rafmacalaba/datause-encoder-data huggingface dataset:
quantifies the impact of business risks pertaining to crime and corruption in a developing country setting. Similar to our study, Bah and Lang (2015) use Enterprise Survey data for countries in Sub-Saharan Africa and report a somewhat limited role of variation in business distortions in explaining cross-country variation in output per capita in their sample. However, the quantitative impact on output observed in Africa is close to what we obtain for Afghanistan.

# **3. Data**

The main sources of data for this paper are the World Bank Enterprise Surveys (ES) and World Bank Informal Enterprise Surveys (IES). Crucial for our analysis, these surveys collect qualitative and quantitative data <mark>to e</mark> valuate the performance of business enterprises and identify constraints to doing business. <mark>The surveys which are</mark> conducted <mark>through interviews with firms in manufacturing, retail, and other services sectors cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures.</mark> <mark>Specifically, we use data from 2014 and 2008, where the 2014 data were collected only on formal sector firms (ES) while the 2008 sample contains both formal and informal sector firms (both ES and IES).</mark> The format of both surveys is very similar to several overlapping questions. In general, the ES is designed such that it is representative of the formal private sector in a country. Given that the actual size and structure of informal sectors is difficult to ascertain, more so in developing countries and emerging economies, it is challenging to get a representative sample for the informal sector. As such, IES may not necessarily be representative at the national level. Despite this caveat, the IES is a reliable source of data on informal firms and presents an excellent opportunity to study these firms to increase our knowledge and understanding of the barriers they face and the business environment they operate in.

For ES2014, firms were selected using stratified random sampling at three various levels of stratification: industry, firm size, and region.<sup>14</sup> Industry stratification consists of manufacturing, retail/wholesale, construction, and other services sectors. Firm size stratification is defined as small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees).<sup>15</sup> Regional stratification includes five regions (Kabul, Herat, Kandahar, Mazar-e-Sharif, and Jalalabad) which include both the city and the surrounding business areas. The sample frame used is based on three different sources: list of manufacturing firms was obtained from the Afghanistan Investment Support Agency (AISA); list for retail firms was generated by the implementing contractor for the five cities of fieldwork and list for

> 14 More information can be found at http://www.enterprisesurveys.org/Methodology.

> 15 For stratification purposes, the number of employees was defined based on reported permanent full-time workers.

8
Policy Research Working Paper 8946

# **Abstract**

In this paper the impact of various agglomerative forces on employment growth in Ghanaian manufacturing is investigated, using data from two firm censuses, as well as population census and trade data. The study is the first to use nationally representative firm data that covers the formal and informal economy to investigate the impact of agglomerative forces on employment growth in an African economy. African economies are rapidly urbanizing, but this has not been accompanied by growth in manufacturing.

A lack of agglomeration economies is one possible explanation for slow manufacturing growth and the attendant premature deindustrialization. The paper follows Combes (2000) in examining the importance of agglomeration economies on employment growth in Ghanaian manufacturing, finding that there is no evidence that population density is associated with faster employment growth. Other agglomeration economies do seem to play a role, although not always in the manner anticipated.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. 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.. 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 corresponding author may be contacted at andrew.kerr@uct.ac.za.

_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
bank presence is associated with shallower credit markets and slower credit growth, as in the “cream-skimming” equilibrium in their model. Focusing specifically on the case of Pakistan, using a panel of 80,000 loans over 7 years, Mian (2006) finds that foreign banks in Pakistan shy away from lending to “soft information” firms such as those that are small, located in rural areas, not affiliated with business groups, or seeking first-time loans and long-term relational financing. Also, he finds that foreign banks are less likely to bilaterally renegotiate and are less successful at recovering loans. Finally, Mian shows that measures of geographical or cultural distance between the firm in the host country and the home country go a long way in explaining the differences found in the lending, recovery, and renegotiation patterns of foreign and domestic banks.

In contrast to the studies discussed above, research by Clarke, Cull, Martinez Peria and Sanchez (2005), Giannetti and Ongena (2005), and Clarke, Cull and Martinez Peria (2006) offers some evidence that foreign bank participation may not always be pernicious for access to credit in host developing countries. Using bank-level data for four Latin American countries (Argentina, Chile, Colombia and Peru) during the mid-1990s, Clarke, Cull, Martinez Peria and Sanchez (2005) investigate whether bank origin affects the share and growth of lending to small businesses. While they find that on average foreign banks seem to lend less to small businesses, they also uncover significant differences between small and large foreign banks. In fact, at least in these countries, large foreign banks often surpass large domestic banks in their share and growth of lending to small businesses.

Instead of using bank level data, Giannetti and Ongena (2005) and Clarke, Cull and Martinez Peria (2006) offer evidence on the effects of foreign bank participation on access to credit using firm-level data. Giannetti and Ongena (2005) employ a large panel of almost 60,000 firm-year observations on listed and unlisted companies in Eastern Europe to investigate the impact of foreign bank lending on firm growth and financing. They find that foreign lending stimulates growth in firm sales, assets, and leverage. Though the effect is smaller for small firms, they conclude that large firms benefit more from foreign bank presence, but even small companies profit from foreign bank entry. Combining responses from a survey of over 3,000 firms operating in 35 developing countries with data on the degree of foreign bank presence across these countries, Clarke _et al._ (2006) find that all enterprises, including small and medium14
country‐pair‐products by 6.3 percent on average, exports of products subject to 1 and 10 percent reductions in tariff rates will increase by 6.6 and 8.9 percent on average respectively as a result of a one‐day reduction in trading times. A one‐day reduction in trading times with a half‐ reduction or full reduction of tariff rates (tariff rate=0) will boost exports by respectively 19.5 and 32.7 percent on average.

_Table 7: Complementarity between infrastructure improvements and trade reforms_

||**_Deepening tra_**<br>|**_de agreements_**<br>|**_Improving m_**<br>|**_arket access_**<br>|
|---|---|---|---|---|
||**OLS**|**PPML**|**OLS**|**PPML**|
||(1)|(2)|(3)|(4)|
|GIS Time|‐0.00228***|‐0.00214***|‐0.00271***|‐0.00262***|
||(0.000185)|(0.000149)|(0.000190)|(0.000159)|
|PTA Depth|0.0287***|0.0277***|0.0190***|0.0168***|
||(0.00329)|(0.00253)|(0.00291)|(0.00213)|
|PTA Depth * GIS Time|‐0.000105***|‐0.000111***|||
||(2.30e‐05)|(2.18e‐05)|||
|Ln(PRF+1)|‐1.728***|‐2.284***|‐3.849***|‐5.254***|
||(0.360)|(0.278)|(0.644)|(0.547)|
|Ln(PRF+1) * GIS Time|||0.00713***|0.00883***|
||||(0.00134)|(0.00108)|
|Observations|1,205,865|8,287,250|1,205,865|8,287,250|
|R‐squared|0.620|0.501|0.619|0.502|

Note: Standard errors in parentheses are clustered by exporter‐importer pair. *** p<0.01, ** p<0.05, * p<0.1

# 4. Assessing the potential impact of BRI on trade

In this section we assess the potential impact of BRI improvement on export flows. First, we study the time improvements that derive from BRI transportation projects using GIS analysis and then we focus on trade effects. The following cases are presented: improvements in infrastructure only, improvements in both infrastructure and border delays and improvements in infrastructure along economic corridors. We also assess the overall effect of BRI transportation projects in the presence of deeper agreements and when better market access is granted to countries.

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_COTE D’IVOIRE: Volatility, Shocks and Growth_

**Figure 7: Terms of Trade, Real Exchange Rate & Real Cocoa Prices - Deviations from Trend**

<!-- Start of picture text --> 0 .4 0 T e r m s o f T r a d e S h o c k s<br>( L o g o f t h e r a t io o f a c t u a l t o t r e n d )<br>0 .3 0<br>0 .2 0<br>0 .1 0<br>0 .0 0<br>- 0 .1 0<br>- 0 .2 0<br>- 0 .3 0<br>0 .2 8 R e a l E x c h a n g e R a t e S h o c k s<br>( L o g o f t h e r a t io o f a c t u a l t o t r e n d )<br>0 .2 4<br>0 .2<br>0 .1 6<br>0 .1 2<br>0 .0 8<br>0 .0 4<br>0<br>- 0 .0 4<br>- 0 .0 8<br>- 0 .1 2<br>- 0 .1 6<br>0 .7<br>C o c o a P r ic e S h o c k s<br>( L o g o f t h e r a t io o f a c t u a l t o t r e n d )<br>0 .5<br>0 .3<br>0 .1<br>- 0 .1<br>- 0 .3<br>- 0 .5<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->
development in the country. In a number of specifications, RZ instrument for this final interaction term with variables capturing the legal origins of the country and a measure of institutional quality, all interacted with a measure of financial development. In particular, I will focus on the specification in Table 4, column 6 of RZ, where the relevant measure of financial dependence is an index of accounting standards recording the types of information provided in annual reports of publicly-traded corporations in a crosssection of countries.

This third example differs from the previous ones in two key respects. First, because RZ rely on the within-country variation in sectoral growth rates, potential violations of the exclusion restriction are less obvious than in the previous two cases. In RZ, the requirement is that the instruments be orthogonal to the country- and industryspecific component of growth, since the regressions contain country and industry dummies. Thus for example, concerns about the exclusion restriction are not that countries with faster growth adopt better accounting standards, but rather that countries with a relatively faster growth in financially-dependent industries would adopt better accounting standards. Nevertheless there might be residual concerns about the validity of the exclusion restriction in this case. The second difference is that RZ use multiple instruments, while the results I show above apply to the case of a single instrument. To make the RZ results fit into the framework of this paper, I choose just one of their instruments and first reproduce the RZ results in this just-identified case. For this purpose I choose their index of efficiency and integrity of the legal system, produced by a commercial risk rating agency, as the one instrument of choice. Doing so gives a result that is of comparable significance to the RZ core result, although the magnitude of the estimated coefficient becomes somewhat larger than what RZ report.<sup>10</sup> I use datasets provided by the authors to reproduce their results. In each of the three examples, I first project the dependent variable, the regressor of interest, and the instrument on all the remaining control variables that these authors treat as exogenous, so that I can identify these residuals as y, x, and z in the discussion above. I also normalize the variance of z to be equal to one, consistent with the discussion above. I

> 10 An alternative is to use just their dummy variable for Scandinavian legal origins as an instrument, which generates results that are quite similar to those reported by RZ.  Conversely, using either dummies for British or French legal origins alone as an instrument does not deliver significant IV estimates of the coefficient on the interaction variable of interest.

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Against this background, we build on the ten-region, 30 sector model of the Russian economy developed by Rutherford and Tarr (2010) to assess Russian WTO accession; we incorporate environmental emissions data in Russia for seven pollutants associated with output (for non-carbon dioxide emissions) and burning of fossil fuel (for carbon dioxide emissions). Emissions are tracked for all 30 sectors in each of the ten Russian regions of our model. Our integrated trade and environment model contains increasing returns to scale sectors, foreign direct investment in business services, product differentiation and endogenous productivity effects from additional varieties of goods or services in imperfectly competitive sectors. In addition, we extend Rutherford and Tarr by allowing fuel substitution in production and consumption. We develop a methodology to quantify the decomposition of the scale, composition and technique effects of all simulations. Compared with earlier CGE research quantifying the impacts of trade on the environment based on CRTS models, we more effectively account for productivity changes that may have an important impact on emissions. In our assessment of WTO accession for Russia we find drastic differences in the economic and environmental impacts between model simulations without and with productivity changes.

With a nominal per capita income above US$14,000 per year in 2013 (and a purchasing power parity per capita income of about US$18,000), it is possible that Russia is above the point on the environmental Kuznets curve where the higher incomes coming from WTO accession may increase the demand for greater environmental regulation. In fact, Antweiler _et al_ ., (2001) have shown in their empirical study of over 40 countries, that trade liberalization has been associated with a cleaner environment due to stronger environmental regulations following trade liberalization. Notwithstanding these optimistic possibilities for environmental regulation (and the stated objectives of the Russian government outlined in the next section), we believe it is crucial to address political economy considerations of environmental regulation. That is, to minimize opposition to environmental regulation, it is crucial to design policies that minimize the costs to industry of achieving a given level of environmental abatement. In a second set of simulations, we assess the costs of three types of environmental regulations to reduce carbon dioxide emissions by 20 percent: market based “cap and trade”; emission intensity standards; and energy efficiency standards. Given that part of the income increase from WTO accession might be used to pay for environmental abatement, in a third set of scenarios, we assess the impacts of “overlapping” policy reforms where we simultaneously implement our central case scenario of _WTO_ accession with each of our three carbon emission reduction policy initiatives. Again, the relative costs of the different environmental regulations are compared.

2
<!-- Start of picture text --> pe<br>ee<br>50 8<br>45 7<br>40 6<br>35 5<br>30 4<br>25 3<br>20 2<br>15 a<br>10 0<br>5 -1<br>0 -2<br>2019 2021 2023 2025 2027 2029 2019 2021 2023 2025 2027 2029<br>90% 70% %w§40% 20% 90% 70% %W40% 20%<br>a<br>4000 20.0<br>3500 17.5<br>3000 15.0<br>2500 12.5<br>2000 10.0<br>1500 75<br>1000 5.0<br>500 2.5<br>0 0.0<br>2019 2021 2023 2025 2027 2029 2019 2021 2023 2025 2027 2029<br>90% 70% m@40% 20% 90% 70% %w§40% m20%<br><!-- End of picture text -->
for a period of 24 months. One of the innovations of the program is that it combined cash transfers with behavioral change accompanying measures to promote early childhood development. Specifically, the behavioral change component included parenting training activities to encourage health, nutrition, psycho-social stimulation, and child protection practices. The behavioral change component was implemented through monthly village assemblies, community meetings and household visits delivered by trained NGO operators and community workers. Participation was encouraged and monitored but it was not a formal condition to receive the cash transfers. We collaborated with the Niger safety net unit in the Office of the Prime Minister to embed an experiment in the roll-out of the program.

In this paper, we present results from an RCT that disentangles the effects of behavioral change promotion from cash transfers to very poor households. The objective is to document whether the interventions led to changes in parenting practices and resulted in improvements in early childhood development. Villages eligible for the cash transfer program were randomly assigned to (i) a control group, (ii) cash transfers only, or (iii) cash transfers with behavioral change promotion (BCC). Within treated villages, we survey cash transfer beneficiary and non-beneficiary households to identify spillovers from the behavioral change intervention.

The study makes three main contributions. First, we estimate the average treatment effect of the BCC intervention on parenting practices and young children’s human capital among cash transfer beneficiary households. Second, we measure local spillovers from the BCC intervention on non-beneficiary households in treated villages. Third, we tease out the relative effects of the behavioral change and cash transfer interventions.

The results show that behavioral change promotion affected a range of practices related to nutrition, health, stimulation and child protection. Local spillovers on parenting practices are found among households not receiving cash transfers within treated communities. Moderate gains in children’s socioemotional development are observed, but there are no improvements in anthropometrics or cognitive development. Cash transfers alone do not alter parenting practices and do not improve early childhood development outcomes. We also find that the cash transfer and behavioral interventions have differential intra-household effects. The behavioral intervention improves dietary diversity among children, but no such improvements are found among adults in the household. The opposite is found for cash transfers, which induce improvements in dietary diversity for adults in the household but not children. The cash transfers also increase household consumption of non-food items, which the BCC intervention offsets.

3
across the two sets of districts. In 2003, when TSC intensity increases sharply in some districts, test scores turn noticeably up in exposed districts, relative to unexposed districts. The rest of this section adds precision and depth to these observations while confirming the robustness of the statistical and causal inference.

# **5.1 TSC intensity associated with greater cognitive achievement**

Table 3 presents the main result of the paper: greater TSC coverage in the first year of life is associated with greater cognitive achievement at age six, as measured by the ASER tests. The coefficients on TSC intensity must be interpreted with care: because TSC latrines are scaled per capita, they estimate a linear approximation to the effect of moving from 0 latrines per capita to 1 latrine per capita. The program did not nearly achieve this and would not aspire to, as members of a household can share a latrine, and some households already had latrines. Therefore, in the row below the TSC coefficients, the table presents the average effect of the program: the coefficient estimate multiplied by the across-district mean of TSC intensity in 2003. Therefore, on average among rural six year olds studied in the ASER survey, during this period the TSC increased the fraction who could recognize letters by about three-tenths of a percentage point.<sup>10</sup> This is an important but not implausibly large effect.

The result is very similar across the three tests and levels of cognitive achievement.<sup>11</sup> Adding the controls does little in any specification to change the estimated coefficients, suggesting that the result is not driven by an omitted variable. Given the rural Indian context, it is reassuring of data quality that girls are consistently found to show worse performance on the tests than boys. Two districts report especially high levels of TSC

> 10Another way of arriving at this result is to regress test scores on an indicator for a district being reported to have received any TSC latrines by that year. As is well-known, this dichotomization will reduce power and attenuate estimates by introducing measurement error into the independent variable. This is associated with a statistically significant coefficient of about 0.015 for reading and math tests, which can be multiplied by about one-fifth of studied children being exposed to the TSC during this period.

> 11Additionally, estimation using logit rather than linear probability finds a similar result; for example, the _t_ -statistic in the case of recognizing letters is 2.13.

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**Table 5**

**Regression Results of Low Congestion with sectoral restriction (country fe)**

|**PANDEMIC & LOC**|**(2.a)**<br>**KDOWN EVOLUTI**<br>0093***|**ON**<br>0080***|**(2.b)**<br>0064***|
|---|---|---|---|
|**Covid death (per Million)**|.<br>(0.002)|.<br>(0.003)|.<br>(0.003)|
|**Public transport closure**|0.595***<br>(0.013)|0.629***<br>(0.015)|0.598***<br>(0.015)|
|**Public transport facial covering**|0.110***<br>(0.005)|0.107***<br>(0.009)|0.105***<br>(0.005)|
|**CON**|**TROLS**|***|**|
|**Working days**||-1.670<br>(0.030)|-1.666<br>(0.030)|
|**Min temp**||-0.003<br>(0.002)|-0.002<br>(0.002)|
|**Avg temp**||-0.016***<br>(0.002)|-0.017***<br>(0.002)|
|**Precipitation**||-0.061***<br>(0.009)|-0.064***<br>(0.071)|
|**City density**|||-0.001*<br>00007|
|<br>**TRENDS IN M**<br>|**OTORIZATION**||(.)<br>-0.226***|
|**Car registration**|||(0.015)|
|**COUN**|**TRY FE**<br>|||
|**Country FE**|Yes|Yes|Yes|
|**Monthly dummy**|Yes|Yes|Yes|
|**Constant**|-1.377<br>(0.295)|-0.862**<br>(0.431)|-0.610<br>(0.431)|
|**N**|68076|68076|68076|
|**N cities**|186|186|186|
|**Wald χ2**|9068.76***|15739.15***|15825.39***|
|**Log likelihood**|-26248.126|-18961.454|-18839.815|
|**LR (chi2)**|1506.92***|2276.12***|2227.78***|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

17