rafmacalaba/datause-encoder-data huggingface dataset:
there are more crisis years experience a decline in inequality. The size of the effect indicated by the point estimate is not huge: a five-year long crisis is estimated to lower the log of Gini by 0.02, equivalent at the sample mean value to moving the Gini from 0.376 to 0.368.

Lopez’s finding can be seen as broadly consistent with the Indonesian evidence, and the other cases mentioned where Gini has declined around the time of crises.

By taking the number of crisis years in a given five-year period as the crisis variable, he is implicitly focusing on short and medium-term impacts of the crisis denouement. However, this variable entails heavy reliance on the accuracy of the crisis end-dates, whereas it is known that these dates are especially uncertain, essentially because the process of resolving failed financial institutions is a long-drawn-out one. Thus, in respect of several crises, no end-point is shown despite the fact that the crisis has been fully resolved. (Lesotho is a good example: taking the lack of an end-date as an indication that the crisis shown as beginning in 1988 was still running in 2002 would be misleading.) Furthermore, the alignment of the Gini and crisis measures is somewhat problematic, given that some of the Ginis are measured early within the five-year period and possibly before even a crisis that scores 0.6 or 0.8 on the index got under way.

These timing issues are likely to bedevil any work in this area; noting them does not detract from the useful exercise carried out by Lopez, but points to the desirability of looking further at the dynamics of the Gini around the known crisis dates.

It is worth mentioning here that another major difficulty in mechanically applying the crisis database is that many of the countries without reported crises are known to have rather dysfunctional controlled banking systems, but ones which have managed to remain solvent or nearly so.

_Gini coefficients before and after crises_ (a) Long-term effects Looking first at long-term effects, it is of interest to compare Gini coefficients measured before any crisis hit, with those measured a sufficiently long interval (say ten years) after any crisis was reported as being in progress.

Because of the frequency and protracted nature of banking crises, there are relatively few datapoints in either category. Thus, confining ourselves to the data in _Povcal_ (and excluding transition economies<sup>21</sup> ), we obtain just 23 pre-crisis Gini observations, with a range from 0.26 to 0.63, a mean of 0.439 and a standard deviation of 0.102. As for “long-after” post-crisis observations, i.e. those 10 years or more after the last recorded crisis date, there are also 23 of these, ranging from 0.29 to 0.74, with a mean of 0.476 and

> banking crises – complicates the interpretation.  Also note that, contrary to other results in the literature

> (e.g. Beck et al., 2004) financial deepening is found by Lopez to worsen inequality.

> 21 For whom the strong, known, dynamics of increasing measured inequality will surely swamp any crisis effects.

13
Policy Research Working Paper 8940

# **Abstract**

Rapid economic growth over the past two decades lifted millions of people out of poverty in Central Asia. But the uneven spread of prosperity left many communities struggling to catch up. To support lagging regions within countries, each of the region’s five national governments has made convergence a pillar of their development strategies. An imperfect patchwork of household surveys allows policy makers to monitor progress and identify some spatial disparities. But these share an important weakness: none of the official surveys in the region is representative when disaggregated to the level of districts. Islands of poverty and prosperity are thus lost in the averages—leading to targeting inaccuracies that can slow the pace of poverty reduction. This study partially addresses the challenge. The accuracy of key welfare indicators is sharpened well beyond what could be achieved for any country alone by: i) unifying survey data from across the region and ii) applying the techniques of small-area estimation. The results provide detailed measures of welfare that in turn can be disaggregated for each district in Central Asia. Comprehensive maps of where the poor and the middle class live are presented, for the entire region and individually for each country.

This paper is a product of the Poverty and Equity Global Practice. 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 author may be contacted at wseitz@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
**Figure 9. Redistributive impact of an increase in tax progressivity**

<!-- Start of picture text --> 0.00<br>-0.01<br>Argentina Brazil Chile Colombia Mexico Peru LAC6<br><!-- End of picture text -->

# _3.4 The redistributive effects of public spending_

Section 2 showed that in European countries fiscal redistribution manages to reduce sharply the Gini coefficient of market incomes, and that public transfers account for the bulk of this effect. In contrast, in Latin America public transfers achieve little on the inequality front. There are two potential (not mutually exclusive) reasons for this difference. One is that the volume of transfers is much smaller in Latin America than in Europe. The other is that the targeting of the given volume of transfers is not very progressive Regarding the volume of transfers, Latin America spends, on average, less than half of what is spent in Europe. For example, Table 5 (based on Lindert et al. 2006) indicates that on average the six Latin American countries considered here devote to transfers about 7.3 percent of GDP. Of this total, about 6.3 percent of GDP goes to social insurance programs (such as pensions and unemployment insurance) and about 1 percent of GDP to social assistance programs such as conditional cash transfers, school meals, and scholarships. In contrast, Europe spends more than 16 percent of GDP on transfers, most of which for social insurance programs. Table 5 also indicates that there is substantial heterogeneity among Latin American countries, with some high spenders (Argentina, and especially Brazil), some moderate spenders (Chile, Colombia) and some low spenders (Mexico and Peru). Thus in principle, even if these flows were targeted progressively, we would expect them to have much less of an impact than in Europe, given their smaller volume.

**<u>Table 5. Transfers in Latin America and Europe (% of GDP)</u>**

||Argentina|Brazil|Chile|Colombia|Mexico|Peru|LAC6|Europe|
|---|---|---|---|---|---|---|---|---|
|Social Insurance|7.7|11.7|6.9|5.9|2.6|3.1|6.3|14.7|
|Social Assistance|1.5|1.4|0.7|0.6|1|0.5|1.0|1.6|
|Total Transfers|9.2|13.1|7.6|6.5|3.6|3.6|7.3|16.3|

Source: Lindert et al. (2006) 18
# **5 Results**

This section presents the results, both from a country-pair and country-centric perspective. Our goal is to analyze the link between GVC participation and both the level (for each country) and the synchronization (for each country-pair) of inflation.

## **GVC and the synchronization of inflation across country-pairs**

In table 1, we use within country-pair variations and control for country-pair fixed effects as well as third country exposure to investigate the link between trade in intermediate inputs and the correlation of inflation for each pair of country. Results in column (1) show that the correlation of GDP deflator (GDPD) inflation increases when total trade proximity increases. This result is indicative that the determinants of GDPD inflation might not be domestic only, and that higher trade integration is associated with a link in the formation on prices. Moreover, we can dis-aggregate this results and separate total trade into trade in final goods and trade in intermediate inputs. Doing so, column (2) unveils an interesting insight about the specific role of Global Value Chains. While an increase in final good trade is _not_ associated with any significant change in inflation co-movement, the effect we observed through total trade is actually solely driven by trade in intermediate inputs.

This result is not very surprising since GDP deflator is defined net of imports. While imported final goods for consumption are simply not taken into account in the construction of GDP deflators, the effect of imported input can still have an impact through the effect of cost shocks on the prices of other firms down the production chain. Overall, our results show that GVC integration as proxied by trade in intermediate inputs creates a link between inflation fluctuation between countries and confirms the view that in a world with production sharing across countries, the determinants of inflation are not purely domestic anymore.

Following results presented in table 1, in table 2 we extend the analysis by using value-added data for trade flows instead of gross trade flows. Doing so is important because, for example, gross import flows might contain value added produce in a third country, in which case domestic inflation would be more likely to be synchronized not with the _direct_ import partner but with the country of origin of value added. Looking at column (1), we first find that the finding related to total trade is _reversed_ : while an 13
two very large ones (the Industrial and Commercial Bank, ICBC, of China and Sberbank, Russia, with 0.43 and 0.23 billion accounts respectively); their inclusion accounts for the fact that the augmented dataset contains more than twice as many accounts – 1.46 billion instead of 0.67 billion – thereby sharply revising aggregate access estimates.

Figure 5 presents the distribution across countries of number of microfinance accounts per 100 adult population using the WSBI series. It also shows the same for microfinance accounts that are not savings bank accounts (based on the CGAP numbers). It suggests a rather better access picture than indicated in earlier work, and in particular shows that ignoring the large government-sponsored savings banks leads to a considerable understatement of the number of bank accounts (though it should be noted that many of these savings banks do not offer credit services to individual customers); A parallel exercise for _banks_ was conducted mainly through a survey of banking regulators (Beck et al. 2005). Data on the number of deposits at 1999-2000 was obtained for 45 countries – 35 of them developing countries. However, this survey covers only those institutions regarded as “deposit money banks” and as such does not cover most microfinance institutions.

A number of difficulties arises in moving from these two databases to a reliable indicator of the proportion of households with financial access. For one thing, there is unfortunately an overlap between the coverage of the two databases on numbers of accounts, as some commercial banks are included in the WSBI sample, and elimination of this overlap has not yet proved feasible. Then again there is the problem of inferring the number of distinct and active accountholders from data on numbers of accounts, as many individuals have multiple accounts. On the other hand, some accounts are recorded even though they have long been inactive. There is sure to be a large variation from country to country and between different types of banks in the degree to which these problems result in the aggregate number of accounts exceeding the number of individuals with accounts. The typical individual in more advanced financial systems likely holds a greater number of accounts than in less advanced economies. And there are widely differing practices with regard to multiple accounts. For example, the ICBC – with the largest number of individual accounts of any bank, is currently carrying out a campaign to consolidate multiple accounts.<sup>37</sup> There are other loose ends. For instance, while the accounts at ICBC are included in the WSBI data, the 146 million active personal deposit accounts at another large Chinese institution, the China Commercial Bank are not, essentially because it is not a member of the WSBI. Both of these banks would be included in the concept of “commercial banks”

> 37 Interestingly, the bank’s notice to customers dated October 18, 2005,  urging them to close or merge accounts containing small balances, identifies no fewer than 11 classes of account which should _not_ be closed.

26
# Annex I. Test of equality in task profile trends between SE and NSE

# 1. Part-Time Employment

Null Hypothesis: �Task���� �Task�������Task���� �Task����� Where: Task���� indicates the average measure of a task category (Non-Routine Cognitive Analytical – NRCA- Non-Routine Cognitive Interpersonal –NRCI-, Routine Cognitive –RC-, Routine Manual – RM-, and Non-Routine Manual –NRM- ) for Part-Time Employment in the Ending point of the analysis.

Task���� indicates the same average measure for Part-Time Employment in the initial point of the analysis.

Task���� is the measure of task intensity for the standard employees in the ending point of the analysis.

Task����is the measure of task intensity for the standard employees in the initial point of the analysis.

## LATIN AMERICA AND THE CARIBBEAN

||||Δ NRCA|||
|---|---|---|---|---|---|
||SE|Part‐time|diff|Equal sign trend|Reject H0|
|Argentina|0.449|0.305|‐0.144|yes|yes|
|Uruguay|0.229|0.0354|‐0.193|yes|yes|
|Brazil|0.543|0.0594|‐0.483|yes|yes|
|Chile|0.437|‐0.0469|‐0.484|no|yes|
|Peru|0.177|‐0.7850|‐0.962|yes|yes|
|Bolivia|0.206|0.7271|0.521|yes|yes|
|Dominican Rep.|‐0.504|‐0.1851|0.319|yes|yes|
|Mexico|0.020|‐0.1047|‐0.125|no|yes|
|El Salvador|0.555|0.2667|‐0.289|yes|yes|
||SE|Part‐time|diff<br>Δ NRCI|Equal sign trend|Reject H0|
|Argentina|0.349|0.108|‐0.241|yes|yes|
|Uruguay|0.183|0.1567|‐0.026|yes|no|
|Brazil|0.613|0.2045|‐0.409|yes|yes|
|Chile|0.352|0.2270|‐0.125|yes|yes|
|Peru|0.717|0.0714|‐0.645|yes|yes|
|Bolivia|0.158|1.0249|0.866|yes|yes|
|Dominican Rep.|‐0.044|0.1151|0.159|no|yes|
|Mexico|0.048|0.0444|‐0.004|yes|no|
|El Salvador|0.446|0.2219|‐0.224|yes|yes|

Page 45 of 51
# **1. Introduction**

Income differences across countries around the world are enormous, so that international migration provides the opportunity for those who move to dramatically increase their incomes (McKenzie et al. 2010; Clemens et al., 2019). Yet only 3.3 percent of the world’s population live outside their country of origin.<sup>1</sup> Moreover, those who do migrate are positively selected on education from almost every sending country, despite returns to skill being higher in many developing countries than in OECD destinations (Grogger and Hanson, 2011). These patterns of low levels of migration and positive sorting on skills present a challenge to classic theories of migration decisions such as the income maximization approach of Sjaastad (1962) and the Roy-model selection approach of Borjas (1987).

The theoretical literature on migration choice has since evolved from a simple consideration of comparing the income gained from moving to the cost of this move, to also incorporate liquidity constraints, risk and uncertainty, imperfect information, and choices between multiple destinations. Different assumptions about how potential migrants trade off these different factors can then give rise to different predictions as to how many individuals will move and to where. Empirically testing these theories is however difficult, given that these attributes are not distributed randomly across destinations or individuals.

We use incentivized laboratory experiments to test how potential migrants trade off different location attributes and use this to distinguish between competing migration theories. We work with samples in Lisbon and Nairobi to examine how sensitive our results are to the study population. We randomly assign observed and unobserved skill levels, and endowments to potential migrants, and then have them make choices between destinations with different returns to skills. We vary the risk of unemployment, whether liquidity constraints bind, whether or not there is full information, and the number of destinations. Using the basic Sjaastad/Borjas model, we would predict extremely high migration rates, with negative self-selection. Adding additional real-world features like risk, liquidity constraints, and incomplete information dramatically lowers migration rates and makes selection less negative. We also document a home bias effect, where simply labeling a destination as home without changing the payoffs reduces migration to other destinations. Finally, we can directly investigate the independence of irrelevant alternatives (IIA)

> 1 <u>http://www.unfpa.org/migration</u> [accessed July 27, 2021].

2
(utilities, electricity) has shown greater resistance to economic developments (largely reflecting the authorities’ policies to protect the living standard of the population). Endprices of electricity increased five-fold between 1997 and 2007 in the Slovak Republic compared to only doubling in Macedonia over the same period. Even though prices of smaller number of goods are being regulated in Macedonia compared to the Slovak Republic; the share of administered prices in the CPI in 2006 was 1.2% and 23.4% in Macedonia and Slovakia (EBRD, 2007), the problem appears to lie in the commercial orientation of the provision of major CPI basket articles.

Additionally, constraints to market entry, based on either the market size or institutional arrangement, can reduce the exchange rate pass-through by allowing monopolistic importers to charge excessive profit margins on their products and adjust prices in an asymmetric way. Equally low competition in the domestic market then supports the survival of such monopolistic structures in the import industry. The lower degree of market competition in Macedonia compared to Slovakia can explain the lower sensitivity of CPI to exchange rate changes. Namely, the Slovak Republic scored 3.3 on EBRD’s index of competition policy<sup>19</sup> in 2007 and 0.39 on OECD’s Index of procompetitive reforms<sup>20</sup> (data is for 2005), compared to Macedonia’s score of 2.3 on the EBRD index and 0.59 on the OECD index.

Finally, the differing exchange rate pass-through may reflect differences in the living standards of the population in both countries (i.e. the construction of the CPI) as well as differences in the extent of trade integration. Macedonian CPI gives a considerably larger weight to food and beverages (around 40% compared to Slovakia’s around 20%) which are mostly domestically produced and as a result shielded from exchange rate developments (see Table 10). Also, imports of goods account for above 80% of Slovak GDP compared to only around 60% of Macedonian GDP. Further more, imports of goods which are closely reflected in the CPI (foods and beverage, fuels and lubricants, transport equipment and consumption goods) account for 35.7% of GDP in the

> 19 The index ranges from 1 to 4+ with 1 referring to “No competition legislation and institutions” and 4+ referring to “Standards and performance typical of advanced industrial economies; effective enforcement of competition policy; unrestricted entry to most markets” (EBRD, 2007, p.211). Most of the advanced transition economies have an Index of competition policy of 3.3, with only Estonia performing better (Index of competition policy of 3.7)

> 20 The index range is between 0 and 1 with smaller value implying better performance on the index. Index data from Miroudut et al. (2007).

25
<!-- Start of picture text --> Figure 32: State Level Water Deprivation by Region<br>a) b) c)<br>Water Deprivation - by State in Water Deprivation - by State in Water Deprivation - by State in<br>North Central - MICS North East - MICS North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Benue FCT Abuja Kogi<br>Kwara Nasarawa Niger Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Water Deprivation - by State in Water Deprivation - by State in Water Deprivation - by State in<br>South East - MICS South South MICS South West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Abia Anambra Ebonyi Enugu Imo Akwa IbomDelta BayelsaEdo Cross RiverRivers Ekiti Lagos Ogun Ondo Osun Oyo<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis

# Shelter Deprivation

National level severe shelter deprivation has remained basically unchanged over the GHS waves 1-4, with around one third of the children living in conditions without sealed flooring. If considered comparable, shelter deprivation, meaning having no flooring in the house, has even been increasing from the GHS to the latest MICS estimates. Irrespective of the survey, the national average is however driven by the rural higher deprivation rate of rural children, where close to half the children are severalty shelter deprived.

43