rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6915

# **Abstract**

Income differences arise from many sources. While some kinds of inequality, caused by effort differences, might be associated with faster economic growth, other kinds, arising from unequal opportunities for investment, might be detrimental to economic progress. This study uses two new metadata sets, consisting of 118 household surveys and 134 Demographic and Health Surveys, to revisit the question of whether inequality is associated with economic growth and, in particular, to examine whether inequality of opportunity—driven by circumstances at birth—has a negative effect on subsequent growth. The results are suggestive but not robust: while overall income inequality is generally negatively associated with growth in the household survey sample, we find no evidence that this is due to the component associated with unequal opportunities. In the Demographic and Health Surveys sample, both overall wealth inequality and inequality of opportunity have a negative effect on growth in some of the preferred specifications, but the results are not robust to relatively minor changes. On balance, although the results are suggestive of a negative association between inequality and growth, the data do not permit robust conclusions as to whether inequality of opportunity is bad for growth.

This paper is a product of the Office of the Chief Economist, Africa Region; and the Poverty and Inequality Team, Development Research 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 fferreira@worldbank.org, clakner@worldbank. org, mlugo1@worldbank.org, or berk.ozler@otago.ac.nz.

_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
# I. SETTING AND DATA

# _Program Features_

Initiated in 1997 and still in effect, _Progresa-Oportunidades_ is a large-scale social program that aims to foster the accumulation of human capital in the poorest communities of Mexico by providing both cash and in-kind benefits, which are conditional on specific behaviors, in the key areas of health and education. The program grants scholarships and school supplies to children aged under 17 conditional on regular attendance at one of the four last grades of primary schooling (grades three to six) or one of the three grades of junior secondary schooling (grades seven to nine). The scholarships increase in amount with school grade level achieved, and in grades seven to nine the scholarships are larger for girls than boys. The program also distributes cash transfers for the purchase of food, provides food supplements, and promotes healthcare through free preventive education intervention on hygiene and nutrition. The distribution of the food stipends and nutritional supplements are conditional on healthcare visits at public clinics. The benefits are delivered to the female head of the household (usually the mother) on a bimonthly basis after verification of each family member ’ s attendance at the relevant facility.<sup>3</sup> The _Progresa_ program is targeted both at the village and household levels. During the first years of the program, poor rural households were selected through a centralized process which encompassed three main steps. First, villages were ranked by a composite index of marginality computed using information on socioeconomic characteristics and access to the program infrastructures from the censuses of 1990 and 1995.<sup>4</sup> Second, 3. Overall cash transfer amounts can be substantial: the median benefits are 176 pesos per month (roughly 18 USD in 1998), equivalent to about 28 percent of the monthly income of beneficiary families.

4. Localities with fewer than 50 or more than 2,500 inhabitants were excluded during the first years of the program. We use the words “locality” and “village” interchangeably when referring to distinct census-designated rural population clusters, (i.e., settlements in which inhabitants live in neighboring sets of living quarters and have a name and locally recognized status, including hamlets, villages, farms, and other clusters). Rural localities (also called rural communities), or villages, are defined as having fewer than 2,500 inhabitants.

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<!-- Start of picture text --> SSA age of the GDP<br>LIC -Fragile<br>LIC...<br>SADC<br>Resource..<br>MIC<br>Madagas..<br>Mozambi..<br>Zambia<br>Malawi<br>Lesotho<br>Namibia<br>South..<br>0 10 20 30 40<br><!-- End of picture text -->
WPS3795 Berger, Frame, and Miller 2005).<sup>2</sup>

# **C. Asset-Based Lending**

Asset-based lending is a transactions lending technology in which financial institutions address the opacity problem by focusing on a subset of the firm’s assets, which are pledged as collateral, as the primary source of repayment. This technology provides working capital financing secured primarily by accounts receivable and inventory.<sup>3</sup> The amount of credit extended is linked on a formula basis using hard data to a dynamically-managed estimation of the liquidation value of the assets used as collateral. The value of collateral is assessed daily in the case of accounts receivable, and typically weekly or monthly for inventory, and linked to the size of the credit available, so that the liquidation value of the collateral always exceeds the credit exposure (Udell 2004).<sup>4</sup> The use of collateral itself, however, does not distinguish asset-based lending from the other lending technologies. The pledging of accounts receivable and inventory is often associated with financial statement lending, relationship lending, and credit scoring, where collateral is used a secondary source of repayment. Under asset-based lending, in contrast, the extension of credit is primarily based on the value of the collateral, rather than the overall creditworthiness of the firm.

# **D. Factoring**

Factoring involves the purchase of accounts receivable by a “lender” known as a factor. As in assetbased lending, factoring focuses on the value of an underlying asset, rather than the overall value/risk of the firm. Factoring is similar to asset-based lending, but there are three important distinctions. First, factoring only involves the financing of accounts receivable, unlike asset-based lending which also involves financing inventory. Second, under factoring, the underlying asset is sold to the “lender.” Third, factoring is essentially

> 2 See Berger and Frame (2006) for more details on small business credit scoring and its effects on credit availability.

> 3 There is relatively little empirical evidence on asset-based finance.  One study finds evidence consistent with practitioner and conventional wisdom that asset-based finance is associated with riskier borrowers (Klapper 1998). Another study of very large credits finds evidence that commercial finance company credits (which are exclusively assetbased) are riskier than commercial bank credits (which can be either asset-based or not), but did not find evidence of associating opacity with asset-based lending using standard opacity measures (Carey, Post, and Sharpe 1998).

> 4 For the least creditworthy asset-based borrowers, lenders often establish special checking accounts into which all collections of receivables must be deposited, reducing potential diversion of these funds by the borrower (Mester, Nakamura, and Renault 2004).  In addition, monitoring cash flows on deposit accounts may provide valuable hard information that may be used in conjunction with any of the lending technologies, provided that all the firms’ checking accounts are consolidated at the lending institution (Nakamura 1993).

5
Policy ReseaRch WoRking PaPeR 4432

# **Abstract**

This paper analyzes how the employment/productivity profile of growth and its sectoral pattern are correlated with poverty reduction. The authors use a sample of 104 short-run growth spells in developing countries, between 1980 and 2001. They also identify some conditions of the labor market and the economic environment that are associated with employment-intensive growth or specific sectoral growth.

The results show that, in the short run, although the aggregate employment-rate intensity of growth does not matter for poverty reduction any more than the aggregate productivity intensity of growth, the sectoral pattern of employment growth and productivity growth is important. Employment-intensive growth in the secondary sector is associated with decreases in poverty, while employment-intensive growth in agriculture is correlated with poverty increases. Similarly, productivityintensive growth in agriculture is associated with decreases in poverty.

Although the study does not address causality, coincidence of these phenomena in this large sample of heterogeneous countries and periods suggests that, in the short run, the sectoral productivity and employment pattern of growth may have important implications for poverty alleviation. Therefore, policies for reducing poverty should not overlook the sectoral productivity and employment implications of different growth policies.

This paper—a product of the Poverty Reduction and Development Effectiveness Department of the Poverty Reduction and Economic Management Network—is part of a larger effort of the department to understand the role of employment as transition channel between growth and poverty reduction and to identify effective policies for the creation of more and better jobs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ppaci@worldbank.org, cgutierrez2@worlbank.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
37

|Table A3: A Brazilian Spatial Price Index (RM Sao Paulo = 1.0)|
|---|
|PNAD Region<br>Spatial Price Deflator|
|RM Fortaleza<br>1.014087|
|RM Recife<br>1.072469|
|RM Salvador<br>1.179934|
|Northeast (other urban areas)<br>1.032056|
|Northeast Rural<br>0.953879|
|RM Belo Horizonte<br>0.958839|
|RM Rio de Janeiro<br>1.002163|
|RM Sao Paulo<br>1.000000|
|Southeast (other urban areas)<br>0.904720|
|Southeast Rural<br>0.889700|
|RM Porto Alegre<br>0.987001|
|RM Curitiba<br>0.987001|
|South (other urban areas)<br>0.904720|
|South Rural<br>0.889700|
|RM Belem<br>1.088830|
|North (other urban areas)<br>1.032056|
|RM Brasilia<br>1.037915|
|Center West (other urban areas)<br>0.968388|

Note: This regional price index is based on the consumption pattems and implicit prices from the PPV 1996 survey, for the Northeast and Southeast regions, and extrapolated to the rest of country according to a procedure specified in Ferreira et. al. (1999), where the exact derivation of the index is also discussed in detail.

We assume, largely due to the lack of earlier comparable regional price information, that the structure of average regional cost-of-living described above remained constant over the period. Temporal deflation was undertaken on the basis of the Brazilian consumer price indices IGP-DI (for 1976), and INPC-R for the three subsequent years. For 1996, the INPC-R was upwardly adjusted by 1.2199, so as to compensate for the actual price increases which took place in the second half of June 1994, and which were not computed into July's index, since the latter was already computed in terms of the URV. This adjustment is becoming the standard deflation procedure at IPEA when comparing incomes across June/July 1994. (See Macrometrica, 1994). In order to center the indices on the first day of the month, which is the reference date for PNAD incomes, the geometric average of the index for a month and for the preceding month was used as that month's deflator. Once again, this is now best practice for price deflation in hyperinflationary periods. Once the deflators were constructed in this way, the values to convert current incomes into 1996 Reais were as follows:

**Table A4: Brazilian Temporal Price Deflators (Selected Years)**

|**1976**<br>**1**4.115|1**1981**|**1**49.512|1**1985**|**1**2257.294|1**1996**<br>1.000|
|---|---|---|---|---|---|

A final possible adjustment to the PNAD data concerns deviations between survey-based welfare indicators (such as mean household income per capita) and National Accounts-based prosperity indicators (such as GDP per capita). The international norm is that household survey means are lower than per capita GNP, both because the latter includes the value of public and publicly
foreign currency debt minus the international reserves. As presented in charts 9 and 10, out of the 4 more indebted countries in 2001, Russia and Brazil had become net creditors by 2009. The other two, Turkey and Mexico, reduced their combined net FX debt from $123 billion in 2001 to $45 billion in 2009. Most impressive is the case of China with a 5-fold increase in its international reserves from about $200 billion in 2001 to over $2 trillion in 2009, while FX debt contracted from $49 billion to $35 billion. To better appreciate the magnitude of the changes in other EM countries, China has been excluded from these charts.

<!-- Start of picture text --> Chart 9 Chart 10<br>Gross external debt vs. Int. Reserves (no China) Gross external debt vs. Int. Reserves (no China)<br>2001 2009<br>200 50 500 50<br>180 FX Reserves FX debt Net FX to GDP (right axis) 450 FX Reserves FX debt Net FX to GDP (right axis) 40<br>40<br>160 400 30<br>140 30 350 20<br>120 300 10<br>100 20 250 0<br>80 200 ‐10<br>10<br>60 150 ‐20<br>40 0 100 ‐30<br>20 50 ‐40<br>0 ‐10 0 ‐50<br>Source: JP Morgan EM Debt and Fiscal Indicators (2009)<br>Source: JP Morgan EM Debt and Fiscal Indicators (2009)<br>(%)<br>USD billion (%) USD billion<br>India Indonesia Malaysia Philippines Thailand Hungary Poland Russia South Africa Turkey Brazil Chile Colombia Mexico Peru India Indonesia Malaysia Philippines Thailand Hungary Poland Russia South Africa Turkey Brazil Chile Colombia Mexico Peru<br><!-- End of picture text -->

**Although international reserves may also be compromised by a high level of private external debt, there is no question that their accumulation dramatically reduced the overall exposure of our EMs group**<sup>**8**</sup> . For the sample of selected EMs, chart 11 shows that the weighted average of the ratio of total external debt to international reserves dropped from 3.5 in 2001 to 1.2 in 2009. The steady declining trend over the decade was only slightly reversed by the global financial crisis in 2008.

> 8 The implementation of more flexible FX regimes in many EMs also contributed to reduce their exposure to shocks and facilitated adjustments in the external accounts.

8
**While maize displaced soybeans in the U.S., other oilseeds displaced wheat in the EU and other wheat exporting countries.** The expansion of biodiesel production in the EU diverted land from wheat and slowed the increase in wheat production which would have otherwise kept wheat stocks higher. In response to the increased demand and rising prices for oilseeds, land planted to oilseeds increased, especially rapeseed and to a lesser extent sunflower. The increase was primarily in the countries that are also major wheat exporters such as Argentina, Canada, the EU, Russia and Ukraine. Oilseeds and wheat are grown under similar climatic conditions and in similar areas and most of the expansion of rapeseed and sunflower displaced wheat or was on land that could have grown wheat. The 8 largest wheat exporting countries<sup>13</sup> expanded area in rapeseed and sunflower by 36 percent (8.4 million hectares) between 2001 and 2007 while wheat area fell by 1.0 percent (Figure 7). The wheat production potential of this land was 26 million tons in 2007 based on average wheat yields in each country, and the cumulative wheat production potential of that land totaled 92 million tons from 2002 to 2007. To illustrate the impact of this land shift on wheat stocks, Figure 8 shows the simulated wheat stocks compared to actual wheat stocks if the land planted to rapeseed and sunflower had been planted to wheat and if wheat stocks had increased by the same amounts. The simulation shows that wheat stocks would have been almost as large in 2007 as in 2001 rather than lower by almost half. Figure 9 shows the relationship between wheat stocks and prices.

<!-- Start of picture text --> Figure 7. Wheat and Oilseeds Area<br>(Index 2001=100)<br>160<br>140<br>120<br>100<br>80<br>60<br>2001 2002 2003 2004 2005 2006 2007<br>Wheat Rapeseed and Sunflower<br>Source: DECPG<br><!-- End of picture text -->

> 13 Eight countries and groups accounted for 90 percent of global wheat exports during 2005-07. These countries and their shares were: U.S. 25.4%. Canada 15.3%, EU-27 11.9%, Russian Federation 9.8%, Australia 9.3%, Argentina 8.8%, Kazakhstan 6.0% and Ukraine 3.2%.

11
country/commodity-group face common shocks, thus implying that new exports and export expansion are simultaneous processes. If these concerns were valid, then the interpretation of our econometric results would be slightly different. In terms of the existing literature (e.g., Hummels and Klenow 2003) the coefficient on the variable that captures the interaction between export profitability and regulatory barriers to entry would need to be interpreted as evidence that regulations affect the relationship between the “intensive” and “extensive” margin of trade, rather than affecting the relationship between export profitability and the introduction of new export products.

In addition to these variables of interest, there are some necessary controls. As discussed earlier, the process of discovery is closely linked to stages of diversification. We therefore control for this relationship by including GDP per capita and GDP per capita squared. We also allow for the possibility that discovery is driven in part by factor endowments by controlling for comparative advantage in each industry measured by presample net exports per capita in that particular commodity group (Leamer 1984). In addition, to control for differences across commodity groups and the different number of products composing each commodity cluster, we include dummy variables for each Leamer commodity group.

Finally, we control for historical discoveries by country/commodity group. This is accomplished using export data at a higher level of aggregation<sup>9</sup> , which is available for a longer time period but only identifies broad, sector-level export discoveries rather than disaggregated product-level discoveries. This measure of historical pre-sample discoveries controls for time-invariant and unobserved characteristics driving discoveries

> 9 SITC Revision 1 at the 3 digit level. See Appendix for data and filter description.

13
declined substantially. Unemployment inflows due to layoffs emerged only in October 1991 (the February 1991 law shortened the notification period to six months) and dramatically increased in 1992.

Given the large declines in employment and the large growth in unemployment during this period, it is hardly surprising that the job opportunities for most individuals have been sharply curtailed (Table 1, panel F). Among those who entered employment in 1987, 61.9 percent found employment in the formal sector within 12 months; among those who entered unemployment in 1990, only 40.8 percent found a formal sector job within the same period of time, a decline of about a third. The data for 1991 show a slight increase, an early sign of a light at the end of the tunnel.<sup>2</sup>

# **2.** HOW THE UNEMPLOYMENT INSURANCE SYSTEM WORKS

This section describes the types of benefits and eligibility rules for unemployment insurance in Slovenia. It proceeds with the arguments that lead one to believe that the Slovenian system during 1990-1992 encouraged unemployed to wait until their unemployment insurance benefits lapsed before taking a job.

# Description of the system

Unlike other socialist economies, Yugoslavia allowed the existence of open unemployment, and set up a system of unemployment insurance to cope with it, in the early 1970s. During the period covered in our analysis (1990-1992) and since then, unemployed workers have had the right to: a) Unemployment compensation; b) Unemployment assistance; c) Training; 2 Slovenia's production has been on the rise since the last quarter of 1993, reaching a remarkable 5 percent in 1994. Unemployment peaked at the end of 1993 and has been declining since then.

4
# **Table 1: List of countries and survey years**

||Demog|raphic and Health|Surveys|Consumption|surveys with an|thropometric data|
|---|---|---|---|---|---|---|
|||Observatio|ns in DHS||Observation|s in the survey|
|Country|Year|Women|Children|Year|Women|Children|
|Benin|2011|13,626|7,193||||
|Burkina Faso|2010|7,218|6,223|2014|n.a.|9,134|
|Burundi|2010|3,751|3,190||||
|Cameroon|2011|6,431|4,585||||
|Congo, Rep.|2011|4,543|4,127||||
|Côte d'Ivoire<br>|2011|3,950|2,967||||
|Congo, Dem.|||||||
|<br>Rep.|2013|7,872|7,791||||
|Ethiopia|2011|13,830|9,144|2013/14|n.a.|2,731|
|Gabon|2012|4,195|3,043||||
|Gambia|2013|3,843|2,828||||
|Ghana|2014|4,153|2,589|2009|2,165|1,968|
|Guinea|2012|3,996|2,969||||
|Kenya|2008|7,286|4,852||||
|Lesotho|2009|1,895|731||||
|Liberia|2013|4,015|3,075||||
|Malawi|2010|6,409|4,283|2013/14|n.a.|2,400|
|Mali|2012|4,402|4,134||||
|Mozambique|2011|11,186|8,622||||
|Namibia|2013|3,393|1,649||||
|Niger|2012|3,896|4,285||||
|Nigeria|2013|30,900|22,499|2012/13|n.a.|2,742|
|Rwanda|2010|5,491|3,507||||
|Senegal|2010|2,188|1,139||||
|Sierra Leone|2013|7,023|3,938||||
|Swaziland|2006|4,190|1,883||||
|Tanzania|2010|8,528|6,402|2012/13|6,170|3,633|
|Togo|2013|4,153|3,023||||
|Uganda|2011|2,297|1,987|2011/12|n.a.|1,494|
|Zambia|2013|13,872|10,769||||
|Zimbabwe|2010|7,382|4,071||||
|Total|2011|205,914|147,498||8,335|24,102|

Note: Observations with missing values and for pregnant women have been dropped. Women between 15 and 49 years of age and children between 0 and 5 years of age are included. The LSMS surveys used are Burkina Faso’s 2014 Multisector Survey; the 2013-14 Ethiopian Rural Socioeconomic Survey; Ghana’s 2009 Socioeconomic Panel Survey; Malawi’s 2013-14 Third Integrated Household Survey; Nigeria’s 2012-13 General Household Survey; Tanzania’s 2012-13 National Panel 38