rafmacalaba/datause-encoder-data huggingface dataset:
Although workers with primary education face a high risk of unemployment, they ere not numerous in most European transition economies, where most of the labor force has at least lower secondary education. Given their high share in the labor force and the relatively high unemployment rate among this group, it is workers with secondary education which are preponderant among the unemployed.

Less skilled workers have lower chances to find work, and accordingly face longer unemployment spells. As a result, they are disproportionately represented among the long-term unemployed. For example, in Poland workers with less than upper secondary education accounted for 60 percent of short term unemployment, and 65 percent of long-term unemployment (2002 data).

Disproportionately high unemployment among less skilled workers is a common phenomenon in developed countries, and transition economies are no exception in this regard. These high unemployment rates are explained by technological change which tends to be biased against less skilled manual labor. At the same time, new generations of capital require higher skills, which explains demand for well educated workers. A more in depth analysis of the changing demand for skills in transition economies is provided in Commander and Kollo (2004).

# **High youth unemployment**

An issue of particular concern in transition economies is high youth unemployment. Indeed young workers (15-24) face the risk of unemployment much higher than that faced by older workers. In most cases the youth unemployment rate is about twice as high as the overall unemployment rate (Table 7). However, relatively high youth unemployment rates are typical of all developed countries. In EU/OECD countries the ratio of the youth unemployment rate to the overall unemployment rate is virtually the same as in transition economies. What distinguishes transition economies from OECD economies is high _absolute_ youth unemployment rates, rather than high _relative_ rates.

26
Figure 6: Toilet cost and subsidy amount over time for subsidy eligible households <!-- Start of picture text --> Toilet cost Subsidy amount<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Year Year<br>Median toilet cost Fitted values Median subsidy amount Fitted values<br>Notes: N = 292 Notes: N = 124<br>50000 50000<br>40000 40000<br>30000 30000<br>20000 20000<br>10000 10000<br>0 0<br><!-- End of picture text --> Table 14: Heterogeneity in impacts by average level of toilet costs in GP as reported by masons

||(1)<br>Sanitation loan|(2)<br>Sanitation loan|(3)<br>Toilet|(4)<br>Toilet|(5)<br>Subsidy|(6)<br>Subsidy|
|---|---|---|---|---|---|---|
|SL|0.178<sup>_∗∗∗_</sup>||0.0633||0.0781||
||(0.0516)||(0.0525)||(0.0484)||
|SL - Low GP toilet costs||0.0969<sup>_∗_</sup>||0.140<sup>_∗_</sup>||0.174<sup>_∗∗_</sup>|
|||(0.0555)||(0.0813)||(0.0810)|
|SL - High GP toilet costs||0.250<sup>_∗∗∗_</sup>||-0.00499||-0.00643|
|||(0.0780)||(0.0710)||(0.0675)|
|High GP toilet costs||-0.0216||0.0721||0.0428|
|||(0.0630)||(0.0819)||(0.0793)|
|Strata FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|~~F~~<br>~~i~~nteraction||0.104||0.195||0.112|
|controlmean|0.0145||0.267||0.168||
|controlmea~~n~~<br>~~l~~ow||0.0231||0.266||0.173|
|controlmea~~n~~<br>~~h~~igh||0.00581||0.267||0.163|
|N|616|616|616|616|616|616|

_Notes_ : Sample restricted to matched subsidy eligible MF client households without a toilet at survey baseline; The dependent variable in (1)-(2) is an indicator equal to one if the MF client had taken a sanitation loan by August 2017 (as per MFI admin data); The dependent variable in (3)-(4) is an indicator equal to one if a toilet was observed by the interviewer during endline survey in August 2017; SL stands for sanitation loan treatment arm; Robust standard errors clustered at the village level are shown in parentheses; *, **, *** indicates significance at the 10, 5 and 1 percent level; The F interaction stat shows the p-value of the F-test of equality between the coefficient estimate of SL - Low toilet costs and the coefficient estimate of SL - High toilet costs; Covariates: Indicator for presence of a child aged 0-2 at baseline, ratio between number of sampled clients and village size, indicator for whether there is any uncertainty around the matching quality; Strata and interviewer fixed effects included;;

# **A Matching process**

The process we used to match the list of clients provided by the MFI to our survey data and to the SBM dataset was based on name matching, using the guide to international names and naming practice 40
**TABLE A15:  Global Insight's Business Conditions and Risk Indicators (WMO) A15: Global Insight's Business Conditions and Risk Indicators** http://www.globalinsight.com World Markets Online (WMO) is an online subscription service from the World Markets Research Center updated daily which provides analysis of the conditions and risks for businesses worldwide. Established in 1996, the World Markets Research Centre is based in London and employs over 190 permanent staff. In 2005, it was incorporated into Global Insight, which also produces DRI ratings (table A14). World Markets Online has developed a risk rating system to enable its clients to compare and contrast the investment climate in over 200 countries around the world. For WMO the principal quality their risk measures endeavor to measure is stability, which they believe businesses need most of all to be able to make secure investments and plan ahead. In addition to stability, WMO believes that businesses also need the right conditions in place; governments must ensure the right policies and safeguards to allow businesses to operate effectively. A country with a high risk rating by WMO is a country where businesses face continual threats to their operations, either from direct physical intervention, or because of the poor conditions and stability in the country concerned. The system rates the quality of conditions and level of stability encountered by investors in each country in terms of political, economic, legal, tax, operational and security environment. Drawing on a worldwide network of information gatherers and analysts, World Markets Research Centre generates a comprehensive range of in-depth country, sector and market services. The process by which the risks are assessed consists firstly of WMO analysts' own experience of the country’s conditions. Daily stories highlight countries’ changing conditions and constantly inform the risk rating levels. In addition to the in-house analysts’ own consensus, World Markets Online also draws upon the expertise and impressions of those working in the field through a wide network of stringers and informal contacts which allows them to access information only available locally as well as to case studies of individual investor's experience. Regular meetings of all the analysts across the regional desks ensure that their ratings are fully comparable globally, and that the factors used for assessment are consistent.

In the table below we list the variables included in each of the governance indicators. In this paper, we use the disaggregated components of the 2002-2005 country risk ratings, prepared for us by a panel of WMO experts.

64
# **South Asia’s outward FDI in 2005-10 totaled US$85.2 billion (mostly from India), surpassing the Latin America and Caribbean region to become the fourth largest FDI investor among developing**

**regions.** The bountiful outward FDI shows South Asia’s increasing competitiveness and integration with the global economy. Motivating South Asian companies to invest abroad are greater access to global markets, distribution networks, foreign technologies, and strategic assets like natural resources and brand names. A series of liberalization measures have paved the way for more cross-border investment by South Asian business. Today, South Asia is the fourth largest region for FDI outflows (Figure 10). After India, the largest flows of outward FDI from South Asia are from Bangladesh, Pakistan, and Sri Lanka. The other South Asian countries, however, show little outward investment (Table 6).

**Figure 10: Average Regional and SAR Net Outward FDI Flows**

<!-- Start of picture text --> Source: World Development Indicators and World Bank staff calculations<br><!-- End of picture text --> <!-- Start of picture text --> Outward FDI Flows from South Asia<br>percent (as percent of GDP)<br>1.60<br>1.40<br>1.20 Bangladesh<br>1.00 India<br>0.80 Pakistan<br>0.60 Sri Lanka<br>0.40<br>0.20<br>0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: UNCTAD Statistics<br>Note: The time series for Afghanistan, Bhutan, Maldives, and Nepal have zero value.<br><!-- End of picture text -->

**Table 6: Outward Foreign Direct Investment Flows from South Asia, 2000-10**

(FDI in million USD)

|**Country**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Afghanistan|0|0|0|0|0|0|0|0|0|0|0|
|Bangladesh|2|20.6|4.1|6.2|5.7|3.3|3.6|21|9.3|29.3|15.4|
|Bhutan|0|0|0|0|0|0|0|0|0|0|0|
|India|514|1,397|1,678|1,876|2,175|2,985|14,285|17,234|19,397|15,929|14,626|
|Maldives|0|0|0|0|0|0|0|0|0|0|0|
|Nepal|0|0|0|0|0|0|0|0|0|0|0|
|Pakistan|11|31|28|19|56|45|109|98|49|71|46|
|Sri Lanka|2|0.001|11.5|27.3|6|38|29|55|61.7|20|45.7|

Source: UNCTAD statistics Some of the possible drivers for outward FDI include (Rajan 2009):

- _Market access:_ By undertaking overseas acquisitions, firms gain entry into the regulated markets of developed countries. The best example is the Indian pharmaceutical industry, where firms with

17
3. On a cost per job basis, Trabajar at a GDP adjusted 1.2 $/day/job is substantially more expensive than Chile's Programa de Empleo Minimo at .4. But as table 1 shows cost per job is inversely related to the labor intensity of the project and this may only reflect that the Chilean program was fundamentally a job creation program and did not heavily value the creation of durable investment projects. Again, both cost per job and labor intensity may be misleading criteria.

The lesson is that, despite the fact that Trabajar may look like a poor performer when measured against programs that have only one of its three possible goals as its objective, it is impossible without a knowledge of the unobservables in equation 2 to conclude that it is a bad or good emergency program. For the same reason, it is hard to assert with confidence that it is "best practice".16 _So, what do we do?_ With the traditional "hard" measures of project quality shown to be misleading, how can we evaluate emergency programs? Although there are no easy or air-tight alternatives, policy makers should probably begin by hewing as closely as possible to our platonic ideal of the smart transfer and view the program as the minimum deviation necessary to transfer the most resources effectively today. Although crude, working through the considerations listed above and weighing their relevance to the situation at hand may help in designing the least cost vehicle for transferring resources. For example, if the government is not set on creating infrastructure projects and no appreciable additional mobilization of resources is likely to occur by undertaking them, then transfer efficiency can be raised by avoiding them. If information problems are large and targeting is a problem, then the costs of a workfare scheme may be unavoidable.

> 16 See for example IBRD 1999, Klugman 1999, Hicks and Griffin 1998.

17
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 19

**Table 7.** Estimates of the relationship between metropolitan fragmentation/coordination and city

productivity premium (OLS).

Y = City productivity premium (λ�������)

|Variable|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|ln(pop 2010)|0.086***<br>(0.0305)|||||
|ln(density)||0.066**|0.229***|0.236***|0.202***|
|||(0.0322)|(0.0649)|(0.0656)|(0.0568)|
|ln(area_km2)||0.115***|0.301***|0.295***|0.276***|
|||(0.0345)|(0.0707)|(0.0713)|(0.0629)|
|ln(no_ adminunits)|||-0.220***<br>(0.0733)|-0.214***<br>(0.0739)|-0.191***<br>(0.0642)|
|no_au_100th_2010|||0.204**|0.203**|0.171**|
||||(0.0853)|(0.0856)|(0.0764)|
|cc_pop2010_ue|||-0.470***|-0.423***|-0.284**|
||||(0.1435)|(0.1501)|(0.1331)|
|gov_body||||-0.046|-0.056|
|||||(0.0553)|(0.0491)|
|its_cov||||0.000<br>(0.0007)|0.001<br>(0.0006)|
|spd_sum||||0.035<br>(0.0299)|0.036<br>(0.0263)|
|pop_radio300km|||||0.007***<br>(0.0019)|
|coast_2010|||||0.062<br>(0.0742)|
|Port|||||-0.119*<br>(0.0657)|
|Constant|-0.025|-0.164|-2.142**|-2.233**|<br>-1.995**|
||(0.4251)|(0.4264)|(0.8666)|(0.8724)|(0.7613)|
|Country dummies|Y|Y|Y|Y|Y|
|Observations|73|73|73|73|73|
|Adjusted R-squared|0.685|0.694|0.745|0.743|0.811|

Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# **6. Conclusions**

This paper studies the impact of metropolitan fragmentation/coordination on economic performance of 73 metropolitan areas in the Latin America and Caribbean region. This contribution offers complementary evidence on the relationship between institutional fragmentation/coordination and economic performance, since most of the available literature in this topic is concentrated on developed countries.
WPS3942

# **Market access, supplier access, and Africa’s manufactured exports: an analysis of the role of geography and institutions** *

## **Ibrahim Elbadawi, Taye Mengistae, and Albert Zeufack The World Bank**

## **Abstract**

In a large cross-country sample of manufacturing establishments drawn from 188 cities, average exports per establishments are smaller for African firms than for businesses in other regions. We show that this is mainly because, on the average, African firms face more adverse economic geography and operate in poorer institutional settings. Once we control for the quality of institutions and economic geography, what in effect is a negative African dummy disappears from the firm level exports equation we estimate. One part of the effect of geography operates through Africa’s lower “foreign market access”: African firms are located further away from wealthier or denser potential export markets. A second occurs through the region’s lower “supplier access”: African firms face steeper input prices, partly because of their physical distance from cheaper foreign suppliers, and partly because domestic substitutes for importable inputs are more expensive. Africa’s poorer institutions reduce its manufactured exports directly, as well as indirectly, by lowering foreign market access and supplier access. Both geography and institutions influence average firm level exports significantly more through their effect on the number of exporters than through their impact on how much each exporter sells in foreign markets.

JEL Classification: F10, F12, O10, O11. Key words: Economic geography; Institutions, International trade; Economic development; Manufacturing; Africa.

## World Bank Policy Research Working Paper 3942, June 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The authors would like to thank Pierre Richard Agenor and Jim Tybout for useful comments on an earlier version of the paper.
# **Table 7. Latin America Vehicle Standards**

|Country||Vehicle Standards|
|---|---|---|
||Imported|LocallyManufactured|
|Argentina|Only new vehicles, equipped<br>with emission control<br>technologies according to<br>Euro 3 standards|As of 2006, new light duty vehicles must comply with<br>Euro 3, Euro 4 as of 2009, likewise for new diesel<br>trucks and buses.|
|Brazil|No importation of used<br>vehicles; imported new<br>vehicles must meet Euro 4<br>standards|Vehicle emissions standards set by IBAMA, based on<br>Euro standards: Euro 2 implemented in 1993, Euro 4<br>planned for 2008 equivalent to PROCONVE IV<br>standard), and Euro 4 in 2009. All new trucks and<br>buses must be Euro 4 in 2009.|
|Chile|Importation of used vehicles<br>is banned.|Emissions testing programs started in 1994 (annual<br>and roadside inspections). Euro 3 standards introduced<br>in 2004, Euro 4 to start in 2009 for passenger cars.<br>Euro 4 for diesel light vehicles required from 2005.|
|Colombia|Importation of used vehicles<br>is banned.|Light duty petrol vehicles must meet USEPA 1987<br>standards. New vehicles must comply with Euro 1;<br>heavy duty diesel vehicles must comply with<br>equivalent of USEPA 1994 standards for buses and<br>1991 standards for other vehicles. New buses must<br>comply with Euro 2, other new heavy duty vehicles<br>with Euro 1.|
|Ecuador|Importation of used vehicles<br>is banned. Model 2000 and<br>newer cars must possess<br>|New light duty petrol vehicles must meet<br>USEPA 1987 standards or Euro 1; new heavy duty<br>diesel vehicles must comply with USEPA 1994<br>|
||catalytic converters|standards or Euro 2.|
|Mexico|Vehicle maxium 10 years,<br>must have a gasoline engine,<br>and must be equipped<br>with a catalytic converter|Since 1993, heavy duty diesel vehicles must meet one<br>of these standards: US 1998, US 2004, Euro 3, or<br>Euro 4. All light duty and passenger vehicles must<br>meet US Tier 1, except on NOx (levels vary) and PM<br>(applies only to diesel).|
|Paraguay|Importation of used vehicles<br>is banned.||
|Venezuela|Importation of used vehicles<br>is banned.|Emissions testing in certain areas, with fines for<br>violators.|

Source: UNEP (2008) Table 8 shows the emission standards adopted by selected countries in Latin America. Argentina, Brazil, and Chile have chosen to adopt EU standards, whereas Colombia, Ecuador, and Mexico have provided flexibility by adopting both the U.S. equivalents and EU standards. As compared to Argentina and Brazil, Chile, and Mexico have introduced more stringent emission standard.

24
is to reduce the software requirements. The RMSM-XX version of the Turkey model will extend the class of models to those requiring a simultaneous solution.

There are many closure rules that are meaningful from the economic point of view. In the application for Turkey we have chosen to implement four alternative possibilities that are shown in Table 2. The four closure rules result from a two-dimensional classification.

TABLE 2: CLOSURE RULES

# FOREIGN CREDIT CONSTRAINT

<!-- Start of picture text --> Yes No<br><!-- End of picture text --> <!-- Start of picture text --> Financial Normative Normative<br>Programming & &<br>USE OF THE Availabilities Requirements<br>MODEL<br>Positive Positive<br>Projections & &<br>Availabilities Requirements<br><!-- End of picture text --> On the one hand, the Turkey RMSM-X can be used both for assessing the effects on the target variables of alternative macroeconomic programs and for obtaining the values of the policy variables that would be consistent with a set of exogenously given targets. In the first case, the policy variables would be determined exogenously, and the model would give us their most likely effects on the target variables. This closure rule defines a positive model which is useful to make projections of the "most 26
expected to force the necessary adjustment by ceasing to finance the imbalances, as happened recently in Mexico.

Current World Bank projections show the aggregate current account deficit of developing countries averaging around 1.6 percent of GDP over the next ten years, only slightly higher than in the past decade (see Chart 5). Allowing for increases in reserves to maintain adequate import cover, this translates into a cumulative net capital flow to developing countries of $1.5-2 trillion between 1995 and 2004. Under these projections, foreign capital will finance around 7 percent of the total investment in developing countries over this period.

Relative to industrial countries' total saving and GDP, the net capital flow to developing countries over the next ten years will remain small-on average a little over 3 percent and 0.7 percent, respectively. Gross flows would be larger, as increasing global capital market integration will see rising two-way flows between developing and industrial countries.

The smallness of these magnitudes suggests that developing countries are unlikely to face a 'thortage" in international capital markets on account of the size of the claim they collectively make on these markets. For individual developing countries, access to capital markets will depend mainly on their own ability to attract capital, based on investors' perceptions of their policies and prospects. The increasing integration of developing countries in international capital markets is reflected in the surge in private capital flows to these countries in the 1990s. Within this aggregate picture of increasing integration, the dependence of private flows on individual country circumstances is reflected in the concentration of these flows in a relatively small number of countries. Between 1991 and 1994, only a dozen countries, mostly in East Asia and Latin America, accounted for close to 90 percent of all private capital flows to developing countries (Chart 9). Developing 14
epidemic in Africa. Section 3 describes the data set and the nature of the HIV/AIDS information campaign in the surveyed area. Section 4 exposes the empirical analysis of HIV prevalence and HIV incidence. Section 5 covers the analysis of sexual behavior. Section 6 discusses the issue of the causality between education and HIV and section 7 concludes.

# 2 Background on the HIV/AIDS Epidemic in Africa

The transmission of the HIV epidemic in Africa occurs overwhelmingly through heterosexual intercourse. There is a long interval between HIV infection–seroconversion–and the actual development of AIDS. Using the data from the Natural History Cohort, a nested cohort study within the GPC, it has been estimated that, for adults, the median time from seroconversion to AIDS was 9.4 years and 9.2 months from AIDS to death (Morgan and Whitworth 2001; Morgan and others 2002). This median survival time is similar to what was found in developed countries before the introduction of antiretroviral therapies. For the majority of HIV/AIDS patients in Africa, however, and certainly for the periods considered in this paper, antiretroviral therapies were neither available nor The long interval between seroconversion and AIDS has several implications that should be kept in mind when studying the dynamics of the HIV/AIDS epidemic. The fi rst implication is that most HIV positive individuals appear healthy. In an environment where voluntary testing is limited,<sup>2</sup> this means that most people ignore their own HIV status, as well as the status of their sexual partners. Another implication of this long interval is that, when the epidemic is in steady-state, any prevalence estimates re fl ect behavior which is on average approximately fi ve years old. This is an important fact to take into account when trying to determine the extent to which some prevalence rates could have been in fl uenced by information campaigns. For example, prevalence rates in 1989—1990 re fl ect behaviors that are on average fi ve years old, while the fi rst prevention campaigns were launched in 1986 at a national level and were, at a local level, reinforced by the establishment of the MRC Programme in November 1989 in the surveyed area. It is therefore reasonable to assume that a substantial component of the prevalence in 1989—1990 is the consequence of sexual behavior that predated the availability of the information about the epidemic.

> 2 In the surveyed area, at round 11, in 2000, 8.9 percent of the individuals older than 17 had obtained the results of an HIV test.

5