rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Growing a successful firm requires a large variety of skills, leading Lazear (2004) to conjecture that entrepreneurs need to be jacks-of-all-trades. He notes (p. 208) that “even if individuals are not endowed with the complete set of skills necessary to start a business, they can acquire those skills”. This notion underlies the vast amounts of money that governments around the world spend on providing business training, aiming to teach the entrepreneur to develop the finance/accounting and marketing/sales skills they lack and that are needed for firm growth. However, given all the other tasks they need to do, it is difficult for entrepreneurs to take much time away from their firms, and so many courses are relatively short in length and result in only modest improvements in business practices (McKenzie and Woodruff 2014). Lasting improvements are further hampered by entrepreneurs’ limited time and attention for making changes on their own. These approaches also go against conventional wisdom in places like Silicon Valley where entrepreneurs expect to relinquish control over some business functions in order to grow their firms.

At start-up, the entrepreneur is typically synonymous with the firm. But as the firm grows, the boundary between the entrepreneur and the firm widens, and the entrepreneur must make decisions as to what activities of the business to carry out herself and what to hire others to do. That is, there is a boundary within the firm (which we refer to as the ‘boundary of the entrepreneur’) as well as at the firm’s edge – i.e., the traditional ‘boundary of the firm’ discussed in the theory of the firm literature about what activities to do inside the business versus contract out (Coase 1937; Williamson 1979; Grossman and Hart 1986). An alternative to training an entrepreneur to develop finance and marketing skills herself is to hire someone who already has these skills, either as an employee within the firm (insourced worker) or as an outside service provider (outsourced professional). Figure 1 uses application data from more than 8,000 Nigerian firms who applied to government programs to show how the likelihood of an entrepreneur doing the finance or marketing activities of the firm varies with the number of workers in the firm. Although the entrepreneur is the main person in charge of financial accounts and marketing when the firm has only a couple of workers, by the time firms reach 10 to 20 workers, half of them have someone other than the entrepreneur implementing these business practices. This suggests that if the goal is to help small firms grow, rather than training entrepreneurs in finance and marketing, it may be 2
income reported in the survey to determine the part corresponding to the transfer. The following percentages were considered to capture this particularity of the Colombian pension system, which varies depending on gender, age, pension amount, and time of retirement. The values shown in Table 1 correspond to the percentage of the pension paid with the National General Budget. The remaining value corresponds to deferred income (i.e. savings).

|**_TABLE1: _**<br>|**_PERCENTAGES_**<br>|**_OF THE PENSION CO_**<br>|**_RRESPONDING TO G_**<br>|**_OVERNMEN_**<br>|**_T TRANSFER_**<br>||
|---|---|---|---|---|---|---|
|**Panel A: Pensioners before**<br>**and women**<br>Range of monthly<br>minimum wage|**Law 100: men**<br>**older than 73**<br>Percent|**older than 78**<br> <br>age|**Panel B: Pensio**<br>**yea**<br>Range of|**ners trans**<br>**rs old and**<br>monthly|**itional regim**<br>**women unde**|**e: men under 78**<br>**r 73**|
|Min.<br>Max.|Men|Women|minimum|wage|Percen|tage|
|1|82.5%|85.4%|Min.|Max.|Men|Women|
|1<br>2|48.5%|51.4%||1|74.4%|79.3%|
|2<br>3|50.5%|54.4%|1|2|61.1%|65.9%|
|3<br>4|53.5%|57.4%|2|3|64.1%|68.9%|
|4<br>5|56.5%|60.4%|3|4|67.1%|71.9%|
|5<br>6|59.5%|63.4%|4|5|70.1%|74.9%|
|6<br>8|62.5%|69.4%|5|6|73.1%|77.9%|
|8<br>9|68.5%|72.4%|6||75.1%|79.9%|
|9<br>Source: (Núñez 2009)|71.5%|75.4%|||||

In the household survey, there are 1,803,623 pensioners, whose total pension value is 32.8 trillion Colombian pesos, a figure very close to the total government budget for pensions (98 percent). Once the percentages of Table 1 are applied, a total subsidy of 21,6 trillion pesos is obtained. Figure 2 presents the results of the distribution of transfers for pensions throughout the income distribution and also presents the results of progressivity in relative terms of these transfers.

<!-- Start of picture text --> Figure 2. Absolute and relative progressivity of government transfers for pensions<br>Panel A. Progressivity in Absolute Terms Panel B. Progressivity in Relative Terms<br>70 9<br>8<br>60<br>7<br>50<br>6<br>40 5<br>30 4<br>3<br>20<br>2<br>10 1<br>0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>pensions<br>% of pension transfer received by each decile % of market income +<br><!-- End of picture text --> Source: World Bank Group staff estimates based on the 2017 ENPH Note: The deciles were put together using market income + per-capita pensions As stipulated in Colombian legislation and as ratified by the Constitutional Court, “a person who has reached the age and met the number of weeks required by law to access the old-age pension, has configured in his favor the right to enjoy said benefit and therefore is in the face of an entitlement, which cannot be taken away.” The fact that pensions in Colombia are considered an entitlement suggests that all income in respect to pensions should 17
Table 1: International bank lending and migration

|Dependent Variable:|(1)|lo<br>(2)|g (Loans;;)<br>(3)|(4)|(5)|Loans;;<br>(6)|
|---|---|---|---|---|---|---|
|logMigration;;||0.185***||O28|OE0h*|OA|
|||(0.038)||(0.051)|<br>(0.038)—|<br>(0.031)|
|logDistance;;|—0.353***|—0.229**|—0.790***|—0.676***|<br>—0.790***|<br>—0.246***|
||<br>(0.088)|<br>(0.092)|<br>(0.139)|<br>(0.137)|<br>(0.115)|<br>(0.052)|
|(ColonialLink);;|o:s99"*|O.780""|1335s"|Ti64**|1:335""|0.181|
||(0.238)|(0.238)|(0.267)|(0.271)|(0.204)|(0.164)|
|(Language);;|0.291|0.041|0.195|0.117|0.294*|—0.212|
||(0.206)|(0.225)|(0.182)|(0.184)|(0.160)|<br>(0.245)|
|(Legalorigin);;|O:689""|0.5748|Os0r*|\O.425""|O056|0.449***|
||(0.108)|(0.116)|<br>(0.124)|(0.116)|(0.111)|(0.120)|
|(Border);;|0.157|0.181|—0.430|—0.453|—0.551*|0.042|
||(0.212)|(0.211)|(0.292)|(0.290)|<br>(0.305)|(0.154)|
|(FiscalTreaty);|0.010*|0.007|0.000<br>=|—0.002|||
||(0.005)|(0.005)|<br>(0.004)<br>—|<br>(0.004)|||
|(DividendTax);|—0.012|-—0.015|—0.022*|<br>—0.024**|||
||(0.011)|<br>(0.011)|(0.011)|(0.011)|||
|(InterestTax);;|—0.014|—0.014|0.014|0.014|||
||(0.012)|(0.012)|(0.011)|(0.010)|||
|(Correlation);;|2.227°**|=1.922***|0.319|0.375|||
||<br>(0.471)|<br>(0.509)|(0.530)<br>=|(0.542)|||
|Countryfixedeffects|No|No|Yes|<br>Yes|Yes|Yes|
|<br>N|824|824|824|824|1,628|1,827|
|L|16|16|16|16|17|ii|
|B|62|62|62|62|175|178|
|Estimator|OLS|OLS|OLS|OLS|OLS|Poisson|

Analysis is based on data described in section I]. Columns (1)-(5) are estimated by OLS. Column (6) is estimated by the Poisson estimator. Standard errors are clustered at the borrowing country level. N, LZ and B denote number of observations, number of lending countries and number of borrowing countries. Columns (1) and (2) include in addition the following country specific variables that are not reported: the log of GDP of countries i and j, the average stock return of country i, a dummy if country 7 is a tax haven and corruption dummies for countries i and j. These variables are dropped out in columns (3)-(6) which include country fixed effects for countries i and j.
<!-- Start of picture text --> Equatorial Guinea<br>100<br>8)<br>a<br>——\<br>4q | \<br>ay | 8<br>0 \ : —<br>4982 4987 4902 {997 2002 2007<br>www Agriculture,value added (*, of GDP)<br>mre Industry,value added (of GDP)<br>Seale: Units<br><!-- End of picture text -->

<!-- Start of picture text --> Congo, Rep.<br>6)<br>Fi<br>4<br>' Si, Nee ~gts ane os ah<br>’ =<br>4982 1987 4992 1997 2002 2007<br>www Paviculture,value added (of GDP)<br>mom Industry,value added (2, of GDP)<br>Seale: Units<br><!-- End of picture text --> <!-- Start of picture text --> Gabon<br>40<br>aa<br>4)<br>aq<br>1982 1987 1992 1997 2002 2007<br>c= Agriculture,value added (% of GDP)<br>wom Industry,value added (% of GDP)<br>Scale: Units<br><!-- End of picture text --> <!-- Start of picture text --> Angola<br>40<br>aa<br>40<br>24 PA<br>1982 {987 1992 1997 2002 2007<br>wees Aariculture,value added (% of GDP)<br>wme— Industry,value added (’, of GDP)<br>Scale: Units<br><!-- End of picture text -->
# Civil Conflict and Firm Performance: Evidence from Cˆote d’Ivoire

Leora Klapper Christine Richmond Trang Tran<sup>_∗_</sup> Keywords: Civil Conflict; Productivity; Africa; Foreign Ownership JEL Classification: D22, F51, L22, M21, O12, O55

## Sector Board: FSE

> _∗_ Klapper: World Bank, lklapper@worldbank.org; Richmond: International Monetary Fund, CRichmond@imf.org; Tran: World Bank, ttran6@worldbank.org. We are grateful to Francois Bourguignon for providing us the data. Financial assistance is gratefully acknowledged from the Knowledge for Change Program (KCP), a multi-donor trust funded program of the World Bank. This paper’s findings interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views or policies of the World Bank or International Monetary Fund, their Executive Directors, or the countries they represent. We thank Anna Alberini, Pamela Jakiela, Charles Towe, Lorenzo Bertolini, Jean-Louis Arcand, Pierre Nguimkeu and workshop and conference participants at the University of Maryland AREC Department, the World Bank, Novafrica, CEA and PacDev for comments and suggestions. All errors are our own.
# **3. Experimental Methodology**

## _3.1 Sampling and CCT Implementation_

The data for our analysis were collected from 36 villages randomly selected from Amhara, Oromiya and Southern Nations, Nationalities and Peoples (SNNP) Regional States. The number of villages from each regional state included in the sample was determined based on the forest cover of each state. These regional states represent about 80% of Ethiopia’s population and over 70% of the land area.

Fourteen households from each village were randomly chosen to participate in the study, giving a total of 504 sample households. Of the 14 randomly selected sample households per village, 10 were randomly assigned as treatment households to receive a _Mirt_ stove and 4 were identified as controls. Three households from each treatment group in each village were randomly selected to participate in CCTs. After receiving informed consent, the CCTs were conducted for both _Mirt_ and traditional three-stone stoves in users’ kitchens by household cooks without any direction other than initial orientation (all in-home, some in group as well) from enumerators. Because of the field setting and the use of standard, local fuels and cooking materials, we expect cooks to behave during CCTs as they would under day-to-day real-life conditions.

As cooks are observed during the CCT, it is possible that they might behave differently than when they are not observed. This is the so –called “Hawthorne” effect in which outside interventions change respondent behavior. What is most important for our case, though, is that in each study period the cooking task is performed identically and is equivalently observed when the CCT is done on the _Mirt_ and the traditional stoves. As the measured effect is the difference between these two CCTs in the same household, we see no reason that a Hawthorne effect would bias the results Using this approach, households receiving the _Mirt_ stove act as their own controls on the same day when the CCT is conducted on their traditional stove. That is, the counterfactual situation is simulated by making the same person prepare a meal on the traditional stove and the ICS. Such an approach avoids the need to assess differences in performance across households or across time, both of which can introduce confounding factors.

9 | P a g e
results. These regressions are reported in Table 10. The results remain qualitatively unchanged, with the impact of Equity Bank’s presence on the probability of having a bank account still positive and highly statistically significant, though somewhat smaller than in our baseline results. However, given that the share of the population with bank accounts tends to be much lower outside Nairobi and Mombasa, the 2-3 percentage point increase in bank account usage in rural and arid and semi-arid districts still indicates a substantial impact of Equity Bank branching.

# _b. Difference-in-Differences Estimator_

Table 11 presents the results from our difference-in-differences estimator for the probability of having a bank account. This table replicates the specifications in Models 1, 3 and 5 in Table 5, using only a subsample of districts _without_ the presence of Equity Bank in 2006 and _including_ time fixed effects in our baseline specification. Therefore, the coefficients reported in the table indicate the change in the probability of having a bank account in districts where Equity Bank started operations between 2006 and 2009 relative to districts without Equity Bank presence during the same period. The results suggest that the impact of the penetration of Equity Bank in a new district on the probability of having a bank account remains positive and highly statistically significant. The main difference with our baseline results is that the magnitude of the impact decreases slightly from 4 percentage points (Model 1 in Table 5) to 3 (Model 1 in Table 11) percentage points.<sup>11</sup>

# **IV.6 Interpretations and Limitations**

A potential concern regarding the interpretation of our results is that Equity Bank might not have actively pursued financial inclusion by entering underdeveloped areas. Rather, our main result could be accounted for by either demand side factors (e.g., more developed areas have more demand for financial services) or by Equity Bank’s anticipation of growth opportunities in certain areas that they hope to capture by opening new branches ahead of other banks. We note that even if such alternative interpretations hold, Equity Bank seems to have seized upon these opportunities more quickly than other banks and, regardless of Equity Bank’s motivation, the end result has been greater financial inclusion.

> 11 Again, smaller impact outside urban areas is partly driven by the low base usage rates in the districts included in the difference-in-differences analysis.

20
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_ 1983 increases from CFAF 9.6 trillion (depreciation rate =10%) to CFAF 10.79 trillion (depreciation rate = 8%) to CFAF 12.38 trillion (6%) to CFAF 14.25 trillion (4%). The lower the depreciation rate, the longer capital “stays in the system,” so the peak slides from 1982 (at 10%) to 1983 (at 6% or lower).

But, the time profile of capital stock remains remarkably similar for quite different depreciation rate assumptions. In particular, regardless of the depreciation rate used, gross capital stock in the economy flattens or decreases after the early 1980s. So, almost regardless of the depreciation rate chosen (within a wide range), the method generates the estimate that capital stock per worker began to decline after the early 1980s.

It appears that when using the “perpetual inventory” method of estimating capital stock, the main determinant of capital stock seem to be the investment flows, or gross capital formation. Initial capital stock and the depreciation rate, although important, are by comparison second-order determinants. This is reassuring as the perpetual inventory method is fairly robust in the presence of uncertainty about initial capital and depreciation.

# **Calculating TFP and Productivity Regressions**

# **_Calculating Total Factor Productivity_**

Conceptually, output is generated by a production function which uses as inputs factors of production: physical capital; human capital (which is the labor force adjusted for quality), and a host of other factors. Physical capital can be inferred from observed investment flows. Human capital can be estimated based on the labor force and educational attainment. The effect of all other factors, or total factor productivity (TFP), is commonly estimated as the “Solow-residual” when the factor shares for the contribution of physical and human capital are subtracted from output.

Starting with the familiar Cobb Douglas production function and assuming constant returns to scale, output Y in time period t is a function of capital K, labor L adjusted for quality by H, and all other factors in that time period, A. Variables are subscripted by t. The marginal return on physical capital, _a_ , is assumed based on experience and other estimates to be 0.35, while the return on human capital is 0.65, for the world in general.<sup>25</sup> Worker quality H is assumed to be a function of average schooling per worker, **s** . The rate of return to education in Africa has been estimated at 13%. Two functional forms are found in the literature:

> 25 See for example Bosworth & Collins (2003), Cohen & Soto (2001) or Gosh and Kraay (2001).

25
- iii -

country cities, the most appropriate disposal facilities will continue to be sanitary landfills, and where a market for compost cAn ra demonstrated, composting plants. Where incineration and/or resource recovery is planned, facilities for safe disposal of rejects and ash are still needed. Where haul distances are greater than 15 to 20 kilometers or travel times exceed 30 minutes, transfer stations should be considered.

- (d) Hazardous Waste Management. To maximize the benefits of a MSWM component, attention should be directed at instituting and improving environmental controls and monitoring to keep hazardous wastes otlit of the municipal system. If industrial wastes are collected by the municipality, all potential sources of hazardous waste should be identified and targeted for appropriate management. Although laws controlling these wastes are enacted at the national or state level, the municipality should play a key role in monitoring inunicipal disposal sites for hazardous wastes, monitoring the generation of industrial and hazardous waste in urban areas, identifying suitable hazardous waste sites, and carrying out periodic inspections of industrial and hazardous waste collection and disposal operations. Particular attention attention should be given to small-scale manufacturing and cottage industries.

- (e) Regulatory Framework. In planning MSWM improvements, efforts should be directed at establishing appropriate solid waste-related lass, ordina;aces, regulations, and corresponding inspection and enforcement responsibilities and procedures at the national, state, and local levels. Effective enforcement of national environmental controls will require national and local authority capacity to monitor compliance and apply enforcement procedures, procedural ease in implementing regulations, nd broad public acceptance. Moreover, regulations should be few i. Aumber, transparent, unambiguous, easily understood, equitable, and considered to have significant positive physical and economic effects.

- (f) Institutional Arrangements. To improve the execution of MSWM engineering components, considerable attention is needed in the area of institutional development. In large cities, solid waste institutions need to be upgraded within the overall administrative hierarchy. The responsibility for collecting waste and delivering it to a disposal facility can remain with the municipalities or private companies. Based otl available data, private firms can be more productive and efficient in carrying out collection and transport operations than municipalities. In planning MSWM improvements, project designers should evaluate the potential for introducing or expanding private sector involvement through a variety of contractual arrangements. To achieve maximum environmental protection and economies of scale, it may be possible to establish a regional or metropolitan autllority responsible for solid waste disposal. In small and intermediate cities, a national solid waste authority should conduct MSWM planning, develop standards, and provide technical and financial assistance. What,.ver institutional
A major issue in the economic development literature has been the relationship between average productivity and the distribution of wealth, in particular the distribution of land. Attention '.as been focused on how features of rural labor markets in low-income settings, such as market segmentation and nutrition-wage interactions, imply that efficiency gains could arise from an equalizing distribution of land (e.g., Mazumdar, 1977; Dasgupta and Ray, 1984). These concerns have led to a large number of empirical tests of technical scale economies (e.g., Bardhan, 1973), of farm household "separability" (Lopez, 1986; Pitt and Rosenzweig, 1982; Benjamin, 1992), and of health-wage associations (Behrman and Deolalikar, 1987).

Studies related to the distribution-efficiency issue in low-income rural areas have tended to ignore risk considerations, although the advantages of large landowners in obtaining credit has been recognized (Sen, 1966). Despite the growing evidence that farmers in low-income environments are risk-averse (Moscardi and deJanvry, 1977; Dillon and Scandizzo, 1978; Binswanger, 1980, Antle, 198? and 1989), however, there has been little empirical evidence on the importance of risk in shaping the actual allocation of production resources among farmers differentiated by wealth. Empirical work on contractual relations in low-income rural areas has also not been successful in obtaining credible or consistent empirical relationships between wealth and risk behavior (Bell and Srinivasan, 1985), in part because of population heterogeneity in risk attitudes.

In this paper we utilize panel data from rural India on investments, wealth and rainfall to examine how the composition of productive and non-productive asset holdings varies across farmers with different levels of total wealth and across farmers facing different degrees of weather risk. In particular, we i) measure the riskiness of farmers' investment portfolios in terms of their sensitivity to weather variation, ii) implement a test of the central feature of 1
# Ratio of producer to world price Ivory Coast and Ghana

<!-- Start of picture text --> 100 Percentage<br>80 -<br>60 L<br>-<br>40<br>20<br><!-- End of picture text --> <!-- Start of picture text --> 1952/53 1957/5a 1962/63 1967/68 1972/73 1977/78 1982/83 1987/88*<br><!-- End of picture text --> year Figure 6 Ivory Coast Ghana 'atios to 5 yr MA (final years forecasL)