rafmacalaba/datause-encoder-data huggingface dataset:
10 _Fig 2:_ _Kakwani index ofprogressivity, and the equalizing effect ofprogressive payments_ <!-- Start of picture text --> 100%<br>E<br>0%<br>Cumulative % of sample, ranked by income<br><!-- End of picture text --> Payments that are progressive on prepayment income will exert an equalizing effect on the income distribution-the Lorenz curve for postpayment income, _Lp,,,_ will lie above the Lorenz curve for preprayment income, Lp,. By contrast, payments that are regressive on prepayment income will exert a disequalizing effect on the income distribution, so that _Lpost will_ lie below _Lp,e_ So, a policymaker who is concerned to ensure that health care payments do not adversely affect the distribution of postpayment income will clearly disfavor regressive health care payments.

It is thus the progressivity of payments _on prepayment income_ that will determine, other things equal, the degree of inequality in postpayment income-i.e. the degree of inequality in households' ability to purchase things other than health care, such as food. This draws attention to another defect of the WHO work, namely its definition of ATP in terms of income _after food 'pending._ Clearly, in the light of the above, one needs to measure both prepayment and postpayment income _before_ food outlays. Knowing how progressive health care payments are on prepayment income defined _net offood outlays_ tells us how health care payments influence the distribution of income available to spend on things other than health care _including food. But_ that misses the policymaker's concern entirely, which is precisely that poor households may have too little left after health care costs to purchase food. Looking at households' income after food expenses have been netted out conceals from us completely their ability to afford food.
quality in the fi rms that are engaged in those activities. More entrepreneurial managers are more likely to engage in technological innovations more often but are also more likely to export or import. In column (1), we add to our baseline speci fi cation (column (6) of Table 3) a dummy variable that equals one if the manager has a college or a post-graduate degree. This variable, which proxies for managerial ability, has a positive and signi fi cant effect on the fi rm’s propensity to innovate. More importantly, the effects of technology diffusion through trade and FDI on technological innovation are robust to the control for managerial quality.

Alternatively, foreign-owned fi rms or exporters may have easier access to fi nance through their multinational parents or due to export-promoting policies. If fi rms are credit constrained, lower costs or increased access to fi nance can increase their ability to innovate.<sup>27</sup> Hence, the estimated effects of openness on innovation could be largely due to open fi rms’ better access to fi nance. The fi ndings in column (2) show that access to fi nance is indeed positively correlated with the propensity to innovate.<sup>28</sup> The effects of minority foreign ownership, exports, and imports on innovation are maintained, suggesting that it is not just a better access to fi nance that underlies those effects, but likely it is the access by more open fi rms to a richer knowledge base.

Some industries and regions with a large presence of fi rms integrated into global markets may provide a particularly dynamic environment for innovation to fl ourish. Thus, it is possible that both the engagement of the fi rm but also the engagement of other fi rms in the same industry and region in international activities matter. In column (3), we control for this external effect by including in the regression the share of fi rms in the industry-region that export and the share of fi rms in the industry-region that import. It is reassuring to see that the effects of minority foreign ownership, exports, and imports on innovation are robust to this control. The effects of these “spillover” variables are positive and signi fi cant for importers, but negative for exporters, suggesting some possible market-stealing effects.

> 27King and Levine (1993) argue that the development of fi nancial intermediaries reduces the costs of identifying entrepreneurs more capable of generating innovations. Increased access to fi nance for fi rms can affect innovation through an improved screening of the quality and probability of success of the projects that are fi nanced as well as through their effects on innovation inputs.

> 28Qualitatively similar fi ndings are obtained by Ayyagari et al. (2006) using a smaller sample of countries from the Investment Climate Surveys.

16
# **Table 5.9: Results for regression model of profitability of the household micro-enterprise**

||Urban|Rural|
|---|---|---|
|Household head characteristics<br>Male|.437<sup>*</sup>|.026<br><sup>**</sup>|
|Age|-.011|-.011|
|Years of school|-.035|.018|
|Speaks Spanish<br>|.634|.037|
|Indigenous|-.139|.247|
|Household characteristics|||
|Number of adults|-.101|.063<br>|
|Population in locality|-.003|.0001<sup>*</sup>|
|Coverage of utilities|||
|Electricity|-2.14|.503<sup>**</sup><br>|
|Water|.931|1.0<sup>***</sup>|
|Fixed phone|1.73|-1.04<br>|
|Cellular phone|2.36|2.7<sup>**</sup>|
|Minutes to closest public phone|.0008|-.001|
|Business characteristics|||
|Capital (Q)|6x10<sup>-8</sup><br><sup>***</sup>|1x10<sup>-5*</sup><br><sup>***</sup>|
|Labor (man-hours)|.001|.001|
|Age of business (years)|-.001<br>|-.004<br>|
|Months worked last year (#)|.092<sup>***</sup>|.105<sup>***</sup>|
|Economic activity|||
|Manufacture|.283|-.144|
|Services|-.124|.281|
|Provider|||
|Large firm|.254|.080|
|Small firm|.030|.013|
|Source of finance|||
|Bank / cooperative / NGO|.898<sup>*</sup><br>|.460<br>|
|Family / friends|.720<sup>*</sup>|.836<sup>**</sup><br>|
|Providers|-.113<br>|1.45<sup>**</sup><br>|
|Savings / assets / inheritance|1.02<sup>**</sup>|.660<sup>*</sup>|
|Type of dwelling<br>House|.507|-.458<sup>*</sup><br>|
|Constant|3.66|5.13<sup>***</sup>|
|Observations|634|478|
|Pseudo R-squared|.1537|.5553|
|Chi 2|332.59|671.08|

_Notes_ : Results reported are those of second-stage regression. Coverage of basic services for electricity, water, fixed and cellular phone were instrumented using availability of these services in census tract as instrument. Estimations include a municipality-level fixed effect. Significant at:<sup>***</sup> 90%,<sup>**</sup> 95%,<sup>*</sup> 90%.

Refers only to enterprises that operate in dwelling.

**Table 5.10: Estimated change in profits due to connection to modern utilities**

||Urban|Rural|
|---|---|---|
|In Quetzales per month<br>Electricity<br>Water<sup>2</sup>|-1,445<br>2,518|399<sup>**</sup><br>1,062<sup>***</sup>|
|Fixed phone|7,585|-395<br>|
|Cellular phone|15,644|8,663<sup>**</sup>|
|As proportion of profits<br>Electricity|-.88|.65<sup>**</sup><br>|
|Water|1.5|1.7<sup>***</sup>|
|Fixed phone|4.6|-.65<br>|
|Cellularphone|9.6|14.2<sup>**</sup>|
|Observations|634|478|

_Notes_ : Refers only to enterprises that operate in dwelling.

44
Table 6

|Dependent variable:|Fiscal adju|stment wh|en needed|based on d|ebt/revenu|e|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|-0.004|-0.007|-0.009*|-0.012**|-0.013|-0.009|-0.020*|-0.026**|
|(% of GDP)|(0.003)|(0.004)|(0.005)|(0.005)|(0.010)|(0.015)|(0.012)|(0.010)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.329||-0.230|-0.238|-0.227|-0.209|-0.225|
|||(0.274)||(0.482)|(0.487)|(0.483)|(0.485)|(0.483)|
|Inflation (t-1)||0.038||0.098|0.096|0.100|0.101|0.084|
|||(0.084)||(0.164)|(0.163)|(0.164)|(0.167)|(0.164)|
|U.S. Interest rate||-0.023**||-0.028**|-0.027*|-0.028**|-0.030**|-0.028**|
|||(0.011)||(0.013)|(0.014)|(0.014)|(0.014)|(0.014)|
|**Political factors**|||||||||
|Gini|||0.004|0.004|0.004|0.005|0.004|0.004|
||||(0.004)|(0.004)|(0.004)|(0.007)|(0.004)|(0.004)|
|Rule of law|||-0.040|-0.020|-0.020|-0.020|-0.065|-0.020|
||||(0.035)|(0.041)|(0.040)|(0.041)|(0.057)|(0.041)|
|Margin of Majority|||0.221*|0.220*|0.220*|0.218*|0.223*|-0.166|
||||(0.132)|(0.129)|(0.129)|(0.130)|(0.129)|(0.206)|
|Election's year|||-0.042|-0.048|-0.048|-0.048|-0.048|-0.047|
||||(0.030)|(0.031)|(0.031)|(0.031)|(0.031)|(0.030)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||0.002<br>(0.012)||||
|Gini * Rigidity||||||-0.000<br>(0.000)|||
|Rule of law * Rigidit|||||||0.002<br>(0.003)||
|Majority * Rigidity||||||||0.023<br>(0.014)|
|Constant|0.303***|0.479***|0.352|0.445|0.449|0.396|0.617*|0.678**|
||(0.079)|(0.117)|(0.301)|(0.298)|(0.301)|(0.350)|(0.322)|(0.309)|
|Observations|1,121|1,081|771|755|755|755|755|755|
|R-squared|0.001|0.009|0.012|0.022|0.022|0.022|0.022|0.026|
|Number of ifscode|147|145|103|102|102|102|102|102|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard err<br>*** p<0.01, ** p<0.|ors clustere<br>05, * p<0.1|d by count|ry in paren|theses|||||

- 24 -
25

**Table 7: Simulated Poverty and Inequality for 1976, Using 1996 coefficients.**

||Mean<br>||Ineq|uality||||Pov|erty|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_______________________|p/c||||________|Z = R$30|/ month||Z = R$|60 / mon|th|
||Income|Gini|E(0)|E(1)|E(2)|P(0)|P(1)|P(2)<br>-|P(0)|P(1)|P(2)|
|1976 observed<br>|265.101|0.595|0.648|0.760|2.657|0.0681|0.0211|0.0105|0.2209|0.0830|0.0428|
|1996 observed|276.460|0.591|0.586|0.694|1.523|0.0922|0.0530|0.0434|0.2176|0.1029|0.0703|
|**_Price_**_Effects_<br>||||||||||||
|a,1for wage earners<br>|218.786|0.598|0.656|0.752<br>-|2.161<br>_|<br>0.0984|0.0304|00.0141 _|0.2876|0.1129|0.0596|
|a,1for self-employed|250.446|_0.597_|0.658|0.770|2.787|0.0788|0.0250|0.0121|0.2399|0.0932|0.0490|
|_=,a_for both|204.071|0.598|-6 0.655|.i754|=|2.9**0**.1114|0.0357|0.0169|0.3084|0.1249|0.0673|
|_a_only, for both<br>|233.837|0.601|0.664|0.774|2.691|0.0897|0.0275|0.0129|0.2688|0.1040|0.0545|
|All1B<br>(but no_a)_for both|216.876|0.593|0.644|0.736|2.055|0.0972|0.0303|0.0143T-|0.2837|0.1114|0.0590|
|Education ,B for both|232.830|0.593|0.639|0.759|2.691|0.0779|0.0234|0.0110|0.2531|0.0953|0.0488|
|Experience 1 for both|240.618|0.600|0.664|0.771|2.694|0.0851|0.0265|0.0125 _|<br>0.2592|0.1000|0.0525|
|Gender P for both<br>|270.259|0.595|0.649|0.751|2.590|0.0650|0.0191|0.0090<br>|_ 0.2160|0.0797|0.0404|
|**_Occupational Choice Effects_**<br>||||||||||||
|y for both sectors (and both<br>heads + others)|260.323|0.609|0.650|0.788|2.633<br>|_<br>0.0944|0.0451|0.0331 _|0.2471|0.1082|0.0671|
|y for both sectors (only for<br>other members)<br>|265.643|0.598|0.6 57|0.57|2.482|0.0721|0.0231|0.0119|-00.2274<br>I|0.0867|0.0454|
|_y, a,1for both sectors<br>|202.325|0.610|0.649|0.788|2.401|0.1352|0.0597|0.0402|0.3248|0.1466|0.0902|
|**_Demographic Patterns_**||||||||||||
|_,ud only, for all|277.028|0.574|0.585|0.704|2.432|0.0365|0.0113|0.0063<br>-|0.1711|0.0554|0.0264|
|__,ud, y, ac, ,B, for all<br>|210.995|0.587|0.577|0.727|2.177|0.0931|0.0433|0.0321<br>-|0.2724|0.1129|0.0677|
|**_EducationEndowment_**_Effects_<br>|393|54|0|_04|||0<br>6|0||||
|e only, for|339.753|0.594|0.650|0.740|2.485|0.0424|0.0136|0.0073|0.1593|0.0567-|0.0287|
|**[ id,**[Le for all<br>|353.248|0.571|0.584|0.688|2.320|0.0225|0.0078|0.0049|0.1131|0.0359|0.0173|
|1.te, pd, y, oc,3,B<br>for all|263.676|0.594|0.600|0.727|1.896|0.0735|0.0374|0.0296 =|0.2204|0.0913|0.0561|
|Source: Based on "Pesquisa Nacional p|or Amostra|de Domic|ilios" (PNA|D) of 1976 a|nd 1996.||||||.-|
Asians make up nearly half of all the foreign professionals in the US, with India being the largest supplier of professionals to the US. In 2000, of the 864,000 foreign professionals working in the US, as many as 472,000 (i.e. 54 percent of all foreign professionals) were born in Asia. India is the largest supplier of skilled professionals to the US. In 2000, nearly 133,000 Indian-born professionals were working in the US in these five professions, implying that one of every 50 professionals in the US was an Indian. But there was considerable variation across professions with three of every four Indian professionals working either as a computer software engineer or a physician or surgeon. On the other hand, only 3.7 percent of foreign-born lawyers, 5.8 percent of the foreign-born architects and 5.9 percent of foreign accountants and auditors were Indian.

**Figure 1a: The number of skilled professionals and the share of foreign-born in the US economy (Number of professionals)                           (Share of foreign professionals    (Share of Indian profess-**

**in US total)                            in foreign)**

<!-- Start of picture text --> Accountants 1,725,235 13.7% 5.9%<br>Lawyers 870,781 5.9% 3.7%<br>Physicians and Surgeons 714,885 26.7% 18.7%<br>Computer Software 702,181 28.9% 31.1%<br>Electrical & Electronics<br>309,203 11.4%<br>Engg. 20.2%<br>Civil Engineers 272,638 11.5%<br>17.0%<br>Mechanical Engineers 270,206 9.8%<br>16.1%<br>Architects 193,757 5.8%<br>15.2%<br><!-- End of picture text --> _<u>Source</u>_ <u>: US Census, 2001.</u> India’s educated manpower is not only large, it is also growing rapidly (Figure 1b). In 1999/00, though only 5.9 percent Indians had graduate degrees or above, this translated into 21.4 million graduate workers. The number of highly educated Indian workers is likely to have increased steeply since then, as enrolment in the higher education system has been rapidly rising since the 1990s. By 2005/06, an estimated 10.5 million students were enrolled in institutions of higher learning. India now has the third largest population enrolled in the higher education system in the world, after the United States and China. The number of professional education institutions has also grown rapidly (Figure 1c). Data on total enrollment in professional educational institutions is not generally available, but according to the All India Council of Technical Education, India produced 464,743 engineers in 2004-05, an increase of 16 percent over 2003-04 and more than double the number of engineers produced by the United Sates and Europe combined.

4
Table 6: Impact on high school graduation by performance in ENLACE 2009 (math)

|Outcome variable|Lowest|tercile|ENLA<br>Middle|CE (Y/N)<br> tercile|Highest|tercile|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|0.004|-0.003|-0.004|-0.005|0.040**|0.037**|
||(0.020)|(0.020)|(0.018)|(0.018)|(0.017)|(0.017)|
|Controls||Yes||Yes||Yes|
|N|2,486|2,452|2,449|2,418|2,386|2,350|
|Mean Dep. Control group|0.57|0.58|0.72|0.72|0.76|0.76|
|SD Dep. Control group|0.49|0.49|0.45|0.45|0.43|0.43|
||*** _p <_0|_._01, ** _p <_|0_._05, * _p_|_<_0_._1|||

The full set of controls include age, dummies for sex and area (urban-rural) and test scores on Enlace 9, one for spanish and other one for mathematics. ENLACE (Y/N) takes the value of 1 if the student took the 12th grade exam in 2012 and 0 otherwise. The three groups are formed using the terciles of performance in ENLACE math 2009.

# **5.4 Interpretation**

The results presented here are consistent with other evidence on the effects of scholarships and conditional cash transfers in EMS education outcomes in Mexico. The most recent evidence identifies the importance of targeting to make scholarships effective. For instance, (Parker and Vogl 2018) rely on a difference-in-difference estimator to show that young children exposed to Progresa-Oportunidades, a well-targeted conditional cash transfer program, increase the likelihood of graduating from EMS. Using administrative data and a difference-in-difference identification strategy, (Araujo et al. 2018) also show a positive and significant effect on EMS graduation associated with the expansion of Oportunidades into urban areas. However, when the cash transfer is not well-targeted it has no effects on EMS graduation rates, as shown by (Dustan 2018) evaluation of “Prepa Si” a universal scholarship program for EMS students in Mexico City. (Dustan 2018) also finds that “Prepa Si” had no impact on test scores measured by the end of upper secondary ENLACE.

22
As we did not have meteorological data for each and every municipality in Chile, this information was estimated. Since average annual temperature in any particular location depends principally on distance from the equator and elevation above sea-level, we estimated a simple model (see Table 2) using information on average annual temperature, latitude, and altitude for all the Chilean stations for which we could obtain “normal” temperature data (from <u>www.worldclimate.org). In order to get sufficient variation in the</u> elevation variable, we included 4 stations located close to Chile (Catamarca, Mendoza, Arequipa, and Oruro).

# **Table 2: Model used to estimate temperature in Chilean municipalities with missing data**

|**Variable**|**Coefficient**|**t-value**|**P-value**|
|---|---|---|---|
|Elevation|-2.1518|-4.48|0.0000|
|Latitude|-0.3883|-8.87|0.0000|
|Constant|27.2469|17.25|0.0000|
|**No. of observations**<br>**R**<sup>**2**</sup>|27<br>0.7669|||

The model indicates that, for every kilometer of elevation, the temperature drops 2.15 degrees Celsius, and for every latitudinal degree further south, the temperature drops 0.39 degrees. This information was used to estimate temperature in all the remaining municipalities, using the altitude and latitude of the municipality capital.

Precipitation does not present such simple regularities, so in order to estimate precipitation for the municipalities where this information was missing; we extended the findings from particular stations to the macro-region to which they belong. For example, for the northernmost macro-region, Tarapacá, we have precipitation data from two stations (Arica and Iquique), so we average the precipitation for these two stations and use this average for the remaining municipalities in the macro-region. This is a reasonable process, as Chile is a long, narrow country divided into 13 relatively homogeneous macro-regions from north to south. The three northernmost macro-regions (Tarapacá, Antofagasta, and Atacama) have desert climates with hardly any rain, but rain increases towards the south to a maximum in the macro-region Aisén, and then it falls again as we move further south.

As can be seen from Table 3, the variation in precipitation within each macro-region is quite small compared to the variation between macro-regions. One exception is the Aisén region, which is crossed by a mountain range, and precipitation is substantially larger on the western side of the range. Therefore all municipalities on the western side will be assigned the same precipitation as Aisén, whereas the municipalities on the eastern side will be assigned the same precipitation as Coihaique.

5
ranged from MK647.16 to MK 1,082.29, depending on the reference weather station. Like the improved groundnut seed, hybrid maize is bred to be disease resistant and highyielding. In pre-release trials in mid-altitude areas of Malawi, DK 8051 had higher yield than all comparison varieties. It outperformed the trial mean by 12.7 percent, and outperformed MH18, another hybrid variety used by farmers in our sample, by 32.7 percent. The DK8051 is also resistant to common diseases including GLS, leaf blight, and other conditions (see Wessels, 2001 for details).

The insurance policy bundled with the loan pays out a proportion (or the totality) of the principal and interest depending on the level of rainfall. In other words, the insured loan is in essence a _contingent_ loan whose repayment amount depends on the realization of rainfall at the nearest weather station. The coverage for both maize and groundnut policies is for the rainy season, which is the prime cropping season, running from September to March. The contract divides the cropping season into three phases (sowing, podding/flowering and harvest) and pays out if rainfall levels fall below particular threshold or “trigger” values during each phase. As Figure 3 shows for a given phase, an upper and lower threshold is specified for each of the three phases. If accumulated rainfall exceeds the upper threshold, the policy pays zero for that phase. Otherwise, the policy pays a fixed amount for each millimeter of rainfall below the threshold, until the lower threshold is reached. If rainfall falls below the lower threshold, the policy pays a fixed, higher payout. The total payout for the cropping season is then simply the sum of payouts across the three phases. The maximum payout corresponds to the total loan amount for seeds and the premium and the interest payment.

The timing of the phases, thresholds and other parameters of the model were determined using crop models specific to improved groundnut and hybrid maize as well as interactions with individual farmers. During the baseline survey, when farmers were asked what affects groundnut production the most, close to 70 percent said rainfall, and less then 20 percent said pests, the next reason in importance. The upper threshold corresponds to the crop’s water requirement or the average accumulated rainfall at the rainfall gauge (whichever is lowest), while the second trigger is intended to capture the water requirement necessary to avoid complete harvest failure. Translated into financial market terminology, the relationship between rainfall and payoffs resembles a “put
To try to shed additional light on these hypotheses we use an alternative dataset that covers a shorter period of time, but includes the type of crime. Whereas the database we have used so far has information on all men that went through the criminal justice process since 1934, a newer database has information on all men that went through the criminal justice process since 2000, but which details the type of crime under process.

A potential drawback of the alternative database is that the type of crime is actually specified for 37.4% of the cases. Therefore, in order to check its validity in Table 4 we first reproduce the results from Table 1 including all observations. For all the time periods, the coefficients on Low Number are not significantly different from the ones obtained in Table 1. Then we separately reproduce the results for those observations where the type of crime is specified and for those observations where the type of crime is not specified. As shown in Table 4, for all the time periods the estimated coefficients on Low Number when we include those observations where the type of crime is specified are not significantly different from coefficients obtained when the sample includes those observations where the type of crime not specified. This result suggests that Low Number is not correlated with missing values in the database.

In Table 5, we estimate the effect of military service by type of crime for cohorts of 1958 to 1965. Remarkably, the coefficient on Low Number in column (7) is negative and significant at the 1% level suggesting an impact of military service on arm-related crime rates. This result is in line with the hypothesis that firearm training received during military service reduces entry costs into crime, though the value of the coefficient indicates that the impact through this pathway is small.

As discussed above, an alternative hypothesis is that military service may negatively affect the labor market prospects of young men by delaying their insertion in the labor market, thus inducing them to commit property crimes. This hypothesis implies that property crime should be lower for those men not serving in military service. The coefficients associated to Low Number in the regression on property crime presented in columns (1) is negative and significant, a result that is in line with this hypothesis.

In column (1) of Table 6 we present results of the impact of conscription on participation in the formal job market. The coefficient of Low Number in this specification is positive and significant at the 10% level, also suggesting that military service has a negative impact on the probability of participating in the formal job market.

9