rafmacalaba/data-use-mentions-extended huggingface dataset:
neighborhoods. In neighborhoods of higher socioeconomic status, restaurant clients also tend to be locals who can afford the upper-tier eating possibilities or visitors who are from equally wealthy neighborhoods. Commuters from poorer areas tend to buy not from restaurants, but rather from markets or street vendors that resemble the food options available in their socioeconomic group.

# **5 Results**

## **5.1 Descriptive results**

Figure 1 shows the geographical distribution of the four socioeconomic groups, the share of women who consume FAFH on a daily basis and the PDRNI of restricted nutrients in 40 neighborhoods of the Lima Metropolitan Area. The first map shows that neighborhoods with a lower socioeconomic background are predominately situated in the north and south, whereas high socioeconomic status neighborhoods are located in the center. A similar distribution can be found for the share of women consuming FAFH on a daily basis.<sup>14</sup> While, on average, 49 percent of all women ages 15–49 consume FAFH on a daily basis (such as in restaurants; see the appendix, table A1), neighborhoods with the highest shares are mainly located in the north and south of the Lima Metropolitan Area. Furthermore, it becomes apparent that most of these neighborhoods show the highest PDRNI of restricted nutrients, while neighborhoods of higher socioeconomic status in the center of Lima exhibit lower PDRNI values. From a simple visual observation, it appears that especially women in neighborhoods of low socioeconomic status consume FAFH and also face a lower nutritional quality of menus in these food environments.

> 14 The average household expenditures per year for the four socioeconomic groups are based on calculations using data of the ENAHO survey. Neighborhoods of low, mid-low, mid- and high socioeconomic status exhibit average per capita household expenditures of S/. 7,500, S/. 9,000, S/. 11,500 and S/. 20,500 (see the appendix, figure A1). Since the socioeconomic strata was based on quintiles of the household
include this interaction term in addition to each variable entering independently. The interaction term examines the possibility that economy _j_ may be less likely to impose new import protection against trading partner _i_ – despite _j_ ’s own domestic economic contraction – if there is a simultaneous negative economic shock abroad. If this were the case, since a negative shock to the domestic economy is captured as an increase in the domestic unemployment rate, we would expect an estimated IRR that is less than 1 for the interaction term and a potentially insignificant IRR estimate for the domestic unemployment change variable. However, we find no evidence of these relationships for this time period and this sample of policy-imposing economies.<sup>17</sup> Finally, in specifications (7) and (8), we address the issue that the five economies might face different choice sets of import restrictions. First, the extent to which WTO commitments restrict an economy’s freedom to raise its applied most-favored-nation (MFN) tariff rates might vary across economies.<sup>18</sup> In specification (7), we control for this by introducing a new variable defined as the annual change in the share of economy _j_ ’s HS-06 products subject to strict WTO discipline, i.e., the share of products for which its applied MFN tariff is equal to (or above) its WTO legal maximum tariff binding.<sup>19</sup> We expect the IRR on this variable to be larger than 1 so that as more imported products 17 During the revision process, we became aware of a paper by Davis and Pelc (2012) that examines related questions on product-level data at the annual frequency for a heterogeneous sample of industrialized and developing economies for 1997-2009. Over this sample period, the authors present evidence consistent with the idea that countries may be less likely to impose protection against partners undergoing a _simultaneous_ contraction. While we present evidence of a similar result for the period covering the Great Recession in section 4.2 below
unit costs per place established across regions, and in slightly higher performance on implementation indicators. In low income countries, the potential for greater effectiveness of nonformal training has, so far, been confounded by the management challenges of a new and more complex institutional design. The relative cost-effectiveness of vocational secondary schools and nonformal training institutions in middle income countries in pre-employment training cannot be judged from World Bank project experience. The available data from Korea demonstrates that these modes can be equally cost-effective, but the Korean context is unique in the strength of the basic education system and the rapid growth of the industrial sector.

6.23 The costs of establishing nonformal training places have been about twice those of secondary places in low income countries. In lower-middle income countrie- secondary places have cost somewhat more. In upper-middle income countries nonformal places have cost nearly seven times those in secondary vocational schools. Nonformal places, of course, are used more frequently in a given time period than those in secondary schools. While data on the outputs of these systems
|3.8|
|HongKong, China|2.3|164.8|1.4|
|Brazil|2.3|1.5|1.4|
|Serbia|1.9|2.6|1.2|

*No data on trade with Belarus were available for Austria, Denmark, and Italy. _Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 39
.3%|0.37|57,834|5.6%|0.48|11.0%|0.11|
|44-48 Wood|22,997|3.6%|1.29|19,983|2.0%|1.1|-2.8%|-0.19|
|72-83 Metals|94,530|14.6%|1.52|88,810|8.7%|1.41|-1.2%|-0.11|
|84-89 Machinery &|||||||||
|Transportation|26,626|4.1%|0.1|37,422|3.7%|0.13|7.0%|0.03|
|Miscellaneous|14,748|2.3%|0.14|37,372|3.7%|0.29|20.4%|0.15|
|**Total**|**647,461**|**100%**||**1,023,849**|**100%**||**9.6%**||

_Source_ : COMTRADE data. _Note_ : HS refers to chapters of the Harmonized System of trade and tariff data of the World Customs Organization.

0
or “late-phase” districts are in white, while the shaded districts are the phase-I districts. In contrast, panel (b), figure 2, shows the employment expenditure that occurred in SGRY or NREGA in 2006. Similarly, panels (c) and (d) of figure 2 show that even during the phase-II implementation of NREGA in 2007, SGRY was operational in the non-NREGA districts. Therefore, the district-level expenditure maps show that SGRY continued to operate in the non-NREGA districts during the phase-wise implementation of NREGA in 2006 and 2007.

> 6 This index was primarily based on district-specific and time-invariant fixed effects.

> 7 Maps for the whole country haven’t been displayed due to restrictions from the Cartography Unit at the World Bank. See the working paper version for the country maps.
3 FOREIGN DIRECT INVESTMENT IN KENYA but new jobs as well. Chinese companies have a higher number of jobs per project because they have fewer projects than other countries. Between 2003 and 2015, FDI from China created 166.92 jobs on average; total FDI generated 100 jobs per project.<sup>10</sup> FDI has a strong ability to create jobs, and table 3 shows some of the progress.

<!-- Start of picture text --> 6000<br>4000<br>2000<br>0<br>China Egypt India Japan Nigeria South AfricaSouth Korea Spain UK United States<br>Country<br>Jobs Created<br><!-- End of picture text --> Figure 29: China is fifth largest creator of jobs. India creates the most jobs (2003-2015) Source: fDi Intelligence from The Financial Times Ltd 2015 Table 3: FDI Projects and related employment (2007-2011)

|Year|Number of Projects|Employment|Employees per<br>project|
|---|---|---|---|
|2007|55|2,847|51.8|
|2008|73|4,341|59.5|
|2009|121|37,045|306.2|
|2010|129|15,753|122.1|
|2011|145|13,289|91.6|

_Source:_ Adapted from UNCTAD/DIAE/PCB/2012/6

**Chinese firms already offer basic skills training** 60 percent of Chinese companies offer formal training programs on skills, safety, and hygiene for local staff; 64 percent of all foreign firms in Kenya offer formal training (SACE 2014; World Bank Enterprise Survey 2013). Companies in the manufacturing sector and state enterprises are more likely to offer formal training than private companies and the service sector. A few Chinese projects aim specifically to provide skills training for Kenyans,
activities of business networks. Such incongruities can have substantial implications for what development programs do in this area. A better understanding of the institutions that support market exchange in SSA would seem essential in developing effective policy prescriptions.

To address these questions, we use data and research results from the Regional Program for Enterprise Development (RPED), a program of manufacturing enterprise surveys across the SSA region, conducted by the World Bank in the 1990s.<sup>2</sup> Among other things, RPED firm-level data can be used to examine the forms of private support institutions found in SSA and to assess how these private orderings shape patterns of market exchange and firm performance. The first section of the paper lays some groundwork with a brief review of the forces driving the formation of private support institutions in the region. We then look at the social capital embodied in these business support institutions and the way this social capital influences firm performance. In the last section, we consider the natural limits private support institutions face in the SSA setting in fostering SME structural transformation and the role for policy and programs.

The paper finds that SMEs in SSA endeavor to get around market failure and the lack of formal institutions protecting property rights and contracts by creating private governance systems in the form of long-term business relationships and networks, as firms do in other parts of the world. But in much of the region, SMEs find it hard even to establish these simple relation-based governance architectures. Economic instability undermines and weakens incentives for long-term, cooperative business relationships and hinders the creation of efficient private institutional arrangements. In this environment tight, ethnically-based, business networks thrive. However, a coordination failure in the indigenous-African manufacturing business community keeps firms from developing network-based governance architectures analogous to those found in ethnic minority communities. It is shown that there are important links between these informal governance institutions and SME performance. Networks raise the performance of “insiders” and, in the sparse business environments of the SSA region, tight networks can

> 2 For a description of
Table A1 Intermediate Input Requirements

||Firm data|Input-output data|
|---|---|---|
|Average intermediate input requirement|0.41|0.42|
|Intermediate inputs sourced from own sector|||
|Food and beverages||0.38|
|Textiles||0.31|
|Minerals||0.04|
|Metals||0.40|
|Other manufactures||0.36|

Source: INDEC. Input-output table for year 1997.

also compute the unit input requirements from an input-output table and we average them across sectors. The resulting coecient is 0.42. The input-output coecients used in the estimation of the production function parameters are presented in Table A1. The data come from the IO Tables for 1997 published by INDEC (Instituto Nacional de Estadística y Censos.

In the model of section 2 we further assume that rms source intermediate inputs from their own sector of production only. This assumption is key for the viability of the estimation procedure as it allows us to treat rms in all sectors symmetrically. It is based on the notion that sectors are aggregate enough so that products are suciently dierent within a same sector, but related through the production chain. For example, the main material in the production of shirts is fabric, and both shirts and fabrics are products within the textile sector. As a back-of-the-envelope test of the assumption we compute coecients for own-sector sourcing from the input-output table. These are 0.38, 0.31, 0.40, 0.04, and 0.36 for food and beverages, textiles, metals, minerals and other manufacturing sectors. The own-sector coecients are very close to the calibrated intermediate input requirement of 0.41 in 4 out of the 5 manufacturing sectors, which implies that the assumption agrees with the data relatively well, with the exception of the small minerals sector. See Table A1.

# Stochastic process for sector and
such as the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), indicating vigorous and dense vegetation. (iv) Grassland refers to vegetated areas dominated by grasses, herbs, and other non-woody vegetation, typically with sparse to moderate tree cover or no trees present. (v) Croplands refer to areas of land specifically designated or utilized for agricultural cultivation, including the cultivation of crops such as grains, vegetables, fruits, and other agricultural products.

41
with the findings in the existing literature that the phone owners are much richer than the full sample of refugees.

Like Rwanda EICV5 and Saint Lucia HBS 2016, three types of weights were calculated to adjust for the sampling bias in the phone owner sample of URHS 2018 in Uganda, namely, PSW (Lee), PSW (Inverse), and PSW & non-PSW.<sup>11</sup> The first subfigure in Figure 4 compares the poverty rates of the phone owner sample by applying the abovementioned weights to the consumption data. The poverty rates among the phone owners applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 38.8%, 43.6%, 43.0%, and 42.6%, respectively. While the last three poverty rates with adjusted weights are slightly but not significantly higher than the poverty rate of the phone owner sample with original weights (38.8%), they

> 11 External data sources on the share of refugees from each country of origin are used to calibrate the weights. Details of the non-PSW adjustments are available in Appendix 3.

18
# **O Figures**

Figure O1: SNAP benefits as a proportion of total food-at-home sales in food stores, 2006-2014 <!-- Start of picture text --> .18<br>.16<br>.14<br>.12<br>.1<br>.08<br>2006 2008 2010 2012 2014<br>Year<br>SNAP Benefits to Food Store Sales<br><!-- End of picture text --> Notes: This figure plots the ratio of SNAP benefits to total food-at-home sales at food stores, as defined by the USDA ERS, from 2006-2014 using data from the USDA ERS and FNS.

## Figure O2: Changes in maximum benefits

(b) Maximum benefits per participant by (a) Maximum benefits, single-person household household size <!-- Start of picture text --> 200<br>200<br>190<br>180<br>180<br>160<br>170<br>140<br>160<br>FarmBill ARRA 120<br>0 2 4 6 8<br>150 Household size<br>2005m1 2010m1 2015m1<br>Year-month Before Farm Bill Farm Bill<br>ARRA<br>Maximum benefits<br>Maximum benefits per participant<br><!-- End of picture text --> Notes: This figure plots out the maximum benefits for a single-person household over time in Figure O2a, and the maximum benefits by household size before the Farm Bill, after the Farm Bill, and after the ARRA in Figure O2b.

85
owner-shareholder. Market share of each bank was defined based on the bank’s assets as a share of total bank assets in the country. We divided the regions into the European Union, East African Customs Union, COMESA and Rest of the World.<sup>36</sup>

# **Ownership Shares of Banks**

Each bank’s market share was then allocated among geographic regions according to the shares of ownership of the bank. We then summed across the banks to obtain total market shares by region. In many cases, however, the Bankscope data were inadequate to allocate ownership shares by region. In these cases, we investigated bank websites, to obtain the required ownership information. The results of our supplementary inquiries are listed below.

The results we get are that owners of the banking sector of Kenya are as follows, in percent: Kenya, 61.8.; EU, 28.7; EAC, 0; COMESA, 0.2; ROW, 9.3. Detailed results on the ownership of the banks are in the tables below.

> 32 <u>http://www.orange.com/en_EN/press/press_releases/cp080917uk.html Accessed 17 April 2009</u>

33 See Paul Buddle Communications, ―The Kenya Regulatory and Fixed-Line Telecoms Overview,‖ March 20, 2008.

34 See Paul Buddle Communications, ―The Kenya Mobile Market Overview,‖ March 20, 2008.

> 35 It combines data from the main information provider, Fitch Ratings, and nine other sources, with software for searching and analysis. Each bank report contains balance sheet and income statements with up to 200 data items. 36 Although we calculated data for the U.S. and the U.K. separately, these were aggregated into the Rest of the World and the European Union, respectively.

87
# **3. Experimental Design**

This study took place in Hayabley, a poor neighborhood of urban Djibouti City. Households were eligible to participate in the study if they had either a pregnant woman and/or a mother of children younger than 2 years old who was registered for and participating in nutrition sessions. Of the 1,055 eligible households, 1,011 (96 percent) were successfully interviewed and enrolled in the study at baseline.<sup>6</sup> Our evaluation exploited the phased rollout of Djibouti’s public works program. After participating in the baseline survey, each of the 1,011 eligible households were randomly assigned to one of 4 groups (A-D, as shown in Figure 1), and offered the opportunity to engage in public works every 6 months, starting in 2014 and ending in 2016. A total of 504 households constituted the first two randomly allocated groups, A and B, which were given priority in the opportunity to work. These two groups were designated as the treatment group. The remaining 507 households, groups C and D, constituted the control group and started the public works, on average, 15 months later than groups A and B (and nine months after the intervention for the treatment group ended). Stratification was conducted by the location of the public works (5 project sites);<sup>7</sup> by nutrition session type: one session for pregnant women, and another for women with child(ren) aged 0-2 years; by the session participant subsets: women in ongoing nutrition sessions and those whose sessions had ended, and by session groups. This stratification results in 65 strata.<sup>8</sup> There are two potential threats to the validity of this study. First, the gradual rollout of the intervention might have generated anticipation effects insofar as women who were not offered to participate in the program in the first round knew that they would eventually receive the offer. By midline most
# **3.4 Implementation fidelity**

We leverage data from the monitoring survey to establish that our experimental design successfully generated different levels of social and geographic decentralization of demonstration activities across arms as Figure 3 illustrates. We present descriptive statistics on the implementation of our experimental design in Table 1. As discussed in Section 3.3, promoted crops are balanced across control and treatment villages and range in number from 1.61 to 1.88 crops. However, no demonstration farmers were designated in control villages, and no demonstration plots were established.

Table 1: Demonstration descriptive statistics

||||Demo in <br>|village<br>|
|---|---|---|---|---|
||Control|Regular|Shared|Decentralized|
||(1)|(2)|(3)|(4)|
|Farmer group|||||
|# of promoted crops|1.88|1.85|1.61|1.85|
|# of demo farmers|0.00|1.52|4.11|8.52|
|Farmer group-crop|||||
|# of demos|0.00|0.82|2.58|4.62|
|# of demo plots|0.00|0.82|1.11|4.62|
|Farmer-crop|||||
|Demo|0.000|0.042|0.129|0.223|
|# of connections to demo|0.000|0.463|0.952|2.683|

_Notes:_ Sample averages of implementation variables by treatment arm across farmer group, farmer group-crop, and farmer-crop observations are presented in this table.

In farmer groups assigned to the regular treatment arm, adhesion to treatment was high with on average 0.82 demonstration plots being set up for each promoted
Afghani per adult equivalent, per month is used for construction of poverty rates. Afghanistan has fourteen poverty lines for rural and urban areas separately in eight regions across the country: North, Northeast, West, West-Central (only rural), South (only rural), Southwest, East, Central regions.

> aThe idea here was to assign clusters in the same month as was done in the previous rounds of the survey. A total of ten households were sampled from every cluster. bThe price data is used for four quarters and 14 geographical regions, rural and urban areas separately in South (all rural), Southwest, North, Northeast, West, West-Central (all rural), Central, and East regions.

45
paper, we examine whether internal monitoring through independent boards and stronger shareholder protections can substitute for external monitoring through the use of short-term debt.

Past research suggests that leverage and debt maturity structure choices could be effective in mitigating agency conflicts (Grossman and Hart, 1982; Jensen, 1986; Stulz, 1990; Hart and Moore, 1995). Good corporate governance and independent boards are also expected to be effective in mitigating agency problems (Bhagat, Carey, and Elson, 1999; Yermack, 1996; Gompers, Ishii, and Metrick, 2003; Klein, 2002). In addition, the law and finance literature suggests that the extent to which debt contracts can be used to mitigate agency problems depends on the institutional environment, and in particular the laws surrounding creditor and investor rights and the quality of enforcement of these laws (La Porta et al., 1998 and 2000; Diamond, 2004; Burkart et al., 2003).

Given the trade-off between monitoring benefits and liquidity risk associated with shortterm debt, we conjecture that firms in countries with better legal protection of shareholders would reduce the need for costly external monitoring using short-term debt. If strong investor protection reduces discretion of insiders and allows for more efficient internal monitoring, then we would expect governance and short-term debt to act as substitutes. An independent board trying to maximize shareholder value, for instance, would choose lower levels of short-term debt if the benefit from external monitoring is low.

To empirically test these conjectures, we gather a large firm-level data set consisting of about 8,453 firms from 23 different countries. Our data set includes a wide range of governance attributes for these firms over the 2003 – 2007 period. These governance attributes, which are collected and maintained at the International Shareholder Services (ISS) database include a wide coverage of key governance features, such as board characteristics, managerial and director compensation, executive stock ownership, auditing practices and shareholder rights. Using these governance attributes, we construct a number of governance related variables that are likely to reflect