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

One regularity in the transition has been a continuing divergence in the amount of unemployment that has been generated in East and Central Europe relative to the states of the former Soviet Union. Table 1 shows that using registrations data by 1995 unemployment in the former region exceeded 10 percent of the labour force. By contrast, unemployment in the FSU was below 2 percent. This discrepancy is sometimes attributed to measurement problems, as also the latter's relatively late start in the transition. Neither provides a wholly convincing explanation. Indeed, where labour force survey data are available, as is the case for Russia, unemployment rates still remain low relative to East and Central Europe, as can be seen in Table 1. Mis-measurement of unemployment cannot account for the gap. And while it is true that many of the states of the FSU have moved slowly on structural reforms, including privatisation, even where this has not been true -- and Russia is the obvious case in point -- unemployment still remains markedly lower than in East and Central Europe. Lags also cannot seemingly account for the large gap.

Another line of argument that has recently gained more currency has been to question official estimates of the drop in output and hence to lower predicted unemployment. According to this view, other indicators, such as energy consumption, suggest that output has actually declined far less than reported. For Kazakhstan, Russia and Ukraine, for example, the cumulative drop in electricity consumption between 1989 and 1994 was under half the reported decline in GDP . This discrepancy is largely absent in East and Central Europe. Further, a growing share of output is accounted for by unofficial activity, commonly coexisting with activity in state or privatised firms, but almost all of which occurs outside the trawl of the tax net. Kaufmann and Kaliberda (1995), for example, come up with some rough estimates indicating that by 1994 the unofficial economy may have accounted for over 35 percent of aggregate output in the FSU and over 40 percent in both Russia and Ukraine. By contrast, the comparable share for East and Central Europe was not much above 20 percent and has actually declined in EBRD, Transition report, 1995, p<sup>1 82</sup> 2
system of generalized child allowances but there were significant payment arrears and the take-up rate was low. Of families with children under 18, only 60 percent reported receipt of a child allowance (World Bank, 1995b). In Kyrgyz Republic, budgetary sequestration resulted in a withdrawal of the child allowance in 1994, and the substitution of a new benefit, the common monthly subsidy. In Estonia, fiscal austerity resulted in flat-rate pensions in 1993 and a withdrawal of social assistance benefits inherited from the Soviet Union.

In Russia and Kyrgyz Republic, increases in the age of the household head were associated with small increases in household welfare, but only in Kyrgyz Republic was there a discernible U-shaped life-cycle pattern. Other demographic variables were not significant, except for the number of adult males in Estonia.

Overall, these results underscore the critical role of the labor market in determining household welfare in the FSU. Those households, who have been able to most effectively capture the returns from their own labor and effort in owing family businesses or private plots, have been able to stay out of poverty. Individuals with university education seem to have been best suited to capture the new possibilities as domestic markets have opened up and economic control and regulation relaxed.

The importance of location, household participation in the labor market, and education in affecting household welfare presents formidable challenges to country authorities seeking to reduce poverty. and to keep poor households from becoming poorer. Whether a household has a private plot or university-educated head are not easy factors to change in the short run, while the extreme locational disparities are so large as to be 75
-110-

# Table 1

This table gives a summary English language presentation of basic data on output, value added and employment levels for the three countries. For the USA the 1977 figures were derived from the General Summary of the 1977 Census of Manufactures (US Dept. of Commerce, 1981a), and the 1975 figures from the Annual Survey of Manufactures 1975-1976 (US Dept. of Commerce, 1979), for Brazil from the Censo Industrial: Brasil (IBGE, 1981a), and for Mexico from the X Censo Industrial 1976, Datos de 1975 (SPP, 1979a). None of these countries follows the internationally standardised ISIC classification. Gross value of output in the USA refers to value of shipments of all products which are produced by the establishments classified in that industry (including interplant transfers within the company to which the establishment belongs). Brazilian and Mexican gross value of output refer to production plus net inventory change. Chapter II explains the different concepts of value added. The employment figures refer to average number of employees for the year.

## Tables 2, 3, 4

The basic census information on production in physical terms and gross value of shipments (in national currencies) is given in tables 2 (USA), 3 (Brazil) and 4 (Mexico). The tables also show unit values derived from the census listings. English translations are provided for all specifications in Brazil and Mexico, which were originally in Portuguese and Spanish respectively. In the very small number of cases in which we were unable to find proper translations, the original specifications are shown between inverted commas. The total census values for some industries are sometimes different from the total we obtained by summing the values of specified items. In these cases we show both the "Census Total" and "Our Total" in tables 2, 3 and 4. Virtually all of these differences are very small. The only big discrepancies are for the Brazilian motor vehicle and petroleum refining industries, where information was probably withheld because of confidentiality requirements (all three countries require suppression of information when there are only three firms or less in an industry).

Table 2 (USA) The USA has its own Standard Industrial Classification (SIC) with four digit industries, e.g. "2111, Cigarettes" and the product detail within these is shown with seven digits. The detailed information can be derived from the Industry Statistics in the 1977 Census of Manufactures, Final Report Volumes (US Dept. of Commerce, 1981b). In fact the totals in table 2 are not entirely congruent in coverage with those for the USA in table 1. Table 1 includes primary products and secondary products of all establishments within the particular industry class; table 2 includes all products which are primary to the industry class, wherever they are produced. In our sample, the totals in table 2 were always smaller than in table 1, so differences in the ratio of the two values are taken into account by means of our coverage adjustment (see Fabricant, 1940, p. 350-1 for a discussion of the matter). In most cases the differences are small.
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A Duration Analysis of CONALEP The marginal effect for a categorical explanatory variable k can be estimated by: where: Hypotheses about coefficients were tested using a likelihood ratio test, which is based on the statistic: χ _c_ 2 = −2( _La_ − _Lo_ ), which under Ho has the distribution χ(2 _d_ 0 − _d a_ ) , where: _Lo_ is the log-likelihood associated with the null hypothesis (constrained model).

_La_ is the log-likelihood associated with the alternative hypothesis (full model).

_d o_ is the number of degrees of freedom for the constrained model.

_d a_ is the number of degrees of freedom for the full model.

Finally, standard regression models were estimated to assess if the reformed CONALEP program increases CONALEP graduates’ earnings .

# **_6._ Results**

Table 5 shows the Kaplan-Meier estimates of the mean and median time of job search after graduation. The median time for cohorts graduating before the reforms were introduced in CONALEP (Survey 94) is 4 months, while for those cohorts in the reformed program (Survey 98) the median time is 3 months. The preliminary finding based on the Kaplan-Meier estimates is important because it shows that graduates from the 1998 Survey found jobs faster than those from the 1994 Survey. If only a simple average of values for surveyed individuals had been taken, the erroneous conclusion would be reached that graduates of the 1998 Survey search for a job longer than individuals of the 1994 Survey do.

13
**Table S4.6.** ITT effects on different knowledge outcomes, FREG- and non-FREG samples separately; using a different set of matching variables.

|||Overall k<br>|nowledge<br>|||Principles<br>|knowledge<br>|||How-to k<br>|nowledge<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FRE|G sample|FREG|sample|Non-FR|G sample|FREG|sample|Non-FRE|G sample|FREG|sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.006|0.007|0.115***|0.110***|-0.006|-0.003|0.071**|0.064**|0.024*|0.025**|0.169***|0.168***|
||(0.014)|(0.013)|(0.023)|(0.020)|(0.017)|(0.016)|(0.028)|(0.027)|(0.014)|(0.012)|(0.026)|(0.023)|
|p-value|0.684|0.572|0.000|0.000|0.711|0.826|0.013|0.017|0.090|0.047|0.000|0.000|
|T2 (extension + video)|0.043***|0.045***|0.136***|0.136***|0.036**|0.041***|0.117***|0.112***|0.052***|0.052***|0.146***|0.149***|
||(0.013)|(0.012)|(0.018)|(0.017)|(0.016)|(0.014)|(0.025)|(0.023)|(0.015)|(0.013)|(0.018)|(0.017)|
|p-value|0.001|0.000|0.000|0.000|0.024|0.004|0.000|0.000|0.001|0.000|0.000|0.000|
|Test T1 >= T2 (p-value)|0.005|0.003|0.195|0.107|0.010|0.004|0.063|0.045|0.040|0.033|0.803|0.801|
|Endline control mean|0.4|70|0.|524|0.|540|0.5|95|0.3|98|0.|450|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.011|0.135|0.139|0.236|0.006|0.102|0.062|0.135|0.016|0.129|0.192|0.306|
|Observations|1,534|1,534|396|396|1,534|1,534|396|396|1,534|1,534|396|396|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and their matched controls. Matching variables are those listed in notes of Table 2 (ITT regressions). Additional control variables identical to those listed in notes of Table 5. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

14
Policy Research Working Paper 9723

# **Abstract**

A large body of evidence suggests sizeable improvements in cognitive and social-emotional skills and subsequent educational attainment following preprimary education interventions as well as increases in earnings later in life. Yet, while the world has nearly reached universal primary education, coverage of early childhood education is still low in many countries. This study uses a novel global dataset of - effect sizes from more than 50 studies conducted in 19 coun tries to examine measures of school participation, cognitive skills, social-emotional skills, and behavior, both during and after preprimary ages. Estimates from meta-regression analysis suggest both strong demand for preprimary services when offered and significant improvements in children’s cognitive skills (0.15 sd) and their executive functioning, social-emotional learning, and behavior (0.12 sd) during the pre-primary period. Moreover, our meta-analytic results indicate statistically significant persistent advantages (0.07 sd) in each type of skill beyond the preprimary period. Pooled heterogeneous treatment effects also suggest higher gains for disadvantaged children. Lastly, cost-benefit analysis using studies from low- and middle-income countries implies benefit-to-cost ratios ranging between 1.7 and 14.2, suggesting high returns to preprimary investments even in contexts with limited state capacity.

This paper is a product of the Development Research Group, the Education Global Practice, and the Development Impact Evaluation Group. 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/prwp. The authors may be contacted at aholla@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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what he describes as an “unintended consequence of testing.”<sup>4</sup> In San Francisco, a very different setting but among the earliest empirical studies on the topic, Boozer and Philipson (2000) find that among unmarried individuals, testing induces a change in behavior only if the tested person was surprised by the test result. Like the Gong study, this finding stresses the importance of prior states of uncertainty, or prior beliefs, in learning the results of a test.

Baird et al (2013) report results of an experiment similar to the one described in this paper.<sup>5</sup> They have a sample of adolescent girls in one district in southern Malawi who were randomly offered VCT in 2009. The randomization was across communities (52 getting VCT and 36 with no VCT). The sample was re-interviewed 10 months later. They find that learning a positive test result lead to an increase in the likelihood of contracting Herpes Simplex Virus, with a higher likelihood of contraction for those surprised by the test result. Among those who tested negative, achievement test scores were improved, a finding they interpret to be that those with longer perceived life horizons have greater incentive to invest in human capital.

In this study, we evaluate the effect of testing on outcomes for both young women and men. We examine the effect of VCT on sexual behavior but also on a range of other interrelated outcomes specific to the transition from adolescence to adulthood. Focusing on this period during the life cycle is of specific policy relevance because of the emphasis on youth as a targeted HIV at-risk population throughout sub-Saharan Africa (Poulin and Watkins 2012).

> 4 The characteristics of Gong’s sample are different from those of the Malawi sample used by Delavande and Kohler and Thornton in several dimensions which could potentially explain the opposite results. Individuals in his sample are less likely to be married and reside in urban areas with higher HIV prevalence than the largely rural areas where the Malawi data was collected. Gong’s sample consists of people who were seeking HIV-related services, and not a random sample of the population. Two-third of the baseline sample attrite by follow-up six months later.

> 5 Although the age ranges are almost the same between Baird et al (2013) and ours (women ages 13-22 and 14-21, respectively), there are notable differences. The women in their sample are more likely to be in school (75 percent compared to 43 percent) and less likely to be married (9 percent compared to 21 percent). Our sample is closer to national statistics from the DHS 2010. Ninety percent of young women in the Baird et al (2013) study report no chance of being infected with HIV, compared to 71 percent in our sample.

6
|**Table 2.2: Net Commod**<br>|**ity Exporter Countries**<br>|||
|---|---|---|---|
|United Arab Emirates|Brunei Darussalam|Guinea|Oman|
|Azerbaijan|Bhutan|Kuwait|Papua New Guinea|
|Bahrain|Central African Republic|Mongolia|Qatar|
|Bolivia|Gabon|Namibia|(Trinidad and Tobago)|

_Authors’ classification._

# _<u>2.3 Stylized facts</u>_

Figures 2.5 and 2.6 show the historical trends of the HH concentration index for a few net commodity exporters between 2000 and 2010. There appears to be significant heterogeneity and volatility in the diversification experiences of these countries. Bahrain, Oman, and the United Arab Emirates have successfully expanded the range of economic activities oriented towards foreign markets in the second half of the last decade. On the contrary, the concentration of exports has gradually increased in Bolivia and the Central African Republic. Some sharp diversification reversals can be observed in Azerbaijan and Gabon in the latter half of the decade. Lastly, the experience of T&T, although starting from a lower concentration level, appears to be rather analogous to Qatar and Oman.

<!-- Start of picture text --> 1<br>Figure 2.5: Evolution of the 0.9 Figure 2.6: Evolution of the<br>0.9 HH index in selected HH index in selected<br>0.8<br>0.8 countries 1 countries 2<br>0.7<br>0.7<br>0.6<br>0.6<br>0.5<br>0.5<br>0.4<br>0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0 0<br>20002001200220032004200520062007200820092010 20002001200220032004200520062007200820092010<br>Central African Republic<br>United Arab Emirates Azerbaijan Gabon<br>Bahrain Bolivia Oman<br>Qatar<br>Source: WITS Source: WITS Trinidad and Tobago<br><!-- End of picture text -->

# 3. <u>ECONOMETRIC SECTION</u>

In this section, the respective importance of the potential drivers of concentration/diversification are tested consistent with the quantitative methodology adopted by Al-Kawaz (2008) and Agosin, et al. (2012). Al-Kawaz (2008) develops an econometric model to identify the main determinants of economic diversification in a group of selected oil producing countries (Indonesia, Iran, Kuwait, Oman, and Venezuela). The author runs pooled weighted least squares regressions over the period 1991-2001 using the UNCTAD diversification index as dependent variable and nine explanatory variables including per capita income and its square value, investment and its square value, inflation, institutional constraint, population, openness and real exchange rate. Al-Kawaz finds investment and higher quality institutions appear to have a strong and positive impact on diversification. Higher inflation discourages the development of new 8
planning have lost value during the transition. At the same time, the value of such characteristics as flexibility, adaptability, ability to learn, market-oriented education, foreign language and computer skills – all of which tend to be stronger for younger workers – has increased. As a consequence, both observed and estimated age-earnings profiles (based on available workers’ characteristics) in the transition economies tend to peak at a much earlier age than in other industrialized countries (see Hazans (2002) for a more detailed presentation).

The Latvian LFS 2002 can be used to estimate age-earnings profiles separately by gender and education<sup>11</sup> . Results are summarized in Figure 23 (models are not presented here). The male earnings profile peaks at 37 years of age when no postsecondary education is acquired and at 34 – 35 otherwise. After this peak, the value of postsecondary vocational education declines with age faster than the value of general secondary education. For females with higher education the peak of the age-earnings profile is 39 years. This profile is flatter than for males. Such a result contrasts with the evidence from Western countries, where age earnings profiles are generally steeper for women. By this measure, women of childbearing age are less disadvantaged in terms of relative wages than in developed market economies. For females with secondary or postsecondary vocational education the advantage of being young reveals itself even more strongly. Age-earnings profiles are remarkably flat. For females with basic education they are downward sloping.

Table 4 confirms that strong position of young employees in the Latvian labor market is a feature shared with the other two Baltic countries. Predicted earnings in the Baltic countries peak not only much earlier than in the West, but also than in the Czech Republic and Slovakia. Earnings peaks in the latter countries are found at 46 – 47 years in the private sector and at 53 in the public sector (Juraida (2001), based on 1998 data).

**<u>Table 4.  Predicted Age of Peak  Earnings</u>**

||**All employees**|**Males**|**Females**|
|---|---|---|---|
|**Estonia, 2000**|33|34|31|
|**Latvia, 2002**|32|36|30|
|**Lithuania, 2000**|36|34|39|

_Notes:_ Controls include education, age, ethnicity, ownership sector, sector of economic activity, job location, local unemployment rate. Occupation and marital status are not controlled for. Only full-time employees included _. Source:_ Latvia: Calculations based on LFS data; Estonia and Lithuania: Hazans (2002).

It must be noted that particular age groups might have an advantage in some characteristics not accounted to in the models, so that actual age-earnings profiles can be somewhat different from the predicted ones. Appendix Figure 20 presents actual age-earnings profiles for the Baltic countries.

> 11  In contrast with OECD (2003a), where data allowed distinguishing level but not the shape of the profiles between educational groups.

33
TABLE 1

# INFLATION IN ISRAEL: YEARLY RATE (PERCENT)

|Year|Inflation<br>rate, %|Year|InflpUon<br>rate,%|Year|Inflation<br>rate,%|
|---|---|---|---|---|---|
|1949|-12.5|1962|10.2|1975|23.5|
|1950|6.0|1963|5.0|1976|39.0|
|1951|20.2|1964|4.4|1977|42.5|
|1952|66.4|1965|7.1|1978.|<br>48.1|
|1953|19.1|1966|7.8|1979|111.4|
|1954|7.5|1967|0.2|1980|132.9|
|1955|4.8|1968|1.9|1981|101.5|
|1956|4.6|1969|3.9|1982|131.5|
|1957|5.2|1970|10.1|1983|190.7|
|1958|4.2|1971|13.4|1984|444.9|
|1959|2.1|1972|12.4|1985|185.2|
|1960|3.4|1973|26.4|1986|19.7|
|1961|9.0|1974|56.2|1987|16.'|

Source: Central Bureau of Statistics, Statistical Abstract of Israel. 1987.

Notes: 1) Change in Consumer Price Index.

- 2) Yearly rate is December index over December index of previous year.

- 3) The annualized rate of inflation for the first 3 quarters of 1985 was 257.0 percent; the annualized rate for the last quarter of 1985 was 29 percent.

corrected from time to time. A crawling peg regime followed, and it was replaced by a free market in 1977.

The three digits inflation was halted abruptly in July 1985. The program adopted included price and wage freezes, reduction of government deficit, and a return to a regime of fixed exchange rates. As part of the new policy, the Bank of Israel limited the expansion of credit and a severe squeeze developed. Produc.-rs who relied on large volumes of short term credit--as many in agriculture did--found themselves in the midst of a painful crisis.

# 2. Outline

In the paper we concentrate on the experience with credit in the family farm sector, the moshavim--villages run by cooperative 2