rafmacalaba/datause-encoder-data huggingface dataset:
and may retard migration responses to moisture changes. We measure noise by the standard error of prediction. Based on the annualized growth rate, calculated as shown in equation (13), we can formulate the predicted value for moisture in any year between census intervals as _W_ ˆ _ijt_ , _smooth_ 3  _Wijt_  _L j_ , _smooth_ 3 _ewijt_ , _smooth_ . From that we form the standard error of prediction: in climate, may increase uncertainty and encourage migration on their own. We took the standard deviation for the 10 years before the end census period ( _t_ ) and for 10 years before the beginning census period ( _t-L_ ) and differenced to get the change in variability (we also tried 17 year periods). Neither two interactive effects with moisture growth, the Gini and _SEPijt_ , nor the change in standard deviation on its own produced consistent or significant effects.<sup>22</sup>

# **6. Climate change and city income**

Having shown evidence of the population effects predicted by our model, we turn to effects on city total income. Our theory indicates that if the local town or city has developed some sort of exportable activity, then reduced (increased) moisture unambiguously raises (lowers) city income. However if the local town exists solely to provide farmers with services (or potentially goods) that are not traded outside the district, then the fortunes of the urban and rural sector are tied. Decreased moisture is then likely to decrease local city income.

Data on income or city product are not consistently available for African cities, so we use an indirect measure. Following the approach in Henderson, Storeygard and Weil (2011, 2012), we test whether the intensity of nighttime light emitted by a city is affected by the amount of rainfall within a 30 km radius around each city in the current or prior year (see Figure 7). The nighttime lights data come from the U.S. Defense Meteorological Satellite Program (DMSP), a weather satellite system that captures visible light between about 8:30 p.m. and 10 p.m. We use annual data from 1992 to 2008 for 30 arc-second grid cells (0.86 km<sup>2</sup> at the equator). The data product typically used for socioeconomic analysis contains only stable lights after temporary light sources such as forest or savannah fires have been removed (e.g., Elvidge et al 1997). We further remove gas flares based on Elvidge et al. (2009). Light intensity for each pixel is expressed as a “digital number” (DN) linearly scaled between 0 and 63.

> 22 High ethnolinguistic fractionalization at the national level might limit migration responses across space, but we found no significant effects, perhaps because it is highly correlated with the moist country dummy. We also considered road accessibility measures, but these are highly endogenous and only available for recent years.

20
# **Registration with Government Agencies and Business Associations**

As Figure 9 clearly suggests, businesses that are already registered at some government agency or professional association (the national business registry - CIPRO, Chamber of Commerce, Business associations, Workers Compensation and other) are more likely to make the next step and register for tax<sup>9</sup> .

# **Figure 9: Registered at government agency or professional association and likelihood for tax registration**

<!-- Start of picture text --> Business not<br>62<br>registered anyw here<br>Business registered at<br>75<br>least somew here<br>Total 66<br>50 55 60 65 70 75 80<br>Percentage of informal businesses likely to register<br><!-- End of picture text --> 9 Chi square = 13.034, significant on 1% level 9
S:WBNA XTX (L) Ratio central govt export tax revenue to exports S: IMFGFS x Export share of GDP S:WBNA YRSCH Average years of schooling of labor force #1980 S:Psacharopoulos and Arriagada (86) WBNA World Bank National Accounts Database WBSI World Bank Social Indicators IMF IFS International Monetary Fund--International Finance Statistics IMF GFS International Monetary Fund--Government Finance Statistics (L) indicates only available for 1974-1989 period 34
Further, the counselors have information on specific needs (demand) of firms and a fit may have arisen for a jobseeker of low skill who was otherwise judged to be less employable. For those rated _highly employable_ , and who had a higher likelihood of being referred to specific matching, treatment effects were close to zero as even in the control group these individuals managed to find employment.

Given their importance for assignment to specific matching, the paper next examines the correlations of worker characteristics with employability ratings from job counselors. Among characteristics observable by jobs counselors, jobseekers over age 30 are more likely to be judged employable, which may be a proxy for prior work experience, and those with less than high school education and those unemployed for more than a year, are judged to be less employable. Gender and characteristics including household size and income are uncorrelated with employability assessments. Exploiting data from a separate survey highlights the importance that personality traits may play in the counseling process. Jobseekers who are extraverted and open to new experiences are viewed to be more employable. While some types of jobs, particularly those in the service sector, may require workers who are more extraverted, attention to how personality traits of jobseekers influence the ratings of job counselors may be useful when considering contracts with the private sector in the future. Not all positions involve facing customers, even in the service sector, and thus will not necessarily require workers to exhibit high levels of extraversion and openness to new experiences.

This paper makes two main contributions to the literature on outsourcing private employment services. First, it examines the relative impact of the services provided by a private employment service provider and finds that job matching in addition to counseling services can have significant positive impacts on employment outcomes, particularly for youth and low-skilled workers who may struggle to otherwise signal their skills in the labor market. Second, it opens the “black box” of the jobseekercounselor relationship to better understand the behavior and incentives of a private employment service provider. Counselors act upon their assessments of jobseeker employability, and jobseekers who are assessed to have limited employment prospects are less likely to receive costly matching services, in linewith a cream-skimming behavior on the part of the service provider.

The rest of the paper is organized as follows. Section 2 provides an overview of the labor market in Bosnia and Herzegovina, as well as a description of the program and evaluation design. Section 3 describes the data sources used for the evaluation. Section 4 lays out the empirical approach used in the paper, and section 5 describes the results of the analysis. Section 6 concludes with a discussion of key findings and their policy implications.

5
- Gallup, John Luke, and Jeffrey D. Sachs. 2000. "The Economic Burden of Malaria." Center for International Development Working Paper No. 52. Harvard University: Cambridge.

- Gove, S. 1997. "Integrated management of childhood illness by outpatient health workers: technical basis and overview." _Bulletin of the World Health Organization_ 75(Supplement 1): 7-24.

- Gwatkin, Davidon and Michel Guillot. 2000. "The Burden of Disease among the World's Global Poor: Current Situation, Future Trends, and Implications for Strategy." Human Development Network: Health Nutrition and Population Discussion Paper. The World Bank: Washington, DC.

- Gwatkin, Davidson R., Shea Rutstein, Kiersten Johnson, Rohini Pande and Adam Wagstaff. 2000. "Socio-Economic Differences in Health, Nutrition, and Population." World Bank, HNP/Poverty Thematic Group, Washington, D.C.

- Kofoed, Poul-Erik Lund, Francisco Dias, Francisco Lopes, and Lars Rombo. 1998. "Correspondence: Diagnosis of Fever in Africa." _The Lancet_ 351(9099): 372.

- MARA. 1998. "Towards and Atlas of Malaria Risk in Africa: First Technical Report of the MARA/ARMA Collaboration." South African Medical Research Council, Durban South Africa. Available at http://www.mara.org.za (January 10, 2002).

- McCarthy, F. Desmond, Holger Wolf, and Yi Wu. 2000. "Malaria and Growth." World Bank Policy Research Working Paper No. 2303. The World Bank. Washington DC.

- Montgomery, Mark R., Michele Gragnolati, Kathleen A. Burke, and Edmundo Paredes. 2000. "Measuring Living Standards with Proxy Variables." _Demography_ 37(2): 155-74.

- McCombie, S.C. 1996. "Treatment Seeking for Malaria: A Review of Recent Research." _Social Science and Medicine_ 43(6).

- Mwenesi, Halima, Trudy Harpham, and Robert W. Snow. 1995. "Child Malaria Treatment Practices among Mothers in Kenya." _Social Science and Medicine_ 40(9).

- Perkins, B.A., J. R. Zucker, J. Otieno, H.S. Jafari, L. Paxton, S. C. Redd, B. L. Nahlen, B. Schwartz, A. J. Oloo, C. Olango, S. Gove, and C. C. Campbell. 1997. "Evaluation of an algorithm for integrated management of childhood illness in an area of Kenya with high malaria transmission." _Bulletin of the World Health Organization_ 75(Supplement 1): 33-42.

- Redd, Stephen C., Peter B. Bloland, Peter N. Kazembe, Ellen Patrick, Rodney Tembenu, and Carlos C. Campbell. 1992. "Usefulness of Clinical Case-Definitions in Guiding Therapy for African Children with Malaria or Pneumonia." _The Lancet_ 340(Nov 7).

- Redd, Stephen C., Peter N. Kazembe, Stephen P. Luby, Okey Nwanyanwu, Allen W. Hightower, Charles Ziba, Jack. J. Wirima, Lester Chitsulo, and Michael Olivar. 1996. "Clinical Algorithm for Treatment of _Plasmodium Falciparum_ Malaria in Children." _The Lancet_ 347(Jan 27).

_33_
pan-regional businesses that operate in Kenya, in East Africa, or across the continent. Global funds operating in Kenya are those firms that have worldwide operations (they are based-in and operate in geographies outside Africa), but also have dedicated teams and funds for Africa. Pan-African managers operate across SSA and may also have a presence in North Africa. East African firms typically focus their activities on member countries of the EAC (Burundi, Kenya, Rwanda, South Sudan, Tanzania, and Uganda) and often consider investments in Ethiopia as well. At present, there are no funds of note that operate solely in Kenya.<sup>5</sup>

**Table 2: Representative PE Fund Managers Active in Kenya**

||**Private Equity Fund Managers**||
|---|---|---|
|**East Africa**|**Pan-Africa**|**Global**|
|Ascent Capital Partners|Africinvest|Abraaj Group|
|Catalyst Principal Partners|Emerging Capital Partners|Actis Capital|
|Fanisi Venture Capital|Helios Investment Partners|The Carlyle Group|
|Centum Investment|Kibo Capital Partners|KKR|
|Transcentury|Phatisa Fund Managers||
|BPI East Africa|Progression Capital Africa<br>TBL Mirror Fund||

# _Source: Primary Research_

In addition to geographic focus, firms may also be classified in terms of the size of their typical investment in a target company, which in turn often correlates to the growth stage of the company that is receiving investment. The funds shown in Table 4 below target investments in the following ranges: investments of US$1-5 million (TBL Mirror Fund, Fanisi, GroFin), US$5-20 million (e.g. Catalyst, Ascent); and $20-100 million (e.g. Helios, ECP). Investments at the small end of the scale correspond to the lower end of the SME spectrum while larger SMEs or mid-market growth equity deals require investment tickets ranging from US$5 million to US$20 million. Investments above US$20 million correspond to large growth equity opportunities, regional roll-ups, buyouts or acquisitions of minority stakes in large and established businesses.

> 5 Acacia, a locally-registered Kenyan schilling-denominated fund that was Kenya-centric was established in 1996 and wound up in 2017.

6
# _<u>See Figure 7 – Meta‐estimates for Education Outcomes</u>_

<!-- Start of picture text --> (a) School Attendance Estimates (%)<br>80%<br>60%<br>40%<br>20%<br>0%<br>‐20%<br>‐40%<br>‐60%<br>‐80%<br>Effect Mean Effect CI<br>Beneficiaries: Poor/Vulnerable HHs Beneficiaries: Childen<br>Beneficiaries: Youth Beneficiaries: Women<br>Beneficiaries: Elderly<br>(b) School Attendance Impacts (%) and Transfer Sizes<br>1 300<br>0.8<br>250<br>0.6<br>0.4<br>200<br>0.2<br>0 150<br>‐0.2<br>100<br>‐0.4<br>‐0.6<br>50<br>‐0.8<br>‐1 0<br>Baseline Mean Estimate Monthly HH Transfer (2011 PPP USD)<br>Percentage of baseline<br>Monthly HH Trasnfer<br>Baseline Mean and Estimate<br><!-- End of picture text --> 37
# Table 4. Sumary<sup>Featuros</sup> of Econometric Studies of Japan's Trade Barriers (contsd)

|Author(s)<br>of Study|Time<br>Period|Countries<br>In Sample|Level<br>of<br>Aggregation|Dependent<br>Variable|Independent<br>Variable<br>|Model|Findings|
|---|---|---|---|---|---|---|---|
|CarlIner<br>(1985)|1967.<br>1972<br>1977, 1980,<br>1982|19<br> <br>Industrial,<br>developing|One economy<br>wide<br> <sup>sector</sup>|Index<br>of Intra-<br>Industry<br>trade<br>trade balance|_tog_GNP<sup>per capita,</sup><br>_log_GP,<sup>distance,</sup><br>absolute<br>value<br>of<br>In manufactures|<sup>Helpean-Krugpan</sup>|NegMtive<br>but In-<br>sigpiftacnt<br>dumy<br>variable<br>for Japan|
|Lawrence<br>(1987)|1970,<br>1980, 1983|13<br>Industrial|21 manufacturing<br>sectors|<br>Imports/total<br>domestic<br>use|Exports/Total<br>OECD<br>use; production/<br>totalOECD produc-<br>tion;<br>distance|Helpman-Krugman|Generally<br>sig-<br>nificant<br>Japanese<br>dumies<br>Indicating<br>underiporting<br>of manufactures|
|Nolsad (1987)|1980|45<br>!ndustrial,<br>developing,<br>separately<br>and together|One economy<br>wide<br>sector<br>|LogImports,<br>log exports,|Log<sup>GOP, logGDP per</sup><br>capita,<br>factor<br>en-<br>dowmnt similarity<br>Index, transport<br>costs|<sup>Stylized</sup><br>verslon<br>of<br>Heckscher-Ohiin<br>and<br> <sup>HlIpsan-Krugman</sup>|Insignificant<br>dumy variables<br>for Japan|
|Sato (1986)|1960, 1970,<br>1960|1S<br>Industrial|One economy wide<br>sector|lports<br>ofone<br>country as a<br>share of**Is-**<br>ports of all 15<br>countries|Log population<br>ratio of net In-<br>ports of primary<br><sup>products toGDP</sup><br>In manufacturing|C*henery<br>(1960)<br>moel|Japanese<br>dumy<br>significant|
for East Gojam; Yigremew, 1997a, and Yigremew, 1997b, for West Gojam; and Gizachew, 2010, for Weg Hemra). For the analysis, data on 2,303,198 individuals in the region of Amhara is aggregated into 212 district-time clusters.

Using census data for the research question at hand has two major advantages vis-à-vis the Demographic Health Survey (DHS) for Ethiopia, the most plausible alternative source of data. First, it allows for a before and after comparison with a baseline (while the first DHS was collected in 2001, i.e., after the land reform in Amhara was implemented). Secondly, the large sample size allows us to generate common fertility measures with a relatively better precision at the district level, which is our primary analysis unit. The most common cohort measure of fertility is the total fertility rate, which gives a point in time estimate for the average fertility of a population (or a sub-population, such as one Ethiopian district). It is built from age-specific fertility rates and captures the average number of children expected to be born by a woman during her childbearing years (see, for instance, Demeny and McNicoll, 2003).<sup>15</sup>

# [Insert Table 1 about here]

Descriptive statistics of the total and age-specific fertility rates at the district level, which are our key variables of interest, are presented in Table 1. Additional outcome variables focus on labor market participation (the proportion of women in a district who are self-employed, employed, and doing housework as main occupation) and marital status (the proportion of women in a district never yet married). Two reform indicators capturing the presence and intensity of the land redistribution in 1997, indicating whether at least one peasant association (the administrative unit subsidiary to a district) was affected, as well as the proportion of peasant associations per district being affected by a redistribution are used. Control variables include the district population in a particular age group (the number of district inhabitants in the age groups 12—17, 18—65, and greater than 65), the district population belonging to a particular religion (the number of district inhabitants who are Orthodox, Muslim or Animist) and ethnicity (the proportion of the district population being of Amharan ethnic origin). A precise definition of each variable as well as its source in the census can be found in the Appendix.

# [Insert Table 2 about here]

Table 2 shows the similarity of baseline characteristics for the full, rural and urban samples by regressing the binary reform indicator on either the main fertility outcomes, additional outcomes, or controls. For all the samples we cannot reject equality, at baseline, in total and age-specific fertility rates, female labor market participation indicators, or religion and ethnicity variables. We find some differences for the population characteristics, but they seem to be non-

> 15 As compared to period measures of fertility such as the crude birth rate and the general fertility rate, the total fertility rate has the advantage of being independent of the age structure of the poulation.

10
population 100,000 or more, which allows comparison with the ENEU survey. The second is rural households in areas of population of 15,000 or fewer. Out of the 105 cohort-period observations, we have 82 observations in urban areas and only 53 observations in rural areas for which 100 or more households are surveyed within the cohort.

We examine mobility in four different measures of household resources. The first is household income from the primary occupation of each member, which is the measure used in the ENEU. The second, total monetary income, includes all household cash income, including income earned from transfers, pensions, rent, interest, and from non-primary jobs. The third measure, full income, adds non-monetary sources of household income, which includes the value of all home-produced consumption and of any goods received as transfers. The fourth measure is full expenditure, which includes all monetary expenditure and homeproduced consumption items. Over the six survey rounds household primary labor earnings has a correlation of 0.91 with total monetary income, 0.83 with full income, and 0.58 with full expenditure.

Panel A of Table 5 presents the estimated slope coefficients from equation (17) for these four measures. For urban households the four measures give very similar levels of absolute mobility. The estimates of β range from 0.86 to 0.89. The rural estimates range from 0.65 (primary wage income) to 0.80 (full expenditure). The point estimates would therefore suggest that there is more absolute mobility in rural areas than in urban areas, and that rural wage income is more mobile than rural expenditure. However the limited number of rural observations results in large standard errors and we can not reject equality of the rural and urban coefficients. The coefficient on log primary wage income for urban households is 0.87 compared to 0.93 for the equivalent measure in the ENEU data. This difference is not statistically significant.

Panel B of Table 5 adds cohort fixed effects and presents the estimated slope coefficients from equation (18). The point estimates suggest very high rates of conditional mobility, with the slope coefficients close to zero. The point estimates also show less conditional mobility in expenditure than in wage income.

23
Table 1: Joint Significance Test

||**Gross-ou**|**tput (YKLM)**|**Value-ad**|**ded (VAKL)**|
|---|---|---|---|---|
|**Sector (ISIC Rev 3.1)**|**F-Stat**|**p-value**|**F-Stat**|**p-value**|
|ISIC 15, and 16|43.01|0.00|4.22|0.01|
|ISIC 17|28.96|0.00|1.58|0.19|
|ISIC 18|29.48|0.00|7.39|0.00|
|ISIC 19|28.51|0.00|3.53|0.01|
|ISIC 20|18.22|0.00|7.63|0.00|
|ISIC 21|54.72|0.00|0.93|0.43|
|ISIC 22|7.82|0.00|3.57|0.01|
|ISIC 23, and 24|12.13|0.00|3.21|0.02|
|ISIC 25|10.98|0.00|8.54|0.00|
|ISIC 26|46.91|0.00|4.97|0.00|
|ISIC 27|37.77|0.00|10.45|0.00|
|ISIC 28|25.81|0.00|2.69|0.04|
|ISIC 29|7.32|0.00|0.87|0.45|
|ISIC 30, 31, 32, and 33|18.77|0.00|2.16|0.09|
|ISIC 34, and 35|20.29|0.00|0.89|0.45|
|ISIC 36|22.46|0.00|5.18|0.00|

Having adopted the translog specification, firm-level TFPR is estimated by: where _f ∈ V AKL, Y KLM_ . TFPR is estimated as a sum of the establishment-level residual _ε_<sup>�</sup><sup>_f_</sup> _ict_<sup>,</sup> constant term ( _c_<sup>�</sup><sup>_f_</sup> _s_<sup>)whicharecommonacrossestablishmentwithineachindustry,country-fixedeffects</sup> ( _FE_<sup>�</sup> _c_<sup>_f_),incomegroupfixedeffects(</sup> _FE_<sup>�</sup> _I_<sup>_f_),yearfixedeffects(</sup> _FE_<sup>�</sup> _t_<sup>_f_)whicharecommonacrossestablish-</sup> ments within industry-country, industry-income group, and industry-year, respectively. The database � � available on the ES portal contains the firm-level estimates, i.e. _TFPRicf_<sup>_Y KLM_</sup> and _TFPRicf_<sup>_V AKL_</sup> , alongside the variables used to estimate TFPR. All estimates take into consideration the survey design for the ES by incorporating both stratification and probability (survey) weight information.

# **4 Estimates**

The output elasticity is given by the first derivative of the production function with respect to each input. For instance, under the gross-output production function, the output elasticity of material inputs for ˆ high-income economies is estimated as _thetam_ = _β_<sup>ˆ</sup> _m_ +2 _β_<sup>ˆ</sup> _mmmict_ + _β_<sup>ˆ</sup> _kmkict_ + _β_<sup>ˆ</sup> _lmlict_ + _β_<sup>ˆ</sup> _klmkictlict_ + _β_<sup>ˆ</sup> _mi_ . The elasticity for low-income counties is the same except _β_<sup>ˆ</sup> _mi_ . The elasticities for material inputs depend on the level of use of all inputs used in the production, including labor and capital and not only on the level of use of material inputs.

Translog coefficients ( _β_<sup>ˆ</sup> _m, β_<sup>ˆ</sup> _mm, β_<sup>ˆ</sup> _km, β_<sup>ˆ</sup> _lm, β_<sup>ˆ</sup> _mm, β_<sup>ˆ</sup> _klm, β_<sup>ˆ</sup> _mi_ ) are the same across establishments within an industry and income group. However, unlike the Cobb-Douglas production function, the output elasticities can vary across firms within the same industry/country, because they depend on the level of use of the inputs of production. Hence, small firms can have different elasticities than large firms (just the same, elasticities can vary between small firms, e.g.). This flexible form is an advantage of translog 8