rafmacalaba/datause-encoder-data huggingface dataset:
abroad” variable (in column 8 of Tables 2, 3, and A7 through A10) by additionally counting a respondent as having migrated if a job offer they reported in the 2012 endline survey is reported as having led to migration overseas in the 2013 survey. We did not modify the “Migrate abroad” variable if in the 2013 survey we learned that a respondent migrated but it was the result of a job offer not reported in the 2012 endline. This is because our objective here was simply to check whether our results would change if we allowed migration pending as of the 2012 endline to actually lead to migration. (To have done otherwise would have led to a false inflation of the treatment effect of “Full Assistance,” because we only surveyed those with job offers in the 2013 survey, and because the “Full Assistance” treatment led to a higher rate of job offers.) Column 2 of Table A6 reports the impacts of our treatments on this modified “Migrate abroad” variable. Our previous results are confirmed: there are no positive statistically significant impacts on migration, and impacts are similar in magnitude to the migration outcomes reported in column 8 of Table 2.

# **A.7. Additional specifications**

In Tables A7 and A8, we present the full set of results from the specifications used in Tables 2 and 3, respectively.

Tables A9 and A10 demonstrate that our previous results are robust to including individuals ages 41-45 in our sample. These individuals, as described earlier, were part of our baseline survey. However, we learned there are few overseas opportunities for new migrants over 40. We restricted our passport sample to individuals aged 20-40 years old, 39
Table 4: Unconditional estimations for manufacturing sector

|||DV: Fem|ale-owned ent|rant share||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Log total incumbent employment<br>in district-industry|0.008+++<br>(0.003)|0.002<br>(0.003)|-0.007++<br>(0.003)|-0.001<br>(0.003)|-0.008+++<br>(0.003)|
|Log female-owned incumbent|||0.027+++||0.025+++|
|businesses in district|||(0.002)||(0.002)|
|Log female-owned incumbent<br>businesses in district-industry||||0.031+++<br>(0.004)|0.021+++<br>(0.004)|
|District Traits:||||||
|Female literacy rate|-0.006<br>(0.011)|-0.007<br>(0.011)|-0.009<br>(0.009)|-0.003<br>(0.009)|-0.006<br>(0.009)|
|Sex ratio|0.026+++<br>(0.008)|0.026+++<br>(0.008)|0.020+++<br>(0.007)|0.019+++<br>(0.007)|0.016++<br>(0.006)|
|Population density|-0.018+<br>(0.010)|-0.020++<br>(0.010)|-0.016+<br>(0.009)|-0.026+++<br>(0.009)|-0.020++<br>(0.008)|
|Education level|0.001<br>(0.009)|0.003<br>(0.009)|0.001<br>(0.008)|0.005<br>(0.008)|0.002<br>(0.007)|
|Age profile|0.034++<br>(0.014)|0.034++<br>(0.015)|0.026++<br>(0.012)|0.023+<br>(0.012)|0.019+<br>(0.011)|
|Infrastructure level|0.025+++<br>(0.009)|0.030+++<br>(0.009)|0.028+++<br>(0.008)|0.025+++<br>(0.008)|0.025+++<br>(0.008)|
|Labor regulations stringency|0.015++|0.015++|0.012++|0.011+|0.010+|
||(0.007)|(0.007)|(0.006)|(0.006)|(0.006)|
|Local Industrial Conditions by Incu|mbent Firms:|||||
|Index of labor market strength|-0.009|||||
|for district-industry|(0.008)|||||
|Index of input-output strength<br>for district-industry|0.021++<br>(0.010)|||||
|Index of labor market strength,||0.023+++|0.002|0.026+++|0.006|
|female-owned businesses||(0.007)|(0.006)|(0.007)|(0.006)|
|Index of input-output strength||0.033+++|0.011+|0.030+++|0.011+|
|female-owned businesses||(0.008)|(0.006)|(0.007)|(0.006)|
|Observations|4,336|4,336|4,336|4,336|4,336|
|Adjusted R-squared|0.306|0.319|0.346|0.328|0.350|

Notes: Regressions consider relative rates of female entrepreneurship across manufacturing district-industries in India. Regressions include industry-year fixed effects. Regressions include unreported controls for log district population and its square, total fertility rate, and travel time to one of the ten biggest cities in India. None of these variables are economically or statistically significant. Regressions weight observations by an interaction of log district size and log industry size. Regressions cluster standard errors by district.
major state; (c) 5 percent on the basis of infrastructure; (d) 5 percent on the basis of the area of states subject to certain normative limits; and (e) 10 percent on the basis of tax effort defined as the ratio of per capita own tax revenue to the square of per capita income. This formula differs from the previous ones by reducing (for income taxes) the weight of population; increasing the weight given to "distance" of per capita income; introducing a weight for infrastructure; and removing the "gap filling weight." (World Bank, 1995, p.57) It is expected that this fornula will strengthen the redistribution function of the Finance Commission transfers.

The detailed procedure for applying the above formula is as follows:12 Step 1. Divide the whole pool for transfers into five parts, 20%, 60%, 5%/6, 5%, 10%.

Step 2. Allocate 20 percent of the pool on the basis of population. That is, the ith state gets P,/P of the 20 percent, where Pi is the ith state's population, and P is the country's total population. The population figures used are those in the 1971 Census.

Step 3. Allocate 60 percent of the pool on the basis on income distance. The respective "distances" are multiplied by the population of the states and the share of each state is obtained by dividing the product for that state by the sum of the products for all states. That is, the ith state gets PiDI£jPjDj of the 60 percent, where Pi is the ith state's population, and Di is the per capita income distance of the ith state from the state with the highest per capita income (Goa in the case of the Tenth Commission). Goa is taken to be the same as for the state with the second highest per capita income (Punjab) from that of the next one (Maharashtra) since otherwise Goa will not get any share at all.

Step 4. Allocate 5 percent of the pool on the basis on area, i.e., the ith state gets A,/A of the 5 percent, where A, is the ith state's area, and A is the country's total area. An adjustment is however made so that no state gets a share higher than 10 percent or less than 2 percent.

Step 5. _5_ percent for infrastructure is on the basis of an aggregate index computed by an expert group. The details are given in Appendix 5 to the Tenth Commission's report.

Step 6. 10 percent for tax effort is allocated using the ratio of per capita own tax revenue to the square of per capita income with the respective products being scaled by population as in the distance criterion. That is, the ith state gets **PiEi/EjPjE** of the 10 percent where E, 2 is the ith state's effort index defined as E, = (R,fP,)/(Y,/P,).

12 The author would like to thank Mr. S. Guhan for providing me with detailed information on the Finance Commission formula.

20
# FIGURE 13: SHARE OF GOVERNMENT, HEALTH. AND EDUCATION SERVICES IN VALUE ADDED:

# USSR AND OECD

<!-- Start of picture text --> 30%-<br>DNISWE<br>20%-_ DN<br>NED NE6D<br>IRL<br>SL N uaK~~~~~~~~~~~S<br>10%-8<br>U9LP UEAI<br>0%-<br>8 8.5 9 ~~~~~~~~~~~~~~~9.5 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
<!-- Start of picture text --> 1000<br>00 1<br>ott] 4<br>700 {<br>600 4<br>500 4<br>00 4<br>yt) |<br>100 i<br>Q15 1 05 0 05 1 1s<br>Domestic interest Rates (%)<br><!-- End of picture text -->

<!-- Start of picture text --> 0. ————__<br>700, _<br>600} 4<br>00F 7<br>400} 4<br>zor 4<br>100} |<br>olé 4 2 0 2 4 6<br>Foreign interest Rates (%)<br><!-- End of picture text -->

<!-- Start of picture text --> 1400<br>1200 1<br>1000 |<br>an 1<br>ao 4<br>400 4<br>tt] 4<br>Q15 1 05 0 05 1 1sJ<br>GOP Growth (%)<br><!-- End of picture text -->

<!-- Start of picture text --> 1600 ———_<br>1600+ 4<br>1400}rzof 4|<br>1000+ a |<br>eo} 7<br>eo 4<br>400} 4<br>200} 4<br>97 x x 10 1 x x<br>Depreciaton (%)<br><!-- End of picture text -->

<!-- Start of picture text --> 1<br>ee3 saasnnanenapasananayee gnsnsgezanansesnsennagseanenenaytttass<br>a i<br>p<br>aan2 =<br>a Be<br>rad<br>0.2<br>2000 «#2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> <!-- Start of picture text -->
1.2<br>4:<br>3<br>o& O08 + + |<br>eo gel &<br>2= 06 $<br>a<br>04 —_ | |<br>a |<br>0.2<br>2004 2005 2006 2007 2008 2009<br><!-- End of picture text -->
from a set of polar orbiting satellites operated by the US Department of Defense. This satellite data was chosen because it passes above each location on earth between 6am and 6pm every day (Basist et al. 2001). These satellites are equipped with sensors that measure surface temperature by detecting microwaves that pass through clouds. However, these satellites do not measure precipitation directly. Hence, the paper relied on the precipitation data of the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This data set, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, interpolates weather variables across Africa based on ground station measurements of precipitation.

Soil data from FAO (2003) were then matched with each observation. The FAO data provide information about major and minor soils in each location as well as slope and texture. Data concerning the hydrology were obtained from the University of Colorado (Strzepek and McCluskey 2006). Using a hydrological model for Africa, flow and runoff were calculated for each district in the surveyed countries. Data on elevation at the centroid of each district were obtained from the United States Geological Survey (USGS 2004). The USGS data are derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

# **4. Empirical Results**

FAO classified Africa into 16 AEZs, the distribution of which is shown in Figure 1. The Sahara desert occupies a vast land area in the north. There are also desert zones in the eastern and southern edge of the continent. Just beneath the Sahara in West Africa is a lowland semi-arid zone, followed by lowland dry savannah, lowland moist savannah, and a lowland sub-humid zone. The lowland humid forest then stretches from Cameroon across Central Africa. Eastern Africa is composed of some desert, lowland dry savannah, and some high elevation humid forest and high elevation dry savannah which are located around Mount Kilimanjaro and part of Kenya. Southern Africa consists of lowland or mid elevation moist savannah, and lowland or mid elevation dry savannah.

As each AEZ is endowed with unique agro-ecological resources, we expect that certain 7
vendor Indic.

0 Are there -- or are there going to be -- public standpipe/ hydrant operators ? (if the answer Is no, change the indicator to "1I,otherwise leave as 0") concess. Indic. 0 Estimated delivery rate of vendors (if no information, use 2 cum/day/vendor): cum/day/ vendor 0 Opportunity cost of vendor labour (may be different from their net Income as a result of monopoly or restricted entry -- if no Information, use Rp 2500/day/vendor): Rptday/ vendor 0 Average delivery rate of standpipes: cum/day/ standpipe 0 Opportunity cost of standpipe/hydrant operator services (may be different from their net income as a result of a monopoly -- if no Information, use Rp 2500/day/concessionaire): Rplday/ conc. 0 Average time required to get 1 cubic meter water from standpipe (hauling excluded): hour 0 Average distance from standpipes to those households which rely on water from standpipes: meter 0 Value of private time (if no information, Rpl50/hour in Java/Bali, Rp200/hour in the Outer islands is proposed): Rpthour 0 6. Did local people boll the drinking/cooking water before project implementation ?(if the answer is yes, change the indicator to l, no I f1', if no write 110"f) boiling indic. 0 31
<!-- Start of picture text --> Figure 6. The Fiscal Cost of Reducing the Gini Coefficient by one point (COP 1,000 billion).<br>30.0<br>24.7<br>25.0<br>21.9<br>20.1<br>20.0<br>16.9<br>15.0<br>11.4<br>10.0<br>7.0<br>5.5<br>4.2<br>5.0<br>1.4<br>-<br>Declaration of Conditional Tax pension Reduce the Reduce pension VAT exemptionsIncrease income Family Subsidy Reduce VAT<br>income for cash transfers income income tax transfers taxation<br>independent exemptions<br>workers<br><!-- End of picture text --> Source: Table 7. Note: The graph is illustrative, in some case the fiscal program reduces the Gini coefficient by less than one point. For instance, taxation of pension income reduces the Gini coefficient by only 0.2 points. As of May 23, 2012, one US dollar was equivalent to about 1,800 Colombian pesos.

# **_(3) Exploiting the Synergies: Combining Proposals_**

55. **Considerations of fiscal impact and effectiveness can be used to identify attractive policy proposals.** The approach is straightforward: policy options that generate fiscal savings can be combined with those programs which are the most effective in reducing inequality, thus maximizing their combined impact. This approach focuses on how to allocate fiscal resources most efficiently to ensure the highest social return in terms of reduced inequality.

56. **To illustrate this approach and the potential of fiscal redistribution in Colombia, a package of measures is proposed, but not necessarily recommended.** Before presenting the proposal, it is important to introduce a few methodological caveats (see Annex 2 for details). First, as an approximation of marginal program effectiveness, average rates of effectiveness reported in Table 7 are used. This may result in some lack of precision, which can be resolved through a more detailed analysis using the underlying household data. Second, the analysis is static as it does not account for possible behavioral responses of individuals to changes in taxes or public transfers. Third, the structure of existing programs is taken as given when computing the rates of effectiveness. In other words, the degree of progressivity, or the opposite, embedded in the existing tax and spending system is assumed unchanged. Finally, the approach is limited by the data available. No attempt was made to estimate the potential inequality reducing impacts of non-existing programs. As a consequence, there is no pretense that the policy package proposed is necessarily the optimal one.

57. **The static simulations presented here are intended to complement existing modeling efforts.** The objective of the simulations is to give a snapshot view of the relationship between the fiscal and income inequality reducing impacts. In this sense, the results are intended to complement other types of exercises, possibly based on CGE modeling, rather than a substitute 21
prospective international investors. Most of the papers cited above have, in fact, used these subjective indicators as measures of good governance.

This section makes use of the information provided by two independent country-risk evaluators, Jnternational Country Risk Guide (ICRG) and Business Environment Risk Index (BERI), which present ratings for various aspects of governance. ICRG provides ratings on five variables, including the rule of law, measuring the extent to which there are mechanisms for peaceful dispute resolution; expropriation risk, assessing the risk of nationalization of enterprise equity; and repudiation ofcontracts by government, measuring the state’s commitment to fulfill its contractual obligations and proxying for its ability to enforce contracts between private-sector agents. It also analyzes corruption in government, evaluating the extent to which bribes to public officials are needed to expedite bureaucratic requirements, circumvent regulations, force a change in policy to benefit a given interest group, or obtain public contracts without a lawful bidding process; and quality of the bureaucracy, evaluating both procedural clarity and the technical competence of government officials. Although in principle these ratings measure different aspects of governance, in practice they are highly correlated. Because of this, an overall index is presented here. This index is based on the simple average of the ratings for the five variables. A similar procedure is used with the BERI ratings, which consist of three underlying variables: enforceability ofcontracts, nationalization risk, and bureaucratic delays. | The ICRG and BERI indices presented below provide information related to two aspects of good governance. The first is the quality of public institutions (evaluated by the variables corruption in government and quality of the bureaucracy/bureaucratic delays), which describes the ability of the state to implement its policies through both an appropriate structure of government and honest and technically competent bureaucrats. The second is the quality of public services (evaluated by the _ variables rule of law, enforceability of contracts/repudiation of contracts by government, and expropriation/nationalization risk), which determine an environment where contractual obligations are respected both by the state and private agents, where legal disputes are effectively resolved in the - judicial system, and where legal rules for investment, employment, and taxation are not arbitrarily modified by the state.

.

The BERI Index (see Chart 17, left panel) shows that Argentina, Chile, Ecuador, and Peru made some progress in the area of governance from 1990 to 1995. For the latter two countries, this meant a recovery toward their 1985 level after a fall in the late 1980s. By contrast, governance in 27
|||Table 4C:|Regression|results from|the full samp|le of153 coun|tries and al|l years 1988-|2015||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||)<br>Logo|Q)<br>ftotal loans (|8)<br>millions of|@)<br>2010 $)|6)<br>Logo|6<br>fHD loans (|0<br>millions of2|8<br>010 $)|9)<br>LogofH|1)<br>D sectorloa|dy<br>ns (millions|of2010 $)|
|IBRD country|-0.124|-1.053***|= -0.508*|= -0.651***|-0.270|-0.916***|-0.774***<br>|= -0.868***|-0.297|-0.933%**|-0.587***|-0.691***|
|(1=yes)|(0.311)|(0.239)|(0.275)|(0.249)|(0.172)|(0.154)|(0.239)|(0.245)|(0.184)|(0.164)|(0.212)|(0.219)|
|Logpopulation||0.767***||1.324*||0.437***||0.764||0.456***||1.164**|
|||(0.058)||(0.741)||(0.046)||(0.529)||(0.049)||(0.512)|
|LogGNIper capita||0.276**||0.441*||0.258***||0.290**||0.222**||0.298**|
|||(0.121)||(0.233)||(0.080)||(0.130)||(0.087)||(0.143)|
|Net aid flow||0.053***||0.017||0.032||-0.009||0.052***||0.006|
|(millions of 2010 dollars)||(0.020)||(0.015)||(0.020)||(0.022)||(0.016)||(0.017)|
|Number ofobservations|3,753|3,402|3,753|3,402|3,753|3.402|3,753|3,402|3,753|3,402|3,753|3,402|
|R2|0.010|0.399|0.566|0.597|0.013|0.202|0.308|0.317|0.013|0.261|0.382|0.394|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|3.185|3.376|3.185|3.376|1.372|1.458|1.372|1.458|1.454|1.546|1.454|1.546|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 3%, TM* Significant at 95%.
## FIGURE 6a

# Investment Change & TOT Shocks to 1986 Middle Income Countries

<!-- Start of picture text --> 0.04<br>Jamaica 0<br>0.03<br>to 0.02 - , ameroon 0<br>0.01 -Tu rkey<br>0 -euGrinea*<br>It idonesia 0 Yugoslavian Paua New Guinea 0<br>-0.01 - Costa Rica -orea + El Salva0or<br>Mt rocco o<br>-0.02 Mal sia 0 Thailand O<br>CD -0.03 - Tunisia 0<br>tN -0.04 - Algeria 0 Honduras 0 zil * Guyana 0<br>~ -0.05 - Mexic0amin. p0 Hungoryo<br>4.1 -0.05 Guatemala 0 Chile U<br>-G4.06 - gypt 0.<br>-0.07 - Ecuador* Peru O<br>I 0 Uruguay o<br>> -0.08 -<br>c<br>c -0.09 - Argentina U<br>-0. 1 Po gal<br>Nigeria * Panama 0<br>-0.f' uelaEa<br>-0.12 -<br>-0.13- Phil pines 0<br>-0.14 - Jor 1an 0<br>-2.5 -1.5 -0.5 0.5 1.5 2.5<br>TOT Shock to 1986<br><!-- End of picture text --> Correlation Coefficient: .20
||(0.0693)|(0.0692)|(0.070)|(0.070)|(0.070)|(0.070)|(0.070)|(0.069)|
|---|---|---|---|---|---|---|---|---|
|Constant|1.9861***|2.2644***|1.986***|2.278***|1.986***|2.275***|1.987***|2.260***|
||(0.1198)|(0.2538)|(0.119)|(0.266)|(0.120)|(0.261)|(0.119)|(0.256)|
|Observations|14,656|14,656|14,656|14,656|14,656|14,656|14,656|14,656|
|R-squared|0.0650|0.0671|0.065|0.067|0.065|0.067|0.065|0.067|

|||_Table A_|_- 8: Firms that in_|_novate and go_|_vernment-owned_|_banks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|INNOV|
|SOB|-0.1301|-0.0997|0.079|0.090|0.149|0.133|0.071|0.096|
||(0.0835)|(0.0722)|(0.219)|(0.225)|(0.230)|(0.238)|(0.241)|(0.240)|
|SOB_MNA|-0.0915|-0.1514|0.037|-0.368|1.206|1.124|0.755|0.377|
||(0.2209)|(0.2243)|(0.690)|(0.724)|(1.622)|(1.625)|(1.314)|(1.348)|
|INTER|||-0.492|-0.456|-0.668|-0.586|-0.550|-0.537|
||||(0.456)|(0.494)|(0.498)|(0.534)|(0.565)|(0.586)|
|INTER_MNA|||0.006|0.464|-1.920|-1.803|-0.903|-0.480|
||||(1.188)|(1.217)|(2.962)|(2.972)|(2.085)|(2.124)|
|ROA||-0.0298||-0.037||||-0.033|
|||(0.0246)||(0.024)||||(0.024)|
|LTA||-0.0432||-0.033||-0.033||-0.039|
|||(0.0315)||(0.036)||(0.036)||(0.033)|
|lsize|0.0459|0.0442|0.044|0.043|0.044|0.042|0.045|0.044|
||(0.0342)|(0.0338)|(0.035)|(0.034)|(0.035)|(0.034)|(0.034)|(0.034)|
|lage|0.0636**|0.0642**|0.064**|0.064**|0.064**|0.065**|0.064**|0.064**|
||(0.0280)|(0.0276)|(0.028)|(0.027)|(0.028)|(0.028)|(0.028)|(0.027)|
|SOE|-0.2443|-0.2607*|-0.204|-0.231|-0.159|-0.162|-0.186|-0.208|
||(0.1505)|(0.1553)|(0.157)|(0.161)|(0.164)|(0.165)|(0.162)|(0.165)|
|FEM|0.0419|0.0452|0.050|0.051|0.050|0.053|0.045|0.047|
||(0.0837)|(0.0837)|(0.083)|(0.083)|(0.084)|(0.084)|(0.083)|(0.084)|
|Constant|-0.5740***|-0.2099|-0.567***|-0.286|-0.568***|-0.296|-0.571***|-0.240|
||(0.1257)|(0.2491)|(0.128)|(0.272)|(0.127)|(0.267)|(0.127)|(0.255)|
|Observations|11,127|11,127|11,127|11,127|11,127|11,127|11,127|11,127|
|R-squared|0.0890|0.0928|0.092|0.095|0.094|0.096|0.091|0.094|

|||_Table A- 9: Firm_<br>|_s that invest in_<br>|_green activities_<br>|_and government_<br>|_-owned banks_<br>|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|GREEN|
|SOB|0.0598|0.0378|-0.026|-0.065|-0.056|-0.051|-0.021|-0.082|
||(0.1216)|(0.1222)|(0.270)|(0.282)|(0.285)|(0.292)|(0.365)|(0.380)|
|SOB_MNA|0.1132|0.1968|-0.313|-0.311|-0.326|-0.312|-0.711|-0.748|
||(0.1784)|(0.1909)|(0.805)|(0.808)|(0.870)|(0.867)|(1.393)|(1.407)|
|INTER|||0.209|0.260|0.283|0.260|0.226|0.338|
||||(0.421)|(0.463)|(0.462)|(0.498)|(0.739)|(0.788)|
|INTER_MNA|||0.488|0.589|0.697|0.676|1.042|1.164|
||||(1.129)|(1.140)|(1.543)|(1.539)|(2.045)|(2.066)|
|ROA||0.0576||0.063||||0.061|
|||(0.0372)||(0.038)||||(0.038)|
|LTA||0.0094||0.001||0.008||0.006|
|||(0.0389)||(0.043)||(0.044)||(0.041)|
|lsize|0.2725***|0.2714***|0.274***|0.272***|0.274***|0.274***|0.273***|0.272***|

39