s, tariffs were cut from an average of about 40 percent to about 15 percent.

3
exercise essentially seeks to confirm that the MSE ratio for the true case is large relative to the distribution of the MSE ratio among the placebos.

We implement the placebo test by sequentially assigning every potential control state to the placebo treatment and verifying whether it is unusual to find MSE ratios as large as Rajasthan’s when the treatment is assigned at random to other states.

# **3.2 Difference-in-differences analysis with firm-level panel data**

In the main empirical analysis presented in this paper, we use firm-level panel data to conduct a difference-in-differences analysis of the impact of the main amendment, the repeal of IDA Chapter V-B for firms with fewer than 300 workers. Since firms with fewer than 100 workers were never subject to Chapter V-B in the first place, the amendment changed firing cost among firms that had between 100 and 300 workers at baseline (that is, the year the amendment was passed). We consider these firms to be our treatment group, and compare the post-treatment change in their outcome with that of firms employing more than 300 workers at baseline. The latter is an appropriate control group as it continued to be subject to IDA Chapter V-B even after the amendment. Moreover, we compare this differential change in the outcomes of the treated and control groups of firms across Rajasthan and other states (that is, we estimate a triple difference). As contract workers are not technically on the payroll of firms under IDA, we define the threshold based on regular workers alone, in line with Chaurey (2015).

The main difference-in-differences specification is as follows: Here, _yit_ is an outcome of interest in firm _i_ and year _t_ . _Postt_ is a dummy for post-treatment years, _Treati_ is a dummy equal to one if firm _i_ had 100-300 regular workers in the baseline year, and _Rajasthani_ is a dummy equal to one if the firm _i_ is located in Rajasthan. The regression includes year-specific dummies _Y eart_ and firm fixed effects _μi_ . The equation is estimated for firms that had 100 or more workers at baseline; hence, firms with more than 300 regular workers at baseline constitute the control group.

The main coefficient of interest is _γ_ , which captures the differential post-treatment change in the outcome of firms with baseline employment between 100-300 (relative to firms with baseline 10
# **1 Introduction**

Latvia was one of the hardest hit countries in the world during the 2008-2010 global financial crisis; during 2008-2010, Latvia’s gross domestic product (GDP) contracted by 21 percent. During 2009 alone, Latvia’s GDP contracted by 18 percent (Figure 1). Household budget surveys indicate that poverty rates increased by eight percentage points in one year - from 10.1 percent in 2008 to 18.1 percent in 2009 (Ajwad, Haimovich, and Azam, 2012). Net job creation was negative as layoffs rose sharply. Between 2008 and 2010, 126,000 jobs were lost, equivalent to 11.2 percent of the pre-crisis workforce. In 2008 Q3, unemployment rates began to rise and reached a peak of about 21 percent in 2010 Q1, compared to about seven percent in 2007-08. Government administrative data show that 40 percent of workers laidoff were not eligible for unemployment benefits, primarily because they failed to meet the requirement of having paid nine months of contributions into the unemployment insurance fund in a 12-month period.<sup>1</sup> In September 2009, in response to rapidly rising unemployment rates, the Government of Latvia launched a public works program known as the Workplaces with Stipends (WWS) program, darba praktiz ̄e ̆sana, simtlatnieku programma, or the 100-Lats-programma.<sup>2</sup> The program aimed to strengthen the social safety net in response to the unprecedented drop in economic activity.<sup>3</sup> Specifically, the program created temporary labor-intensive employment for people who had lost their jobs but were ineligible for unemployment benefits; also, the program benefitted communities through maintenance activities.

Public works programs are an important safety net intervention used widely around the world. Countries have introduced public works programs with diverse objectives such as

> 1Unemployed workers ineligible for unemployment benefits comprised a variable percentage, but as the crisis dragged on, the percentage rose to more than 50 percent as many more workers lacked sufficient work history required to secure benefits.

> 2Lats is the currency of Latvia, and 1 Lats was $2 on March 31, 2011.

> 3Prior to the crisis and relative to its neighbors, Latvia allocated a very small share of social welfare spending to programs designed to target the poorest households. The low spending on poverty-targeted benefits resulted in low coverage indicators—about six percent of households in the poorest quintile were covered by poverty-targeted safety nets (World Bank, 2010).

2
11 Figure 5 Annual Growth Rates, 1985-90 and 2000 for World and Bank Regions <!-- Start of picture text --> 3.4 -<br>3.2-<br>3.0-<br>c 2.8-<br>* 2.6<br>L 2.4-<br>wE<br>2.2-<br>2.0- 7<br>I.-<br>4 1.6-<br>1.4-<br>z 1.2- '<br>1.0-<br>0.8-<br>0.6-<br>0.4-<br>la byBn1.8o,19525<br>0.2-<br>0.0<br>World Africa EiAN LAC Asio<br>1 9 20 2 01985-90 2000<br><!-- End of picture text -->

# Figure 6 Contribution to World Population Growth by Bank Region. 1985-2150

<!-- Start of picture text --> 60-<br>50-<br>0<br>40-<br>'a.~~~~~~~~~~~~~~~<br>o 30<br>z<br>W 20-EM<br>0<br>10<br>0-<br>1 980 2 000 2 020 2 040 2 060 2 080 2 100 2 120 2 140<br><!-- End of picture text -->
47

**Graph 21**

# RETURN ON HOUSING AND DOMESTIC CREDIT GROWTH IN **LAC**

<!-- Start of picture text --> Argentina Chile<br>40 .16 70- 5<br>Domesti credit<br>30- .12 60. 4<br>II, ~~~3<br>20 Dorrmestic credt o.08<br>10 , r A .04 30- 5 I<br>0 .00 2 2 0 0/ '<br>-10 91 92 93 94 I* I l 95 Doetccei 96 97 98 99 00 01 -, -.064 10 75 76 77 I 78 -- 79 80 81 .2 82 2<br>Colombia Mexico<br>40 .08 80. .5<br>30S r.06 s.4 60A Domestc cedft<br>DoMestic cred it<br>0 - -~~~~~~~~.04 40. .3<br>-20~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.<br>-20 H~~~~~~ousi -4 40Hosing -.1<br>-30, ....-. 06 -80 ,,,,,,,,,,,,,,,, -. 2<br>1990 1992 1994 1996 1998 2000 80 82 84 86 88 90 92 94 96 98 00<br><!-- End of picture text --> Sources: Argentina: domestic credit to the private sector (line 32d..zf IMF IFS); Return on housing is the growth rate of the housing price index (1 999=1 00) from INDEC. Chile: Credit from Central Bank of Chile; Housing prices firom Maloney. Growth **rate** of average prices in constant unit prices in Lo Curro and La Dehesa. Colombia: Financial sector total net credit from revista del Banco de la Republica and Central Bank of Colombia webpage; Return on housing is the growth rate of the housing price index (Dec. 1998= 100) from DANE. Mexico: domestic credit is lending by commercial banks to 'the non-bank private sector, the source is the central bank of Mexico; Return on housing is the growth rate of the housing price index (1994=100) from Banco de Mexico.
Table 7: Impact of expected dowry on fertility and sex ratio, NFHS rural data

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Dependent variable:**||**Nu**|**mber of bi**|**rths**||
|_Firstborn girl_ * _Expected dowry_||0.008|0.002|0.003|0.004|
|||[0.015]|[0.012]|[0.013]|[0.013]|
|_Expected dowry_||-0.084|-0.004|-0.004||
|||[0.057]|[0.037]|[0.016]||
|_Firstborn girl_|0.273***|0.249***|0.287***|0.287***|0.284***|
||[0.032]|[0.050]|[0.039]|[0.038]|[0.039]|
|N|60,248|60,248|59,737|59,737|59,737|
|Dep var mean for Firstborn boy|3.095|3.095|3.095|3.095|3.095|
|**Dependent variable:**||**Fraction**|**sons (pa**|**rity** _≥_**2)**||
|_Firstborn girl_ * _Expected dowry_||0.008*|0.008*|0.007|0.007|
|||[0.004]|[0.004]|[0.004]|[0.004]|
|_Expected dowry_||-0.0002|-0.005**|-0.008***||
|||[0.002]|[0.002]|[0.003]||
|_Firstborn girl_|0.030***|0.008|0.009|0.009|0.010|
||[0.004]|[0.010]|[0.010]|[0.010]|[0.010]|
|N|51,243|51,243|50,810|50,810|50,810|
|Dep var mean for Firstborn boy|0.530|0.530|0.530|0.530|0.530|
|**X**_i_|||x|x|x|
|Caste FE + YOB FE + State FE|||x|x|x|
|State*YOB FE||||x|x|
|Caste*YOB FE||||x|x|
|Caste*State FE||||x|x|
|Caste*State*YOB FE|||||x|

NOTES: This table reports the coefficients corresponding to specification (3). Each column is a separate regression. The dependent variable in the top panel is the total number of births and in the bottom panel is the proportion of male births among second and higher parity births. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Dep var mean is the mean of the dependent variable for firstborn-boy households. YOB refers to the year of birth of the firstborn child. **X** _i_ controls for parents’ age, years of schooling, religion, and household standard of living at the time of survey. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.

34
standard socio-demographic and farm characteristics, as they are mainly driven by the constant in our regressions. However, we do notice that female-headed households face lower returns to female household labor in export crop cultivation. Conversely, their smaller plot size and lower number of fields managed result in relatively higher returns from these factors for export crop production. When removing land area from the decomposition, we find that both the endowment and the structure effect increase the gender gap, as expected (results available upon request).

**Table 5. Oaxaca-Blinder decomposition of the gender differential in agricultural productivity with region fixed effects (2008)**

||**Log [Se**<br>|**lf-reported P**<br>|**roductivity (X**<br>|**OF/ha)]**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops|All crops|Export crops|Food crops|
|**overall**|||||
|Male headed households|11.356***|11.356***|11.221***|11.399***|
||(0.036)|(0.036)|(0.048)|(0.040)|
|Female Headed households|11.122***|11.122***|10.957***|11.120***|
||(0.061)|(0.061)|(0.104)|(0.062)|
|difference|0.234***|0.234***|0.264***|0.279***|
||(0.059)|(0.060)|(0.102)|(0.063)|
|explained|-0.144**|-0.075|0.074|-0.226***|
||(0.058)|(0.060)|(0.097)|(0.066)|
|unexplained|0.377***|0.309***|0.190*|0.505***|
||(0.060)|(0.061)|(0.103)|(0.069)|
|**explained**|||||
|Log [Total land area cultivated in ha]|-0.437***|-0.456***|-0.267***|-0.413***|
||(0.046)|(0.047)|(0.067)|(0.047)|
|Household head age (years)|-0.028***|-0.025***|-0.035***|-0.018**|
||(0.008)|(0.008)|(0.014)|(0.008)|
|Married|0.012|0.006|0.044|-0.035|
||(0.039)|(0.039)|(0.062)|(0.049)|
|Divorced/Separated|-0.006|-0.005|0.006|0.006|
||(0.013)|(0.012)|(0.014)|(0.014)|
|Widowed|0.031|0.036|0.026|0.039|
||(0.037)|(0.037)|(0.067)|(0.047)|
|Household head has Primary education|-0.002|-0.003|-0.008|0.001|
||(0.005)|(0.005)|(0.008)|(0.006)|
|Household head has Secondary education|-0.002|-0.002|0.003|-0.008|
||(0.005)|(0.005)|(0.006)|(0.006)|
|Household head has Tertiary education|0.002|0.001|0.003|-0.000|
||(0.001)|(0.001)|(0.002)|(0.001)|
|Household head disability|0.003|0.004|-0.001|0.003|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Dependency ratio|<br>-0.003|<br>-0.003|<br>0.003|<br>-0.001|
||(0.005)|(0.005)|(0.006)|(0.006)|
|Household Male Labor Use (Number)|0.123***|0.119***|0.069**|0.102***|
||(0.019)|(0.019)|(0.029)|(0.026)|

11
17 productivity, higher inequality sector (say, manufacturing or modern commercial agriculture), Kuznets hypothesized that inequality would rise during an initial phase of the process (as labor begins to move across sectors), and then eventually decline (as most workers are already in the modern sector, and the intersectoral gap loses significance). Kuznets found empirical support for this inverted-U inequality trajectory in the data he had available at the time, for the US, England and Germany. Some cross-sectional studies have found evidence consistent with an inverted-U relationship between inequality and mean income, and there is a hint of this relationship in Figure 1.<sup>26</sup> As data on changes in inequality over time have accumulated for many more countries, however, it has become apparent that the inverted-U relationship hypothesized by Kuznets does not hold in general. It does not hold systematically for individual countries for which there are long time-series of inequality measures. Bruno et al. (1998) compiled time series data on inequality measures amongst growing developing countries and found almost no cases that conformed to the prediction of the Kuznets Hypothesis. And its “dynamic version”, which postulates a relationship between rates of GDP growth and changes in inequality, does not seem to hold on average either. Using all countries in the _PovcalNet_ data set for which there are more than one survey, Ravallion (2007) plots proportional changes in the income Gini against proportional changes in mean income for 290 observations, representing 80 countries. (This can be thought of as a re-estimation of the relationship in Figure 1 in which we restrict the sample to developing countries and allow for the existence of country-level fixed effects, potentially correlated with mean income.) A small negative correlation (r=-0.15) is found in the data, which is insignificant at the 10% level. Among growing economies, inequality tends to rise as often as it falls.<sup>27</sup> Thus we have: _<u>Stylized Fact 1: Economic growth tends to be distribution-neutral on average in</u> developing countries, in that inequality increases about as often as it decreases in growing economies._

> 26 Following the most common specification in the literature on testing the Kuznets Hypothesis, we regressed the Gini index on a quadratic function of log GDP per capita using the data in Figure 1.  The coefficient on log GDP was positive and that on its squared value was negative, and both coefficients were significant at the 1% level. The turning point was within the range of the data.

> 27 Among economies experiencing contractions during the spells used by Ravallion (2007), inequality increases are somewhat more frequent than inequality reductions.
- Rosenzweig, M. and K. Wolpin. 1986. “Evaluating the effects of optimally distributed public programs,” _American Economic Review_ , 76:470-482.

- Sandjaja, Sihadi, S. Mulyati, Arnelia, M. Saidin, Herudarini, Suhartato, Y. Widodo, A. Atmawikarta, Sudjasmin, Sudikno, I. Zuraedah. 2001. Studi Dampak Program Makanan Tambahan terhadap Status Gizi dan Kesehatan Bayi dan Anak, unpublished report, PPPG &  Bappenas.

- Strauss, J. and D. Thomas. 1995. Human resources: empirical modeling of household and family decisions, in T.N. Srinivasan and J. Behrman (eds.), _Handbook of Development Economics, Volume 3A_ , Amsterdam: North Holland Press.

- Strauss, J., K. Beegle, A. Dwiyanto, Y. Herawati, D. Pattinasarany, E. Satriawan, B. Sikoki, Sukamdi, F. Witoelar. 2004. _Indonesian Living Standard Before and After the Financial Crisis: Evidence from Indonesia Family Life Survey_ , ISEAS, Singapore.

- Vidmar, Suzanna, John Carlin, Kylie Hesketh and Tim Cole. 2004. “Standardizing Anthropometric Measures for Children and Adolescents with New Functions for _egen_ ,” The Stata Journal 4(1): 50-55.

- Yamauchi, Chikako. 2010. “Community-Based Targeting and Initial Local Conditions: Evidence from Indonesia’s IDT Program” _Economic Development and Cultural Change_ (in press).

Wooldridge, J. 2002. _Econometric Analysis of Cross Section and Panel Data_ , MIT Press 25
PLicy RESEARCH WORKING PAPER 1433

# Summary findings

What kind of privatization program is best suited to stimulate enterprisc restructuring in former centrally planned economies? One view, expressed by mobt Western business and political leaders, is that privatization is best pursued case by case, with emphasis on sales to new owners, including foreign investors. Another view, espoused by a minority of "radical" economists and economists-turned-politician, was that restructuring is best pursued through economic incentives; combined with "mass" privatization of state enterprises so that they become widely held (public) joint stock corporations.

The ultimate test, of course, is future productivity growth and rising welfare standards. We cannot yet measurc these. The disaggregaped data on production and employment by industry required for such a measure either are not available or are unreliable. - The only objective measure available comparable across countries - is export performance. Trade data reveal to what extent firms have been able to reorient themselves to create and exploit competitive advantages. We now have four years of data, cnough to get an idca of what is going on, and to comparc one Central or Eastern European country's performance against another's.

The data suggest that the Czech Republic, the country chat has pursued mass privatization most actively and credibly, has also done best in restructuring its industries and reorienting them toward world markets. Those that - pursued a gradualist approach Hungary being the - main example have changed their export structure less, but export growth has also been above average.

This paper- a product of the Finance and Private Sector Development Division, Europe and Central Asia, and Middle East and - North Africa Regions, Technical Departmcnt is part of a larger cffort in the region to analyze progrcss-and policy options in transition economies. Copies of this paper are availablc free from the World Bank, 1818 H Strect NW, Washington, DC 20433. Please contact Ms. Faten Hatab, room H8-087, extension _35835 (2S_ pages). March 1995.

_The Picy Resarch Workig Paper ers dissemnafes the fing of work_ w _progress_ **_to_** _aecowage the exchang of ies aboua deilpment isus An objectie of the seris is to get the fndings ot qrlry, eu if dtie presentatios are kss dtn fidly polshed The paper cary the ames of the authors a should be used anadcco&dngy. The find cinterpwtatons, and conclusions are the ,ahort own and sh not eb ahRbuted to he World Bank its Exeaiue Board of Directrs, or ay of its member_ **_courtne&_** Produced by the Policy Research Dissemination Center