rafmacalaba/datause-encoder-data huggingface dataset:
proposal could, in the short term, benefit a politician since citizens will have their incomes raised by the redistribution part (and could then be grateful to them for this influx of incomes).

In order to address the pertinent questions related to this proposal, country case studies could be undertaken to test the feasibility of redistributive and tax mechanisms as presented. The recent discovery of oil reserves in some African countries opens up new opportunities as well as challenges for public engagement, public discussion and donor support to intervention in institutional arrangements for the entire value chain, including resource collection and management.

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**Figure 4: Labor market for teachers: high public budget**

<!-- Start of picture text --> S<br>w ’’<br>w*<br>W<br>D<br>Q1 q * q *’ Q2<br><!-- End of picture text --> For public policies that aim at inducing changes in aggregated teacher profile by changing teacher remuneration, key information is needed on the slope of _S_ , and the level of _w_ * that can induce the desired level _q_ *.

# **IV. Estimating teacher supply**

The econometric model that this paper uses is based on Dolton (1990), which modifies that of Zabalza et al. (1979) and Willis and Rosen (1979), and considers that there are two possible outcomes to a college graduate’s decision. The graduate can either decide to become a teacher ( _a_ ) or not ( _na_ ). It is assumed that the earnings streams in these two regimes may be parameterized by a simple geometric process. In this model, decisions are considered from the perspective of life-cycle earnings.

11
# **Figure 3 Bank exposure to SMEs (II)**

The information shown in this figure is derived from the data we collected from banks. The source of the panels is the same but the information is displayed differently in each case. Figure 3.A shows the average financing to SMEs as a percentage of total bank financing to the private sector in the most involved, medium involved, and least involved banks of the aggregated sample of Argentina and Chile for 2006. The most involved banks are the top third of banks with the highest share of SME loans as a percentage of total bank loans to the private sector. The least involved banks are the bottom third of banks with the lowest share of SME loans as a percentage of total bank loans to the private sector. The medium involved banks are the ones that do not fall in either category. Figure 3.B illustrates financing to SMEs by type of bank for 2006. The banks that answered the information presented in both panels represent 64% of total private sector loans in Argentina and 80% in Chile.

# **A. Financing to SMEs as a share of financing to the private sector**

_By level of involvement_ <!-- Start of picture text --> 70% 62%<br>60%<br>50%<br>40%<br>28%<br>30%<br>20% 8%<br>10%<br>0%<br>Most involved Medium involved Least involved<br>(Top third) (Bottom third)<br>B. Financing to SMEs as a share of financing to the private sector<br>By type of bank<br>70%<br>60% 56% Argentina Chile<br>50%<br>40%<br>31%<br>27%<br>30%<br>20% 16% 12%<br>10%<br>0%<br>Private domestic banks Foreign banks Public banks<br> SME loans over private sector loans<br>SME loans over private sector loans<br><!-- End of picture text -->

# **B. Financing to SMEs as a share of financing to the private sector** _By type of bank_
implement the improvement plan developed during the diagnostic phase. Every area would be covered by different consultants and with different schedules but would typically involve weekly meetings for four hours per visit, spread over three to five months. Once per month, the team would meet with the whole firm’s management to discuss improvements and re-define priorities and next actions. The total consultant time was 500 hours, consisting of 100 hours of providing training, and then approximately 100 4-hour sessions per firm of individual consulting. The cost of this individual intervention was US$28,950 per firm receiving treatment.

Based on our discussions with firms and own observations of the process, the implementation appears to have involved an emphasis on teaching firms how to measure and monitor key performance indicators, and on providing firms with the set of tools needed to better understand how their business is performing. It appears that there was less direct implementation from the consultants. For example, the consultants might go through the financial and performance data from the firm and suggest the need for the firm to consider new product lines or develop new markets abroad, but seldom make more direct recommendations (e.g. you should try exporting product X to Ecuador, or you should start using this production technology).

# **3.3 Group Consulting Treatment**

The idea behind the group consulting treatment was to test whether the same gains in management improvements could be achieved more efficiently through working with small groups at a time, motivated in part through the way agricultural extension services are often implemented. The group treatment arm aimed to lower costs in two key ways. First, by working with multiple firms at once, and potentially having them also learn from one another, each consultant’s time was spread over more firms. Secondly, rather than consultants having to travel to the firms, most of the meetings took place in central meeting places such as conference rooms, cutting down on consultant travel time.

Groups were formed of 3 to 8 firms located in the same region, such that members are not direct competitors to one another, but are instead producing complementary products with similar management problems.<sup>10</sup> These groups were formed after the randomization, in November 2013. However, unfortunately a different government budgetary entity was designated to pay for this

> 10 The composed groups are 1 group of 8 firms, 4 groups of 7 firms, 2 groups of 6 firms, 1 group of 4 firms, and 1 group of 3 firms.

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# **Health Crisis, Mobility Restrictions, and Group Trade: Evidence from Small-Scale Cross-Border Transactions in the Great Lakes Region**<sup>**1**</sup>

## **John C. Keyser,**<sup>**a**</sup> **Charles Kunaka,**<sup>**b**</sup> **and Peter Walkenhorst**<sup>**c**</sup>

_<u>JEL classification</u>_ F14 (Empirical studies of trade) F15 (Economic integration) O17 (Formal or informal sectors)

### _<u>Keywords</u>_

Informal cross-border trade; COVID-19 pandemic; cooperative arrangements a) Senior Agricultural Economist; Agriculture and Food Global Practice; The World Bank; jkeyser@worldbank.org.

b) Lead Specialist; Finance, Competitiveness and Innovation Global Practice; The World Bank; ckunaka@worldbank.org.

c) Lecturer; American University of Paris, 5 Bd de la Tour-Maubourg, 75007 Paris, France; pwalkenhorst@aup.edu.

1 The paper benefitted from support provided by the Umbrella Facility for Trade trust fund, which receives contributions from the Governments of the Netherlands, Norway, Sweden, Switzerland, and the United Kingdom.
**<u>Table 1: Water and Sanitation Definitions</u>**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**JMP/WHO**|**Technology**|**Improved/**<br>**Unimproved**|**Private/ Public**|
|||**WATER CA**|**TEGORIES**||
|Piped water into dwelling/yard/plot|improved|piped|improved|private|
|Public tap orstandpipe|improved|piped|improved|public|
|Tubewellorborehole|improved|well|improved|public|
|Protected dug well -private|improved|well|improved|private|
|Protected dug well -public|improved|well|improved|public|
|Protected spring|improved|well|improved|excluded|
|Unprotected spring|unimproved|well|unimproved|excluded|
|Unprotected dug well-private|unimproved|well|unimproved|private|
|Unprotected dug well-public|unimproved|well|unimproved|public|
|Tanker-truck/Bottled water|unimproved|excluded|excluded|excluded|
|Surface water|unimproved|surface|surface|surface|
|||**SANITATION**|**CATEGORIES**||
|Flushtoilet/sewersystem/septic tank|improved|flush|improved|private|
|Flush/pour flushto pitlatrine|improved|latrine|improved|private|
|Ventilatedimproved pitlatrine|improved|latrine|improved|private|
|Pitlatrine withslab|improved|latrine|improved|private|
|Specialcase|improved|excluded|excluded|excluded|
|Sharedflushtoilet|unimproved|flush|improved|public|
|Sharedlatrine|unimproved|latrine|unimproved|public|
|Pitlatrine without slab|unimproved|latrine|unimproved|private|
|Hanging toilet or hanginglatrine|unimproved|latrine|unimproved|private|
|Flush/pour flushto elsewhere|unimproved|latrine|unimproved|private|
|Bucket|unimproved|open|open|open|
|Nofacilities orbushor field|unimproved|open|open|open|

Notes: _Detailed categorization is available from the authors by request._ In a last specification, and only for a selected number of surveys, we distinguish between public and private water and sanitation access (see column 4, Table 1). Public water access implies on average a longer distance to the household and hence longer transport and storage times, and is thus likely to reduce the water quality consumed in comparison to private access to the same technology. Similarly, public sanitation can– for example, due to free-rider problems - be expected to be less hygienic than private toilets. For water access, the distinction between private and public access is explicitly coded in most surveys; surveys where no difference between private and public water access is made are not used for this part of our analysis. For sanitation, few surveys explicitly asked whether a latrine or flush toilet was used by several or only one household. In general, we assume toilets to be private, and only recode them as public when sharing with other household is explicitly stated.

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but not directly relevant to the bidding strategy, because this does not represent the intensity of competition that bidders actually face.<sup>10</sup> The Hausman (1978) exogeneity test technique is used to examine whether bidder participation is endogenous or exogenous. If the Hausman exogeneity cannot be rejected, the conventional SUR estimator is more efficient to estimate Equation (1).

**Figure 2. Average number of firms applying for a public tender**

<!-- Start of picture text --> 6<br>5 4.7<br>4<br>3.5<br>2.9<br>3<br>2<br>1<br>0<br>Number of firms that Number of firms that Number of firms that<br>expressed interest applied made presentation<br>Source : Author’s survey data.<br><!-- End of picture text -->

# **IV.  Data**

The sample data were collected from the public ESCO projects in Japan.<sup>11</sup> The market began developing in the mid-1990s and has been steadily growing until recently. The total market amounted to JPY40 billion or about US$350 million in 2007. About half were implemented in the industrial sector; another half in the residential sector. The public sector accounts for some 20 percent of the total market. The intensity of market competition has increased in

> 10 The underlying idea is the same as the use of the number of plan holders or eligible bidders in the existing literature (e.g., De Silva et al. 2008; Li and Zheng 2009). 11 See Annex III for more data on the Japanese ESCO market.
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major cities, such as Arusha, Mbeya, Mwanza and Tabora, the urban milieu accounts for about 45 percent of total enterprises in Tanzania.

Notably, however, the quality of electricity supply is not necessarily reliable even in urban areas. According to the BEEPS data, electricity was among the most crucial concerns that many firms raised in the region (Figure 4). The main empirical analysis of this paper uses the 2006 BEEPS for Burundi, 2007 for Kenya, 2005 for Rwanda, 2006 for Tanzania, and 2006 for Uganda. Newer data in 2011 were available for Rwanda when this paper was prepared. But the analysis does not use them to maintain consistency across the countries. According to these surveys, in Tanzania and Uganda, electricity was identified as the most crucial business constraint in 2006. In Rwanda, for which more recent data are available, the electricity problem seems to have been partially resolved but remains one of the major constraints. Power outages are a chronic problem in the region. In 2007, Kenya, Tanzania and Burundi experienced outages for 53, 63 and 143 days, respectively (Eberhard et al., 2011).

Transport infrastructure was also identified as a major constraint in the region. It is of particular concern to firms involved in international trade. While about 30 percent of the domestic firms were concerned about transportation, nearly 60 percent of the exporting and importing firms felt constrained by transport (Figure 5).<sup>3</sup> Transport infrastructure is clearly a matter of concern for those who are engaged in trade and other international businesses.

Transport infrastructure to access a seaport is particularly crucial to landlocked countries, such as Burundi, Rwanda and Uganda. The nearest major seaport is 1,100 km away from Kampala, Uganda. Kigali and Bujumbura are 1,400 km away from the ports of Mombasa and Dar es Salaam, respectively. Even Nairobi is located 500 km distant from the port of Mombasa (Figure 6). When vehicle operating costs, freight tariffs and waiting time costs at transport nodes, such borders and ports, are taken into account, the transport costs to the port

> 3 Exporting firms are defined as those who exported at least 10 percent of products to abroad. On the other hand, importing firms are defined as those who responded that they imported any material inputs and/or supplies directly from abroad.
use annual averages of monthly household expenditures for the average household and for the bottom quintile household. The expenditure data are taken from the Indonesia Database for Policy and Economic Research (INDO-DAPOER, World Bank Group).<sup>9</sup> The air pollution data set contains estimates on mean annual (1999 to 2015) concentrations of fine particulate matter (PM2 _._ 5), coarse dust particles of 2.5 micrometers in diameter, that proxy a wider range of air pollutants. The data points are available at a 0.01-degree resolution and have been derived from a combination of satellite-, simulation- and monitor-based sources. The authors address several inconsistencies in satellite-derived PM2 _._ 5 data by calibrating their estimates with ground-based observations and reducing the noise of seasonal anomalies.

INDO-DAPOER contains key economic, social and demographic indicators at the districtlevel, primarily sourced from various surveys and the Indonesia Central Bureau of Statistics (BPS). We use the annual means of monthly per capita household expenditures (in IDR) and the same indicator for the average across the 20 percent poorest households, which are available from 1999 to 2014. We are primarily interested in distinguishing between average economic developments and changes in poor household income. The data set includes poverty rates and local estimates of GDP, but the coverage is poor.

We are primarily interested in the environmental-economic interactions in urban environments. To narrow the focus, we used a gridded population data set (Gridded Population of the World, v4 at 30 arc-seconds resolution) to distinguish urban from rural districts. We defined urban areas as a contiguous patch of pixels with population density higher than 300 per square kilometer and a population count higher than 5,000. This is similar to the approach followed by OECD and EC-DG Regio to define global Functional Urban Areas, scaling down the population counts to be relevant in a subnational context. Our approach identifies 219 areas with urban clusters. To establish a link between urban air pollution and the INDO-DAPOER database, we summarized the PM2 _._ 5 annual grids to the district-level using the mean value for pollution grids sensed over urban patches in each district. This captures output directly from urban activity, and reduces the outside influence of fires and agricultural activity.

> Since we are particularly interested in pollution, we drop any areas that at one or more

> 9https://data.worldbank.org/data-catalog/indonesia-database-for-policy-and-economic-research. Some district names and boarders have changed over time. To construct the time series, we used the database’s “District Proliferation Crosswalk” file to match observations in the data to the district definition provided by the Global Administrative Areas repository (GADM) available at http://www.gadm.org/. Indonesia’s latest district configuration covers 497 districts of which 427 were successfully matched.

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Table 4. Women’s Marriage Outcomes, by Religion Post the Gujarat Riots of 2002

||(1)<br>Age at<br>Marriage|(2)<br>Married Before<br>Age 18|(3)<br>Age at<br>Cohabitation|(4)<br>Age at<br>First Sex|(5)<br>Age at<br>First Birth|
|---|---|---|---|---|---|
|Post X Riot|-0.326<sup>_∗∗∗_</sup>|0.043<sup>_∗∗∗_</sup>|-0.261<sup>_∗∗∗_</sup>|-0.395<sup>_∗∗∗_</sup>|-0.021|
||(0.081)|(0.015)|(0.070)|(0.056)|(0.080)|
|Post X Riot X Muslim|-0.155|0.031<sup>_∗_</sup>|-0.149|-0.065|-0.160|
||(0.234)|(0.018)|(0.243)|(0.205)|(0.253)|
|Observations|415441|425254|405697|418219|389744|
|R-squared|0.247|0.164|0.216|0.228|0.128|
|Control Average|19.47|0.323|19.49|19.41|21.04|
|District Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|

_Note_ : This table reports coefficients from specification 3. Standard errors are clustered at the state level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

Table 5. Women’s Educational Outcomes Post the Gujarat Riots of 2002

||(1)<br>Years of<br>Education|(2)<br>At least Completed<br>Primary School|(3)<br>At Least Completed<br>Secondary School|(4)<br>Completed<br>Higher Education|
|---|---|---|---|---|
|Post X Riot|-0.388<sup>_∗∗∗_</sup>|-0.016|-0.018<sup>_∗∗_</sup>|-0.016<sup>_∗_</sup>|
||(0.096)|(0.010)|(0.008)|(0.008)|
|Observations|423957|423957|423957|423957|
|R-squared|0.483|0.330|0.369|0.211|
|Control Average|7.405|0.781|0.648|0.130|
|District Fixed Efects|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|

_Note_ : This table reports coefficients from specification 1. Standard errors are clustered at the state level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

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The rest of the paper proceeds as follows. Section 2 briefly describes the data set used. Section 3 discusses alternative reasons why SMEs could be less likely to invest in job training than larger firms. Section 4.1 documents the differences across firm sizes in the intensity to train and section 4.2 analyzes the heterogeneity of these findings across alternative samples within and across countries. Section 5.1 discusses the extent to which training differences across firm sizes could relate to differences in the return of this investment or in the role of different market imperfections impeding SME development. Section 5.2 discusses the empirical evidence supporting each factor. Section 6 concludes and draws policy implications.

# **2. Data**

Our analysis explores a large firm level data set, the _Enterprise Survey_ , collected by the World Bank across several developing countries. For each of the 99 countries in our sample, we select the most recent wave of data available. The only exception relates to a few countries where we have included a previous wave to the most recent one to insure a more comprehensive coverage of the relevant variables explored in our analysis. The final data set covers more than 48,000 firms operating in 99 developing countries and surveyed between 2002 and 2007. The _Enterprise Surveys_ are one of the best data sets to analyze the employer provided job training across developing countries. First, the surveys collect comparable information for several firm characteristics across all the countries. This comparability allows us to document cross country and within country profiles of firms offering job training. Second, the survey collects information on training intensity at the firm level as well as several other firm and workforce characteristics. These include the firm size and age, human capital composition of the workforce, measures of R&D and technology adoption and firm openness. In addition, there is also detailed information on the firm’s geographical location and its sector of activity (2-digitISIC classification). Third, the surveys reach a substantial number of countries across all SME policy on productivity growth. One reason could relate with firm size not being an exogenous determinant of growth so that SME policies could distort firm size and hurt economic efficiency (Kumar, et al., 2001, Caves et al., 1980). Alternatively, policies promoting a sound business environment (through low entry/exit barriers, well defined property rights, and effective contract enforcement) could be more conducive to market competition but the benefits are not only restricted to SMEs.

9
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Verwimp, Philip. 2003 (b). “Testing the Double Genocide Thesis for Central and Southern Rwanda.” _Journal of Conflict Resolution_ 47: 423-42.

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