rafmacalaba/datause-encoder-data huggingface dataset:
for designing effective guarantee schemes. Our review is based on general guiding principles derived from general insurance principles, a thorough literature review, and international practice<sup>7</sup> . For the international benchmarking, we selected a number of credit guarantee schemes in developing and developed countries that are reasonably well-established, including Canada’s SLFP, Chile’s FOGAPE, Colombia’s Fondo Nacional de Garantías, France’s OSEO, Hungary’s Garantiqa, India’s CGTMSE, Korea’s KODIT, the Netherland’s BMKB, Romania’s National Credit Guarantee Fund for SMEs, Taiwan’s SMEG, and the US SBA. Table 2 summarizes the design components and the outcomes that are assessed.

**Table 2: Basic Design Components and Outcomes Assessed**

||**MAIN OBJECTIVES OF THE SCHEME**<br>|
|---|---|
|**Main objectives**|Mission statement of the PCG scheme|
||**RULES OF THE SCHEME**<br>|
|**Eligibility criteria**|Characteristics of eligible firms (size, sectors, age) and eligible financing|
|**Coverage ratio**|Percentage of risk taken by the guarantee fund|
|**Fees**|Price of the guarantee|
|**Payment rules**|Triggers related to the payment of the guarantee|
|**Collateral and down payment**|Collateral and down payment required when using the guarantee|
||**MANAGEMENT OF THE SCHEME**|
|**Operational mechanism**|Individual, portfolio or hybrid approach|
|**Credit risk management**|Credit risk management tools (credit scoring and rating, credit registry)|
|**Capacity building**|Assistance to participating institutions designed to increase their lending<br>and risk management capacity|
||**OUTCOMES OF THE SCHEME**|
|**Outreach**|Number of eligible firms that are covered by the scheme|
|**Additionality**|Capacity to target firms that are effectively credit constrained|
|**Financial sustainability**|Capacity to contain losses and maintain an adequate level of equity given<br>the expected liabilities|

# **4 Reviewing the Design of Partial Credit Guarantee Schemes in MENA**

- _4.1 Main Objectives of the Scheme_

**MENA PCGs generally have broader objectives than those in the benchmark countries.** As shown in Annex 1, the mission statements of Guarantee Schemes in benchmark countries emphasize access to finance for SMEs that lack adequate collateral (Annex 1). By contrast, Annex 2 shows that MENA Guarantee Schemes have broader developmental objectives, such as supporting export capacity (Jordan, Morocco), fostering entrepreneurial spirit (UAE), improving the financial sector’s skill base (Syria, Iraq), facilitating investment in innovation (Morocco), and supporting national industrialization programs (Saudi Arabia).  These broad

> 7 Beck and al. 2008; Honohan 2008 ; Green 2003; European Commission 2006;  Deelen and Molenaar 2004.

8
Table 4: HTC Impacts on Objective and Self-Reported Outcomes by Prior HIV Beliefs

||Pa|nel A: High Pri<br>Obje|or Beliefs of H<br>ctive|IV Infection Bef|Subjective<br>ore HTC and|Tested Nega|tive<br>Self-R|eported|||
|---|---|---|---|---|---|---|---|---|---|---|
||||||||1 if S|Nb|||
||=1 if HSV-2<br>Positive|=1 if HSV-2<br>Positive|Achievement<br>Test Score|Achievement<br>Test Score|Likelihood<br>of HIV<br>Infection|Probability<br>Live to 50|= ex<br>Active Past<br>12 Months|umer<br>Partners Past<br>12 Months|=1 if Engage<br>in Risky Sex|=1 if in<br>School|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|HTC|-0.029|-0.039|0.487**|0.400**|-0.123***|0.219|0.011|-0.029|-0.005|0.133|
||(0.040)|(0.030)|(0.217)|(0.185)|(0.031)|(0.592)|(0.080)|(0.084)|(0.079)|(0.093)|
|Number of observations|143|143|142|142|143|143|143|143|143|143|
|Mean in control|0.091|0.091|-0.299|-0.299|0.159|4.816|0.449|0.505|0.421|0.295|
||Pa|nel B: Low Pri|or Beliefs of H|IV Infection Bef|ore HTC and|Tested Posit|ive||||
|HTC|0.299**|0.215**|-0.010|-0.166|0.524***|0.195|-0.035|0.009|-0.100|0.090|
||(0.139)|(0.098)|(0.304)|(0.288)|(0.146)|(1.242)|(0.149)|(0.155)|(0.156)|(0.209)|
|Number of observations|53|53|50|50|51|51|51|51|49|51|
|Mean in control|0.218|0.218|-0.254|-0.254|0.016|5.885|0.664|0.664|0.569|0.231|
|Includes Controls|No|Yes|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. The achievement test score is the average percent across math, English and cognitive tests (standardized). The subjective likelihood of HIV infection takes on a value from 0 to 1 where zero is no self-reported chance of HIV infection. Risky sex takes on a value of 1 if the respondent does not always use condoms with all partners, and is zero for those who always use condoms or have not engaged in sexual activity in the past 12 months. Regressions that include controls include the following: age, an indicator for never having had sex, and school enrollment status at baseline, all of which are demeaned and fully interacted with the treatment indicator. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence 19
WPS4071 <mark>B</mark> ACKGROUND <mark>P</mark> APER TO THE <mark>2007 W</mark> ORLD <mark>D</mark> EVELOPMENT <mark>R</mark> EPORT

# **_How are Youth Faring in the Labor Market? Evidence from Around the World_**

**_Jean Fares, World Bank_**

**_Claudio E. Montenegro, World Bank_**

## **_Peter F. Orazem_**<sup>**_*_**</sup>

**_Iowa State University_**

#### **Abstract.**

This paper uses a new standardized micro database for a large set of developing countries to (i) describe the patterns of labor market outcomes for youth, and (ii) to explain the contributions of supply and demand factors to youth outcomes. The paper shows that youth face various difficulties in transitioning to work. This is reflected in their relatively higher unemployment rate, higher incidence of low paying or unpaid work, and a large share of youth who are neither working nor in school. This is especially true for young girls who are found outside the labor market, some engaged in home production. Finally, the paper also finds that cross-country estimates show that changes in the youth relative cohort size is unlikely to have a large effect on how youth are faring in the labor market.

### World Bank Policy Research Working Paper 4071, November 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._

> * We are very thankful to Mehmet Ziya Gorpe, Natsuko Kiso, Anette Richter, and Victor Sulla, who provided excellent research assistance.

1
# **Figure 5a: Net benefit ratio for rice**

<!-- Start of picture text --> 0.04<br>0.02<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5<br>Rural<br>-0.02<br>National<br>-0.04<br>-0.06<br>-0.08<br>Urban<br>-0.1<br>Log of per eq adult expenditure<br>National Urban Rural<br>Source: Authors’ estimation using ELIM 2006.<br>Figure 5b: Net benefit ratio for millet and sorghum<br>0.185<br>Rural<br>0.135<br>National<br>0.085<br>Urban<br>0.035<br>9.5 10.5 11.5 12.5 13.5 14.5 15.5<br>-0.015<br>Log of per eq adult expenditure<br>National Urban Rural<br>Net Benefit Ratio<br>Net Benefit Ratio<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.
**8**

another. PRIVATE CREDIT is also a broader measure of banking sector development than that used **by** Levine and Zervos **(1998)** since it includes all financial institutions, not only deposit money banks.' Finally, unlike past studies, we carefully deflate the banking statistics. Specifically, financial stock items are measured at the end of the period, while **GDP** is measured over the period. Simply dividing financial stock items **by GDP,** therefore, can produce misleading measures of financial development, especially in **highly** inflationary environments.' Thus, PRIVATE CREDIT improves significantly on other measures of financial development.

To assess the robustness of our results, we use two other measures of financial development. LIQUID LIABILITIES equals the liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of banks and nonbank financial intermediaries) divided **by GDP.'** Unlike PRIVATE CREDIT, **LIQUID** LIABILITIES is just an indicator of size. The other measure is COMMERCIAL-CENTRAL BANK, the ratio of commercial bank domestic assets divided **by** commercial bank plus central bank domestic assets. COMMERCIAL-CENTRAL BANK measures the degree to which the banks versus the central banks allocate society's savings. The intuition underlying this measure is that commercial banks are more likely to identify profitable investments, monitor managers, facilitate risk management, and mobilize savings than central banks.

'For example, King and Levine (1993a,b) use a measure of gross claims on the private sector divided **by GDP.** But, this measure includes credits issued **by** the monetary authority and government agencies, whereas PRIVATE CREDIT includes only credits issued **by** banks and other financial intermediaries. Also, Levine and Zervos **(1998)** and Levine **(1** 998a) use a measure of deposit money bank credits to the private sector divided **by GDP** over the period **1976-1993.** That measure, however, does not include credits to the private sector **by** non-deposit money banks.

> **6** Some authors try to correct for this problem **by** using an average of financial intermediary balance sheet items in year t and t-1 and dividing **by GDP** measured in year t [King and Levine 1993a]. This however does not fully resolve the distortion. This paper deflates end-of-year financial balance sheet items **by** end of year consumer price indices **(CPI)** and deflates the **GDP** series **by** the annual CPI. Then, we compute the average of the real financial balance sheet item in year t and t- 1 and divide this average **by** real **GDP** measured in year t.

> **7** Among others it has been used **by** King and Levine (1993a).
# **Small Business Training to Improve Management Practices in Developing Countries: Reassessing the Evidence for “Training Doesn’t Work”**<sup>**#**</sup>

David McKenzie, _World Bank_ _Keywords:_ Business training; microenterprises; management practices; soft skills _JEL Classification codes:_ O12, O17, L26, I26

> # I thank Chris Woodruff for his collaboration on an earlier review of the business training literature; Tim Ogden for helpful discussions which helped prompt me to write this current review; and Stephen Anderson, Shawn Cole, Graeme Harris, Seema Jayachandran, Bill Maloney, Caio Piza, Simon Quinn, Petr Sedlacek, Bilal Zia, and an anonymous referee for helpful comments.
Table 1: Alternative Estinates of Household Expenditure Per Capita (CFAF/year) based on the 1985 CILSS

|l___________|Grootaert<br>(1992)|Glewwe<br>(1987)|Kanbur<br>(1990)|
|---|---|---|---|
|Abidjan|376,108|402,300|377,600|
|Other Cities|271,864|253,300|237,000|
|East Forest|164,472|146,500|136,500|
|West Forest|239,134|182,200|170,100|
|Savannah|152,573|107,700|100,800|
|Cote d'Ivoire|237,853|216,500|202,800|

Table 2: Alternative Estimates of Household Income (CFAF/year) based on **the 1985** CILSS <!-- Start of picture text --> Grootaert Kozel<br>(1992) (1990)<br>Abidjan 1,704,295 2,823,011<br>Other Cities 1,402,945 1,934,893<br>East Forest 925,730 }<br>West Forest 1,228,056 }1,022,597<br>Savannah 767,173 }<br>Cote d'Ivoire 1,206,414 1,600,743<br><!-- End of picture text --> S
so That is, the coefficient on G/Y Is negative and strongly significant. Ram (1986) criticized the functional form used by Landau. Essentially, the point is that if Y is a function of 0, then Y -- the percentage change in Y -- should be a function of the change in G, and not the level of G. For this reason, Ram (1986a) developed a model of public and private output which tests the impact of the growth in government spending with the growth in output. He finds a strong positive effect of government spending, both using cross-sectional analysis and time-series regressions for each country. Ram's findings are strongly confirmed using ay sample of 111 countries; the percentage growth rate in government spending is contrasted with output growth in Figure 8; the positive correlation coefficient is highly significant in a bivariate regression. Ram interprets this correlation to mean that government inputs of capital and labor are often more productive, and may confer positive externalities on the private sector.

While Ram was the first to provide a theoretical foundation for his econometric estimation, his analysis suffers from two shortcomings. First, his theoretical model expresses G as the contribution of government capital and labor to value added in GDP. However, the G that must be used by researchers is expenditures on goods and services. Hence measured G is a use, and not a source of income; the only measured contribution of government to GDP is through its payroll.
We find agglomeration and trading costs are more strongly correlated with employment growth than the business environment is. Firms with access to large domestic and international markets expand employment more quickly than other firms do. Firms that are exposed to strong capacity, as proxied by the share of large firms in the city, also tend to expand employment faster. Although fewer business environment variables are robustly correlated with job growth, crime and labor inflexibility negatively affect it.

We also look at whether the business environment and agglomeration affect employment growth differently in large and small firms, and in old and young firms. We find several differences across types of firms. For example, young firms benefit more from telecommunications development and capacity agglomeration.

# **Empirical Framework**

Many things that affect economic growth, such as access to finance (Levine, 1997), might also increase demand for labor and, thus, employment growth. Given that most of the mostly small and medium-sized firms in the Enterprise Surveys—the data used in this study—primarily serve domestic customers, we would expect demand for these firms’ products to increase as the economy grows. If growth is factor neutral, we would expect that firms’ demand for workers would grow as the domestic economy grows. Thus, improving access to finance might encourage job growth. If the economy was already at full employment, increased demand for labor might have only a modest impact on employment growth (i.e., it would mostly increase wages). However, many developing countries have large, unproductive, informal and rural sectors that could provide workers to formal firms and thus increase employment. We would therefore expect factors that affect economic growth to also affect employment growth in developing countries.

Other things might also affect employment growth without necessarily affecting economic growth. In particular, policies that favor small firms might encourage firms to remain small. For example, if regulations are more heavily enforced for large firms, burdensome regulations might discourage firms from becoming large and reduce employment growth at the firm level even if they do not affect economic growth.

3
reasonably precise. Using the basic specification with a single quartic in the dropout score as a benchmark, the 95 percent confidence interval for the mathematics test is (-0.10, 0.14), and that for the vocabulary test is (-0.11, 0.11).

Conceivably, although the CSP program may not have increased learning for the average recipient, it could have improved test scores for children who attended higher quality schools. To test for heterogeneity by school quality, we use administrative data from the Education Management Information System (EMIS) for the year before students started receiving CSP scholarships. Specifically, we interacted the indicator variable for CSP recipients with variables corresponding to school-level measures of the share of teachers with upper secondary education; and the share of teachers with 5 or more years of experience.<sup>15</sup> We then ran our basic RD specification including the indicator variable for CSP recipients, as before, as well as the interaction between CSP recipients and the relevant measure of school quality. (Note that the main effect for these quality measures drops out because all of our regressions include school fixed effects.) These results are presented in the upper panel of Table 4—for the mathematics test (left-hand panel) and the vocabulary test (right-hand panel). The coefficient of interest in these regressions is that on the included interaction term. In no case is this interaction term significant. We conclude that children who were offered scholarships did not have higher test scores than they would have without the program even when they attended schools with more educated or more experienced teachers.<sup>16</sup> The lower panel of the table focuses on various measures of classroom crowding. In the first row, the regression includes a quartic in the total number of students in a school in the year before the CSP program began, and an indicator variable for “large” schools, as defined by program administrators. Recall that 20 more scholarships were awarded by the CSP program in “large” schools, with total enrollment above 200, than in “small” schools. Controlling for a polynomial in school size, “large”

> 15 Mean share of teachers with upper secondary schooling across schools is 0.57 (with a standard deviation of 0.29); mean share of teachers with 5 or more years of experience is 0.47 (with a standard deviation of 0.27). 16 We also estimated models in which these continuous variables were transformed into indicator variables for high and low values of quality. The results from these models were qualitatively similar to those we report.

16
average inflation rate between the ESPS and PSF surveys. The second line corresponds to the satisfaction of individual basic needs. It is computed using the average non-food per adult equivalent expenditure of households for which food consumption per adult equivalent is between 0.95 and 1.05 the nutrition poverty line. This average is added to the nutrition threshold to obtain the basic needs line. Table S1.4 reports the values of the poverty thresholds for Dakar, other towns and rural areas.<sup>6</sup>

**Table S1.4: Nutrition and basic needs poverty lines**

||Nutrition poverty line|Basic needs poverty line|
|---|---|---|
|Dakar|396|835|
|Other towns|369|647|
|Rural areas|356|558|

Source: « Pauvreté et Structure Familiale » (PSF) survey, authors' calculations. Note: Poverty lines per urbanization strata in Senegal.Values are given in CFA francs.

Using these thresholds, we can compute FGT indexes for both the PSF and ESPS surveys.

The results are reported in table S1.5. Not much difference is found between the values of the FGT indexes. PSF finds more people under the nutrition threshold (16.7% versus 12.5%), but less under the basic needs one (42.8% versus 45%).

**Table S1.5: FGT indexes for PSF and ESPS surveys**

|Index|Nutrition po<br>PSF|verty line<br>ESPS|Basic needs po<br>PSF|verty line<br>ESPS|
|---|---|---|---|---|
|Poverty headcount|0.167|0.125|0.428|0.450|
|Mean poverty gap|0.042|0.027|0.143|0.130|
|Meansquared poverty gap|0.016|0.009|0.064|0.052|

Source: « Pauvreté et Structure Familiale » (PSF) and « Enquête de Suivi de la Pauvreté au Sénégal » (ESPS) surveys, authors' calculations based on 13988 and 123486 individual observations for PSF and ESPS surveys respectively Note : Poverty measures obtained with the two surveys.

> 6 We repeated the same exercise on the ESPS sample. The resulting basic needs thresholds are at bit lower than those of PSF (507, 592 and 780 CFA per adult equivalent and per day for rural areas, other towns and Dakar respectively).

14
patterns for export dynamics that are similar across countries. Therefore, the learning from a specific country should be taken cautiously, but definitely provides lessons to other countries. Ideally, the comparison of the impacts of EPAs should incorporate the heterogeneity among countries’ agencies and institutions. For this reason, accumulating further knowledge on the effects of these programs in different contexts is critical to fully understand how and in which circumstances they work.

# **4 Conclusion**

This paper summarizes a few stylized facts from a novel database based on a survey on EPAs’ characteristics conducted by the World Bank in collaboration with the ITC in 2010. Most agencies have the legal status of public-private institutions. Also, several EPAs went through at least one type of institutional change in the short period between 2005 and 2010. In many cases they became a public-private or private institution. On average, these agencies have an executive board with nine members, including about 50 percent of members representing the private sector. The median EPA has about 86 employees, but this varies largely across regions.Most EPAs have the diversification of markets and products among their priorities. Moreover, a large share of them prioritize major markets. EPAs spend more on small and medium firms, instead of large ones; spend more on established exporters, instead of new/occasional exporters or non-exporters; and spend more on the provision of marketing services (e.g. trade missions) and export support services (e.g. training, technical assistance), relative to other services.

The findings of the literature on the effects of EPAs on export performance suggest that many of them have been succeeding in their objective. There is evidence of significant effects of EPAs’ support on the intensive and extensive margins of exports. Yet, there are several questions that still deserve further attention. Are there spillover effects of EPAs’ support on the export performance across different margins of trade for untreated firms? Is promoting exports in the extensive margin at the firm level enough to boost diversification at the country level? What are the optimal size and resources (in per capita terms) to be allocated to EPAs? Understanding these and other key questions regarding the operations of EPAs, can provide helpful insights to improve the effectiveness of export promotion policies.

13