rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9325

# **Abstract**

Many countries in Sub-Saharan Africa face a rapidly growing population and labor force in demand of good jobs. Ethiopia has reacted to this challenge by prioritizing largescale industrial development through the construction of industrial parks to drive exports, job creation, and growth. However, the African experience with industrial parks so far has been mixed. To provide further evidence on the welfare effects of factory jobs in Ethiopia, this study conducted an experiment that facilitated the job application and onboarding process for young female job seekers at three factories. Using panel data from 827 applicants, the study finds that the extra support increased the likelihood of being employed in the treatment group in the short run, largely driven by wage and factory work. Further, the intervention raised reported monthly income by nearly 30 percent in the treatment group. However, the study also finds an adverse impact on health outcomes as well as downward adjustments of applicants’ expectations and perceptions of the earnings potential and desirability of factory work in response to the treatment.

This paper is a product of the Africa Gender Innovation Lab, Africa Region. 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://www.worldbank.org/prwp. The authors may be contacted at gtefera@worldbank.org; nbuehren@worldbank.org and mgoldstein@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
many fold draws). As a result, these out of time results should be viewed as only a rough check of how much one should trust the within-time results.

Models based on the bureau data, which is lower dimensional, tend to be relatively stable over time. On the other hand, standard models using phone indicators see substantial deterioration (AUC declines from 0.71 to 0.63 for Random Forest and from 0.76 to 0.60 for logistic stepwise). Our modified phone indicator models that use only within week variation are much more stable (AUC increases from 0.62 to 0.64 for Random Forest and decreases from 0.63 to 0.59 for stepwise OLS FE).<sup>13</sup> All phone indicator models continue to outperform models using credit bureau data on this cut of the data (AUC 0.55-0.58). Our performance also lies within the range of the one comparable published benchmark of out of time performance of traditional credit scoring we could find in the literature, from a developed setting (AUC 0.57-0.76, Table S4). Those and our results suggest that bureau models can face at least slight deterioration when tested out of time, with the caveat that we have tested only one time period. The out of time performance of our methods is expected to improve to improve when trained on multiple cohorts (just as credit bureaus have evolved the data they collect by observing default patterns over many cohorts).

After the results from this pilot, the telecom implemented a scoring system using data and methods similar to what we suggested, suggesting they viewed it to be profitable.

# **5. DISCUSSION**

Mobile phone data appear to quantify nuanced aspects of behavior that are typically considered soft, making these behaviors ‘hard’ and legible to formal institutions (Berger & Udell, 2006). Further, these data are already being captured. The method can assist with the provision of financial products to the poor in several ways.

> 13 The CDR-W Random Forest model is likely to underperform when trained on the same time period with cross validation: it learns less structure when an equivalent sample size is split across multiple time periods (as is the case with out of sample test, which trains on a random subset of loans across weeks).

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than intended to serve the local market, and many exports are done by multinational affiliates. As a result, the export promotion agency and the foreign investment promotion agency, CINDE, jointly coordinate the government response to foreign companies that are potential exporters. In fact, CINDE attracted Intel, while PROCOMER was established the year Intel arrived. There is evidence that foreign direct investment promotion agencies are themselves effective. Harding and Javorcik (2011) show using a difference-in-differences approach that when sectors are targeted by investment promotion agencies, they receive more foreign investment from the US economy compared to other sectors. This effect is concentrated in countries with the most cultural distance from the United States, and those countries with less effective governments and higher corruption—indicating that effects are biggest where red tape and information asymmetries were previously most severe.<sup>5</sup> A fruitful line of future research is to measure the complementary between these two agencies, with a focus on the potential interactions between domestic and multinational firms. Some larger countries have investment promotion agencies with mandates to promote both domestic and foreign investment. An open question is whether the absence of a dedicated foreign investment promotion agency in these countries diverts government attention from attracting export-oriented multinationals.

_4) Dedicated revenue with regular evaluation._ PROCOMER was originally funded by a levy on exports from firms operating in Costa Rica’s tree trade zones, but the revenue base was later expanded to a levy on all exports. This dedicated revenue gives firms who pay the tax an interest in ensuring the agency provides value. The budget is also evaluated annually. Most recently, tax exemptions for investors were compared to benefits in terms of additional tax revenue, incremental wage increases, and pension contributions, and it was found the agency generates about $2.50 of those benefits for every $1 in tax incentives offered to foreign investors (República de Costa Rica, 2022). Ideally, this analysis should be completed by an independent government agency, like the Ministry of Finance or Treasury.

_5) Unique online portal for potential exporters and buyers._ Effective export promotion agencies have client relationship management tools that help them respond to the idiosyncratic needs of firms at scale. In Costa Rica, this system is built on web-based surveys available to potential exporters and investors that evaluate each group’s readiness to start exporting. Some

> 5 Evidence from Europe suggests foreign investment promotion agencies can also be effective in high-income countries, especially in attracting investment to less-developed regions of those economies (Crescenzi, Di Cataldo, and Giua, 2021).

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33 occur in the Asia and Pacific region (excluding Japan), Middle East and parts of Africa, and Eastern Europe. Only the United States is projected to show a decline in new vehicle registrations.

Looking to the longer term, vehicle population and use projections were developed based on historical trends and expected population and income growth rates. Such projections are inherently uncertain because of the inability to predict international crises, prolonged economic downturns, future oil prices, and other events affecting automotive transportation. Two methods were used to project the future vehicle fleet. Conservatively, vehicle growth can be expected to follow historical trends as reflected by a straight linear regression. This indicates a global vehicle population approaching 1 billion by the year 2030. If, on the other hand, per capita trends are regressed and then multiplied by population estimates, the global vehicle population would be substantially higher. Overall each projection indicates that the global vehicle population will increase at an average rate of about 2-3% per annum, over the time frame investigated. Individual countries in Asia and Latin America, however, are likely to experience higher growth rates. The global projections were compared with several individual country estimates [Matsunami 1988, Olsson 1986, SFIT 87, Gillingham 1988, Myers 1988, OECD 1983, ROV 1986, SASO 1987, Sjostrom 1988, Strategic Analysis Europe 1988, NZMTF 1987, TC 1987, Kroon 1988, TC 1988, Gospage 1985a, Gospage 1985b, Hartig 1988, Lenz 1985, DSA 19871. In general, the estimated global growth rates compare reasonably with individual country estimates.

# **Urbanization, Economic Activity, and Motorization**

The degree of urbanization is strongly influenced by the magnitude of economic activity, while the level of motorization is linked to economic activity and urbanization. Energy consumption is significantly influenced by both the degree of urbanization and the level of motorization. The regression analyses summarized in Table 13 suggest a reasonable positive correlation between income and motorization and between urbanization and motorization.

**Table 13. Generalized relationships between urbanization, economic activity, and motorization**

|**X**|Y|Relationship|df|F|**R2**|
|---|---|---|---|---|---|
|**GNP per**<br>**Capita'**|**Motor**<br>**Vehicles**<br>**per**<sup>**1 0 0 0**</sup><br>**persons**|**Y**= 0.00879*X'.'0|24|108.1|**0.82**|
|**Urban**<br>Pop.b|**Motor**<br>Vehicles<br>per 1000<br>**persons**|Y =**0.01726 83*XOm**|**24**|**30.64**|0.57|

f **In 1985 US dollars;** F **peret**
Policy Research Working Paper 5032

# **Abstract**

Using data on more than 56,000 enterprises in 90 countries, this paper finds that objective conditions in the business environment vary substantially across firms of different sizes and that there are important nonlinearities in their impact on employment growth. The paper focuses on four areas: access to finance, business regulations, corruption, and infrastructure. The results, particularly on the impacts of finance and corruption on growth, depend on whether and how the analysis accounts for the possible endogeneity of the business environment. Controlling for endogeneity revises the finding that small firms benefit most from access to finance, particularly for sources of finance associated with investment and growth. The findings are also sensitive to how “small” is defined. Differentiating micro (less than 10 employees) from other small firms shows that, while small firms can be disadvantaged in such an environment, micro firms tend to be proportionally less affected by a weak business climate—and, on occasion, it can help them to grow. Overall, allowing different size classifications provides insights into the impact of the business environment that are lost in more aggregate analyses.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to understand the microeconomics of growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mhallward@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
For many borrowers, VF loans are just one source of credit; a third of VF borrowers also borrow from the Bank for Agriculture and Agricultural Cooperatives (BAAC), a more traditional agricultural development bank that extends relatively large loans, mainly to farmers, at comparatively low interest rates, and is sometimes held up as a model of a successful rural financial institution (Fitchett 1999).

About half of VF borrowers say that they did so in order to finance agricultural activities, and a third or more now borrow to pay for “daily expenses”, which may be thought of as consumer credit. About one in ten VF loans are devoted to non-farm businesses, a low proportion given the initial expectations (or hopes) that the Village Funds would promote non-traditional economic activity.

# **3. How Village Funds Operate**

Before trying to explain the performance of Village Funds, we first need to provide some additional institutional details.

|**Table 2. Use of Village Fund, 2004, 2009, and 2010**|**2004**|**2009**|**2010**|
|---|---|---|---|
|Percentage of households borrowing from the Village Fund|36.3|29.4|29.3|
|_of which:_borrowed from VF and BAAC|_15_|_11_|_10_|
|borrowed from VF but not BAAC|_23_|_16_|_19_|
|Borrowed from the BAAC but not VF|_6_|_10_|_-_|
|Did not borrow|34|39|n.a.|
|Number of Village Fund loans since 2002||5.0|5.9|
|**Amount borrowed from Village Fund (baht per loan)**||||
|All Thailand|16,183|15,790|16,905|
|Households in poorest expenditure/capita quintile|_17,312_|_14,080_|_15,953_|
|Rural households|_16,462_|_15,724_|_16,965_|
|Female borrowers|_15,322_|_15,497_|_16,830_|
|Memo: Expenditure per capita (baht per month)|3,398|3,941|4,980|
|Interest rate charged on VF loans(percentp.a.)|6.0|6.0|6.0|
|**Stated purpose of borrowing:**||||
|Agriculture (equipment, inputs, other)|50.9|46.7|46.8|
|Non-farm business|13.9|12.8|10.2|
|Health/Education|4.6|5.0|2.4|
|Repay debt|n.a.|5.9|1.8|
|Household daily expenses/other|30.8|29.8|38.8|
|Did loan raise income? % saying yes|71.1|71.3|69.5|
|**Repayment: % reporting**||||
|In full and on time|-|92.2|92.6|
|In full, but late|-|3.4|2.5|
|In part|-|2.4|2.1|
|Not yet repaid|-|2.1|2.8|
|Wouldyou applyfor a VF loan again? % sayingno|-|11.1|10.1|
|Memo: Number of Observations(adults)|80,950|100,408|100,221|

_Source:_ Socio-Economic Surveys of 2004, 2009, and 2010. These each included a special module that asked 5
**Table 5: Simulations of policy and fiscal interventions**

|**Indicators**<br>|**Manda**<br>**substitut**<br>**biodiese**|**tory**<br>**ion for**<br>**l (5%)**|**Mand**<br>**substitu**<br>**biodie**<br>**bioethan**|**atory**<br>**tion for**<br>**sel &**<br>**ol (5%)**|**Increa**<br>**export t**<br>**Soy & So**<br>**Oil (1**|**se of**<br>**axes,**<br>**ybean**<br>**0%)**|**Compe**<br>**subsidy**<br>**of bio**<br>**(20**|**nsated**<br>**to sales**<br>**fuels**<br>**%)**|
|---|---|---|---|---|---|---|---|---|
||**y1 **|**y5 **|**y1 **|**y5 **|**y1 **|**y5 **|**y1 **|**y5 **|
|**_Macroeconomic_**|||||||||
|**_Indicators_**|||||||||
|GDP|-0.1|-0.1|-0.3|-0.1|-0.1|-0.2|0.0|0.0|
|Trade Balance|-0.2|-0.1|-0.3|-0.1|-0.1|-0.2|0.0|0.0|
|Fiscal Result|-0.1|0.0|-0.2|-0.1|-0.1|-0.1|0.0|0.0|
|Rate of Unemployment|0.1|0.0|0.2|0.0|-0.1|0.0|0.0|0.0|
|**_Welfare Indicators_**|||||||||
|Poorest Household|-0.2|-0.1|-0.3|-0.1|-0.1|-0.2|0.0|0.0|
|Richest Household|-0.1|0.0|-0.2|-0.1|-0.1|-0.1|0.0|0.0|
|**_Sectoral Activity Level_**|||||||||
|Agriculture|-0.2|-0.1|-0.3|-0.2|-1.3|-1.5|0.0|0.0|
|Manufactures|-0.1|0.0|-0.2|0.0|0.3|0.2|0.0|0.0|
|Services|-0.1|-0.1|-0.3|-0.1|0.1|0.1|0.0|0.0|
|**_Specific sectors Activity Level_**|||||||||
|Soy|-0.2|-0.1|-0.3|-0.2|-9.0|-10.4|0.0|0.0|
|Maize|-0.3|-0.1|-0.4|-0.2|3.4|3.8|0.0|0.0|
|Sugar Cane|-0.2|-0.1|1.3|1.8|1.3|1.5|0.0|0.0|
|Rest of agriculture|-0.1|-0.1|-0.2|-0.2|3.8|4.3|0.0|0.0|
|Soybean oil|-0.2|-0.1|-0.3|-0.2|-7.3|-8.6|0.0|-0.1|
|Diesel|-0.2|-0.2|-0.3|-0.3|-0.1|-0.1|0.0|-0.1|
|Gasoline|-0.2|-0.2|-0.3|-0.3|-0.1|-0.1|0.0|-0.1|
|Biodiesel|633.7|489.7|632.8|489.2|6.0|89.7|22.5|542.4|
|Bioethanol|-0.2|-6.3|13514.6|15637.6|0.2|1.2|22.7|539.3|
|**_GHG emissions index_**|-0.6|-0.6|-1.0|-0.8|-0.6|-0.8|0.0|0.0|

Note: y1: 2007, y5: 2012. Figures are deviations from percentage change in the baseline of the corresponding year.

**_Export taxes_** . There is a slight reduction in GDP as well as investments, following the increase of export taxes on soybean and soybean oil, as expected because of the additional distortion imposed to the economy.  On the other hand, the increase in export taxes on soybean and soybean oil impacts positively on production of biodiesel: production of biodiesel increases by 6% for the first year with respect to the benchmark, and exports grow almost 12%. The response is stronger in the long run, since more mobile capital is available to be allocated to the production of biodiesel. These exercises illustrate the potential relevance of indirect policy instruments on the reaction and growth of biofuel

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# **Figure** **<u>3 – Breakdown of virtual-C imports to EU15 (percent)</u>**

<!-- Start of picture text --> EU15<br>CHN<br>21%<br>Row<br>38%<br>USA<br>15%<br>eit<br>RUS 12%<br>JPN 8%<br>3% IND<br>3%<br><!-- End of picture text --> Note: virtual carbon imports have been estimated using the BTIO approach. Source: authors 23
**Table** 7: **Consumption Effects of Food Stamps**

|_Independent Variables_|_Expend_<br>_(Excludin_<br> <br>_Consump_|_iture_<br>_g Non-_<br>_tion)LI_|_Dependent_<br>_Expend_<br>_(Includin_<br>_Consump_|_Variables_<br>_iture_<br>_g Non-_<br>_tion) -k_|<br>_Food Exp_|_enditure_|
|---|---|---|---|---|---|---|
||_Parameter_|_T-ratio_|_Parameter_|_T-ratio_|_Parameter_|_T-ratio_|
|Intercept|0.574|1.13|0.844|1.98|-0.101|-0.25|
|<br>Food Stamp|0.115|2.39|0.108|2.67|0.042|1.09|
|Change in Household|0.104|_0.65_|0.092|0.68|0.207|1.60|
|Structure|||||||
|Secondary Education|-0.029|-0.75|-0.017|-0.51|0.082|2.63|
|Post-Secondary Educ.|0.042|0.56|0.031|0.49|0.004|0.07|
|No. of Children:|||||||
|less than 4 yrs.|0.006|0.33|0.008|0.51|0.02|1.27|
|<br>4 - less than 9 yrs.|0.047|2.05|0.027|1.39|0.045|2.44|
|9- less than 14 yrs.|0.013|0.65|0.009|0.53|-0.001|-0.06|
|<br>14- less than 19 yrs.|0.124|5.97|0.099|5.69|0.080|4.80|
|<br>No. of Adults:|||||||
|19 - less than 25 yrs.|0.095|4.79|0.079|4.77|0.077|4.79|
|255-lessthan30yrs|0.123|4.05|0.119|4.68|0.103|4.23|
|<br>30- less than 45 yrs.|0.096|3.39|0.091|3.83|0.072|3.16|
|<br>45 - less than 55 yrs.|0.138|4.18|0.144|5.19|0.184|6.92|
|<br>55 or older|0.005|0.18|0.025|0.97|0.072|2.86|
|Stone/brick/concr. Wall|0.202|0.86|0.054|0.27|-0.116|-0.61|
|Block Wall|-0.177|-0.81|-0.194|_-1.05_|-0.133|-0.75|
|Other (not wood) Wall|-0.415|-1.16|-0.766|-2.54|0.166|0.57|
|Metal Roof|-0.427|-1.49|-0.746|-3.11|0.181|0.79|
|Concrete floor|-0.353|-0.814|-0.067|-0.18|-0.035|-0.10|
|Wood floor|-0.361|-0.820|-0.081|-0.22|-0.154|-0.44|
|Tile/marble floor|0.275|0.618|0.475|1.27|0.207|0.58|
|Toilet with sewer|-0.472|-2.08|-0.182|-0.95|-0.067|-0.36|
|Toilet without sewer|-0.542|-2.95|-0.085|_-0.55_|0.003|0.24|
|Rural|0.393|7.25|0.385|8.47|0.364|8.35|
|Pregnant or Lactating|0.129|0.22|0.046|0.25|0.306|1.76|
|FemaleHeadedHH|0.106|_0.05_|0.124|3.28|0.157|4.33|
|Handicapped|1.886|3.29|1.636|3.41|0.463|1.01|
|<br>Month of interview in 89|0.249|1.75|0.192|1.61|0.240|2.09|
|Month of interview in 90|-0.095|-0.43|-0.042|-0.22|0.306|1.70|
|Month of interview in 91|-0.349|-1.82|-0.186|-1.15|-0.214|<br>-1.38|
|Adjusted R-Square||0.31||0.35||0.37|
|<br>F-Statistic||15.9||19.0||20.2|
|Number of Observations||967||||967|

1/ Non-consumption expenditure refers to expenditure on taxes, insurance, transfers, depreciation of assets. The change in household structure attempts to capture whether there has been a significant change in household structure (lifecycle). It is a dummy variable equal to I if the change in the age of the household head between 1989 and 1991 exceeds 2. All variables, except the month of interview, and the dummy for wood material for walls in 1989 (Wall 1989) are first differences.

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# **4.2 Currently recommended microbial risks assessment and management framework**

# _4.2.1 Viral, bacterial and protozoan risks_

The currently recommended management framework for the control of the viral, bacterial and protozoan risks resulting from wastewater use in agriculture includes five steps (Table 4.1):

## _Step 1: Define the tolerable maximum additional burden of disease_

A tolerable maximum additional burden of disease resulting either from working in wastewaterirrigated fields or from consuming wastewater-irrigated food must be selected. Burdens of disease are now expressed as ‘DALY losses’ per person per year (pppy). The DALY loss associated with a case of a disease can be considered as the ‘health cost’ of one episode of that disease. Tolerable DALY losses are small—for example, 1 × 10<sup>−</sup><sup>_n_</sup> pppy, where _n_ is in the range 1−10 (in order of magnitude terms).

The Australian National Guidelines and the 2006 WHO Guidelines both adopt a tolerable maximum additional burden of disease of 10<sup>−6</sup> DALY loss pppy. However, the 2006 WHO Guidelines state:<sup>18</sup> Wastewater treatment may be considered to be of a low priority if the local incidence of diarrheal disease is high and other water-supply, sanitation and hygiene-promotion interventions are more costeffective in controlling transmission. In such circumstances, it is recommended that, initially, a national standard is established for a locally appropriate level of tolerable additional burden of disease based on the local incidence of diarrheal disease—for example, ≤10<sup>−5</sup> or **≤10**<sup>**−4**</sup> **DALY [loss] per person per year** (emphasis added) (WHO 2006, volume 2, section 4.5).

Given the high global incidence of diarrheal disease, which in order-of-magnitude terms is 0.1−1 pppy (Mathers _et al_ ., 2002; Kosek _et al_ ., 2003; _cf_ . Table A.1 in Annex A), a tolerable maximum additional burden of disease of 10<sup>−4</sup> DALY loss pppy is a more appropriate choice, or at least a more appropriate initial choice in low- and middle-income countries. As detailed in section A3.1 of Annex A, this results in an increase in diarrheal-disease incidence of only 1 percent.

## _Step 2: Derive tolerable disease and infection risks_

The tolerable maximum additional burden of disease established in Step 1 must be ‘translated’ into tolerable disease and infection risks pppy, for one or more ‘reference’ pathogens, as follows: TolerableDALYloss <u>pppy</u> Tolerablediseaserisk pppy  DALYlossper case of disease Tolerable infection risk pppy = Tolerable disease risk <u>pppy</u> Disease/infection ratio _Choice of ‘reference’ pathogen._ Both the Australian National Guidelines and the 2006 WHO Guidelines applied QMRA to determine infection risks for three ‘reference’ pathogens:

- rotavirus (a viral pathogen),

- _Campylobacter_ (a bacterial pathogen), and

- _Cryptosporidium_ (a protozoan pathogen).

> 18 This is also stated in WHO, 2007, 2008a, and 2010; see also Haas, 1996.

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