rafmacalaba/datause-encoder-data huggingface dataset:
economies and across different social groups, the data cover sub-national units and distinguish by gender and skill level.

We divide the Western Balkans group into 54 sub-national regions corresponding to the ISO3166 international standard division at the two-digit level<sup>1</sup> . For the European Union, we take each of the 27 countries as our unit of analysis. In total, our QSM has 81 spatial units that respond heterogeneously to the policies we simulate.

To use the model and perform our counterfactual analysis over these locations, we construct a database with basic information on wages and employment for four groups of workers: high-skilled males, low-skilled males, high-skilled females, and low-skilled females, divided into three economic sectors: agriculture and mining, manufacturing, and services.

We employ two international databases: the International Income Distribution Database (I2D2) and the Working Time Statistics (WTS) to compute the national and sub-national level measures needed. In addition, we use the Global Roads Inventory Project (GRIP) for information on road infrastructure.

For EU countries, we use the WTS database to calculate the country-specific wage ratio across skill levels and the wage ratio across gender. Then, we multiply the wages in each country and sector by the skill and gender ratios to have data at the skills-gender level in each sector. We follow the same procedure to obtain employment levels.

Meanwhile, we use the I2D2 database for the Western Balkans because it allows us to analyze the data at the sub-national geographical level. This database enables cross-Balkan comparisons by standardizing representative household surveys conducted separately in each economy. However, given the independence of the data sources, not all the variables are available for all the Western Balkan economies. For example, Serbia and North Macedonia do not collect information about the economic sector from the workers. To overcome this problem, we combine the I2D2 with the WTS database by calculating the aggregated salaries and employment levels for every region and multiplying them by the proportion of wages across the three economic industries calculated at the national level. Additionally, some of the regions in the Western Balkans lack information on the salaries for specific sectors. Therefore, to circumvent this second problem, we input them using a regression model based on the industry, education, and age variables.

Finally, we use data from the GRIP to calculate the travel times across the Western Balkans region and the EU countries. This dataset gathers and harmonizes information on all the road infrastructure for over 222 countries worldwide.

> 1Table 12 lists all the sub-national regions of the Western Balkans included in the analysis

3
observed data from indicator k. The parameter βk captures the slope of the relationship between true but unobserved governance and the observed data in indicator k. Since unobserved governance has a standard deviation of one, βk is the increase in the observed data associated with a one standard deviation improvement in unobserved true governance. Finally, the standard deviation of the error term, σk captures how informative the observed indicator of governance is about true governance. If σk is large (small), the observed data provides a very imprecise (precise) signal of true governance.

# _The Conditional Distribution of Governance_

The assumptions of the UCM detailed above allow us to summarize our knowledge about the unobserved level of governance in each country by calculating the distribution of unobserved governance _conditional on the observed data for each country_ . Let y j = (y 1j,..., y jK j ) denote the observed data for country j from each of the Kj data sources in which it appears. Since we have assumed that unobserved governance gj and the error terms εjk are jointly normally distributed, this implies that the joint distribution of the observed data, yj, and unobserved governance, gj, is also normal. This in turn means that the distribution of governance _conditional on the observed data for each country_ will also be normal. Our estimate of governance for each country will simply be the mean of this conditional distribution, and the precision of this estimate will be the standard deviation of this conditional distribution.

Applying standard results for the multivariate normal distribution, the conditional distribution of gj given the observed data yj is normal, with the following mean and variance: In particular, the mean of governance conditional on the data available for country j, that we use as our estimate of governance, is just a weighted average of the standardized scores from each data source. The weights applied to each data source are inversely words, data sources that provide a more precise signal of governance receive a greater weight in the conditional mean, and hence in our estimate of governance. The variance of the conditional distribution summarizes how reliable or precise is this estimate of governance. This variance is smaller the larger is the number of data sources available for each country, and the more precise are each of those data sources.

We refer to the expression in Equation (2) as the "governance estimate" for each country, and the square root of the variance in Equation (3) as the "standard error" of the 99
interesting distinction with the data from Jordan is that there are now four observations of per capita consumption for each household in the sample, and how one uses these four observations on welfare matters for the measurement of poverty.

_i. Measuring poverty based on four quarters – status quo and cross-section approaches_ The current approach, which we refer to as the _status quo_ , is to take the simple average for the household, scale this to an annual figure and assess whether the average is below the poverty line (z). More formally, the status quo defines a household _i_ as poor if ~~�~~ �<sup>��, �����</sup><sup>~~�~~</sup> �<sup>�</sup> ∑�������⁄4� or the household’s average value of consumption of the four quarters. If a household is poor in first three quarters out of four, but their consumption in the last quarter is large enough to bring their average over the poverty line, they are assessed as not poor. This averaging essentially dampens the volatility that is typically observed in consumption data, shortening and thinning the tails of the distribution.

If we contrast this with the one-visit approach, this household would have been classified as poor if it had been interviewed during any of the first three quarters, and it would have been classified as nonpoor if and only if it had been interviewed during the last quarter. In expectation then, the one visit approach would identify the household as poor, while the averaging method identifies it as nonpoor. Table 1 indicates that it is relatively rare for a household to be poor in three quarters, but have an average consumption level greater than the poverty line. This only occurs for about 1 percent of the population. Nonetheless, 1 percent is still about 61,000 people in the population. The only household type for which both methods would unambiguously assess a household as poor is if the household is poor in all four quarters. Table 1 reveals that this is the 7
expense of other suppliers of imports, so that the net increase in total imports is modest. For instance, most of the US$62 million increase in motor vehicle imports from the EU is merely trade diverted from other sources—the actual newly-created trade is only US$12 million. This dominance of trade diversion is determined by a number of factors, including the initial tariff level, the degree of substitution among suppliers, as well as the initial market share of the preferred supplier (see Box 2).

**Figure 2.  Origin of Nigeria's imports before and after reform**

<!-- Start of picture text --> ROW<br>-3.7%<br>INDIA<br>-2.8%% Original<br>CHINA imports<br>-2.6%<br>USA<br>-4.8%<br>EU<br>+7.4%<br>New<br>AFRICA<br>-4.4% imports<br>ECOWAS<br>-2.0%<br>0 5 10 15<br>bln. US$<br><!-- End of picture text --> Source: Computed by authors based on TRIST model and database Table 7 reports the results of different EPA scenarios for a number of economic aggregates.<sup>12</sup> In the central EPA scenario (Scenario 1) with actually applied tariffs and the top 20% of protected lines excluded total imports are 0.5 percent (US$ 119 million) higher. The overall applied tariff rate decreases from 7.5 percent to 6.3 percent. Tax revenues from trade (both tariffs and VAT) fall by US$ 271 million, equivalent to a loss of 0.6 percent of total government revenue or about

> 12  These “aggregated” or macro numbers are indicative only and need to be interpreted with caution.  They are simply the sum of the tariff-line level partial equilibrium simulations, and are not taking into account the various economy-wide resource constraints and reallocations, or any intersectoral economic linkages.

15
# cooperative unions and societies to improve their services to farmers.'

Nevertheless, relatively few farmers benefitted from the relatively high prices (e.g. Tsh. 140160/Kg. for SO) which private traders were offering at the very beginning of the season. While trade figures for the entire season are not yet complete, the share of farmers in the export value is likely to have fallen back below 50% (see Table 17). Given the profit margins earned on exports (see below) and given the level of producer prices prior to the announcement of guideline prices, it would appear that official Interventions resulted in a transfer of income from farmers to private traders/cooperatives/banks amounting to some Tsh. 525 million.' Direct taxes on producers, in the form of local cesses, probably amounted to about Tsh. 350 million (e.g. an average of Tsh. 10/Kg).

|___________|Table**17:**R<br>____________**_**<br>_ _ _ _ _ _ _ _ _ _|ecent Trends in Pr<br>_ _ _ _ _ _ _<br>(1sh ./K g .)'|oducer Prices||
|---|---|---|---|---|
|Season|Standard Grade<br>**_**<br>_<br>**_**<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|Undergrade<br>_<br> <br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|Weighted<sup>2</sup><br>V|Share of Export<br>alu e|
|1989/90|84|_56_|82.6|57%|
|1990/91<br>__|<br>_73_<br>|_73<br>_|108.2<br>_|56%|
|1991/92|137|89|132.2|66%|
|1992/93|120 - 140|80 - 90|110.0 -127.5|47 - 54%3|

1 Does not take into account local cesses paid by farmers which vary significantly by district (e.g. between T.Sh. 2 and 18 per kg.) 2 For 1989/90 and 1990/91 weighted 95% SG + 5% UG. For 1991/92 and 1992/93 the respective ratios are 90/10 and 75/25.

3 Only raw nuts exported. Estimated average FOB value at $700/ton.

Source: Marketing Development Bureau and Author's Field Work During the season, both farmers and traders did move cashew nuts across district and regional borders in order to counter or take advantage of differentials in guideline/actual producer prices and in local cesses. This 'migration' of the crop also took the form of chasing the available cash as in some villages, districts, and regions there were periods in which cooperatives, the TCMB, and locally operating private traders did not have cash to make purchases. As a result of these 'migrations' district-level figures for marketed production showed considerable anomalies with huge recorded increases in some areas and major declines in others.

44One future difficulty is foreseen here. It is still the cooperative unions and societies which provide most of the input loans and inputs to farmers, with repayment coming through deductions on the delivered crop. With private traders taming an increasing proportion of the cashew crop (and other crops), input loan recovery way become a severe problem for the cooperatives.

45 Given export prices and the shilling exchange rate, still very comfortable margins would have been earned by the private trade with a producer price of Tsh. 140/Kg.. At this producer price, the cooperative unions who sold to the TCMB could have covered their finance charges and still had a modest profit to begin repayment of arrears.

36
# **IV. CONCLUSIONS**

Over the long run, financial development supports and promotes economic growth. However, the path to development is far from smooth; and along the way, economic growth can suffer from the financial fragility that characterizes maturing systems. The process of financial development entails a deepening of markets and services that channel savings to productive investment and allow risk diversification; these positive aspects of financial development lead to higher economic growth in the long run. As economies mature, however, the same process can present weaknesses evidenced by systemic banking crises, cycles of booms and busts, and overall financial volatility. Whether intrinsic to the process of development or induced by policy mistakes, these elements of financial fragility can hurt economic growth and will do so until maturity is reached.

Recognizing the possibility of a dual effect of financial intermediation on economic growth, this paper estimates an encompassing empirical model of long- and short-run effects using a sample of cross-country and time-series observations. We find that a positive long-run relationship between financial intermediation and output growth can coexist with a negative short-run relationship, which indeed is the case for the average country in the sample. Since the methodology allows us to obtain the short-run effects of financial intermediation on growth country by country, we can attempt to link these effects to the aspects of financial liberalization that the literature proposes as harmful to growth. We find that financially fragile countries, namely those that experience banking crises or suffer high financial volatility, tend to present significantly negative short-run effects of intermediation on growth. For more stable countries, this effect is in average nil.

Finally, attempting to relate our results to the empirical growth literature, we go back to the classic context of growth regressions using panel data. We find that the volatility and crisis aspects of financial intermediation are relevant growth determinants, along with the usual measures of financial depth. However, whereas financial depth leads to higher growth, financial fragility --as captured by financial volatility and banking crises-- has negative growth consequences. The total impact of financial liberalization 19
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Policy Research Working Paper 7484

# **Abstract**

This paper investigates the occupational mobility and job quality of young people in Indonesia and relates this to the concept of “scarring.” The concept of labor market scarring in this paper is the occurrence of low or zero returns to certain types of work (for example, self-employment). Scarring is expected to occur whenever an individual spends periods working in occupations in which their human capital is either stagnant or deteriorating. Fixed effects estimations using panel data from the Indonesian Family Life Survey reveal that a period in self-employment is associated with negative returns for youth (about 3 to 4 percent per year penalty), but not for older adults. In addition, there are clear patterns of persistence in self-employment over time with few individuals progressing from petty self-employment to businesses with permanent workers.

This paper is a product of the Social Protection and Labor Global Practice 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://econ.worldbank.org. The authors may be contacted at tpackard@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
on the _savings_ side of the banking sector _outside_ metropolitan areas.<sup>5</sup> Concerning other factors explaining banking sector development, we find that macroeconomic stability and broad measures of institutional quality are less important in Africa than in other developing countries, while the natural resource ‘curse’ (as in, e.g., Sachs and Warner, 1995, 2001) is no worse in Africa than elsewhere. Finally, regarding indicators of stock market development, such as market capitalization and turnover, we do not find any of the factors associated with stock market development in the rest of the world to be significant in Africa. In large part, this is because most African stock markets remain thin, illiquid and dysfunctional; and only very recently have Sub-Saharan African countries made a policy commitment to their development.

We also conduct _firm_ -level analyses which enable us to control for both country-level variables and firm characteristics in the same regressions. Employing the widely used World Bank global Investment Climate Surveys (ICS), which are comprised primarily of firms that are not listed on stock markets, we examine factors determining firms’ access to _external_ finance in the form of bank loans (from both domestic and foreign banks) and/or credit cards.<sup>6</sup> The idea is that if a country’s banking sector is more developed, firms, and in particular privately owned firms, should have easier access to these types of formal finance. We run regression models where we include the same country-level factors as in our cross-country studies as well as firm characteristics that could explain substantial variations in the demand for and in the use of financial services. Once again, we include the African dummy and interact it with both country-level and firm-level variables.

The results of the micro-level regressions confirm that population density is an important

> 5 Mbiti and Weil (2010) use the same household surveys in Kenya as in our paper, and find that increased use of M- Pesa, one of the leading mobile phone based money transfer systems, lowers the propensity of people to use informal savings mechanisms but raises the probability of their use of banking services.

> 6  This is consistent with prior research showing that financial institutions provide the most important source of external finance in most developing countries (e.g., Beck, Demirgüç-Kunt, and Levine 2000).

5
Annex 1: Data Sources for the Worldwide Governance Indicators

|**WGI**<br>**Code**|**Organizaton**|**Data Source**|**Country Coverage***<br>**(most recentyear)**|**Frequency***|**Type****|**Methodology and Data Availability**|
|---|---|---|---|---|---|---|
|ADB|African<br>Development<br>Bank|Country<br>Performance<br>Assessments|37 African Development<br>Bank client countries<br>eligible for concessional<br>fnance.|Every two years;<br>used in WGI<br>since 2004.|Expert/GOV|Expert assessment along 16 dimensions, scored by AfDB<br>country economists subject to centralized review.<br>Questonnaire and process are similar to the World Bank<br>Country Policy and Insttutonal Assessment (PIA), the IFAD<br>Rural Sector Performance Assessment (IFD) and the Asian<br>Development Bank Country Performance Assessment (ASD).<br>Link to methodologyhere.Data are publicly available only for<br>countries eligible for concessional fnance.|
|AFR|Afrobarometer|Afrobarometer<br>Surveys|35 African economies in<br>Round 9.|Approximately<br>every 2-3 years;<br>used in WGI<br>since 2002|Survey/HH|Detailed descripton of survey instruments and sampling<br>methodology are availablehere.Record-level survey data is<br>publicly available.|
|ASD|Asian<br>Development<br>Bank|Country<br>Performance<br>Assessment|26 Asian Development<br>Bank client countries.|Every two years;<br>used in WGI<br>since 2005.|Expert/GOV|Expert assessment along 16 dimensions, scored by ADB<br>country economists subject to centralized review.<br>Questonnaire and process are similar to the World Bank<br>Country Policy and Insttutonal Assessment (PIA), the IFAD<br>Rural Sector Performance Assessment (IFD), and the African<br>Development Bank Country Performance Assessment (ADB).<br>Link to methodologyhere.Data are publicly available only for<br>countries eligible for concessional fnance.|
|BPS|World Bank and<br>European Bank<br>for<br>Reconstructon<br>and<br>Development|World Bank<br>Business<br>Environment<br>and Enterprise<br>Performance<br>Survey (BEEPS)|30 transiton economies<br>in Eastern Europe and<br>Central Asia.|Every three<br>years, used in<br>WGI 2000-2015.|Survey/Firm|Enterprise surveys conducted by the World Bank Enterprise<br>Survey (WBES) unit and cofnanced with the European Bank for<br>Reconstructon and Development. Before 2023, this was the<br>only region for which the WBES produced surveys with<br>sufcient frequency to be included in the WGI. Beginning in<br>2023 the WBES has global coverage on a three-year rotatng<br>cycle and is included as a new data source in the WGI (see<br>source WBS below). Record-level survey data is publicly<br>available through the WBES website. Link to methodology and<br>questonnaireshere.|
|BTI|Bertelsmann<br>Foundaton|Bertelsmann<br>Transformaton<br>Index|137 countries covering<br>a global sample.|Every two years;<br>used in WGI<br>since 2002.|Expert/NGO|<br>Expert assessment along 47 dimensions scored by country<br>experts recruited by Bertelsmann Foundaton. Assessments<br>are subject to centralized review and follow a detailed<br>checklist with scoring criteria. Link to full methodology here.|