rafmacalaba/datause-encoder-data huggingface dataset:
|**Table A1. T**|**ransition Matrices betw**<br>|**een Tenure Typ**<br>|**es, overall and**<br>|**for Households F**<br>|**ormed before**<br>|**and after 197**<br>|**8**<br>|
|---|---|---|---|---|---|---|---|
||In 1978|Pure Owner|Bargadar|Owner cum|Pattadar|Landless|No of obs|
||||<br>|tenant<br>||||
|**In 2008**|||**Entire**<br>|**sample**<br>||||
||Pure Owner|72.99|8.33|19.34|7.12|11.69|33,261|
||Bargadar|0.00|51.88|5.32|2.43|3.48|3,295|
||Owner cum tenant|0.00|9.01|55.34|2.03|4.21|3,675|
||Pattadar|5.01|5.64|3.79|64.88|8.77|8,695|
||Landless|22.00|25.14|16.20|23.55|71.86|47,000|
||No of obs.|36,075|2,498|2,161|3,257|51,935|95,926|
|**In 2008**||**Hous**|**eholds establis**|**hed before 1978**|**only**|||
||Pure Owner|86.32|8.08|15.23|2.67|14.07|6,603|
||Bargadar|0.00|67.88|3.81|3.27|4.80|736|
||Owner cum tenant|0.00|11.92|76.14|3.57|6.79|896|
||Pattadar|5.94|4.85|3.30|85.74|11.79|1,878|
||Landless|7.74|7.27|1.52|4.75|62.55|5,286|
||No of obs.|6,281|495|394|673|7,556|15,399|
|**In 2008**||**Hou**<br>|**seholds establi**<br>|**shed after 1978 o**<br>|**nly**<br>|||
||Pure Owner|70.18|8.39|20.26|8.28|11.28|26,657|
||Bargadar|0.00|47.93|5.66|2.21|3.25|2,559|
||Owner cum tenant|0.00|8.29|50.71|1.63|3.78|2,780|
||Pattadar|4.81|5.84|3.90|59.44|8.25|6,817|
||Landless|25.01|29.56|19.47|28.44|73.44|41,714|
||No of obs.|29,794|2,003|1,767|2,584|44,379|80,527|

_Source:_ Own computation from 2008/9 West Bengal listing survey.

24
by 1.9 percentage points, extreme poverty by 4.8 percentage points, and year-round starvation by 3.4 percent points. PRIME also affects seasonal outcomes. For example, during the lean ( _monga_ ) period, PRIME increases monthly employment by 14.8 percent and reduces starvation by 3.4 percentage points. It also reduces food deprivation by 5.2 percentage points during non- _monga_ period. The results clearly suggest that PRIME has contributed to reducing both seasonal and chronic poverty among the ultra-poor.

Do the effects of PRIME differ from those of regular microfinance? If we believe that PRIME is a better program than regular microfinance for the ultra-poor, then its impacts should be greater. We have just estimated the outcome regression weighted by the inverse of participation probability (estimated propensity score) in the case of binary treatment. We can extend this concept to participants of PRIME, regular microfinance, and the control group to estimate the relative impacts of PRIME and regular microfinance. The probability of receiving PRIME, regular microfinance, or neither treatment is estimated following the MNL estimates shown in Table 6.

The outcome regression is then weighted as follows: where

> _P_<sup>ˆ</sup> _PRIME_ = estimated probability of PRIME participation and

> _P_<sup>ˆ</sup> _MFI_ = estimated probability of regular microfinance participation.

32
References: Ahuja, Vinod and Deon Filmer, 1996 "Educational Attainment in Developing Countries: New Estimates and Projections Disaggregated by Gender". _Journal of Educational Planning and Administration_ Vol X(3):229-254.

Barro, Robert and Jong-Wha Lee, 1993. "International Comparisons of Educational Attainment," _Journal of Monetary Economics, 32:363-394._ Behrman, Jere R., Nancy Birdsall and Miguel Szekely, 1998. "Schooling Gaps, Family Background, and Macro Conditions in Latin America," mimeo prepared for Brookings Institution Center on Social and Economic Dynamics/Inter-American Development Bank Workshop on Social Mobility, Brookings Institution, 4-5 June 1998.

Behrman, Jere R. and James C. Knowles, 1997. "How Strongly is Child Schooling Associated with Household Income?" University of Pennsylvania and Abt Associates. Mimeo.

Benefo, Kofi, and T. Paul Schultz, 1996. "Fertility and Child Mortality in C6te d'Ivoire and Ghana," _World Bank Economic Review._ 10:123-158.

Bonilla-Chacin, Maria and Jeffrey S. Hammer, 1999. "Life and Death among the Poorest." The Johns Hopkins University and Development Economics Research Group, The World Bank. Mimeo.

Castro-Leal, Florencio, Julia Dayton, Lionel Demery, and Kalpana Mehra, 1997, "Public social spending in Africa: do the poor benefit?" mimeo, PRMPO, The World Bank.

Dollar, David and Roberta Gatti, 1999. "Are Gender Inequality, Income, and Growth: Are Good Times Good for Women?" Gender and Development Working Paper No. 1, DECRG, The World Bank. Washington, DC.

Dubey, Ashutosh and Elizabeth King. 1994. "A New Cross-Country Education Stock Series Differentiated by Age and Sex", mimeo, The World Bank.

Filmer, Deon, Elizabeth King, and Lant Pritchett, 1998. "Gender Disparity in South Asia: Comparisons Between and Within Countries"' World Bank Policy Research Working Paper No. 1867. The World Bank.

Filmer, Deon and Lant Pritchett, 1998. "Estimating wealth effects without income of expenditure data -- or tears: Educational enrollment in India," World Bank Policy Research Working Paper No. 1994. DECRG, The World Bank. Washington, DC.

Filmer, Deon, and Lant Pritchett, 1999a "The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries". _Population and Development Review 25(1)._ Filmer, Deon and Lant Pritchett, 1 999b, "Determinants of Education Enrollment in India: Child, Household, Village and State Effects," _Journal of Educational Planning and Administration._ 40
# PRE Working P Seri

||Iie|Author|Date|Contact<br>foLpaper|
|---|---|---|---|---|
|WPS607|Abolishing Green Rates: The Effects<br>on Cereals, Sugar, and Oilseeds<br>in WestGermany|Donald F. Larson<br>Simon Glance<br>Brent Borrell<br>Merlinda Ingco<br>Jonathan Coleman|March 1931|D. Gustafson<br>33714|
|WPS608|Cross-Country StudiesofGrowth<br>and Policy: Methodological,<br>Conceptual, and Statistical Problems|Ross Levine<br>David Reneit|March 1991|CECMG<br>39175|
|WPS609_|A Sensitivity Analysis of Cross-|Ross Levine|March 1991|CECMG|
||CountryGrowthRegressions|DavidRenelt||39175|
survey and administrative data it would be useful to better understand why the distributions differ when they do, especially because the few answers that have been given are for rich countries and are not consistent, as we now describe.

A priori, unit non-response of high income or wealth individuals would seem to be the most important factor explaining why surveys under-capture the top of these distributions. Evidence for this proposition comes from J **ohansson-Tormod and Klevmarken (2022), who use administrative data and two wealth surveys from Sweden in -2002 and 2003. The data is unusual because the authors could match both survey responders and non-responders to the admin data they accessed. The authors find that the survey underestimates mean wealth within the top 1 percent by 40 percent relative to the admin data. For the top percentile,** J **ohanssonTormod and Klevmarken (2022)** show that the mean of the top 1% using the survey response values is very similar to the mean when using the same survey respondents (i.e. excluding nonrespondents) but using the wealth values from the admin data for those survey respondents (which they can do because the surveys and admin data are linked). But the means for the top 1% are very different when comparing the survey respondents using their wealth information from the admin data and the full set of respondents plus non-respondents again using their wealth information from the admin data. This implies that under-reporting is not a concern, but that unit non-response is.

J **ohansson-Tormod and Klevmarken (2022) use two surveys, finding similar results in both. One of these is Share. The documentation for the survey indicates that for the Swedish Share survey the only adjustment of the design weights is calibration to eight age and sex totals from external demographic data. It is then perhaps unsurprising that unit nonresponse is found to be the cause of the differences in the top 1%.** Burkhauser et al. (2017) compare UK household surveys and admin data based on tax records. They show that the surveys diverge from tax data around the 95<sup>th</sup> percentile of the income distribution and argue that under-reporting rather than unit non-response is responsible for this divergence, because the 95<sup>th</sup> percentiles and below are similar in both data sources. Burkhauser et al. (2023) provide further evidence in support of under-reporting being more important than unit non-response in the UK, in the context of investigating the share of women in the top 1%. They show that the top 1% in surveys and tax data have very similar observable characteristics, and that these are distinctly different to the 9 percentiles below the top 1%.

We noted above that Flachaire et al. (2022) found that under-reporting at the top of the distribution was important in Uruguay using linked survey and tax data, but the authors did not investigate unit 26
Table 7 Machine Learning: GATES Analysis

||Most Afected|Least Afected|Diference|
|---|---|---|---|
||_γ_5<br>(1)|_γ_1<br>(2)|_γ_5-_γ_1<br>(3)|
||**Panel A: **|**savings and cons**|**umption**|
|Log total savings|-0.025|-0.293|0.300|
||[1.000]<br>|[1.000]<br>|[1.000]<br>|
||(-0.820,0.821)|(-0.990,0.636)|(-0.954,1.340)|
|Log formal savings|0.524|0.598|-0.168|
||[0.596]|[0.604]|[1.000]|
||(-0.461,1.199)|(-0.346,1.616)|(-1.480,1.265)|
|Log informal savings|-0.488<br>|-0.191<br>|-0.209<br>|
||[0.791]|[1.000]|[1.000]|
||(-1.802,0.813)|(-1.239,0.674)|(-1.894,1.525)|
|Log total remittances|0.003|-0.563|0.496|
||[1.000]|[0.667]|[1.000]|
||(-1.347,1.201)|(-1.798,0.398)|(-1.965,2.984)|
|Log total consumption|-0.024|-0.494|0.465|
||[1.000]|[0.006]|[0.049]|
||(-0.335,0.295)|(-0.828,-0.166)|(0.053,0.910)|
|Shock index|-0.027|-0.073|0.055|
||[1.000]|[0.807]|[1.000]|
||(-0.172,0.100)|(-0.220,0.076)|(-0.153,0.237)|
||**Panel B: **|**account use and **|**learning**|
|Deposits|0.065|0.031|0.039|
||[0.601]|[0.762]|[1.000]|
||(-0.057,0.184)|(-0.037,0.136)|(-0.174,0.211)|
|Withdrawals|1.298|0.802|0.498|
||[0.000]<br>|[0.000]<br>|[0.000]<br>|
||(1.042,1.602)|(0.567,1.007)|(0.260,0.721)|
|Send-money|1.019|0.338|0.662|
||[0.000]|[0.000]|[0.000]|
||(0.705,1.280)|(0.220,0.494)|(0.307,0.953)|
|Total transactions|1.730|1.131|0.605|
||[0.000]|[0.000]|[0.001]|
||(1.289,2.167)|(0.805,1.453)|(0.217,0.996)|
|Direct transactions|0.536<br>[0.000]|0.224<br>[0.000]|0.304<br>[0.000]|
||(0.450,0.645)|(0.152,0.305)|(0.191,0.441)|
|Indirect transactions|-0.099|-0.277|0.193|
||[0.274]|[0.002]|[0.136]|
||(-0.204,0.014)|(-0.446,-0.101)|(-0.012,0.379)|
|Outside transactions|0.943|0.470|0.453|
||[0.000]|[0.001]|[0.131]|
||(0.571,1.164)|(0.225,0.787)|(-0.014,0.869)|

Notes: The table reports sorted group average treatment effect coefficients for savings and mobile money payroll account use at endline. The sample is divided into _K_ =5 groups, _γK_ , based on the quintiles of the machine learning proxy predictor _S_ ( _Z_ ). Medians over 100 splits. 90% confidence intervals in parentheses, _p_ -values for the hypothesis that the parameter is equal to zero in brackets.

47
<!-- Start of picture text --> 150 Ak 10<br>a |<br>po uly ba<br>Wy rn<br>a Ai 8<br>: . wii<br>100 I =,Yeu7% ! \Aaa<br>1 an or Vi 6<br>1 ‘ i i<br>i I J i<br>| Fs My 1 re<br>! yt ¥ i. lft 4<br>1 oft an<br>' \s 1<br>i<br>i<br>0 0<br>1960 1970 1980 1990 2000 2010<br>Year<br>——=— Oil price in constant dollars, left scale<br>=== == Gonflicts in MENA, right scale<br><!-- End of picture text -->
competition, in particular, has a positive influence on the innovation rates of firms. By contrast, having a state-owned competitor does not have a significant effect on firm innovation. We also find evidence that managerial education and experience, and the education level of the workers are important determinants of firm innovation.

Our findings are most closely related to two recent papers examining the role of finance in facilitating innovation and entrepreneurship. Using data on Italian firms, Benfratello et al. (2006) show that local banking development has a positive effect on the probability of a firm introducing process or product innovation and this is particularly so for small firms and for firms in sectors more dependent on external finance. Their paper however does not examine how access to external finance at the firm level affects innovation. Gompers et al. (2006) on the other hand, study the supply side of capital. Using data on venture capital financing of US firms they show that suppliers of capital have a unique ability to identify the most skilled and successful entrepreneurs. In our paper we examine if the firms that do get external capital are indeed more innovative than firms that are not dependent on external finance.

The rest of the paper is organized as follows. Section 2 presents the motivation and our hypotheses. Section 3 discusses the data and summary statistics and section 4 presents the empirical model. We discuss our main results in section 5. Section 6 concludes.

# **2. Motivation**

# **_2.1. Finance and Innovation_**

There is a large empirical literature establishing that financial development promotes long term economic growth.<sup>2</sup> Specifically, at the macro cross-country level, King and Levine (1993) and Levine, Loayza, and Beck (2000) show that financial development promotes growth. Cross-country time-series studies by Bekaert, Harvey, and Lundblad (2001, 2005) also show that financial liberalization boosts economic growth by improving the allocation of resources and the investment rate. Demirguc-Kunt and Maksimovic (1998) and Rajan and Zingales (1998) provide evidence at the firm and industry level showing that reduced access to external finance is associated with lower

> 2 See Levine (2005) for a review.

4
crisis, bank deposits accounted for only **3%** of total pension fund assets, while the share of mortgage bonds rose to **_51%_** and of state securities to 44%. Since then, the share of<sup>mortgage bondsdeclined</sup> steadily, reaching **16%** in **1990.** However, the real value of mortgage bonds in the portfolio of pension funds increased **by** **_85%_** over this period.

Table **10**

|||Inves|tmentProfil|es||
|---|---|---|---|---|---|
||Bank<br>Deposits|State<br>Securities|Mortgage<br> Bonds|Corporate<br>Bonds|Corporate<br>Equities|
|**1981**|**61.9**|28.1|9.4|**0.6**||
|**1982**|**26.6**|**26.0**|46.8|**0.6**||
|**1983**|**2.7**|44.5|**50.7**|2.2||
|1984|**12.9**|42.2|43.1|**1.8**||
|**1985**|**20.9**|42.6|**35.3**|**1.1**||
|**1986**|**23.2**|46.7|**25.5**|**0.8**|**3.8**|
|**1987**|**28.3**|41.5|21.4|**2.6**|**6.2**|
|**1988**|**29.5**|49.7|**20.6**|6.4|**8.1**|
|**1989**|**21.5**|41.6|**17.7**|**9.1**|**10.1**|
|**1990**|17.4|44.1|**16.1**|**11.1**|**11.3**|
|**1991/6**|14.3|**39.5**|14.6|**11.7**|**19.9**|

Source: Habitat **(1991)** The most significant changes in the second half of the 1980s were the continued ir -rease in the share of central bank securities to **42%** in **1990,** within a more or less constant tot- for state securities, and the considerable expansion of holdings of corporate bonds and corporate equities, each with **11%** of total pension fund assets in **1990. By** June **1991,** equity holdings reached **20%** of pension fund assets, mostly as a result of the large appreciation of equity values in the booming Santiago stockmarket.

The investment profiles of pension funds have been shaped **by** the tight investment rules that have imposed strict limits on equity investments. The significance of pension funds for each class of instruments, and the potential for further expansion once investment rules become more liberal, is shown **by** the share of pension fund holdings in the total market for each class of instrument (Table **11).** Thus, pension funds held 20% of all bank deposits in **1990,** **_56%_** of mortgage bonds, **38%** of central bank securities, nearly **100%** of treasury securities, **_56%_** of corporate bonds and **9%** of corporate equities. Total pension fund assets corresponded to 20% of all liabilities of the Chilean financial system and to 40% of total bank credits.

The growth of pension fund investments in corporate securities since **1986** highlights the impact that pension funds can have on the development and deepening of the Chilean capital markets. But the relatively low share of total corporate equities held **by** pension funds underscores the large potential that still exists for a much greater impact.

Pension fund resources are expected to continue growing at a fast rate. Even if the rate of return cn pension fund assets is equal to the growth rate of **GDP,** the continuing inflow of net annual contributions equivalent to 3.4% of **GDP** imply that as a minimum pension fund resources will reach

**25**
# **I. Introduction**

Young people everywhere experience considerable difficulties entering the labor market. On average, across regions, youth unemployment is about 2 to 3 times the national unemployment rate. Youth unemployment is typically concentrated among the less educated workers, but in some labor markets the unemployment rate is high even among the most educated workers.

While such unemployment spells may be temporary, and the associated income loss also short-lived, there is some evidence that the consequences of these early unemployment spells may impose enduring disadvantages on individual workers. Over the last two decades a growing literature has emerged on the post-unemployment labor market disadvantage of youth, or what is otherwise known as a labor market “scar.” Following an initial experience of unemployment, many individuals have been found to be vulnerable to new rounds of unemployment (“unemployment scar”), lower postunemployment wages (“wage scar”), or both.

Much of this literature has been drawn from labor market experiences in advanced economies (e.g., OECD 1998, Gregg and Tominey 2005, Arulampalam 2000). Much less is known about scarring in transition economies, where unemployment and labor market adjustment have been prominent public policy issues following the early transition period but where little is known about their lasting consequences.<sup>1</sup> In large part, this has been due to the relative scarcity of longitudinal data.

This note examines early unemployment spells and their longer-term effects among the youth in Bosnia and Herzegovina. It uses data drawn from the Living in BiH/Living Standards Measurement Survey (LSMS), a rare, longitudinal database covering up to 4,800 working-age individuals over the 2001 to 2004 period. It documents the labor market outcomes of individuals up to three years after the initial period, and estimates their covariates.

The rest of the note is organized as follows: Section II provides a brief survey of the literature on youth unemployment and scarring. Section III discusses the data sources and the descriptive statistics. Section IV presents the empirical framework and reports the main results. Section V summarizes the findings and provides some concluding observations.

# **II. Brief Review of Related Literature**

Many young men and women face significant difficulties entering the labor market. According to most measures, youth are more likely to be unemployed than adults, although significant variations in unemployment exist between urban and rural sectors, between developed and developing countries, as well as between poor and rich

> 1 One important exception is a recent study of Hungary (Audas and others 2005). There have also been studies of labor market transitions in former socialist economies (see Boeri and Terrell 2002 for a brief survey of the literature).

2
predictors. In addition, several studies (Eichengreen, Rose and Wyplosz (1995 and 1996), Milesi-Ferretti and Razin (1995), Otker and Pazarbasioglu (1995), Kaminsky and Reinhart (1996), find support for including past crises as predictors.

Demirguc-Knut and Detragiache (1998) also use a probit analysis but with a broader set of vanables than Kaminsky and Reinhart (1996 and 1998) who focus on the relationship between banking and currency crises and on the impact of financial liberalization (which is the only significant predictor of banking crises in their study). Demirguc-Knut and Detragiache (1998) study a panel of 53 countries over the period 1980-95. One of the key factors in banking crisis prediction according to them is extemal vulnerability measured by broad money to intemational reserves.

Edwards (1989) finds that the probability of devaluation is affected predictably by the variable net foreign assets/M1. Klein and Marion (1994) also find that the ratio of net foreign assets of the monetary sector to MI is a significant predictor as is the same ratio squared. The latter however is not robust to changes in the specification. Net foreign asset positions conceal some useful infornation about what is really going on; additionally, the Edwards (1989) and Klein and Marion (1996) studies had limited coverage since data on net foreign asset positions (henceforth NFAs) is readily available for only a limited number of countries. Frenkel and Rose (1996) estimate a multivariate probit to find that concessional debt/total debt, foreign direct investment/total debt and foreign interest rates all help predict crises one year in advance. However, these studies rely either on short-term external debt data as reported by member countries to the World Bank or net capital flows (readily available from the Intemational Financial Statistics of the IMF, hereafter IFS) and their composition.

Kaminsky and Reinhart (1998) do not include any financial flows in their regressions. Corsetti, Pesenti and Roubini (1998), include short-term foreign debt, and non-performing loans as a proportion of bank assets (as a proxy for weakness in the domestic banking sector). Their data on the banking sector is useful. They primarily base their information on domestic banks' external liabilities to foreign banks in 18 developed countries. Their figures match the World Bank-IMF-BIS database on external liabilities. Their coverage though is poor, and 4