rafmacalaba/datause-encoder-data huggingface dataset:
Over the last 15 years the attitude of policy makers towards manufacturing in Africa has changed significantly. The strategy of import substitution, which involved protecting the domestic manufacturing sector by means of barriers to imports and discouraging exports, was embraced in Africa for a long time and was almost certainly part of the reason for the poor economic outcomes. At the center of the current policy discussion is the idea that improved economic performance in Africa’s private sector can contribute to poverty reduction by generating more well paid jobs. Since the early 1990s, large scale data collection projects have been fielded in Africa with the objective of generating data on manufacturing firms and plants informative of why things had gone wrong in the sector and how matters could be improved. The purpose of this paper is to review the findings that have come out of this research with a special view to what we think are the most important policy implications. Our purpose is thus not to extend the work that has been done but to draw on it to address a range of questions relevant to understanding the links from industrial policy to poverty reduction.

We are not the first to take stock of what can be learned from data on manufacturing firms in Africa. Tybout (2000) reviews the research on firm performance in developing regions, including Africa. Mazumdar and Mazaheri (2003) focus exclusively on African manufacturing. Collier and Gunning (1999) discuss the problems faced by Africa’s manufacturing sector as part of their survey paper on Africa’s general economic problems. All of these studies cover data on African firms confined to the period 1991-1995, i.e. the first wave of the RPED data (see Section 2). More recently, Kingdon, Sandefur and Teal (2004) and Fafchamps and Söderbom (2004a) have provided overviews of the conditions for job creation and labor demand in Africa, drawing on the firm surveys among other sources. Our review is based on more recent data than what was referred to in Collier and Gunning (1999), Tybout (2000), and Mazumdar and Mazaheri (2003), and in looking at a wide range of results that have come out of the research on the firm data it has a broader focus than the review papers by Kingdon et al. and Fafchamps and Söderbom (2004a).

One of the most remarkable characteristics of African economic performance is the wide range of outcomes across countries. There exist spectacular success stories as well as mediocre economic performers. There are relatively large as well as very small economies. There are countries that have experienced drawn out political turmoil and others that have seen long-term political stability. There are countries 2
Policy Research Working Paper 8037

# **Abstract**

Like many emerging economies, South Africa has identified exports as an engine for more inclusive, job-intensive growth. However, employment growth did not follow the substantial export growth that South Africa experienced in the 2000s. This paper uses a newly developed World Bank database—the Labor Content of Exports—to show that the composition of South Africa’s export growth helps to understand the weak relationship between export and employment growth. Minerals exports, which propelled export as well as wage growth, are not job intensive and as a result supported far less job growth. Minerals have also increasingly become an enclave sector with few backward linkages to the domestic economy. In contrast, manufacturing exports support jobs and wages primarily in input-providing sectors, where indirect manufacturing employment is nearly 4.5 times greater than direct manufacturing employment. The paper also documents a shift in the labor content of global value chain–intensive manufacturing sectors away from direct manufacturing to indirect services. Such a shift has been biased toward skilled labor. As a results of these trends, labor in services sectors has been the main beneficiary of South Africa’s export growth, absorbing more than half of the growth in wage income from exports over the 2000s, primarily by supplying inputs to other sectors’ exports.

This paper is a product of the Trade and Competitiveness 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 chollweg@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
introduca separately a DEBT MODULE that details the interaction between the country and the foreign credit market. The particular details of this model are discussed in section TII. The basic input into the DEBT model is the existing pipeline debt by creditor as reported in the Bank's Debt Report System (DRS) and the terms and conditions of new debt (maturity and grace periods, interest rate and time profile of gross disbursements). At this stage the transfer of data between the DRS and JAVELIN is not yet automatic but this problem will be resolved in the near future.

The nature of the transfer of data between the DEBT model and the **RMSM-X** model depends on the circumstances facing the country. If the country faces a foreign borrowing constraint the total available credit is sent from the DEBT model to RMSM-X as well as the interest rate. No data return from RMSM-X to the ,EBT model. If the country's foreign horrowing is not constrained, the DEBT model provides the interest rate to the RMSM-X. The RMSX-X model returns total demand for credit which is then matched by the DEBT model's pipeline and a gap. The gap is distributed across foreign creditors in the DEBT model, based on a set of assumptions.

Finaily, we design standard output required for various purposes in a STANDARI) TABLES model. This includes the standard attachment to CSPs, CEMs and other Bank documents. The STANDARD TABLES model imports the data from the three other models. Part of the historical data come from the HISTORICAL DATA model and the DEBT model. Projections come from the RMSM-X model and the DEBT MODULE.

5
**Measuring Inequality of Opportunity with Imperfect Data: The Case of Turkey**

Francisco H. G. Ferreira _The World Bank_ Jérémie Gignoux _Paris School of Economics_ Meltem Aran<sup>1</sup> _Oxford University_

**Keywords:** Inequality of opportunity, wealth index, imputed consumption, Turkey **JEL Codes:** D31, D63, J62

> 1 We are grateful, without implication, to Deon Filmer, Jesko Hentschel, Peter Lanjouw, David McKenzie and participants at the SPO-World Bank Social Policy Workshop in Ankara, on 22 October 2008, for comments on an earlier version of this paper. The views expressed in this paper are those of the authors, and should not be attributed to the World Bank, its Executive Directors, or the countries they represent. Correspondence to fferreira@worldbank.org.
<!-- Start of picture text --> Development Economics Research Group, the World Bank<br>Servicesi, Trade Restrictionsinti Database S Home<br>f Key restrictions Main policy measures All policy measures<br>ume services Trade Restrictions Index | Submit<br>Sectors Clear all Select all Please choose at least one sector, one mode or one country from the options available<br>Overall on the left. If you do not makea choice in the particular dimension, the result will include<br>Financial all options available.<br>Show = Banking<br>results (Lending by banks For detailed instructions on using this database. click here.<br>(Acceptance of deposits by banks<br>-Linsurance When a sector/subsector is selected, selection on its subsector will not be affected.<br>(Automobile Insurance<br>(Life Insurance<br>[1 Telecommunications Oo Reinsurance Your current selection:<br>ORetail(Transportation Gectors: d<br>(Professional Unrestricted<br>— Modes:Unrestricted<br>(overall sssenassssna5=555==55==5=5=55=======<br>(Mode 1 Countries:<br>(Mode 3 India<br>[Mode 4 Seesssscssssss=2222=22=22==2=2=====<br>Countries & Regions Clear all Select all<br>(East Asia and Pacific iS<br>(Europe and Central Asia<br>(Latin America and the Caribbean<br>(Middle East and North Africa<br>=f South Asia<br>(Bangladesh<br>India<br>(Nepal<br>(Pakistan<br>(Sri Lanka<br>(Sub-Saharan Africa<br>(High Income OECD<br>(CEv-20<br>(Gulf Cooperation Council hd<br>Development Economics Research Group, the World Bank<br>Servicesi Trade Restrictionsinti Database . Home<br>STRI Key restrictions Main policy measures [fj<br>7<a——,—— ‘ll policy measures Submit |<br>Sectors Clear all Select all Policy measures Clear all Select all<br>© Overall [= (Sector Openness<br>Financial (Form of Entry<br>Show (Telecommunications DLicensing<br>results Retail (Operations<br>=-[ Transportation (Regulatory Environment<br>D Air Passenger Domestic (Condition of Provision of Service<br>D Air Passenger International (Restriction on Service Provider<br>(Maritime Shipping International (Type of Entry<br>(Maritime Auxiliary Services LJ Entry Condition<br>(Road Freight Domestic<br>8 ORail Freight Domestic Please choose at least one sector, one mode or one country from the options available<br>LProfessional=-LJAccounting and Auditing onall theoptionsleft. available.Ifyou do not make a choice in the particular dimension, the result will include<br>(Accounting Aa<br>Modes For detailed instructions on using this database. click here.<br>- a Whena sector/subsector or policy measure is selected, all its child nodes will be<br>Cmode 3 selected.<br>[i Mode 4<br>Countries= & Regions- Clear all _ Select all Your current selection:<br>(East Asia and Pacific 5 Ss .<br>Latin(Europe America and Centraland the AsiaCaribbean Retaileee<br>[South(Middle AsiaEast and North Africa Modes:. ed<br>[Bangladesh sasseseessesesessesessssssssses25<br>ane Main policy measures:<br>Elpakistan Unrestricted<br>(sub-Saharan(Sri Lanka Africa Fountries:Coping<br>tiigh income OECD eeeseeeeseesesseesssessssesssse===<br>(CEv-20<br>(Gulf Cooperation Council x<br><!-- End of picture text -->
22

- the degree of liberalization involved was relatively low, because trade flows between the US, Mexico and Canada were already relatively free. In particular, the extent of US trade liberalization was very small; insofar as significant concessions were made, they were on the Mexican side;<sup>22</sup>

- as a consequence, economic estimates of the direct macroeconomic effects of NAFTA on the US were tiny (of the order of a few tenths of one percent of GDP over a decade, and a few tens of thousands ofjobs, less than the margins of error in the quarterly national accounts statistics);<sup>2 3</sup>

- yet NAFTA aroused strong political opposition in the US, while it was relatively popular in Mexico, and was regarded as by far the most important economic priority by Mexican policymakers (in Canada it was largely a non-issue, since the principal political battle over trade liberalization had been fought over the Canada-US FTA).

This suggests that "non-traditional" gains (or losses) are likely to have been a significant factor behind NAFTA.

A number of authors have made arguments relating to the time-consistency or insurance mechanism set out above to explain Mexico's desire to join NAFTA, despite the small direct gains found by most estimates (e.g. Whalley (1996)). However, it is not all clear what types of domestic policy reform were involved. NAFTA does not cover macroeconomic policy at all, so it is difficult to see how it could have operated as a commitment device. If it was intended as such, it does not appear to have been effective in this instance, since NAFTA did not ultimately work to make Mexican fiscal and monetary policy more credible, as the 1994 devaluation demonstrated.

Nor did NAFTA cover domestic microeconomic reforms such as privatization or deregulation; indeed Mexico has made relatively little progress in these areas since NAFTA, recently backtracking on its commitment to privatize the state petroleum monopoly. It does commit Mexico to some fairly modest provisions with respect to the observance of existing labor and environmental law; but since these provisions were

> 22 See, for example, Hufbauer (1992).

> 23 See, for example, National Commission on Employment Policy (1992).
||**T**|**able 1.**|**Banking**|**Sector**|**Developm**|**ent**||||
|---|---|---|---|---|---|---|---|---|---|
||**1990**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|
|**Credit to the Priva**<br>(Percent of GDP)|**te Sector**|||||||||
|**_Central and Easter_**|**_n Europe_**|||||||||
|**Bulgaria**||7.2|5.8|3.7|3.8|21.1|35.6|12.6|12.7|
|**Czech Rep.**||||50.8|59.5|59.4|57.4|66.4|58.0|
|**Hungary**|46.2|38.7|33.1|28.1|26.1|22.5|22.0|24.0||
|**Macedonia**||||59.3|48.8|25.6|29.8|30.6||
|**Poland**|3.1|11.1|11.4|12.2|11.2|12.0|14.9|17.1||
|**Average**||||30.8|29.9|28.1|31.9|30.1||
|**_Baltics_**||||||||||
|Estonia||18.0|7.5|11.2|14.1|14.8|18.0|25.8|25.3|
|Latvia||||17.3|16.4|7.8|7.2|10.5|14.1|
|Lithuania||||13.8|17.6|15.2|9.9|9.6|9.5|
|**Average**||||14.1|16.0|12.6|11.7|15.3|16.3|
|Cis||||||||||
|**Georgia**||||||||||
|**Kazakhstan**||||45.3|24.8|7.1|5.1|5.0||
|Kyrgyz Rep.||||||12.5|9.0|3.4|5.3|
|**Ukraine**|||2.6|1.4|4.6|1.5|1.4|2.4||
|**Average**||||||7.0|5.2|3.6||
|**OECD/1**|74.6|75.4|78.3|74.5|74.5|77.0|78.9|83.0||
|M2||||||||||
|(Percent of GDP)||||||||||
|**_Central and Easter_**|**_n Europe_**|||||||||
|**Bulgaria**||71.9|74.7|77.6|78.0|64.9|71.2|33.6|29.3|
|<br>**Czech Rep.**||||69.6|73.1|80.5|75.4|71.2|68.3|
|**Hungary**|43.8|47.4|51.2|49.6|45.5|42.3||||
|**Macedonia**||||72.9|13.6|13.5|13.0|15.2||
|**Poland**|34.0|32.3|35.8|35:9|34.5|34.1|35.4|37.6||
|**Average**||||61.1|**49.0**|47.1||||
|_Baltics_||||||||||
|**Estonia**||**126.9**|30.2|28.4|26.9|25.5|27.0|30.3|28.4|
|**Latvia**||||31.6|34.2|23.4|23.0|27.4|25.4|
|Lithuania||||23.1|25.8|23.3|15.8|19.0|19.5|
|**Average**||||27.7|29.0|24.1|21.9|25.6|**24.4**|
|CIs||||||||||
|**Georgia**||||||||||
|**Kazakhstan**||||||||10.2||
|**Kyrgyz Rep.**||||||17.2|14.2|13.7|14.6|
|**Ukraine**|||50.1|32.5|26.7|12.7|11.5|13.6||
|**Averae_**||||||14.9|**12.8**|||
|**OECD/2**|79.6|77.5|82.1|71.0|83.8|84.5|87.4|88.4||

_Source: International Monetary Fund, "International Financial Statistics. "_ /I Data for high-income OECD countries except for Germany (1990) and Luxembourg (1993), for which data is not available.

/2 Data for high-income OECD countries except for Germany (1990), Sweden (all years) and UK (all years) for which data is not available.

result of bank crises and bank liquidation. On the other hand, the _CEE_ countries did not experience as large an increase or decline (see section 3).

3
Lewis and Lockheed specifically identify discrimination at school and mistreatment by teachers as reasons for keeping children out of school (Ringold, Orenstein and Wilkens 2003; Narayan 2000). All this leads to lower school participation and to less learning. For example, a cross-national study estimated that the combined effects of gender and marginalization (immigrant status and isolated residence) lowered mathematics scores a staggering 35 points, equivalent to one-third of the difference between the highest scoring country and the international average (Woessman 2000).

The high costs of schooling as well as its low returns reduce demand. For poor parents in particular, the opportunity, social and direct costs of schooling are high. In addition, weak employment opportunities, and perceptions about the low returns in the labor market to those who have attended school, create weak demand. While the poor quality schooling and discrimination mentioned above are important, cost appears to be paramount.

The direct costs of primary school, in the form of school fees, family contributions and unofficial fees, can represent a significant portion of a families’ disposable income, preventing even families who wish to send children to school from enrolling them (Bray 1998, Kattan and Burnett 2004). Other direct costs of education (school uniforms, textbooks, transportation) can be additional barriers. For marginalized groups, who typically have both low incomes and limited demand for schooling, these costs prove insurmountable. Costs associated with sending girls to school can be even higher, as, for example, when lack of transportation to school can mean long, possibly unsafe walks, particularly for adolescents. In Central Asia, for example, costs of schooling increased substantially during the transition following the dissolution of the USSR, leading to increasing gender disparities in enrollment (Silova and Magno 2004).

Families may have a preference for paying costs associated with schooling boys, as the returns would be higher given labor market opportunities, and, socially, girls in many societies are “married away” joining the husband’s family and no longer providing for or living with her family. The general male preference often found among excluded groups adds to the disadvantage experienced by girls. Opportunity costs are also high for marginalized families and Basu and Tzannatos (2003) find that the need for child labor is the single most important reasons for poor families not sending children to school in developing countries.

Labor market returns are often poorer for minority groups and women, particularly minority women, due to labor market discrimination and possible differences in language and other skills.

12
time because it lacked information on policy removals.<sup>8</sup> Constructing and examining stock measures also allows me to better assess the incidence of TTBs in the face of heterogeneity in the timing of newly imposed barriers and the length of time that such barriers stay imposed.

My approach takes advantage of extremely detailed data from the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2010a) in order to construct relatively comprehensive information on the “stock” and “flow” of such barriers at the 6‐digit Harmonized System (HS) product level across countries and over time. I begin by focusing on the share of imported products at the HS‐06 level (“counts”) before comparing this with a second, complementary (“values”) approach which relies on HS‐ 06 (non‐oil) import value data from the United Nations _Comtrade_ database to construct year‐by‐year coverage ratios of imports subject to TTBs. More details on the underlying data are available in the Appendix and are described comprehensively in Bown (2010a).

# **2.1 Methodology**

My first methodological approach takes an importing economy’s set of HS‐06 products as the unit of observation. I build from Bown and Tovar (forthcoming, figure 1) which focused on India’s use of antidumping over the period 1992‐2003 to construct count measures of the annual stock of HS‐06 products subject to Indian antidumping, thus reflecting information on newly imposed trade barriers, previously imposed trade barriers, and the removal of previously imposed barriers. In addition to applying the Bown and Tovar approach to a new set of countries, I also adapt it along three important dimensions: i) I examine not only cumulative stocks but also flows; ii) I examine not only antidumping, but also HS‐06 products subject to other TTB policies such as countervailing duties, global safeguards, and China‐specific safeguards; and iii) I normalize the count of affected HS‐06 products by the economy’s stock of HS‐06 products with non‐zero imports in that year.

More formally, let _k_ be the policy‐imposing (importing) economy and let _mik_ , _t_<sup></sup> {1,0} be an indicator for whether the economy had non‐zero imports of product _i_ in year _t ._ The HS‐06 product _i_ is in the economy’s time‐varying set of HS‐06 products with non‐zero imports, defined as<sup>_I_</sup> _tk_<sup>. Next let</sup><sup>_b_</sup> _ik_ , _t_<sup></sup> {1,0} be an indicator for whether the importing economy _k_ “applies” a temporary trade barrier

> 8 Exceptions include recent research examining the question of antidumping policy removal and the Sunset Review process stemming from the Uruguay Round Agreements such as Moore (2006) and Cadot, de Melo  and Tumurchudur (2007).

8
impact of NAFTA (the FTA benefitting Canada and Mexico) is a bit difficult to distinguish from the common border effect. This distinction is made more difficult by the fact that a common border is in itself an important determinant of the decision by the U.S. to offer such trade preferences which is suggested by the significant effect that this variable has on the probability of being eligible for trade preferences.

It is worth comparing the results of the regular TOBIT estimation (column 2 of Table 3) with the Heckman selection model (Column 1 Table 5) to identify the effects of the selection bias. The most important result is that the coefficients of the FTA, CBI and Andean utilization varaibles are significantly lower in the Heckman Selection model. For example, the estimates for the impact goes from 84% to 50% for the FTAs and from 33% to 3% for CBI (when we use average utilization ratios) and the Andean program is no longer significant. On the other hand, the estimated effects of the GSP and AGOA are higher under the Heckman selection model; GSP no longer has a negative effect and AGOA’s effect doubled. These results suggest that FTA/CBI/Andean beneficiaries are actually benefitting more from the political alliance variables in the selection equation, and the preferences themselves seem to be an endogenous response to these geo-strategic considerations. The opposite is probably at play with respect to the rather large number of countries that benefit from GSP and AGOA.

# 5 Conclusion

The main objective of this paper was to identify the impact of various U.S. reciprocal and unilateral preferential market access programs while at the same time control for the geostrategic motivations of the U.S. Our main conclusion is that such programs have economically large and statistically significant effects on the exports of the beneficiary countries, but geostrategic and political interests play an important role in determining the probability of gaining preferential access to U.S. markets. In fact, some of the commonplace results reported in the existing literature seem to be biased by the exclusion of these non-economic considerations. For example, exporting countries’ geographic distance from the U.S. is not a significant 19