rafmacalaba/datause-encoder-data huggingface dataset:
|Control of Corruption (lagged)|Control<br>of<br>Corruption<br>indicator<br>from<br>Worldwide Governance Indicators. Higher<br>values of the variable imply lower corruption.<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of months covered in the calendar<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|---|---|
|Profit Tax Rate|The amount of taxes on profits paid by the<br>business expressed as a percentage of total<br>commercial profits of the business as per the<br>Doing Business methodology, World Bank. To<br>make the data comparable across countries,<br>several<br>assumptions<br>are<br>made<br>about<br>businesses. The main assumptions are that they<br>are limited liability companies, they operate in<br>the country's most populous city, they are<br>domestically owned, they perform general<br>industrial or commercial activities, and they<br>have certain levels of start-up capital,<br>employees, and turnover.<br>The values of profit tax are for the last fiscal<br>year that is covered by the ES. In cases where<br>the fiscal year spanned more than one<br>calendar year, the figure used is the weighted<br>average over the calendar years covered,<br>where the weights used are the proportion of<br>months covered in the calendar year.<br>Source: Doing Business, World Bank.|
|DTF|<br>A measure of the overall regulatory burden on<br>the private firms. Higher values of the variable<br>imply less burdensome regulations from the<br>firms’ point of view. The overall regulatory<br>burden is constructed by taking the simple<br>average over regulatory burden or distance to<br>the frontier in the following five areas: starting<br>a business, obtaining construction permit,<br>paying taxes, trading across borders, and<br>resolving insolvency. The variable is lagged by<br>2 years from the fiscal year covered by the|

61
- Becker, T ̈orbj ̈orn I. & Paolo Mauro (2006). “Output Drops and the Shocks that Matter”. _IMF Working Paper_ 06/172, Washington, DC: International Monetary Fund

- Bernard, Andrew B., Stephen J. Redding & Peter K. Schott (2006). “Mutli-Product Firms and Trade Liberalization”. _Working Paper_ 12782, Cambridge, MA: National Bureau of Economic Research

- Bevan, David L., Paul Collier & Jan Willem Gunning (1993). “Trade Shocks in Developing Countries: Consequences and Policy Responses”. _European Economic Review_ 37(2–3) (April): 557–565

- Blundell, Richard & Stephen R. Bond (1998). “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models”. _Journal of Econometrics_ 87(1) (August): 115–143

- Bussi`ere, Matthieu & Marcel Fratzscher (2008). “Financial Openness and Growth: Short-run Gain, Long-run Pain?” _Review of International Economics_ 16(1) (February): 69–95

- Calder ́on, C ́esar, Norman V. Loayza & Klaus Schmidt-Hebbel (2005). “Does Openness Imply Greater Exposure?” _Policy Research Working Paper_ 3733, Washington, DC: The World Bank

- Calder ́on, C ́esar A. & J. Rodrigo Fuentes (2006). “Complementarities between Institutions and Openness in Economic Development: Evidence for a Panel of Countries”. _Cuadernos de Econom ́ıa_ 43(127) (Spring): 49–80

- Cavallo, Eduardo A. & Jeffrey A. Frankel (2008). “Does Openness to Trade Make Countries More Vulnerable to Sudden Stops, or Less? Using Gravity to Establish Causality”. _Journal of International Money and Finance_ 27(8) (December): 1430–1452

- Chang, Roberto, Linda Kaltani & Norman V. Loayza (2009). “Openness Can Be Good for Growth: The Role of Policy Complementarities”. _Journal of Development Economics_ 90(1) (September): 33–49

- Chinn, Menzie D. & Hiro Ito (2008). “A New Measure of Financial Openness”. _Journal of Comparative Policy Analysis_ 10(3) (September): 309–322

- Dehn, Jan (2000). “The Effects on Growth of Commodity Price Uncertainty and Shocks”. _Policy Research Working Paper_ 2455, Washington, DC: The World Bank

- Dennis, Allen C.K. & Benjamin A. Shepherd (2007). “Trade Costs, Barriers To Entry, And Export Diversification In Developing Countries”. _Policy Research Working Paper_ 4368, Washington, DC: The World Bank

- di Giovanni, Julian & Andrei A. Levchenko (2009). “Trade Openness and Volatility”. _Review of Economics and Statistics_ 91(3) (August): 558–585

- Easterly, Wiliam R., Roumeen Islam & Joseph E. Stiglitz (2001). “Shaken and Stirred: Explaining Growth Volatility”. In Boris Pleskovic & Nicholas H. Stern (editors), _Annual World Bank Conference on Development Economics_ , volume 2000, pp. 191–211. Washington, DC: The World Bank

- Easterly, William R. & Aart C. Kraay (2000). “Small States, Small Problems? Income, Growth, and Volatility in Small States”. _World Development_ 28(11) (November): 2013–2027

30
emerging economy and using a rich set of panel data with baseline immediately before the onset of COVID and follow-up across three rounds of post-COVID surveys.

# **2. Study Context**

## **<mark>2.1. Context</mark>**

<mark>This study leverages a large youth employment and opportunities project currently being implemented by the Government of Kenya (GoK) with funding from the World Bank. The Kenya Youth Employment and Opportunities Project (KYEOP) seeks to</mark> increase employment and earning opportunities among vulnerable youth in the country by providing skills training, entrepreneurship support, and access to relevant job market information.

<mark>A key component of KYEOP is a support program for self-employment that targets youth aged 1</mark> 8-29 years with low formal education levels. We are currently implementing a largescale impact evaluation of this program that provides selected youth with the following forms of support to help them start or grow a business: (a) a business grant of Ksh. 40,000 (about US$ 360) (b) business development services in the form of formal training and counseling (BDS); or (c) both grants and BDS. This study comprises 9,380 youth with the selection stratified across 15 counties in Kenya, as well as by gender.

The program and its evaluation were initiated in June 2019 with a call for applications followed by a screening process to determine eligible applicants. Among those eligible, a baseline survey was conducted in January/February 2020 and selection into the three treatment arms and a control arm was conducted right after.

Owing to the large scale of the program, the intervention rollout was organized by groups of five counties. This study focuses on the first group of five counties where BDS started in February 2020, and grants were disbursed in two tranches, with the first tranche in March 2020 and the second in July 2020.<sup>4</sup> The timing of the original study is such that the baseline survey was completed right before the pandemic reached Kenya in early March 2020. We immediately organized three rounds of phone surveys in the five treatment counties to assess the mitigation impacts of these support measures on individual and business outcomes. This resulted in a study sample of 1,054 youth across the 5 counties and three rounds of surveys that cover the period from February to October 2020.

## **<mark>2.2. COVID-19 in Kenya</mark>**

The first case of COVID-19 in Kenya was declared on March 13, 2020. The GoK decided almost immediately to implement social distancing measures. It announced on March 15 a

> 4 The five counties are Kisumu, Kwale, Mombasa, Nairobi, and Nakuru.

6
_Table 1. Distribution of households by Prefecture and treatment arms_

|Numberof h<br>Prefecture|ouseholds byTre<br>Total<br>Number<br>of<br>Households|atment g<br>%|roup and b<br>Number<br>Househol<br>Control|yPrefecture<br>of<br>ds<br>Treatment|Treatmen<br>0 GNF|t branch<br>70k GNF|140k GNF|
|---|---|---|---|---|---|---|---|
|Mali|725|17%|389|336|74|172|90|
|Télimélé|1 423|34%|679|744|182|382|180|
|Siguiri|636|15%|318|318|76|178|64|
|Kérouané|1 433|34%|681|752|186|379|187|
|Total|4 217|100%|2067|2 150|518|1 111|521|
|Source: Bas|eline, Education m|odule||||||

25
_6_ match the ones that we would classify as male dominated if we were to select based on their labels alone, with the possible exceptions of software development and business services. We are confident that the sectors identified as male dominated in this paper are ones that could be generalized as so for Ethiopia in the absence of a full census of firms. We use this definition of crossovers throughout this paper to explore differences in outcomes between crossover and noncrossover firms.

# **IV. Results**

# _A. Firm Performance_

We compare mean differences between business outcomes for crossover and noncrossover female businesses to test whether female enterprise owners in maledominated sectors have higher returns than female owners in female-concentrated sectors in Ethiopia.

We begin by investigating the differences in profits, revenues, costs, and number of workers between firms owned by crossovers and noncrossovers.<sup>6</sup> Many of the women who cross over into male-dominated sectors make higher profits than women in female-concentrated sectors, as was found in the Ugandan sample of In Table 1, we present the means of a number of business performance outcomes, as well as whether the female owner had business partners and the age of the business for the full sample of enterprises (column 1), separately for crossovers (column 2) and noncrossovers (column 3), and the differences in means between crossovers and noncrossovers (column 4). The asterisks in column 4 signify a significant difference in the particular outcome variable between crossover and noncrossover enterprises. The results in Table 1 show that crossovers perform better than noncrossovers.

Crossover firms have, on average, double the profits of noncrossover firms. This profit difference is statistically significant and was found to be robust with alternative definitions of crossovers. We found no difference in revenues for crossovers and non-crossovers when revenues are defined over the last month and for a typical month. Crossover businesses also have statistically significantly higher operating costs than non-crossover businesses. One possible explanation for the revenue, cost, and profit results is that a large proportion of the noncrossover businesses are in the retail sector (50 percent), which is typically a sector associated with higher revenues but lower profit margins. On average, the crossover firms are

> 6We look for differences in average outcomes for crossovers and non-crossovers and estimate for business _i_ :

(1) _yi_ = _βCROSSOV ERi_ + _δXi_ + _εi_ where _yi_ is the outcome of interest and _CROSSOV ER_ is an indicator variable that equals 1 if the firm is a crossover (operates in a male-dominated sector). All regressions cluster standard errors within subcity groups (there are 20 subcities in the sample). We report regression estimates controlling for the age of the business and city dummy variables ( _Xi_ ).
# **Figure 20. Long-term growth and inflation forecasts**

<!-- Start of picture text --> A. 2020 GDP growth forecasts over time B. 2020 inflation forecasts over time<br>Percent Percent<br>7 Forecast in 2010 Forecast in 2010<br>5<br>6 Forecast in April 2016 Forecast in April 2016<br>4<br>5<br>4 3<br>3<br>2<br>2<br>1<br>1<br>0 0<br>World EMDEs United Euro Japan World EMDEs United Euro Japan<br>States Area States Area<br><!-- End of picture text --> Sources: Consensus Economics, World Bank.

Notes: A: GDP-weighted average growth rates for World and EMDEs (emerging market and developing economies). The World aggregate is based on 45 countries for which long-term consensus forecast data are available. EMDEs consist of 22 economies. B: GDP-weighted average CPI inflation rates for World and EMDEs. From these two aggregate groups, Venezuela, RB, is excluded due to high inflation forecast.

An additional and perhaps more controversial step might involve so-called “helicopter money,” a concept initially floated by Friedman (1969). The practical implementation of such a policy was first suggested by Bernanke (2002) to address persistent deflation in Japan. The idea is to stimulate aggregate demand by increasing public spending and/or cutting taxes through monetary financing. The implementation of such policy might involve infrastructure spending or direct tax rebates to households financed through an increase in money supply. The growth in aggregate demand, all else being equal, could eventually inflate prices. However, more work on the logistical aspects and implications of these types of policies is necessary before their implementation.

**_Why is a broader mix of policies needed?_** The benefits of unconventional monetary policy might diminish over time, and the risks associated with their aggressive use point to the need for a broader mix of macroeconomic policies (Kose and Terrones 2015; World Bank 2016b). Specifically, monetary policy should be complemented by fiscal and structural policies that could support aggregate demand and help counter adverse shocks in the future (Cœuré 2016). Moreover, in a context of ultra-low interest rates and borrowing costs, and given the secular decline in public investment in many advanced economies, there is increased room for a mix of spending measures and tax reforms. The positive returns on public investment and the possible crowding in of private investment in infrastructure, education, and health make a case for a significant reorientation of public expenditure in that direction (IMF 2014; World Bank 2016a; Ball, De Long, and Summers 2014).

47
design.

I apply the following strategy to separate out these eects. I use a binary employment status, 1 for employed - 0 otherwise, as the dependent variable instead of hours supplied to estimate the impact on extensive margins. To examine the impact on intensive margins, I analyze the hours supplied for a particular employment by limiting the sample to those who are engaged in that employment. That is, depending on the left-hand side outcome variable, the analyzed sample is conditioned on being employed in that particular sector.

Tables (3) and (4) reports 2SLS estimates of the impact of migration on wage employment and self-employment outcomes, respectively, by sex using IV1 as the instrument. Panel A of the tables shows the impacts on extensive margins while panel B shows the eects on intensive margins. Again, the instrument is reasonably strong with all _F_ -statistics from the rst stage above the threshold value of 10 except for the hours supplied for non-agricultural wage employment, which is at 7.1. Both male and female adults from the migrant sending families have smaller involvement in wage-employment, both on extensive and intensive margins when compared to adults from families without migrants. Meanwhile, migration has greater negative eect on left-behind men's propensity to participate in market employment than women's, 31.1% vs 20.5%, (Table 3, Panel A). However, among working adults, left-behind women supply fewer hours in wage-employment than the left-behind men (Table 3, Panel B), which could be a result of Nepali women, if employed outside their homes, having mostly part-time jobs. At the same time, having migrants in the family aects left-behind women's selfemployment decisions on both extensive and intensive margins, but not men's. Compared to women from households without migrants, women in households with migrants are 28.0% more likely to be engaged in self-employment, mostly working in their own farms. However, among those employed, women decrease weekly hours worked by 13 hours (Table 4). Overall, migration has negative eect on all the left-behind household adults' involvement in wage-employment but it is only women who increase their involvement in self-employment. In a country with traditional household norms and social culture, where women tend to be subordinate to men, it is not surprising that only women redistribute their time allocation in response to sending some family members abroad.

Table (5) presents the 2SLS estimates of the impacts of migration on the time spent in household production and other activities by the left-behind adults and children using IV1 as the instrument.<sup>12</sup>

> 12Fetching water, collecting rewood and dung, taking care of animals, making mats, knitting, weaving, tailoring

14
# **Table of Contents**

|1.|Introduction ............................................................................................................................. 1|
|---|---|
|2.|What Is a Strategic Investment Fund? ..................................................................................... 1|
|3.|The Emergence and Growth of Strategic Investment Funds ................................................... 9|
|4.|Structure and Market Validation ........................................................................................... 11|
|5.|A Double Bottom Line .......................................................................................................... 13|
|6.|Public Capital Multipliers ...................................................................................................... 14|
|7.|Investment Strategy ............................................................................................................... 16|
|8.|Common Challenges.............................................................................................................. 19|
||_A. Attracting Private Sector Investment ......................................................................................... 19_|
||_B. Sourcing Investable Projects ..................................................................................................... 20_|
||_C. Balancing Policy and Commercial Objectives .......................................................................... 21_|
||_D. Securing the Right Staff ............................................................................................................. 23_|
|9.|Conclusion ............................................................................................................................. 24|
|Ref|erences ..................................................................................................................................... 26|
|Ap<br>wit|pendix A. Strategic Investment Funds and Sovereign Wealth Funds Investing Domestically<br>h a Development Objective ...................................................................................................... 31|
|Ap|pendix B. Estimating the Economic Rate of Return and Policy Efficiency of Investments .... 41|
||8<br>10|0.10<br>0.19|-<br>-|-<br>0.00125|0.11<br>0.11|-<br>0.093|4.39<br>2.3|126<br>88.12|-<br>0.0066|-<br>0.086|
|---|---|---|---|---|---|---|---|---|---|---|
|HK|10|0.24|-|0.0025|0.48|0.024|1.5|83.3|-|-|
|TK|1|0.21|0.12|-|0.49|-|1.67|113|-|-|
||8|0.21|-|-|0.49|-|1.56|109|-|-|
||10|0.17|-|0.0007|0.48|0.107|2.36|118.8|-|-|

Sources:

> 1. (Maithel et al., 2012); 2. (Eil et al., 2020); 3. (Weyant et al., 2014); 4. (Weyant et al., 2016); 5. (ICIMOD, 2019c); 6. (Seay et al., 2021); 7. (Maithel et al., 2014); 8. (Rajarathnam et al., 2014); 9. (Nepal et al., 2019); 10. (Abbas et al., 2023); 11. (Stockwell et al., 2016); 12. (Jayarathne et al., 2018). Notes:

a Nepal-specific value. b Indian kilns. The main fuel used by kiln technology for data sources is as follows. 1: BTK (mainly coal, but also rubber tires and wood logs); ZZK (coal, sawdust); VSBK (coal, coal powder, and slurry as internal fuels); DDK (biomass fuelwood); and TK (coal). 2: All kilns are coal-based. 3: BTK (coal, coal slurry, wood, rubber); ZZK (coal, sawdust, petcoke); VSBK (coal, coal slurry, coal powder); DDK (wood) and TK (coal ash and coal slurry). 4: CK (coal, bagasse, iron and paper plant ash, coal fines); Indian BTK (coal, mustard stalk, wood); Nepalese BTK (coal); Indian ZZK (coal, petcoke, sawdust); Nepalese ZZK (coal and sawdust). 5: Information not available. Note that values for ZZK, VSBK, HK, and TK are calculated based on the value of FCBTK. 6: same as in data source 3. 7: CK (coal and biomass); BTK (coal, biomass, petcoke, rubber tires); ZZK (coal, biomass, petcoke, rubber tires); VSBK (coal); HK (coal, biomass); TK (coal, petcoke). 8: CK (coal, biomass); BTK, ZZK, and VSBK are coal. 9: BTK (coal, rick husk, and briquette) and ID ZZK (coal, rick husk, sawdust). 10: Same as in 2 and 7. 11 and 12: CK (Coal and fuelwood) and ZZK (coal and bagasse).

For source 4, CK values are for large clamps and the figures in the parenthesis are for small clamps. For others, values are for Nepal and the values in the parenthesis are for India.

52
Table 1: Absolute income _β_ -convergence across municipalities, 1992-2014

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||**1992-2014**|**1992-2000**|**2000-2014**|**2000-2005**|**2005-2010**|**2010-2014**|
||||**a. All mu**|**nicipalities**|||
|_ln yit−τ_|_−_0_._007***<br>(0.001)|0.001<br>(0.003)|_−_0_._019***<br>(0.001)|_−_0_._043***<br>(0.003)|_−_0_._020***<br>(0.003)|_−_0_._013***<br>(0.003)|
|Obs.|2,361|2,361|2,361|2,361|2,361|2,361|
|R<sup>2</sup>|0.102|0.000|0.342|0.313|0.076|0.022|
||||**b. Urban m**|**unicipalities**|||
|_ln yit−τ_|_−_0_._008***<br>(0.001)|_−_0_._003<br>(0.004)|_−_0_._019***<br>(0.001)|_−_0_._045***<br>(0.003)|_−_0_._020***<br>(0.004)|_−_0_._009***<br>(0.004)|
|Obs.|944|944|1,017|1,017|1,022|1,022|
|R<sup>2</sup>|0.138|0.002|0.334|0.323|0.076|0.012|
||||**c. Rural m**|**unicipalities**|||
||_−_0_._018***|_−_0_._027***|_−_0_._031***|_−_0_._077***|_−_0_._035***|_−_0_._068***|
|_ln yit−τ_|(0.001)|(0.004)|(0.002)|(0.003)|(0.004)|(0.008)|
|Obs.|1,417|1,417|1,344|1,344|1,339|1,339|
|R<sup>2</sup>|0.235|0.050|0.415|0.395|0.062|0.188|

_Source_ : World Bank calculations.

_Note_ : The table presents estimates of the parameter _β_ in equation (3), weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth rate in the mean per capita income of municipalities. _ln yit−τ_ are municipalities’ initial per capita income. All variables are in log-scale and in real per capita terms at August 2014 prices. Urban (rural) municipalities are defined as those with more (fewer) than 15 thousand inhabitants. The intercepts are shown in table 1 in the ancillary file. Robust standard errors are in parentheses *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 rural municipalities was consistently faster and statistically significant in each subperiod (see table 1, panels b and c). While no evidence of convergence across urban municipalities was found in the 1990s, convergence occurred in rural ones at an annual rate of 2.7 percent. Moreover, although the speed of convergence halved in both groups during 2005–10 relative to the previous five years, the pace had recovered across rural municipalities by 2010–14, whereas it slowed even further in urban ones (table 1, panels b and c, columns 4–6).

To examine the conditional income _β_ -convergence hypothesis, whereby paths of mean per capita income growth are conditional on factors such as the initial conditions and structural characteristics of municipalities, the specification in (3) is rewritten as 12
<!-- Start of picture text --> 49<br>Figuse '7: GDP Growth Rates and Government<br>Spending: 1970-80<br>0<br>0 ~~~00 0 0 0<br>0a<br>,°t,~~~~~ OSC .3j. o; a o0 °<br>0 00 ~~~~~~~~0 T0 0<br>0 0000 0<br>0 00 %M0 00<br>0 0p0 0 0<br>.o 0 0<br>-.05<br>-. 05 t<br>0 .1 .2 .3 .4<br>Ratio of Government Spending to GDP<br><!-- End of picture text -->