rafmacalaba/datause-encoder-data huggingface dataset:
Table 7: Correlation between industry traits and mean input counts, 2000

||Organized||Unorganized sec<br>|tor<br>|
|---|---|---|---|---|
||sector|Total|Male owned|Female owned|
||(1)|(2)|(3)|(4)|
|Log labor intensity|-0.189|-0.246*|-0.169|-0.246*|
|Log capital intensity|0.173|0.039|-0.036|-0.154|
|Log materials intensity|0.131|0.080|0.053|0.103|
|Log average wage|-0.012|0.003|0.045|0.262*|
|Log financial dependency|0.134|-0.064|-0.045|0.053|
|Log import dependency|0.034|0.142|0.044|0.140|
|Organized sector share of establishments|0.128|-0.032|0.019|0.045|
|Organized sector share of employment|0.470*|0.211|0.245*|0.215|
|Organized sector share of output|0.365*|0.213|0.223*|0.300*|
|Female share of unorg. sector establishments|-0.036|0.006|0.152|-0.153|
|Female share of unorg. sector employment|-0.160|-0.200|0.016|-0.258*|
|Female share of unorg. sector output|-0.408*|-0.255*|0.022|-0.253*|

Notes: See Table 5. Table documents correlations between input counts and industry traits.
social empowerment, and so forth (Cunningham et. al. 2008), would increase the cost estimates many-fold.

Policy recommendations to expand investments in girls can be classified into three general areas: investing in girls, counting girls, and advocating for girls.<sup>21</sup>

# **Invest in girls**

Increasing funding for adolescent girls and tracking what it achieves<sup>22</sup> will benefit both today’s girls and tomorrow’s girls by providing services to today’s girls and learning from those experiences to better provide for girls tomorrow. Incentives for school attendance through conditional cash transfers (Schady and Fiszbein 2009) or scholarships (Angrist et. al. 2002) have been shown to keep girls in school are cost effective interventions. While the evidence of success and cost effectiveness in supply-side interventions is much scarcer than demand-side interventions, governments could consider expanding primary school facilities to house secondary school classes, investing in non-formal schooling options to reach the most vulnerable girls, and tracking enrollment, completion rates, and the percentage of girls at grade for age to measure progress (Cunningham et. al. 2008b).

Girls’ engagement in the labor market could be enhanced through building marketable skills, facilitating the labor force entry process, and alleviating gender constraints and expectations. Skills could be enhanced be improving the relevance of educational curricula, developing after-school tutoring and mentoring programs, providing financial education programs, and funding internships, apprenticeships and training opportunities to promote girls’ transitions to safe and productive livelihoods. Teaching job intermediation skills or providing information for the location of jobs may facilitate the school-to-work transition. And working with families, who may make decisions on behalf of girls, to value the contribution that girls make to the labor market

> 21 A recent global movement to support adolescent girls has defined a set of policy recommendations that are essential, yet feasible, for governments to implement and for the development community to support. The full list of recommendations includes 10 policy actions.  For more information, please see: http://www.coalitionforadolescentgirls.org/10_actions.  Only those relevant to the analysis in this paper are reported here.

> 22 Official Development Assistance figures from 2005-2006 state that 2.17% of total aid ($54.3 billion) list gender equality as the principle objective. Assuming the majority of that aid goes to grown women, less than two cents per aid dollar is directed to girls.

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# Table A.10: Husband’s and Wife’s Fertility Preferences

|Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Husband’s ideal number of sons**|||
|Fraction of female leaders<br>-0.021|0.026|-0.269|
|(0.156)|(0.180)|(0.203)|
|Observations<br>44,130|31,059|13,071|
|Outcome mean<br>1.209|1.253|1.105|
|**Panel B: Wife’s ideal number of sons**|||
|Fraction of female leaders<br>0.184|0.162|0.158|
|(0.120)|(0.131)|(0.170)|
|Observations<br>61,735|43,676|18,059|
|Outcome mean<br>1.187|1.243|1.053|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are husband’s ideal number of sons in Panel A and wife’s ideal number of sons in Panel B. Column (1) presents estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

37
pointing towards risk reduction as a potential reason for persistence of fragmentation, links between fragmentation and variability of output remained tentative at best (Blarel 1992).

A study of impacts of fragmentation on cost and variability for a 1995-2006 panel of 18,000 Japanese rice farms that cultivated a mean of 4 fragments with a Simpson index of 0.8 supports this: Fragment number and Simpson index increased average cost but reduced variance of output per hectare. In the case of Japan, high wages and well-functioning insurance markets implied that fragmentation costs outweighed the benefits but this may be different in other settings (Kawasaki 2010).<sup>5</sup> The size of fragmentationinduced costs and benefits will thus depend on wage levels, the scope for mechanization, and availability of other options to reduce risk or insure against it. If labor costs are high and mechanization widely practiced, the costs imposed by small or highly fragmented plots can easily outweigh potential benefits. This could explain why returns of programs to consolidate holdings, often with land use planning, in developed countries are reported to have been high (Simons 1987).

From a methodological perspective, this implies that accounting for potential endogeneity of plot sizes will be important, though this has been difficult to achieve in practice (Blarel 1992). We address this is by limiting our sample to households who cultivate only inherited land.<sup>6</sup> This still leaves the option that farmers adapt to their holdings’ level of fragmentation by choosing different types of crops. Access to data on input cost and output at the fragment level, together with the fact that virtually all farmers grow either rice or wheat, allows us to restricting the analysis to fragments with rice or wheat only to estimate the impact of a more fragmented holding structure at the farm level (i.e. including all fragments) in this context.

# **2.2 Analytical approach**

To explore if fragmentation affects production cost, we follow the literature (Berndt and Christensen 1973; Binswanger 1974), measure fragmentation by the number of plots and the Simpson index, and estimate a trans-log cost function of the form

> 5In Bulgaria, fragmentation (as measured by the Simpson index) was found to reduce farm profitability but to boost richness of species as measured by a biodiversity index (Di Falco _et al._ 2010 van Rensburg 2010).

> 6While land transfers do occur, a large share (72 %) of those in our survey is between members of the same family (Foster and Rosenzweig 2010). If experience elsewhere is any guide, the productivity gains that can be achieved from such transactions are below those that could be realized from a more formal land rental market (Deininger and Jin 2008).

6
60

# _2. Tools of Estimation_

LIMO h a 'task' within TROLL that provides sensitivity analysis of dynamic system models.<sup>3</sup> 3 It takes a nonlinear TROLL model as an input and provides a linearized solution by evaluating the first order derivatives around the simulation path. This solution then becomes the basis for linear analysis. It may be noted that, once the nonlinear model is linearized at a particular point in time, the perturbation-related terms _(CAxj_ and DAN) remain fixed, and the dynamic behavior of the linearized model is governed by the matrices **_A_** and _B._ LIMO implements parameter sensitivity, multiplier and dynamic analyses by diagonalizing these matrices through their characteristic roots and vectors.

TESTMOD is another TROLL task that provides more limited sensitivity tests of dynamic simultaneous equation systems. The major difference between LIMO and TESTMOD is the estimation of linearized model solutions by LIMO prior to any sensitivity analysis. However, for models which are reasonably linear, the solutions of the two TROLL tasks are generally very close.

# _3. Results_

This part examines the structural sensitivityof MULTIMOD. The parameter perturbation results are presented first, followed by the multiplier analysis results. To facilitate the interpretation and comparability of the results, the deviation of the results from the baseline are reported in elasticityscaled form.34 33 See TROLL Program, LIMO, Technical Report No. 34, 1983, and TROLL Program TESTMOD, Peter L.B. Hollinger and John Neese.

3tlasticity is one form of scaling. Depending on the nature and units of the variables involved, different forms of scaling may be employed, for example, ratio scaling, beta scaling (See Pindyck (1985). For details on elasticity scaling, see Kuh, _et al,_ (1985).
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Financing Kenya’s infrastructure

**T** o meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Kenya needs to expand its infrastructure assets in key areas (table 9). The targets outlined below are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

**Table 9. Illustrative investment targets for infrastructure in Kenya**

||Economic target|Social target|
|---|---|---|
|ICT|Install fiber optic links to neighboring capitals and<br>submarine cable|Provide universal access to GSM signal and public broadband<br>facilities|
|Irrigation|Develop additional 55,000 hectares of economically<br>viable large-scale irrigation|**None**|
|Power|Develop 990 MW of new generation capacity and<br>270 MW of interconnectors|Raise electrification to 50 percent<br>(100 percent urban and 32 percent rural)|
|Transport|Achieve regional (national) connectivity with good quality 2-<br>lane (1-lane) paved road|Provide rural road access to 80 percent of the highest-value<br>agricultural land, and urban road access within 500 meters|
|WSS|**—**|Achieve Millennium Development Goals|

Sources: Mayer and other 2009; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009.

Meeting these illustrative infrastructure targets for Kenya would cost $4 billion per year for the years through 2015. Capital expenditure would account for 72 percent of this requirement. Meeting growing demand for power and improving the reliability of power supply will require an estimated $1 billion per year to install almost 1,000 megawatts of new generation capacity and 270 megawatts of cross-border interconnectors. The water and sanitation sector is the area with the highest spending needs: almost $2 billion will be needed each year to meet the Millennium Development Goals, with capital expenditure accounting for three-quarters of the total. While less than the amounts needed for power and water and sanitation, requirements for transport and ICT are also high in absolute terms, amounting to around half a billion dollars a year in each case (table 10).

Kenya’s infrastructure spending needs are high in absolute terms—and even more so relative to GDP (figure 12). At $4 billion, in absolute terms, the country’s needs are among the highest in Africa. Relative to the size of Kenya’s economy, that spending would amount to a staggering 21 percent of 2006 GDP. Investment alone would absorb around 15 percent of GDP, comparable to what China invested in infrastructure during the mid-2000s.

22
**Table 2: Variable composition and data sources**

|Variable|Composition|Data sources|
|---|---|---|
|Sovereign rating|Numerical values as in Table 1.|Standard & Poor’s.|
|GDP growth|3-year moving average of GDP growth.<br>Annual percentage growth rate of GDP<br>at market prices based on constant local<br>currency. Aggregates are based on<br>constant 2000 U.S.dollars.|World Bank, World<br>Development Indicators.|
|Log of GNI per<br>capita|Natural log of GNI per capita. GNI per<br>capita is the gross national income,<br>converted to U.S. dollars using the<br>World Bank Atlas method, divided by<br>the midyearpopulation.|World Bank, World<br>Development Indicators.|
|Reserves/ Imports +<br>Short term debt|Total reserves divided by sum of<br>imports and short-term debt. Total<br>reserves (includes gold, current US$).<br>Imports of goods and services (constant<br>2000 US$). Short-term external debt,<br>residual maturity.|World Bank, World<br>Development Indicators for<br>reserves and imports, and Bank<br>of International Settlements for<br>short-term debt.|
|External<br>debt/Exports +<br>Remittances|External debt divided by sum of exports<br>and remittances. External debt stocks<br>(US$), total of ‘Liabilities to BIS banks,<br>consolidated, total’, ‘International debt<br>securities, all maturities’, ‘Official<br>bilateral loans, total’ and ‘Multilateral<br>loans, total’. Exports of goods and<br>services (constant 2000 US$).<br>Remittances in US$.|Joint External Debt Hub for<br>constructing external debt.<br>World Bank, World<br>Development Indicators database<br>for exports and Global Migration<br>Report for remittances.|
|GDP volatility|5-year standard deviation of GDP<br>growth|World Bank, World<br>Development Indicators.|
|Rule of law|Rule of law with values from +2.5 to -<br>2.5 spanning the best and the worst<br>scores,respectively.|Estimates from World<br>Governance Indicators (Kraay,<br>Kaufmann and Mastruzzi,2010)|
|Inflation|Inflation, consumer prices (annual %).|World Bank, World<br>Development Indicators.|
|Government debt|General government gross debt (US$)|International Monetary Fund,<br>World EconomicOutlook.|
|log of GDP|Natural log of GDP|World Bank, World<br>Development Indicators.|

The model is run separately for data pertaining to ratings as of July 2008 (before the crisis set in) and December 2012. A pooled regression across the two periods is also carried out. Additional explanatory variables in the pooled model are a dummy for the year 2008 and a term interacting GDP volatility with the 2008 dummy (to check if the relationships were impacted by the financial crisis, see below).<sup>4</sup>

> 4 Following Ratha, De and Mohapatra (2011), we eliminate outliers where the residual is greater than one standard deviation of the dependent variable’s distribution. For the 2008 estimates there are no such outliers; for 2012, the outliers are Argentina, Cyprus and Spain; and for the pooled regression, the outliers are Argentina and Cyprus.

10
# **2. Background and Analytical Framework**

# **_2.1 Innovative Screening Methods: The EFL Tool_**

To solve the information asymmetries that banks face when screening SMEs—limited access to reliable information needed to assess their growth potential and risk profiles, and difficulties gathering that information at a low cost—banks in the United States have shifted their focus away from business operations and toward the business owner. In the mid-1990s, large U.S. banks started developing credit-scoring models based on (i) data they had collected on SMEs or that was available via commercial credit bureaus, and on (ii) SME owners’ personal consumer data obtained from consumer credit bureaus (Berger and Frame, 2007; Berger and Udell, 2006).<sup>7</sup> In the United States, the wide adoption of credit scoring has led to an increase in the credit extended to SMEs; an increase in lending to relatively opaque, risky businesses; an increase in lending to low-income areas, and to areas outside the banks’ local markets; and an increase in loan maturities (Berger, Frame, and Miller, 2005). Not all countries, however, have well-developed credit bureaus that gather the level of information on SMEs and consumers needed to build a reliable credit-scoring model. The average credit bureau in Latin America and the Caribbean complies with only half of best practices, as defined by the World Bank in its Doing Business Report, and covers only 39.3 percent of the adult population (Doing Business Report 2014). Credit bureau coverage is even lower in many other world regions (with the notable exception of the highincome countries in the Organisation for Economic Co-operation and Development, OECD).

Thus, even though credit scoring represents a potential solution to the problem of improving SMEs’ access to credit, it can take many years to pass legislation that will lead to improvements in the quality and depth of information recorded by credit bureaus as well as in the bureaus’ coverage. In addition, banks may be reluctant to share proprietary information with other banks (Bruhn, Farazi, and Kanz, 2013), and even after credit bureaus are set up and are working well, building an accurate credit-scoring model often requires many years of credit history. In the meantime, credit markets in developing countries may have to rely on alternative lending technologies to screen potential clients.

> 7 Mester (1997) states that standardized models developed commercially for lenders without enough loan volumes to build their own models “found that the most important indicators of small-business loan performance were characteristics of the business owner rather than the business itself. For example, the owner’s credit history was more predictive that the net worth or profitability of the business.” This result reflects the correlation between personal and business success, and the commingling of the finances of the business and the owner (Berger et al., 2005).

5
Table 2.5a. Shapley Decomposition of Regional Differences in Poverty for 2001

|||Headcoun|t|P|overty Ga|p|Squar|ed Povert|y Gap||Watts||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|
|Douala|-29.29|-29.85|0.56|-10.71|-10.52|-0.19|-4.84|-4.64|-0.19|-14.76|-14.32|-0.45|
|Yaoundé|-26.84|-30.08|3.24|-10.13|-10.56|0.43|-4.70|-4.73|0.04|-14.10|-14.45|0.35|
|Adamaoua|8.20|14.54|-6.34|2.60|7.02|-4.42|0.83|3.70|-2.88|2.94|10.27|-7.33|
|Center|8.00|15.07|-7.07|2.19|6.42|-4.24|1.08|3.28|-2.21|3.67|9.40|-5.73|
|East|3.80|9.34|-5.54|2.58|5.70|-3.12|1.20|3.07|-1.88|3.48|8.43|-4.95|
|Far-North|16.11|22.73|-6.62|6.05|11.11|-5.05|2.62|5.96|-3.33|7.97|16.45|-8.48|
|CoastT|-4.70|2.47|-7.17|-2.70|0.71|-3.40|-1.38|0.36|-1.74|-3.95|1.02|-4.97|
|North|9.90|13.30|-3.40|2.71|6.36|-3.65|0.81|3.30|-2.49|3.05|9.24|-6.19|
|North-West|12.30|12.69|-0.39|8.11|7.08|1.03|5.15|4.10|1.05|13.45|11.00|2.45|
|West|0.15|10.17|-10.02|-1.69|4.33|-6.02|-1.36|2.21|-3.57|-3.19|6.22|-9.41|
|South|-8.63|3.94|-12.57|-5.43|1.61|-7.04|-3.13|0.76|-3.89|-8.34|2.24|-10.58|
|South-West|-6.36|-2.49|-3.86|-2.28|-1.05|-1.23|-1.04|-0.54|-0.50|-3.25|-1.53|-1.72|

Source: Authors’ Calculations Table 2.5b. Shapley Decomposition of Regional Differences in Poverty for 2007

|||Headcount||Po|verty G|ap|Square|d Povert|y Gap||Watts||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|Difference|Scale|Inequality|
|Douala|-34.40|-26.66|-7.73|-11.44|-8.57|-2.87|-4.81|-3.51|-1.30|-15.10|-11.23|-3.88|
|Yaoundé|-33.96|-26.34|-7.62|-11.35|-8.54|-2.80|-4.79|-3.55|-1.23|-14.99|-11.25|-3.73|
|Adamaoua|13.05|17.73|-4.68|2.17|7.41|-5.23|0.39|3.69|-3.31|2.35|10.51|-8.16|
|Center|1.29|14.76|-13.46|-2.83|5.76|-8.59|-1.93|2.69|-4.62|-4.43|7.99|-12.43|
|East|10.51|16.43|-5.92|3.37|7.98|-4.61|1.20|4.25|-3.05|4.14|11.57|-7.44|
|Far-North|25.97|24.77|1.20|12.26|14.59|-2.33|6.18|8.43|-2.25|17.23|22.02|-4.79|
|Coast|-8.82|3.09|-11.91|-4.66|1.34|-6.00|-2.32|0.65|-2.97|-6.51|1.87|-8.38|
|North|23.76|24.15|-0.39|8.67|12.63|-3.96|3.55|6.68|-3.13|11.32|18.35|-7.03|
|North-West|11.10|9.66|1.44|4.30|5.61|-1.31|1.81|3.00|-1.19|5.67|8.15|-2.48|
|West|-10.95|2.85|-13.80|-5.68|0.98|-6.66|-2.76|0.47|-3.22|-7.87|1.36|-9.23|
|South|-10.64|-2.96|-7.68|-4.94|-1.27|-3.67|-2.38|-0.62|-1.76|-6.80|-1.77|-5.03|
|South-West|-12.39|-4.46|-7.93|-5.45|-1.78|-3.67|-2.55|-0.85|-1.70|-7.46|-2.47|-4.99|

Source: Authors’ Calculations 13
# **References**

- Adida, Claire L., Adeline Lo and Melina R. Platas. 2018. “Perspective Taking Can Promote Short-term Inclusionary Behavior Toward Syrian Refugees.” Proceedings of the National Academy of Sciences 115(38):9521–9526.

- Alesina, Alberto, Armando Miano and Stefanie Stantcheva. 2022. “Immigration and Redistribution.” The Review of Economic Studies 90(1):1–39.

- Alesina, Alberto and Eliana La Ferrara. 2005. “Ethnic Diversity and Economic Performance.” <u>Journal</u> of Economic Literature 43(3):762–800.

- Alesina, Alberto, Michela Carlana, Eliana La Ferrara and Paolo Pinotti. 2024. “Revealing Stereotypes: Evidence from Immigrants in Schools.” Forthcoming: American Economic Review .

- Alesina, Alberto, Paola Giuliano, A. Bisin and J. Benhabib. 2011. Preferences for Redistribution. North Holland pp. 93–132.

- Andreoni, James and Lise Vesterlund. 2001. “Which is the Fair Sex? Gender Differences in Altruism.” The <u>Quarterly Journal</u> of Economics 116(1):293–312.

- Batson, Charles Daniel. 2011. Altruism in Humans. Oxford University Press, USA.

- Behm-Morawitz, Elizabeth, Jennifer Lewallen and Brandon Miller. 2015. “Real Mean Girls? Reality Television Viewing, Social Aggression, and Gender-Related Beliefs Ps cholo of Po ular Media Culture .

- Among Female Emerging Adults.” <u>y gy p</u>

- Berthold, Anne, Carola Leicht, Nicole Methner and Petra Gaum. 2013. “Seeing the World with the Eyes of the Out-group: The Impact of Perspective Taking on the Prototypicality of the Ingroup Relative to the Out-group.” <u>Journal</u> of Experimental Social Psychology 49(6):1034–1041.

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