rafmacalaba/datause-encoder-data huggingface dataset:
Görlich, Dennis, Toman Omar Mahmoud and Christoph Trebesch (2007), “Explaining Labor Market Inactivity in Migrant-Sending Families: Housework, Hammock, or Higher Education”, Kiel Working Paper No. 1391, Kiel Institute for the World Economy.

Grigorian, David A. and Tigran A. Melkonyan (2011), “Destined to Receive: The Impact of Remittances on Household Decisions in Armenia”, _Review of Development Economics_ , 15(1), pp139–153.

Havlik, Peter (2005), “Unit Labor Costs in the New EU Member States”, wiiw Statistical Report, No. 1, The Vienna Institute for International Economic Studies (wiiw), Vienna.

Hausman, J.A. (1978), “Specification Tests in Econometrics”, _Econometrica_ , Vol 46, No 6, pp 251-72.

Ivlevs, Artjoms (2016), “ Remittances and Informal work”, _International Journal of Manpower_ , Vol 37, issue 7, pp1172-90.

Justino, Patricia and Olga N. Shemyakina (2012), “Remittances and Labor Supply in PostConflict Tajikistan”, Working Paper Vol 12, No 388, Institute of Development Studies.

Kalaj, Ermira (2013), “Do remittances alter labor market participation?”, _Journal for Labor and Social Affairs in Eastern Europe_ , Vol. 16, No. 4, pp.417-432.

Kim, Namsuk (2007), “The Impact of Remittances on Labor Supply: The Case of Jamaica”, Policy Research Working Paper 4120, World Bank.

Landesmann, Michael and Doris Hanzl-Weiss (2013), «Structural adjustment and Unit Labor Cost Developments in Europe’s Periphery: Patterns before and during the Crisis”, Research Reports 390, The Vienna Institute for International Economic Studies (wiiw), Vienna.

Lartey, Emanuel K.K., Frederico S. Mandelman and Pablo A. Acosta (2008), “Remittances, exchange rate regimes and the Dutch Disease: A panel data analysis”, Working paper no 200812, Federal Reserve Bank of Atlanta.

Lewney, Richard, Jörg Claussen, Graham Hay, Evripidis Kyriakou and Günther Vieweg (2012), “The cost competitiveness of European industry in the globalisation era – Empirical evidence on the basis of relative unit labor cost (ULC) at sectoral level”, Industrial Policy and Economic Reform Papers No. 15, Enterprise and Industry Directorate-General, European Commission.

Lopes, Humberto, Luis Molina and Maurizio Bussolo (2007), “Remittances and the real exchange rate”, Policy Research Working Paper 4213, World Bank.

Mishra, Prachi (2014), “Emigration and wages in source countries: a survey of the empirical literature”, in _International Handbook on Migration and Economic Development_ , ed Robert E.B. Lucas, Edward Elgar, pp241-266.

Ndiaye, Ameth Saloum, Oumoul Khayri, Ya Cor Ndione Sessinou and Erick Abel Dedehouanou (2016), “Migration, remittances, labor market and human capital in Senegal”, Working Paper 2016-10, Partnership for Economic Policy.

Ordóñez, Javier, Hector Sala and José I. Silva (2015), “Real unit labor costs in Eurozone countries: drivers and clusters”, _Journal of European Labor Studies_ , (2015) 4:15.

24
Policy ReseaRch WoRking PaPeR 4431

# **Abstract**

Brazil’s slow pace of poverty reduction over the last two decades reflects both low growth and a low growth elasticity of poverty reduction. Using GDP data disaggregated by state and sector for a twenty-year period, this paper finds considerable variation in the povertyreducing effectiveness of growth—across sectors, across space, and over time. Growth in the services sector was substantially more poverty-reducing than was growth in either agriculture or industry. Growth in industry had very different effects on poverty across different states and its impact varied with initial conditions related to human development and worker empowerment. The determinants of poverty reduction changed around 1994: positive growth rates and a greater (absolute) elasticity with respect to agricultural growth contributed to faster poverty reduction. But because there was so little of it, economic growth played a relatively small role in accounting for Brazil’s poverty reduction between 1985 and 2004. The taming of hyperinflation (in 1994) and substantial expansions in social security and social assistance transfers, beginning in 1988, accounted for a larger share of the overall reduction in poverty.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to understand pro-poor growth and the determinants of poverty dynamics in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fferreira@worldbank. org; pleite@worldbank.org; and mravallion@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
across "natural regions" and departments.2 But INEI has combined variables which are common to both the 1993 census and one such survey conducted in 1995 to develop imputed district-level measures of income and poverty (INEI 1996).

This procedure is conceptually similar to that used to estimate infant mortality at the district level. INEI estimated income in 1995 on the basis of the household survey, and then regressed income in every department on its correlates--household composition, education levels, access to basic services such as water, sewerage and electricity, ownership of durable goods, such as television, radio and refrigerator, and other variables included in both the census and the survey. The coefficients from the 24 department-level regressions were then used to impute average income in every district, and the fraction of the population in each district below an income-based poverty line.

# **3. The analytic framework**

# _Measures ofperformance_

The simplest measure of targeting focuses on leakage and undercoverage rates (Grosh, pp. 16-17; Baker and Grosh 1994). A poverty line is chosen to separate "poor" from "non-poor". Leakage rates are then defined as the fraction of total program resources which go to the nonpoor, and undercoverage rates as the fraction of the poor who do _not_ benefit from the program. By this measure, better geographic targeting indicators result in lower leakage and lower undercoverage rates.

A second approach simply ranks individuals by an indicator of welfare-say, per capita expenditures-and then cumulates the fraction of households and the fraction of resources transferred by different indicators. The results are often presented in terms of so-called "concentration curves" (see, for example, Milanovic 1995), and I follow this practice below. By this measure, the best targeting indicator is that whose concentration curve is above all others at every point. The concentration curve method does not require use of a poverty line, which may be an advantage given the fact that setting poverty lines can be contentious.

Alternatively, one might want to compare the changes in various poverty measures which are likely to result when transfers are based on alternative targeting indicators (Chaudhuri and Ravallion 1994). This is an exercise in comparative statics: what is total poverty before and

> 2 These natural regions are Lima, and the urban and rural areas of the coast, sierra (highlands), and selva (3ungle), respectively. Natural regions do not, in general, correspond to the administrative regions mentioned before.

3 The methodology applied by INEI for these imputations is similar in spirit to that proposed in Hentschel et. al (2000).

5
# 10. Dollar Revaluation Cost

11. Foreign Cost of Borrowing 12. Cost Difference 13. Revenue from Financial Repression 14. GDP 15. Total Central Government Revenue 16. Revenue as percent of GDP 17. Revenue as percent of total tax revenue 18. GDP Deflator 19. Inflation 20. Per Capita Income World Bank DRS Table 2, column 9 (adjusted to eliminate the effects of reschedulings) at t divided by column # 1 at _t_ - 1

- #7 + #9 + #10

- #11 - #4

- #12 * [(#1(t) - #2(t)) + _-_

- (#1(t 1)- #2(t -1))/2 IFS GFS **.** nd Country Source D

- #13/#14

- #13/#15

IFS Percent change in # 18.

IFS 23
-33 -

problem ot poor service quality quite apart from problems caused by the distribution network. Non-technical causes Include the failure of the utility to meter and/or bill consumers and failure to control Illegal connections. The latter, of course, only adds to the problem of overloading of the distribution network.

Except for specific studies for some countries, very few projects approved In the 1980s assigned an overall rating of service quality to each utility (on a one-to-five good-to-poor scale). The results Indicated that 11 out of 26 utilities had a poor to very poor quality of service, taking Into account brownouts, blackouts, voltage fluctuations, and systematic load shedding, while ten utilities provided a good quality of service. Of the 11 utilities with poor service, six were on at least their third Bank-financed power project which means that the Bank had been Involved with them for at least ten years, and In some cases, more than fifteen years. Similar findings were obtained for levels of total losses.

For the most part, the available data could not discern technical losses as a percentage of total losses. A review of available ESMAP reports on loss reduction studies reveals a wide range of technical losses, as follows.

|<br>|Technical a/<br>Losses %<br>|Non-technical<br>Loss %<br>|b/<br>Total<br>Losses %<br>|
|---|---|---|---|
|Sri<br>Lanka<br>|14<br>|4|18<br>|
|Panama<br>|17<br>|5<br>|22<br>|
|Sudan<br>|17<br>|14<br>|31<br>|
|Bangladesh<br> <br>|14<br>|17<br>|31<br>|
|Liberia<br>c/<br>|13<br>|22<br>|35<br>|
|Malaysia (Sabah State)<br>|11<br>|17<br>|28<br>|
|Ivory Coast|8|4|12|

a/ Technical energy loss as a percentage of net generation. b/ On a base of net generation due to metering and biling errors, theft, and Illegal connections.

c/ Increased during period 1982-1985.

# 3/ "Power Sector Performance Review", Energy Department, 1986.
# II. <u>Findings based on data from host governments</u>

To obtain a more realistic picture of what is happening on the ground, the study made an effort to collect data from host country governments. Host governments tend to capture the data more comprehensively and accurately than the home government, simply because most host countries subject all FDI projects, large or small, to government approvals or registries at the point of business entry.

Collecting data from African host countries has not been easy. Although all countries gather general FDI data, data specifying firms’ origins, overtime changes, sector distributions and economic impact are not always readily available. Moreover, the quality of data is very uneven and generally poor. Many agencies in a country may be involved in FDI statistics; each has only a limited view of the whole picture. For instance, investment agencies may be charged giving initial project approvals, company registries responsible for legal establishment of the businesses, and central banks track the actual capital flows. In addition, other line ministries may have a responsibility to track the FDI impact on economic sectors, job creation, exports and tax revenues generation. Ideally, data from all sources are needed to reflect FDI from its intentional initiation, through actualization, and to economic impact generation, but this would require sophisticated FDI data systems which few African countries have at present.

To gain at least a preliminary understanding, the research started with a mini-survey among the national investment promotion agencies (IPAs). Targeting IPAs as the starting point makes sense where they are mandated as the “One Stop Shop” for FDI promotion and facilitation. These IPAs often do their best to gather FDI statistics through coordination with other relevant government agencies even if not directly being in charge of all FDI aspects.

Six national IPAs participated actively in the survey and provided extremely useful data as requested. These included four of the top five African recipients of Chinese investment according to MOC statistics: Nigeria, Zambia, Ethiopia and Ghana.<sup>13</sup> The two additional countries were Liberia and Rwanda. Together, the six countries count for about one-third of all Chinese investment projects in Africa as certified by the MOC.

Most participating IPAs provided specific data on the Chinese investment, both “committed” and “actual,” by project number and capital amount, by year of entry and by sector. Most provided data comparing Chinese investment with overall FDI inflows to their countries. Regarding economic impact, most IPAs were able to include numbers of jobs created but unable to provide data on export earnings and tax revenues generated. Finally, participating IPAs generously shared perceptions of Chinese investment, regarding both positive impact and major concerns.

> 13 South Africa, ranked as number 2 recipient, was omitted by this research for the reason that it is a mid-income country.

11
cr: V ~ **~** ~ ~ ~ ~ ~~OC

**VWPS 1202**

- Ths paper a product of the Debt and Intemnational Finarce Division, International E-conomics - DepAzMcnt is part of a larger effort In the department to evaluate foreign Investment as a source of financing for developing countries. Copies of the paper are available from tht; World Bank, 1 81I8 H Street NW, Washingt, DC 20433. Please contact Roe Vo rom SB-042, exteon 31047 (October 1993,62 pages)

Foreigv? direct investment in the developing world has grown rapidly in recet years, making it one of the most importat sources of financing to developing countries. Sader presents a databasw on about 1,1I00 global privatizatlor transacdons from 1988 through 1992.

Between 1988 and 1992, developing country governments eamned almost US$62 billon in revenues from the sale of state-owned assets. About a third of those revenues came from foreign sources. Privatization in Latin America represents about 66 percent of privatization in the developing world. Privatization in Europe, including Eastern Europe, accounts for 17 percent, and privatization in East Asia, 13 percent. The beaviest foreign participation is in Eastern Eumpe,; primarily for lack of domestic financing.

Foreign investors' general participation in privatization programs was strong, providing developing countries with substantial amnounts of foreign exchange.

The relative size of the privatization program and the degree of openness to foreigners are important detenninants of foreign direct investment. Each dollar in privatization revenue generates an additional 35 cents in new foreign direct investmnei **n** t flows, and a 1 percent increase in foreign participation adds another _50_ cents.

In addition to the direct inflow of funds through the sale of assets, many developing countries also increasingly attracted foreign investment outside of their privatization programs. Privatization of infrastructure and the financial sector especially seem to have sent important signals to foreign investors, indicating an imnprov **e** d conomic envirounment and possibly the eventual elimination of bottlenecks. Improved expectations about the profitability of investment projects render these countries more attractive to foreign investors.

T'he Prlicy Reseach Working Pape Series disseninates the findings of work under way in the Bank. An objective of the seTies is to get thee _findings_ out quickly, even if presentations are less than fully polished. The findings, interpretations, and conchisions in theme papers do not necessarily represent official Bank policy.

Produced by the Policy Reseach Dissemination Center
index of the degree to which the government protects private property and enforces laws that protect private property. The data are for 2000 and were obtained from the Index of Economic Freedom constructed by the Heritage Foundation. The index is available for a large number of countries and has been recently used in several papers including Johnson, Kaufmann, and Zoido-Lobaton (1998), LLSV (1999, 2002), Beck, DemirgucKunt and Levine (2003) and Claessens and Laeven (2003). Within our sample, property rights varies from a score of 5 for countries with good property rights protection like the United States to 1 for countries like the Congo Democratic Republic, Libya and Vietnam.

The countries in our sample belong to different legal traditions and the data on legal families is taken from LLSV (1998). Since the literature has argued that common law countries have a significant advantage over civil and socialist law traditions and regressions typically only distinguish between common law countries and civil law countries (see, for instance, LLSV(1998), Stulz and Williamson (2003), Beck, DemirgucKunt and Levine (2003)), we use the dummy variable **Common Law,** which takes the value 1 for English common law countries and 0 for countries of all other legal traditions. Moreover, it is not clear what the distinctions between the civil law (Scandinavian, German and French) countries really mean. For instance, Nenova (2003) shows that the benefits from control are lower in countries with a Scandinavian civil law tradition than in common law countries while Coffee (2001) argues that social norms rather than legal regimes can explain these lower benefits of control. Small sample sizes of German and Scandinavian civil law countries also prevent us from making finer distinctions between the civil law countries.

14
The Engel curve deflator gives implied price changes that suggest very little linkage between urban and rural sectors within each region. According to the WCPD deflator, the average gap between rural and urban inflation within a region is just 1.6 percentage points using variable weights or two percentage points using fixed weights, suggesting that price changes in urban areas and their hinterland largely move together. But for the Engel curve deflator, the average gap in the inflation experience of the rural and urban sectors within a region is thirteen percentage points, and this seems unlikely to be true given the arbitrage opportunities that it implies.

# V. IMPACTS ON INEQUALITY AND POVERTY

The Engel curve deflator interprets the higher average food shares of households living in poor rural areas as evidence of a high cost-of-living in these areas. Consequently, the level of inequality appears higher in real terms than in nominal terms if the Engel curve deflator is used. In contrast, the WCPD price indexes show real inequality to be less than nominal inequality because regions and sectors that are nominally richer are found to have a higher price level; this positive relationship between nominal incomes and the price level is consistent with the Balassa-Samuelson effect. These differing effects of deflation are illustrated in figure 4 in the form of nominal and real Lorenz curves for Vietnam in 2010, with only the variable-weight version of the WCPD deflator used since the fixed-weight version gives very similar results. The Gini coefficients corresponding to these Lorenz curves are 0.427 for nominal consumption, 0.404 for real consumption when the WCPD deflator is used, and 0.465 if the Engel curve deflator is used. Thus, the use of the Engel curve deflator would introduce a bias of six Gini points into the measurement of real inequality, which is a relatively large effect.

28
assume that book values of net loans equal market values of gross loans. In most cases, however, there is a discrepancy between the market and book value of loans. A particular case is when loan data are distorted by inadequately reported nonperforming loans. Application of such distorted data to efficiency models might lead to incorrect conclusions, because banks with excessive loan growth will show high technical efficiency (ceteris paribus), while they take on excessive risks. Inadequately reported nonperforming loans are most likely to be a large factor of data distortion in emerging markets with weak enforcement of banking regulation. In such countries a bank efficiency model might estimate a bank to be in better shape that they actually are. In fact, the most efficient banks of a banking system as measured by a bank efficiency model are not necessarily the least risky banks. In the case of East Asia it seems likely than inadequate loan loss provisions were a large factor of data distortion as well, because, until 1997, loans were not classified as nonperforming loans until no payments were made for over one year, leading to underreporting of bad loans to regulators.

The extremely high loan growths in the five East Asian countries during the period 199296 (up to 151% for the Philippines - see Appendix, Table 4) suggest excessive risk taking behaviour of the East Asian banks. Since the technical efficiency measure does not take into account that excessive loan growth can indicate excessive risk taking instead of increased bank performance, technical bank efficiency measures are not a perfect indicator for bank performance, but are merely an indicator for how well banks are in transforming their inputs into loans, both bad and good loans. Especially in countries where poor regulatory frameworks and weak enforcement of banking regulations enable banks to take excessive risks, technical efficiency measures will provide a misleading insight in the performance of banks.

The earlier mentioned studies by Leightner and Lovell (1997), Leightner (1997), Gilbert and Wilson (1998) and Hao et al. (1999) on Thai and Korean banking data use traditional bank efficiency measures to assess the bank performance. We argue that their results are misleading. Leightner and Lovell (1997), for example, find that Thai banks experienced high loan growth rates during the period 1990-94 and argue that high growth rates indicate an unusual level of success of the banking system. For Korea, Hao et al. (1999) find that banks with faster loan growth rates were most efficient during the period 1985-95. Although these high loan growths do indicate a success in terms of generating loans one cannot draw general conclusions on the success of the banking system without looking at the risk of these loans. Ex-post we know that high loan growth of Thai and Korean banks was a sign of excessive lending, not of excellent performance.

8