rafmacalaba/datause-encoder-data huggingface dataset:
The Soviet Union performance during the last two decades of its existence was in fact rather unequal not only in respect to the Central Asian republics but also relatively to the Trans-Caucasian republics and Moldova. The Southern republics of the Union including Armenia, Azerbaijan, Georgia, Kyrgyzstan, Moldova, Tajikistan and Uzbekistan (CIS-7 henceforth) were underperforming already in the 1970s and 1980s relatively to other republics. The CIS-7 experienced lower growth than other CIS republics in the 1970s and this gap increased during the 1980s (table 1.1). In 1989, the average GNP per capita at PPP prices was estimated at 4,191 USD for the CIS7 as compared to 6,711 USD for the other FSU republics (De Melo et Al. 1997).

**Table 1.1 Initial Conditions: Output and Income**

||NMPgrowthpc(1)|NMPgrowthpc(1)|
|---|---|---|
||1971-1985|1986-1989|
|Armenia|4.9|2|
|Azerbaijan|2.1|2.9|
|Georgia|4.6|-0.8|
|Moldova|5.2|-1.2|
|Kyrgystan|3.1|2.2|
|Tajikistan|2.2|0.3|
|Uzbekistan|1.4|0|
|Median CIS-7|3.1|0.3|
|Median FSU non CIS-7|3.4|2.6|

Source: Adapted from Wolf (2003); (1) Net Material Product growth per capita Industry was relatively underdeveloped and the populations remained largely rural. While non CIS-7 republics had a median urban population growth between 1975 and 1985 of +9% per year, the CIS-7 had a growth of -2%. In 1990, less than half of the populations of the CIS-7 lived in urban areas as compared to an average of 65% for the other FSU republics. The CIS-7 - with the exception of Azerbaijan - were also not particularly rich in natural resources and were generally net receivers of either implicit or explicit subsidies from Russia.

Relatively lower performances were evident for almost all socio-economic indicators. Despite the historically lower salaries and wages, the CIS-7 show lower growth in these measures in the 1980s as compared to other republics of the Union. These countries entered the transition period with lower industrial output per capita, lower wages and lower productivity as compared to other CIS republics (table 1.2). They were also endowed with a relatively underdeveloped infrastructure, low shares of fixed capital and fewer highly educated workers. The trade pattern showed significant less integration with the rest of the world and longer distances with potential foreign markets<sup>4</sup> .

Trans-Caucasian countries performed relatively better than Central Asian countries but the overall picture is that the CIS-7 entered the transition period significantly under-equipped as compared to other republics of the Soviet Union.

> 4 Wolf, H. (2003) " _The Initial Conditions_ ", paper presented at the Lucerne conference of the  CIS-7 initiative, January 20-22, 2003

5
their children’s schooling outcomes. It does so by analyzing a household survey from Rwanda, the EICV, that contains, in part because of the 1994 Genocide and the HIV/AIDS epidemic, a very large proportion of children living in households from which their biological parents are absent. The data also contain information about the type of relationship that links the children to their “adoptive” families and about the educational attainment of the absent biological parents. These two features control for non-random placement of the “adoptees,” one of the main sources of bias in using the adoption strategy.

The results of the analysis suggest that the nurture component of the intergenerational transmission of human capital is important, in particular for mothers, contrary to recent evidence proposed by Behrman and Rosenzweig (2002) and Plug (2004). In the preferred specification, even after controlling for non-random placement by including the schooling of the biological parents and the type of relationship linking the child and the head of his new household, the education of the most educated female adult in the new household has a positive and significant effect on the schooling of the child welcomed in the household. The magnitude of the effect is similar to the effect in a biological mother-child relationship.<sup>16</sup> The effect of the education of the most educated male in the relationship is smaller than in a biological father-child relationship, but remains positive and significant. When boys and girls are analyzed separately, it appears that, when the nurture component is isolated, the mother’s education matters more for girls, while the father’s education has a stronger effect on boys’ educational achievement. The analysis of interaction terms indicates that the positive effects of the education of the “adoptive” parents are only present for children related to the head of their new household (grandchildren and other relatives).

The external validity of this study might be questioned since it is difficult to argue that children being “adopted” into recomposed households in the years following the Genocide in Rwanda are representative of the general parent-child relationship. In addition, non-biological children seem to be discriminated against, according to Hamilton’s rule, where the children most distant from the household head are worse off. However, given the rising numbers of orphans from conflicts and from the HIV/AIDS epidemic in Africa and elsewhere, the analysis offers important policy lessons. In particular, the study demonstrates that placing orphans in households where they have relatives minimizes their educational losses and favors the intergenerational transmission of human capital.

> 16 Björklund, Lindahl and Plug (2004), using data from Sweden, find a positive and significant effect of the adoptive mother’s schooling, but with a smaller magnitude than in biological families. Sacerdote (2004) comes to a similar conclusion with a sample of Korean-American adoptees.

14
# PAYMENTS

_Most people receive payments—such as wages, payments for the sale of agricultural products, or payments in the form of remittances or government transfers. And most make payments—such as for school fees, for utility bills, or in the form of remittances. The 2014 Global Findex survey introduced new questions to explore these payments. It asked what kinds of payments people receive, what kinds they make, and how they carry out these transactions— whether in cash or digitally._ The Global Findex survey asked questions about seven types of payments that can be grouped into three broad categories: wage payments, government transfers, and payments for the sale of agricultural products ( _payments from businesses or government to people_ ); payments for utility bills and for school fees ( _payments from people to businesses or government_ ); and domestic remittances, both those sent and those received ( _payments between people_ ). The survey collected data on all seven types of payments in developing economies. In most high-income OECD economies, however, it collected data only on wages, government transfers, and utility bill payments.<sup>1</sup> This chapter distinguishes mainly between payments in cash and digital payments. But it also distinguishes between two types of digital payments: those sent or received through an account and those sent or received through an over-the-counter (OTC) transaction—a transaction completed in cash at a financial service provider, which transfers the money electronically on behalf of the sender and recipient. The distinction between these two types of digital payments is an important one. By using an OTC transaction rather than cash, people benefit from the greater efficiency of making payments through a financial service provider. But only by using an account can they also keep the money in a safe place until they need it.

Some respondents who reported sending or receiving a payment, when asked about the payment channel used, provided a “no,” “don’t know,” or “refuse” response to all possible options. These respondents form an additional category, typically consisting of only a small share of adults, and are reported as those using “other” methods.

## Payments from businesses or government to people

This section looks at how people receive three common types of payments from businesses or government, distinguishing between payments received into an account and payments received in cash only. By definition, all digital payments in this section are payments into an account.<sup>2</sup>

### How do people receive wage payments?

Globally, 32 percent of adults reported having received at least one wage payment from an employer in the past 12 months—52 percent of adults in high-income OECD economies and 27 percent in developing economies.<sup>3</sup>

> THE GLOBAL FINDEX DATABASE 29
# Policy Uncertainty and Aggregate Fluctuations: Evidence from Emerging and Developed Economies<sup>∗</sup>

Haroon Mumtaz<sup>†</sup> Franz Ulrich Ruch<sup>‡</sup>

**Keywords:** Policy uncertainty; Monetary Policy; Fiscal Policy.

**JEL Codes:** D80; E32; E43; E63.

∗This paper benefited from valuable comments and suggestions from Antonio Fatas, Jongrim Ha, Ayhan Kose, Francisco Arroyo Marioli, Franziska Ohnsorge, John Rogers, and Garima Vasistha. We would like to thank Marcin Holda, La-Bhus Fah Jirasavetakul, Chris Redl, and Antonio Spilimbergo for providing their measures of economic policy uncertainty. We would also like to thank Jiayue Fan for research assistance. The findings, interpretations and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.

> †Queen Mary University of London. Email: h.mumtaz@qmul.ac.uk

> ‡Development Economics, Prospects Group, The World Bank. Email: fruch@worldbank.org
variates that are expected to influence the reported earnings ratio, and  _k_ is a random mean-zero error term. The co-variates are discussed in the following paragraphs.

In terms of variables that reflect the wife’s ability to monitor her husband’s economic conditions abroad, we include an indicator variable for wives who are unaware of their spouses’ expenses in Qatar. If a wife does not know her husband’s expenses, it is likely that she would also be less aware about his earnings. Hence, by part (i) of prediction 2, we expect this indicator variable to be negatively associated with the reported earnings ratio. Also, as wives’ main outside source of information about their husbands’ economic conditions is their social network, wives with paid jobs in India are likely to have a wider and more diverse social network and, thus, to be in a better position to inquire into the labor market returns of their spouses. Furthermore, working spouses should also be better at assessing their husbands’ overseas economic conditions as would more educated ones be as well. Therefore, assuming that working and educated wives face a lesser problem of asymmetric information, wives’ employment and education should have a positive impact on the reported earnings ratio.

Regarding the predicted role of beliefs on reporting behavior, that is, part (ii) of prediction 2, we measure the wife’s beliefs with the median migrant earnings in the district that her household belongs to. We compute median earnings using the household reports of migrant earnings in the Arabian Gulf countries from KMS 2011 to avoid the influence of outliers. Our prediction is that the lower the median, the smaller the ratio.<sup>15</sup> We also control for a number of other factors that may affect reporting behavior. A closer coincidence of preferences between the couple is likely to reduce underreporting, with an

> 15. The Arabian Gulf countries share similar compensation structures. There are fourteen districts in Kerala. While a more disaggregated level of reported migrant income would have been preferred to account for the relevant information flows to each household, we were only able to identify the district where spouses resided in our matched sample. However we note that there is a strong correspondence between the _taluk_ or sub-district level ( _n_ = 72 ) and

> district level ( _n_ =14 ) median earnings ( _r_ = 0.52 ).

20
# **Placing Bank Supervision in the Central Bank:**

## **_Implications for Financial Stability Based on Evidence_**

## **_from the Global Crisis_**<sup>**_*_**</sup>

Martin Melecky<sup>#</sup> Anca Maria Podpiera World Bank World Bank

**Keywords** : Central Banks, Macroprudential Supervision, Bank Supervision, Financial Stability, Banking Crises, the Global Financial Crisis.

**JEL Classification** : G21, G28, E58

> * We thank Davide Salvatore Mare, Maria Soledad Martinez Peria, and Thierry Tressel for helpful comments on earlier drafts of the paper. The views expressed in this paper are those of the authors and do not reflect the views of the World Bank or its affiliated organizations.

> # Corresponding author: <u>mmelecky@worldbank.org; Office of the Chief Economist, South Asia Region, World Bank, 1818 H</u> Street NW, Washington D.C. 20433, USA.
villages with higher baseline enrollment selected into the school feeding programs. We might have seen greater effects if the program had 100% takeup or had been targeted to relatively disadvantaged villages. More consistent implementation might also have produced more compelling effects.

Non-takeup villages in our sample had the opportunity to enumerate reasons for nonparticipation. The top four reasons elicited in each district among non-takeup villages are presented in Table 10. In all districts and for both feeding modalities, the most common response was that the WFP food delivery point was too far away; another frequent response was lack of access to a road. These hurdles will not be easily overcome in the field as WFP continues its school feeding program expansion in Lao PDR. Problems were also cited with the necessity to build the food storage warehouse and to recruit sufficient village volunteers to run the program. This suggests a threshold level of social capital and social efficacy that is required for villages to participate in school feeding, which may discourage the villages that have the most to gain from participating. Finding a solution to these takeup problems should be a priority for the WFP in Lao PDR and in other settings where program implementation is challenging.

The present study was one of three country studies in school feeding impact assessment undertaken by the World Food Programme and the World Bank between 2004 and 2008. The Burkina Faso study site (Kazianga, et al., 2010) finds a large and significant positive effect on child weight-for-age for younger siblings of eligible children, with benefits accruing primarily for young boys. THR and OSF provided roughly similar benefits. Results indicated that the benefits accruing to young children would have cost 9 times the value of the food transfer had it been provided as a direct transfer. The study argues that failure to account for spillover effects may lead to underestimates of benefits associated with school feeding programs.

26
nological opportunities might be particularly low in countries such as Indonesia and Mexico, where heavily subsidized prices result in energy-efficiency being not a major concern for the average producer. Our finding that the positive impact of fuel prices on investment is muted for foreign and exporting plants provides further support to the market frictions and bounded rationality hypothesis, as information frictions and deviation from profit-maximizing behavior are likely to be more limited for these The rest of the paper is organized as follows. Section 2 discusses some of the key institutional features of the case studies; Section 3 describes data; Section 4 sets out the identification strategy; Section 5 provides the results, and Section 6 concludes.

# **2 Institutional context**

Indonesia and Mexico are particularly suitable contexts to study the relation between fuel prices and firms’ performance. First, they are two of the largest emerging countries, which together account for 2.6% of world’s CO2 emissions.<sup>11</sup> A significant part of the emissions is due to fuel consumption, which is sustained by long-standing policies of subsidized fuel prices in both countries, including for industrial users. These subsidies translate into some of the lowest fuel prices among a large sample of countries for which this data are available (panel a) in Figure 1).<sup>12</sup> The low prices have contributed to relatively high consumption of fossil fuel by domestic industries in both countries. Fuels account for roughly 65% of total energy consumption in the average manufacturing plant in Indonesia and 68% in Mexico, but only 40% in France (Marin and Vona, 2017), where fuel prices are considerably higher in purchasing power parity terms. On the other hand electricity prices in both countries are in the middle range within the same sample

> 11The figure refers to 2014 according to World Bank Data on emissions.

> 12In Indonesia the subsidy has been largely phased out in the public budget at the end of 2014, but energy prices continue to be implicitly subsidized by the state owned monopolists of electricity production and distribution and of fuels distribution, which also generates concerns for their economic sustainability. In 2014 the Mexican government has started implementing a gradual reform of the energy sector and it introduced a carbon tax, although the tax was limited in scope.

5
have historically been very high (12% in average) in Jamaica circa 1990, they have recently dropped 10% in 1990 to 7% in 1999, recovering somewhat in the new millennium.<sup>9</sup> Decreasing returns to education is perhaps a symptom that after reaching almost universality in primary and secondary enrollments, Jamaica is now focusing in improving the quality of its schools.<sup>10</sup> Increasing wage with high unemployment suggests that labor markets are not working efficiently in capturing the excess labor available, and/or reservation wages in the work force are too high relative to productivity. The latter hypothesis would be true if, for instance, the labor force is ageing significantly, in which case the average reservation wage of the labor force would be increasing because of dropping willingness to work. In this case, employers would agree to pay a premium for experienced workers given that the supply of newly educated young workers is getting relatively thinner.

There is some evidence that the labor force is indeed aging in Jamaica. The share of young workers under 35 decreased from 47% in 1990 to 42% in 2002, and the average age of labor force has consequently increased by 6%, from 36.2 to 38.5.<sup>11</sup> Not surprisingly, Jamaica has the oldest labor force in the LAC region.<sup>12</sup> This phenomenon is also likely to be caused by the high levels of emigration to the US and the UK.

Growth of wages in excess of productivity has been a major factor in the loss of export competitiveness, particularly in apparel exports in Jamaica.<sup>13</sup> Therefore, to design effective policies to increase labor productivity and employment in Jamaica, it is crucial to understand the role of factors that may increase reservation wages in excess of productivity. In the next section I consider the role of remittances in widening the wedge between productivity and reservation wages.

> 9 Psacharopoulos and Patrinos (2004), World bank(2006c)

> 10 Approximately 80% of Jamaican adults have now a secondary or higher level of formal education. PIOJ and STATIN, various issues.

> 11 World Bank(2006c)

> 12 IDB (2003)

> 13 World Bank (2005)

6
**Appendix Table 25: Children in monetary poor households in 2022 at the $2.15 line in 2017 PPP by education of household head**

|**Education of head**|**Extreme poor**<br>**(millions)**|**Headcount**<br>**poverty rate(%)**|**Share of extreme**<br>**poor(%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|No education|94.7|32.6|38.0|22.6|
|Primary|99.9|24.3|40.1|31.9|
|Secondary|48.5|11.4|19.5|33.2|
|Tertiary|6.3|3.9|2.5|12.3|
|**Total**|249.5|19.4|100.0|100.0|

**Appendix Table 26: Children in monetary poor households in 2022 at the $3.65 line in 2017 PPP by education of household head**

|**Education of head**|**Children living in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor (%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|No education|189.1|65.0|35.4|22.6|
|Primary|200.9|48.9|37.6|31.9|
|Secondary|125.1|29.3|23.4|33.2|
|Tertiary|19.4|12.2|3.6|12.3|
|**Total**|534.5|41.5|100.0|100.0|

**Appendix Table 27: Children in monetary poor households in 2022 at the $6.85 line in 2017 PPP by education of household head**

|**Education of head**|**Children living in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor (%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|No education|266.0|91.4|29.8|22.6|
|Primary|317.7|77.3|35.6|31.9|
|Secondary|255.9|59.9|28.7|33.2|
|Tertiary|52.6|33.1|5.9|12.3|
|**Total**|892.1|69.2|100.0|100.0|

**Appendix Table 28: Children in monetary poor households in 2022 at the $2.15 line in 2017 PPP by industry of work of household head**

|**Industry of head**|**Extreme poor**<br>**(millions)**|**Headcount**<br>**poverty rate(%)**|**Share of extreme**<br>**poor(%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Agriculture|80.8|30.8|71.0|38.3|
|Industry|10.1|7.4|8.8|19.7|
|Services|17.2|7.7|15.1|32.7|
|Other|5.8|9.1|5.1|9.3|
|**Total**|113.8|16.6|100.0|100.0|

**Appendix Table 29: Children in monetary poor households in 2022 at the $3.65 line in 2017 PPP by industry of work of household head**

|**Industry of head**|**Children living in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate (%)**|**Share of poor (%)**|**Share of**<br>**population (%)**|
|---|---|---|---|---|
|Agriculture|153.4|58.4|58.8|38.3|
|Industry|37.0|27.4|14.2|19.7|
|Services|53.0|23.6|20.3|32.7|
|Other|17.6|27.7|6.7|9.3|
|**Total**|260.9|38.0|100.0|100.0|

18
**How Can We Learn Whether Firm Policies Are Working in Africa? Challenges (and Solutions?) for Experiments and Structural Models**<sup>*****</sup>

David McKenzie, _World Bank_ _Keywords:_ Firm productivity; Randomized Experiments; Structural Models; Impact Evaluation _JEL codes:_ O12, N67, D22, C93

> * I thank Marcel Fafchamps for spurring me to write this paper, Garth Frazer for very helpful comments on a first draft, Jessica Antista of Technoserve, and Johane Rajaobelina and Michel Botzung of the IFC’s Business Edge unit for sharing their numbers on firms served, and Matthew Groh for excellent research assistance.
# **Table 4. IV Regression (instruments for T: Ethnic Diversity and Latitude)**

|Dependent<br>Var|Private|Credit|NPL/tot|al loan|
|---|---|---|---|---|
||Without<br>apmindex|With<br>apmindex|Without<br>apmindex|With<br>apmindex|
|T_245|41.5***|52.4***|-6.2***|-13.7*|
||(6.25)|(3.74)|(-2.75)|(-1.98)|
|legor_uk|6.48|13.7|2.02|-.47|
||(.31)|(.67)|(.98)|(-.13)|
|legor_fr|-12.2|-14.7|5.1|2.28|
||(-.59)|(-.76)|(2.44)|(.59)|
|legor_ge|8.46|13.5|2.51|1.81|
||(.42)|(.75)|(3.69)|(.74)|
|legor_so|-43.6|-79.5|8.01|10.6|
||(-1.87)|(-4.52)|(3.66)|(3.63)|
|apmindex||-7.03||2.67|
|||(-1.59)||(1.56)|
|Constant|-141.3***|-143.8***|38.41***|57.7**|
||(-3.26)|(-2.75)|(2.85)|(2.19)|
|# obs|146|114|80|69|
|R<sup>2</sup>|.64|.70|.67|.46|

Note: _1) Legor_uk are those countries with English legal system origin, legor_fr – French legal system origin; legor_ge – German legal system origin; legor_so – socialist legal system origin. (see the appendix 2 for source and variable description)_ _(2): numbers in parentheses are t statistics._ _(3): Coefficients with * denote significance at the .10 level, ** at the .05 level and *** for the .01 level. (4): The instruments passed the over-identification test, with Hansen J statistic quite small (p-value for the χ_<sup>_2_</sup> _larger than .95). We also looked at the first step regression in the 2SLS, to make sure the instruments are correlated with the endogenous explanatory variable with the p-value for the F-test smaller than .05)._ 21