rafmacalaba/datause-encoder-data huggingface dataset:
behind the robust empirical finding that – other things equal – landlocked countries tend to grow more slowly than the rest. However, these geographic disadvantages do not pose an insurmountable obstacle to development -- they can be offset with good transport and communications facilities.<sup>1</sup> Africa’s problem is that poor infrastructure adds to its geographic disadvantage.

Aside from external trade, there are many concrete indications that deficient infrastructure hampers Africa’s development in other ways. Reinikka and Svensson (1999) use data from Uganda’s industrial enterprise survey to test the impact of poor infrastructure – as reflected by an inadequate supply of electricity – on firm-level investment, and find that unreliable electricity is a significant investment deterrent. Diao and Yanoma (2003) show that growth in the agricultural sector is constrained by high marketing costs, which largely reflect poor transport (as well as other infrastructure) facilities. Estache and Vagliasindi (2007) argue that an insufficient power generation capacity limits growth in Ghana. Lumbila (2005) finds that deficient infrastructure may hinder the growth impact of FDI in Africa.

This paper offers an empirical assessment of the impact of infrastructure development on growth and inequality, with a focus on Sub-Saharan Africa. The paper uses a comparative cross-regional perspective to place Africa’s experience in the international context. Drawing from an updated data set of infrastructure indicators covering 100 countries and spanning the years 1960-2005, we estimate empirical growth and inequality equations including a standard set of control variables augmented with infrastructure development indicators. The empirical approach extends previous literature in several dimensions: it encompasses different core infrastructure sectors, considers both the quantity and quality of infrastructure, and accounts for their potential endogeneity. We use the empirical estimates to illustrate the contribution of infrastructure development to growth and equity across Africa.

The paper follows recent empirical studies of the contribution of infrastructure to the level and growth of aggregate output and productivity (Sánchez-Robles 1998; Canning 1999; Demetriades and Mamuneas 2000; Röller and Waverman 2001; Esfahani

> 1 In other words, geography is only part of the story. Limao and Venables (2001) conclude that poor infrastructure is responsible for a good portion of Africa’s record-high transport costs and its abnormally low intra-regional trade.

3
in mission areas are more successful at achieving their goals. To this end, two empirical tests are conducted, one using data on project ratings and the other using survey data on development outcomes from the Demographic and Health Survey (DHS).

# **3.1 Project Ratings**

Each World Bank project is headed by a team leader who is also responsible for evaluating its success (with respect to the stated goal) upon completion. After the initial evaluation, the World Bank’s Independent Evaluation Group (IEG) performs a second assessment based on available project documentation. Furthermore, the IEG performs an additional in-depth evaluation of approximately 25 percent of the projects, which includes on-site visits and additional analyses (Denizer, Kaufmann, & Kraay, 2013). Each layer of evaluation rates the projects on a six-point scale from “highly satisfactory” to “highly unsatisfactory.” The data contain rating information on 43 percent of the projects included in the grid analysis, thus allowing tests to be run on whether projects implemented in the vicinity of missions display higher ratings. Since rating information is at the project level rather than the location level, here the analysis departs from the grid-level dataset and relies instead on a project-level dataset (recall that each project is implemented across several locations). The baseline specification to be estimated by OLS is where _p_ and _k_ are indices for project and country, respectively.

Rating _pk_ is a binary variable that equals 1 if the rating of project _p_ is “satisfactory” or better and equals 0 otherwise (the most recent available IEG evaluation is used, which means desk reviews in 87 percent of the cases); MissionLocations _pk_ is the project’s fraction of precision 1 and precision 2 locations that are within 25 km of a mission station (this radius implies observational units that roughly correspond to the size of cells in the grid structure). The selection of relevant covariates follows Denizer, Kaufmann, and Kraay (2013). In terms of country-level variables, the controls include growth in average GDP per capita over the life of the project (from the World Bank) and the sum of Freedom House scores on civil liberties and political rights.<sup>25</sup>

> 25Denizer, Kaufmann, and Kraay (2013) also included CPIA ratings from the World

26
such as accountants, sales representatives and analysts.<sup>23</sup> The asymmetric impact of automation is in line with the hypothesis that industrial robots substitute for workers performing production occupations, but complement some non-production occupations. For example, an industrial robot performing production tasks may send more precise, real-time information on the production process that would improve the performance of non-production workers, such as process analysts.

Table 5: plant level impact of robots on employment and wages.

||(1)<br>Employment|(2)<br>Wages|
|---|---|---|
|ETR _×_ secondary|-0.006**|-0.018|
||(0.002)|(0.012)|
|Observations|53,517|53,517|
|R-squared|0.939|0.653|
|Plant FE|yes|yes|
|Industry-year FE|yes|yes|
|Other technologies|yes|yes|

The table presents OLS estimates of the relationship between plants’ exposure to robots and employment. The dependent variables are the log of plant level employment and average wages. Secondary is the 2006 share of plants’ employment with secondary education. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

Table 6: Plant level impact of robots on production and non-production employment and wages

||(1)<br>Production<br>employment|(2)<br>Production<br>wages|(3)<br>Non-production<br>employment|(4)<br>Non-production<br>wages|
|---|---|---|---|---|
|ETR _×_ secondary|-0.023**|0.008|0.050***|0.060|
||(0.009)|(0.012)|(0.009)|(0.051)|
|Observations|53,517|53,517|53,517|53,517|
|R-squared|0.904|0.596|0.829|0.657|
|Plant FE|yes|yes|yes|yes|
|Industry-year FE|yes|yes|yes|yes|
|Other technologies|yes|yes|yes|yes|

The table presents OLS estimates of the relationship between plants’ exposure to robots and employment by type (columns 1-4). The dependent variable is the log of plant level (total, production and non-production) employment. Secondary is the 2006 share of plants’ employment with secondary education. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

> 23Indeed non-production workers have higher education than production workers, with 12 years of education on average versus 10 years for production workers.

20
own website to communicate with clients or suppliers, _email_ = 1 if firm uses email to communicate with clients or suppliers. This technology indicator captures both worker skills and productivity.

In a second step, we substitute regional determinants of exporting and agglomeration economies for regional dummies. Following the economic geography literature, we include variables related to investment climate, which yields the following equation: where the regional determinants of exporting are defined as follows:

- _customs_ =  average number of days to clear imports from customs in logarithms by region

- _electr_ = hours of power outages per month in logarithms by region

- _license_ = average number of days to obtain either operating license, import license or construction-related permit in logarithms by region

- _credit_ = the percentage of firms with a credit line by region, and

- _corrup_ is the percentage of firms expected to pay informal payment to public officials by region.

The calculation of these regional indicators is based on all firms in the sample, regardless of a firm’s specific industry. We used weights provided by the Enterprise Analysis Unit to calculate regional averages. Note that we only include some regional determinants of exporting due to data limitations of our available regional investment climate indicators (see Tables 1 and 2). Thus, we only use one variable to capture infrastructure ( _electr_ ), transport and trade facilitation ( _customs_ ), permits and licenses ( _license_ ), corruption ( _corrup_ ), and access to finance ( _credit_ ), respectively. We don’t capture regulation and tax, and by focusing on regional investment climate, it might be possible that we omit other regional determinants of exporting, e.g., related to average skills and productivity. However, we believe that these are indirectly captured by agglomeration economies.

Finally, we include three types of regional agglomeration economies, namely (i) a region’s number of firms as percentage of a country’s total number of firms ( _agg_size_ ), (ii) a region’s number of direct and indirect exporters as percentage of a country’s total number of direct and indirect 19
<!-- Start of picture text --> (% of rural nonfarm employment by industry)<br>40%<br>Wh<br>20%<br>10%<br>o%<br>Manufacturing CGonstrection Social services Trade, transport<br>and<br>1983 91993-94 2004-05 92009-10 communication<br><!-- End of picture text --> <!-- Start of picture text --> (% annual growth rate of rural nonfarm employment by industry)<br>8.8<br>12.0%<br>9.0%<br>6.0%<br>3.0%<br>0.0%<br>S08 Manufa: te] Social services Total<br>6.0% ©———____?_$?<br>B83t093 mgs i004 m04t009<br><!-- End of picture text -->
delinquency rate, it was ordered into liquidation.<sup>10</sup> As many microlenders around the world weaken their group liability approach and shift towards individual lending, understanding the role of group liability in enhancing performance has become a critical question in microfinance programs, moving forward. Yet, the empirical literature provides little guidance for policy makers and microfinance practitioners, since few empirical studies have examined group liability contracts with other lending strategies.

In this paper, we exploit an exogenous change in liability structure in an Indian microfinance program, where the program shifted from individual liability to a group liability structure. We find evidence that for the same borrower, the shift to group liability reduces default rates and improves savings discipline. Under the group liability setting, required monthly loan installments are 6% less likely to be missed and compulsory savings deposits are about 19% less likely to be missed, relative to individual liability. Thus, our findings indicate that group lending outperforms individual lending in loan repayment and savings discipline.

The microlender we study, Saath, has operations that are fairly typical of MFIs that lend in urban areas. Overall, our results suggest that group liability in microfinance may be a particularly effective contract in improving repayment behavior, especially for clients who are already borrowing. A promising avenue for further research is to study the precise mechanisms for this improved behavior, that is, the relative merits of peer screening versus peer monitoring under group liability contracts. Further, if peers are indeed effective screeners or monitors, an open research question is whether group liability contracts can be made even better, perhaps by introducing some formal group or sub-group level repayment incentives or insurance mechanisms.

# **References**

- Ahlin, C. and R. M. Townsend (2007, February). Using repayment data to test across models of joint liability lending. _The Economic Journal 117_ , F11–F51.

- Armend ́ariz de Aghion, B. (1999). On the design of a credit agreement with peer monitoring. _Journal of Development Economics 60_ , 79–104.

> 10See: http://financialaccess.org/node/3547

19
**Figure 8:  Market Risk on Equities – RG 05**

|**DKr mln.**|||
|---|---|---|
||Market value|**Red**<br>**Yellow**|
|1. Equities|||
|2. Derivatives|||
|3. Total|||

# **3. Credit Risk**

The credit risk is measured by applying credit risk weights on the different categories of bonds and reported in risk report RG08. Government bonds are assigned a weight of 0. Supra and Agency bonds are assigned weights between 0.10 and 0.01563 depending on class and duration. Corporate bonds are assigned a weight of 1. The same weights are used for both the red and yellow scenarios. The total credit risk is 8 percent of the weighted amount.

It is important to note that the credit risk on corporate and mortgage bonds does not yet take into account the risk of default as reflected in different credit ratings. As the traffic light system evolves toward a fuller application of risk-based rules, the applied risk weights will need to reflect the investment grade of corporate and mortgage bonds.

**Figure 9:  Credit and Counterpart Risk – Credit Risk RG 08**

|**~~DKr mln.~~**|Unweighted amount|Weight|Weighted amount|
|---|---|---|---|
|1. Government bonds||0,0||
|Mortgage loans||||
|2. -remaining term < 6 months||0,01563||
|3. -6 months≤remaining term≤24 months||0,0625||
|4. -remaining term > 24 months||0,1||
|Other claims on banks and such||||
|5. -remaining term < 6 months||0,03125||
|6. -6 months≤remaining term≤24 months||0,125||
|7. -remaining term > 24 months||0,2||
|8. Remaining claims||1,0||
|9. Total||||
|10. Total credit risk (weighted total amount<br>multiplied by0,08)||||

# **4. Counterpart Risks on Derivatives**

The counterpart risks on derivatives are calculated by using the market value multiplied by a weight depending on the type and duration of contracts and by a factor [ _a_ ]that depends on the credit risk classification of the security issuer.

23
Policy Research Working Paper 9887

# **Abstract**

This paper studies factors that could account for the asymmetric impact of the COVID-19 pandemic in Latin America, by exploiting microdata from the World Bank’s high-frequency phone household surveys conducted immediately after the onset of the pandemic. The paper codifies the occupation variables in these surveys, constructs measures of the individual’s potential for work from home, and estimates fixed-effects models of job loss and other labor outcomes. In line with previous studies, the findings show that the impact of the COVID-19 shock was (i) harder for women and (ii) strongly decreasing in the ability to work from home. Importantly, the analysis finds that the mitigating effect of working from home on the severity of the impact was especially relevant for women with children. These effects were larger in countries/periods in which the containment measures implemented by governments against the spread of the disease were more stringent. The paper also provides suggestive evidence on a plausible mechanism underlying the results: women with children were more likely to stay home due to school closures and the traditional intrahousehold distribution of childcare responsibilities, and thus the possibility of working from home was crucial for them to keep their jobs.

This paper is a product of the Poverty and Equity Global Practice. 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 lgasparini@cedlas.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
# **<u>Table A.27. The spillover effect on individual-level outcomes</u>**

|||||Dependen|t variables||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Districts in provinces with the|-0.0113|0.0013|0.0128|0.1267|0.0050|0.0015|0.0098|0.1724|
|program|(0.0252)|(0.0105)|(0.0197)|(0.1430)|(0.1154)|(0.1149)|(0.0147)|(0.1849)|
|Age|0.0303***|0.0063***|0.0095***|0.1107***|-0.0121***|-0.0108***|0.1588***|0.1932***|
||(0.0012)|(0.0005)|(0.0005)|(0.0053)|(0.0038)|(0.0036)|(0.0069)|(0.0315)|
|Age squared|-0.0003***|-0.0001***|-0.0001***|-0.0015***|0.0004***|0.0004***|-0.0082***|-0.0100***|
||(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0003)|(0.0015)|
|Male|-0.0614***|-0.0157***|0.0954***|1.1164***|-0.1978***|-0.1817***|0.0170**|0.0220|
||(0.0085)|(0.0040)|(0.0049)|(0.0510)|(0.0252)|(0.0239)|(0.0072)|(0.0217)|
|Ethnic minorities (yes=1, Kinh=0)|0.1322***|-0.0723***|-0.0071|0.0997|-0.1579**|-0.1482**|-0.1172***|0.6665***|
||(0.0180)|(0.0080)|(0.0114)|(0.1105)|(0.0671)|(0.0622)|(0.0146)|(0.0900)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.0714***|-0.0178**|-0.0560***|-0.4341***|0.8067***|0.7216***|0.2612***|-0.5382***|
||(0.0259)|(0.0085)|(0.0116)|(0.1177)|(0.0444)|(0.0424)|(0.0366)|(0.1772)|
|Observations|167,748|167,748|167,748|167,748|110,518|110,518|57,720|57,720|
|R-squared|0.179|0.055|0.149|0.136|0.122|0.118|0.209|0.287|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

77
- Sacerdote, B. (2001). Peer effects with random assignments: Results for Dartmouth roommates, _Quaterly Journal of Economics_ **116** (2): 681– 704.

- Soetevent, A. R. (2006). Empirics of the identification of social interactions: An evaluation of the approaches and their results, _Journal of Economic Surveys_ **20** (2).

- Veleda, C. (2003). Mercados educativos y segregaci ́on social. las clases medias y la elecci ́on de la escuela en el Conurbano Bonaerense. Working Paper 1, Centro de Implementati ́on de Pol ́ıticas P ́ublicas para la Equidad y el Crecimiento (CIPPEC).

- Veleda, C. (2005). Efectos segregatorios de la oferta educativa. el caso del Conurbano Bonaerense. Working Paper 5, Centro de Implementati ́on de Pol ́ıticas P ́ublicas para la Equidad y el Crecimiento (CIPPEC).

- Vigdor, J. L. & Nechyba, T. (2004). Peer effects in elementary school: Learning from apparent random assignment. Unpublished manuscript, Department of Economics, Duke University.

- Vigdor, J. L. & Nechyba, T. (2006). _Schools and the equal opportunities problem_ , MIT Press, Cambridge, MA, chapter Peer effects in North Carolina public schools.

- Wilson, W. J. (1997). _The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy_ , University Chicago Press, Chicago.

33
3 estimated expenditure needed to reach<sup>this caloric requirement, though the</sup> details of estimation are unclear from<sup>the information given in BBS (1988b).</sup> The BBS results show a substantial decrease<sup>in the proportion of the</sup> rural population who were deemed poor,<sup>from 83% in 1973/74 to 74% in i981/82,</sup> then dropping quite sharply to 51% in<sup>1985-86. The estimates show a similar</sup> (though less dramatic) contraction in<sup>the prevalence of urban poverty. While</sup> the BBS figures suggest that the absolute<sup>numbers of poor increased during the</sup> 1970s, a sharp decline in rural areas<sup>during the 1980s is indicated, though</sup> this was partly mitigated by an increase<sup>in the numbers of poor in urban</sup> areas. Nonetheless, the BBS figures<sup>show an impressive recent record in</sup> poverty alleviation, with aggregate numbers<sup>of poor declining from 67 million</sup> in 1981/82 to 51 million in 1985/86.

Skeptics have pointed to a number of problems<sup>in comparing BBS's</sup> Household Expenditure Surveys over time.<sup>For one thing, there are differences</sup> in sample coverage; for example, while<sup>the 1981/82 HES surveyed about 9,500</sup> households (0.05 percent of the population),<sup>this dropped to just 3,800 (0.02</sup> percent) for the 1985/86 HES. There<sup>have also been some changes in the</sup> questionnaire used, with implications<sup>for the measurement of household incomes</sup> and consumptions, such as in the methods used<sup>for imputing the value of food</sup> consumption from own production.

So there are good reasons to ask: How robust<sup>is this seemingly excellent</sup> recent record on poverty alleviation<sup>to possible inconsistencies over time in</sup> the underlying household expenditure surveys,<sup>on which the estimates of</sup> poverty have been based?.

One possible clue can be obtained from<sup>an alternative source of data on</sup> aggrega-e incomes and consumpZion,<sup>namely Bangladesh's national accounts (NA).</sup>