rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7. Output percentage. Youths from 18-35 years of age of ENCEL 2017.**

||||**Stratum**<br>|**of the so**<br>|**n or daugh **<br>|**ter(n= 3,**<br>|**084)**<br>||
|---|---|---|---|---|---|---|---|---|
|||**NM**|**Commerc**|**e HM**|**LMM**|**LMS**|**Agriculture**|**Total**|
||NM|26|8.2|16.4|12.3|16.4|20.5|100|
||Commerce|9.1|18.2|15.9|19.3|15.9|21.6|100|
|**Stratum of**<br>**the**|HM|8|11.8|30.4|13.5|19.8|16.5|100|
|<br>**provider**|LMM|11|11|19.7|26.2|14.2|17.8|100|
||LMS|9.2|15.8|15.8|8.8|23.8|26.7|100|
||Agriculture|9.2|11.4|11.2|10.6|14.6|43|100|
||Total|9.7|11.9|14.2|12.5|15.9|35.9|100|

Source: Own elaboration from ENCEL 2017.

However, women and migrants are less likely to have an occupation in the agricultural stratum and more likely to have a NM occupation (for any stratum of origin), in comparison with the men and non-migrants, respectively. In the case of the comparison by ethnic background it is observed that indigenous youths are slightly more likely for to have an agricultural destination when their provider’s occupation was in the four lowest strata in the hierarchy, while they are more likely to inherit their provider's occupation if they have an NM origin. The comparisons groups by treatment intensity do not present substantial differences.

# B. _Results of relative mobility_

In this section we present the results of relative mobility for the study’s entire sample, as well as for the differentiated subgroups by sex, migratory status and comparison group according to intensity of PROSPERA’s treatment. The results of the fit and the parameters of the log-linear models for the entire sample are presented in the text. The results of the fit of the log-linear models for the subgroups for sex, ethnic background and intensity of PROSPERA’s treatment are included in the text, while the parameters of these models and the log-multiplicative models are included in Appendix G. The results of the analysis by ethnic background are not presented in this document, since no differences between the subgroups were found.<sup>53</sup>

# B.1 _. Relative mobility of the complete sample, by sex and migratory status_

Table 8 shows the results of the different log-linear models described in Appendix C, in the case of the entire sample of youths, that is, these are models based on the two-way mobility table. According to the values of the BIC, we may conclude that models 10 and 11 have the best fit. These models indicate that the main factors that determine the association between occupational

> 53 These results are available per request of the interested party.

34
# **_3.2.1 Is the nature of international finance the reason for the high cost of capital?_**

The reasons for the high cost of capital in Mongolia cannot, however, be traced to inadequate or expensive international finance. Mongolia’s official debt is primarily concessional, long-term debt. The share of concessional debt in total external debt averaged 85 percent over the past ten years, and is much higher than the average for low income countries (51 percent) and East Asia (20 percent) (Table 4). Mongolia continues to have little trouble obtaining concessional finance as the levels of these external inflows have remained relatively stable over the past decade. In December 2005, concessional loan agreements have been entered into with China, and South Korea, among others. Significant additional grant financing over the next few years is expected.

**Figure 2:  Domestic investment is on the rise in Mongolia...**

<!-- Start of picture text --> 60.0<br>50.0<br>40.0<br>30.0<br>% of investment<br><!-- End of picture text --> <!-- Start of picture text --> 20.0<br>10.0<br>0.0<br><!-- End of picture text --> <!-- Start of picture text --> Domestic investment (% of investment) Foreign investment (% of investment)<br>Source: Mongolian Statistical Yearbook (2004).<br><!-- End of picture text --> _Source: Mongolian Statistical Yearbook (2004)._

**Figure 4:  Real cost of capital has declined primarily due to increase in the inflation rate rather than declines in the risk premium.**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2 2001 2002 2003 2004 2005<br>Interest rate spread (lending rate minus deposit rate)<br>Inflation, consumer prices (annual %)<br><!-- End of picture text -->

**Figure 3:  as the cost of capital declines.**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Mongolia Cambodia Vietnam Kyrgyz Republic<br>%<br><!-- End of picture text --> _Source: World Bank (SIMA)._

**Figure 5:  Cost of capital is high because of high deposit interest rates and risk premiums.**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Mongolia Azerbaijan Cambodia Kyrgyz Vietnam Uruguay<br>Republic<br>Deposit interest rate Interest rate spread (lending rate minus deposit rate)<br><!-- End of picture text --> Inward FDI was high in Mongolia (at 9.8 percent of GDP, compared with the median of only 1.7 percent for Fitch’s ‘B’ rated countries).<sup>19</sup> Rising official reserves and commercial bank assets helped push the 2006 liquidity ratio to over 600 percent (notably higher than the comparable Fitch’s ‘B’ country group median of 170 percent).

19 Insurers in the Fitch’s ‘B’ peer group are viewed as weak with a poor capacity to meet policyholder and contract obligations.

11
**Table 1: Labor Laws in Selected States**

|**Labor Laws**|**Minimum Wage**|**Overtime**|**UI – Benefit**<br>**Formula**|
|---|---|---|---|
|**California**|6.75|Time and half|HQ– 1/23-1/33|
|**Oregon**|7.05|Time and half|AW – 1.25%|
|**Idaho**|5.15|None|HQ– 1/26|
|**Pennsylvania**|5.15|Time and half|HQ– 1/23-1/25|
|**Ohio**|4.25|Time and half|AWW– 50%+DA|
|**Kansas**|2.65|Time and half after<br>46 hours per week|HQ - 4.25%|

**<u>Sources</u>** <u>:  http://www.dol.gov/esa/minwage/america.htm,</u> http://ows.doleta.gov/unemploy/sigprojan2003.asp, http://www.ows.doleta.gov/unemploy/uilawcompar/2003/monetary_entit.pdf HQ – High Quarter Formula: weekly benefit is fraction of quarterly income in the highest income quarter in the base period AW – Annual Wage Method:  weekly benefit is percentage of annual wages in the base period AWW – Average Weekly Wage: weekly benefit is percentage of average weekly wage in the base period

# **Table 2: Business Taxation in Selected States**

|**Taxation**|**Corporate**<br>**Tax Rate**<br>**(%)**|**Tax Brackets**<br>**(No. of**<br>**brackets)**|**Minimum**<br>**Tax ($)**|**State**<br>**Sales Tax**<br>**(%)**|**Local**<br>**Sales Tax**<br>**Max (%)**|
|---|---|---|---|---|---|
|**California**|8.84|Flat rate|800|6.00|2.50|
|**Oregon**|6.6|Flat rate|10|No|No|
|**Idaho**|7.6|Flat rate|20|5.00|3.00|
|**New York**|7.5|Flat rate|100-1500<br>(depending on<br>payroll size)|4.00|4.50|
|**Pennsylvania**|9.99|Flat rate|No|6.00|1.00|
|**Ohio**|8.5|3,000 -50,000<br>(2)|No.|5.00|2.00|

**<u>Sources</u>** <u>: http://www.taxadmin.org/fta/rate/sl_sales.html,</u> http://www.taxadmin.org/fta/rate/corp_inc.html

In Canada, there are obvious differences between the provinces, as Tables 3 and 4 show for some labor regulations and business taxes. Canada’s case is complicated by the fact that some states like British Colombia have an English legal tradition while Quebec’s legal tradition is linked with that of France. India is a large developing country where states have quite different institutional structures. While the Industrial Disputes 20
- couples in which only one partner is HIV positive.  This means that, in the absence of consistent condom use, the HIV negative partner is at great risk for infection.

If someone does not use condoms within marriage mainly because they imply a lack of trust or infidelity and that person were to have extramarital sex, he or she may be more likely to use a condom outside the marriage when the question of fidelity is not part of the equation. It would therefore be logical for married individuals engaging in extramarital sex to use condoms as frequently as unmarried individuals engaging in casual sex. This seems to be the case for married men but not for married women. Gersovitz (2005) summarizes studies that report that men are more likely to use condoms with their casual partners than with their regular partners. Our results confirm that married men use condoms at similar rates to unmarried men during extramarital sex however married women use condoms less than unmarried women when they have extramarital sex. This high-risk behavior will be explored in more detail in the coming sections of this paper.

# **Section 3: Methodology**

This analysis uses nationally representative and comparable data from 13 Sub-Saharan African countries. Data from Burkina Faso, Cameroon, Ethiopia, Ghana, Guinea, Kenya, Lesotho, Malawi, Rwanda, Senegal, and Zimbabwe all come from the most recent DHS which all have similar questions (Burkina Faso 2003, Cameroon 2004, Ethiopia 2005, Ghana 2003, Guinea 2005, Kenya 2003, Lesotho 2004, Malawi 2004, Rwanda 2005, Senegal 2005 and Zimbabwe 2005/06). The data from Côte d’Ivoire and Tanzania (Côte d’Ivoire 2005 and Tanzania 2004) are from the HIV/AIDS Indicator Survey (AIS), which 11
our methodology as it led to the temporary disappearance of excess interaction between inspectors and brokers. It also triggered a novel form of IT manipulation. While manipulation of inspector assignment was eventually weeded out with the help of improved IT infrastructure, our results serve as a reminder that technology is not a panacea in the fight against corruption. Rather, our results illustrate how IT solutions can be captured by bureaucrats and economic operators and serve as a conduit to corruption.

# **References**

- Timothy Besley and Torsten Persson. The origins of state capacity: Property rights, taxation, and politics. _American Economic Review_ , 99(4):1218–44, 2009.

- Roger Gordon and Wei Li. Tax structures in developing countries: Many puzzles and a possible explanation. _Journal of Public Economics_ , 93(7-8):855–866, 2009.

- Frederico Finan, Benjamin A Olken, and Rohini Pande. The personnel economics of the developing state. In _Handbook of Economic Field Experiments_ , volume 2, pages 467–514. Elsevier, 2017.

- Adnan Q Khan, Asim I Khwaja, and Benjamin A Olken. Tax farming redux: Experimental evidence on performance pay for tax collectors. _The Quarterly Journal of Economics_ , 131(1):219–271, 2015.

- Adnan Q Khan, Asim Ijaz Khwaja, and Benjamin A Olken. Making moves matter: Experimental evidence on incentivizing bureaucrats through performance-based postings. _American Economic Review_ , 109(1):237–70, 2019.

- Thomas Baunsgaard and Michael Keen. Tax revenue and (or?) trade liberalization. _Journal of Public Economics_ , 94(9-10):563–577, 2010.

- Pranab Bardhan. Corruption and development: a review of issues. _Journal of Economic Literature_ , 35(3):1320–1346, 1997.

- Benjamin A Olken and Rohini Pande. Corruption in developing countries. _Annual Review of Economics_ , 4(1): 479–509, 2012.

- Andrei Shleifer and Robert W Vishny. Corruption. _The Quarterly Journal of Economics_ , 108(3):599–617, 1993.

- Andrei Shleifer and Robert W Vishny. _The grabbing hand: Government pathologies and their cures_ . Harvard University Press, 2002.

- Eric Zitzewitz. Forensic economics. _Journal of Economic Literature_ , 50(3):731–69, 2012.

- Doing Business. Comparing business regulation in 190 economies. _URL: http://openknowledge. worldbank. org.(date of access: 14.01. 2020)_ , 2020.

- Mark Dincecco and Nico Ravanilla. The importance of bureaucrats in a weak state: Evidence from the philippines. Working paper, available at ssrn 2773884, 2017.

- Thomas B Pepinsky, Jan H Pierskalla, and Audrey Sacks. Bureaucracy and service delivery. _Annual Review of Political Science_ , 20:249–268, 2017.

- Guo Xu. The costs of patronage: Evidence from the british empire. _American Economic Review_ , 108(11):3170–98,

2018.

34
resource rents on the business environment can have a major impact on the ability of domestic entrepreneurs to open a business and for small and medium-sized firms, in particular, to effectively compete in markets. This will likely have a broader effect on entrepreneurship rates in a country. Although cross-national data with wide coverage are hard to come by, a World Bank study using data for up to 90 countries finds that a lower quality business environment will result in lower rates of entrepreneurship, as measured by ―entry density‖ rates (see Klapper and Love 2010). While rents may over significant periods of time help sustain a stable dual system of restricted access for domestic private sector actors, coupled with significant and largely unproductive public distribution mechanisms, this development trajectory poses significant challenges for the pursuit of a more dynamic and inclusive growth strategy.

**Figure 15. World Bank “Doing Business” Rankings, Regional Averages in 2010 (By Region, 2005-2011)**

<!-- Start of picture text --> 0 20 40 60 80 100 120 140 160<br>OECD high income<br>Eastern-Europe/Central Asia<br>East Asia/Pacific<br>Latin America/Caribbean<br>Middle East/North Africa<br>South Asia<br>Sub-Saharan Africa<br>Avg Country Ranking, Overall Ease of Doing Business<br>Avg Country Ranking, Overall Ease of Starting a Business<br><!-- End of picture text --> _Source:_ <u>www.doingbusiness.org. Lower rankings indicate greater ease of doing business</u> and starting a business. Ranking for Libya is missing and therefore not included.

71. Policy measures that might reverse this trend are greater reform of the regulatory environment that individuals and local businesses face. A few oil-dependent countries in the MENA region, such as Saudi Arabia and Bahrain, have made notable progress in recent years in reforming the regulatory barriers to domestic business activities. Others, such as Iran and Iraq, have made less progress (see World Bank 2011b). Overall, countries in the MENA region rank low on the measure of ―overall ease of doing business.‖ With respect to the ―ease of starting a business,‖ only countries in the Sub-Saharan Africa region average a lower ranking in 2010 (Figure 15). Note that Libya‘s ranking is missing from the dataset. A study by the World Economic Forum in 2007 on national business environment ranks Libya lower than Iraq, which the World Bank ranks near the bottom of the world in both categories (see Figure 15). Another way that these countries can foster the growth of the domestic private sector is by directly providing credit to firms, in particular those that are small and mediumsized. Strengthening the financial institutions that produce information on credit history and 23
TABLE 7: SIMULATED REDUCTION IN EXTERNALITX COSTS THROUGH IPM IN COTTON (RS. CR8.) <!-- Start of picture text -->
-----------------------------------------------------------<br>DEGREE OF CONTROL<br>CONTINUING  LOW  MEDIUM  HIGH<br>EXTERNALITY (RS.CRB)  0.6  0.75  0.9<br>(RS.CRS.)<br>LOW  33.4  20.04  25.05  30.06<br>MEDIUM  66.8  40.08  50.1  60.12<br>HIGH  133.6  80.16  100.2  120.24<br>-----------------------------------------------------------<br>TOTAL SAVINGS (INCLUDING SAVINGS IN CATASTROPHIC COSTS)<br>(RS.CRS.)<br>LOW  29.54  34.55  39.56<br>MEDIUM  49.58  59.6  69.62<br>HIGH  89.66  109.7  129.74<br><!-- End of picture text -->

-----------------------------------------------------------
a substitute for lack of income, then PATI should have had a negative effect on crime, since the increase in disposable income would have allowed beneficiaries to increase consumption of certain goods, which may have reduced the incentive or need to engage in economically motivated crimes. In addition, since PATI enforced work requirements, this could have generated a negative impact on crime because beneficiaries will have less time to commit crime. Participation in community activities and training could also have had crime deterrence effects. On the other hand, there could have been a positive community effect on crime arising from the fact that there is more money in the community due to the income transfers from the program. The paper uses panel data at the municipal level for the period 2007 to 2014. Such information contains monthly data on different types of crime rates and social program benefits by municipalities. Fixed effect estimations are used to control for unobservable variables.

The remainder of the paper is structured as follows. We first review the theory and evidence on the impact of development programs on conflict. Section 3 describes PATI’s objectives and operation. Section 4 describes the data and empirical strategy, and section 5 reports the results. The final section concludes.

# **2. Literature Review**

Many safety net programs provide income to needy individuals. Given that the poor are the main recipients of such programs and are more likely to commit crime (Ehrlich 1973; Murray 1984; Lott 1987; Grogger 1991), welfare programs may be expected to have an effect on criminal behavior. Economic models postulate that incentives matter for people’s decisions on whether to participate in crime (Becker 1968; Ehrlich 1973, 1996). However, very few studies have focused on the effect of welfare programs on crime. Most have concentrated on their direct stated objectives of impact on consumption, poverty, education and health outcomes, and others.<sup>1</sup> There is ample evidence in the literature that welfare programs, such as unemployment benefits or vouchers can have an effect on crime rates. Overall, studies from both developing and developed

> 1 However, there is a long list of studies looking for links between crime and unemployment incidence (Freeman 1999) or lower wages (Grogger 1991; Gould et al. 2002; Machin and Marie 2004).

3
Though this provides us with an administrative source of variability in treatment exposure among households across states, we are not guaranteed that the outcomes of interest would be on average the same in these two states in the absence of the intervention. We take several steps to improve the comparability of the treatment and control groups. First, we reduce the geographic distance between control and treatment groups by comparing the outcomes of interest between families residing in the twin cities of Gómez Palacio/Lerdo (control) and Torreón (treatment) that straddle the border of the States of Durango and Coahuila, respectively. Although these cities are split administratively between the two states, they are effectively a single urban area in socioeconomic terms. Households residing near the border in these cities are likely to be similar except for the influence of state policies. Second, we further improve the comparability of the treatment and control groups by sampling from census blocks in the control area that best match the census blocks from the treatment area using preintervention information from the 2000 national census.

Thus, to the extent that the only important difference in state policies is _Piso Firme_ , we can consistently estimate the impact of _Piso Firme_ on the outcomes of interest for households residing near the same border in the city of Torreón, using the households from Gómez Palacio/Lerdo as a control group. We find that _Piso Firme_ significantly increased the share of cement floors, child health, and maternal happiness.

We then use offering households _Piso Firme_ as an instrument for having cement floors to estimate the impact of having cement floors on outcomes. We find that replacing dirt floors with cement floors significantly improves the health of young children. Specifically, we find significant decreases in the incidence of parasitic infestations, diarrhea, and the prevalence of anemia, and a significant improvement in children’s cognitive development. In particular, we find that a complete substitution of dirt floors by cement floors in a house leads to 78 percent reduction parasitic infestations, 49 percent reduction in diarrhea, 81 percent reduction in anemia and a 36 to 96 improvement in cognitive development. Finally, we also find that replacing dirt floors by cement floors leave adults substantially better off. Specifically, we observed that a complete substitution of dirt floors by cement floors in a house would lead to a 59 percent increase in self-reported satisfaction with housing, a 69 percent increase in self-reported 3
- 34 -

The result suggests that solely for metering purposes, one effective measure is to increase the block tariff threshold.<sup>20</sup>

**<u>Table 8. IV and Nelso-Olsen estimation with metering assumed to be endogenous</u>**

||IV|Nelson-Olsen|estimator 1/|
|---|---|---|---|
||ln|||
|Dependent variable|_q_<br>(2nd stage)|_I_2/|ln_q_|
|_I_|–1.024 ***||–1.477 ***|
||(0.166)||(0.225)|
|ln_q_||–1.011 **||
|||(0.485)||
|ln _~~q~~_||0.148||
|||(0.279)||
|ln_q_0||0.191||
|||(0.245)||
|ln_MP_|–0.088||–0.123|
||(0.189)||(0.268)|
|ln_D_|0.031 ***|0.026 *|0.029 ***|
||(0.007)|(0.016)|(0.010)|
|ln_TEMP_|0.043|–0.006|0.045|
||(0.067)|(0.095)|(0.093)|
|ln_PREC_|–0.020 *|–0.010|–0.014|
||(0.011)|(0.015)|(0.016)|
|ln_Y_|0.035 ***|0.031 *|0.037 ***|
||(0.010)|(0.017)|(0.014)|
|ln_HH_|0.040 *|0.066|0.049|
||(0.024)|(0.059)|(0.031)|
|D(_Power for lighting_)|0.141 *|0.117|0.139|
||(0.084)|(0.147)|(0.159)|
|D(_Power for heating_)|0.026|0.040|0.029|
||(0.020)|(0.039)|(0.027)|
|D(_Power for cooking_)|0.016|0.047|0.025|
||(0.020)|(0.053)|(0.028)|
|D(_Power for water heating_)|0.052 ***|0.050|0.054 *|
||(0.020)|(0.033)|(0.029)|
|D(_Power for others_)|0.050|0.039|0.049|
||(0.031)|(0.034)|(0.037)|
|D(_Gas for lighting_)|–0.025|–0.030|–0.029|
||(0.052)|(0.086)|(0.093)|
|D(_Gas for heating_)|0.005|0.000|0.005|
||(0.017)|(0.022)|(0.024)|
|D(_Gas for cooking_)|0.003|0.005|<br>0.006|
||(0.022)|(0.025)|(0.028)|
|D(_Gas for others_)|0.083|0.118|0.097|

20 This measure also has an adverse effect on the electricity demand management, as discussed. If the threshold is raised, the demand would increase.
**Table C.3. Real sectoral value added for base and non‐base simulations (% annual growth 2018‐2030)**

||2017|base|ginv‐<br>tdir|ginv‐<br>dbor|ginv‐<br>fbor|pwe|remit|
|---|---|---|---|---|---|---|---|
|Private|89|4.97|5.12|4.73|5.15|5.05|5.00|
|Government|3|5.09|5.09|5.09|5.09|5.09|5.09|
|Total|93|4.98|5.12|4.74|5.14|5.05|5.00|

Note: The 2017 column shows levels as GDP shares (%), while the simulation columns show annual growth rates 2018‐2030.

Source: Authors’ calculations based on simulation results.

# **Table C.4. Government receipts and spending in 2017 and for simulations in 2030 (% of nominal GDP)**

|Item|2017|base|ginv‐<br>tdir|ginv‐<br>dbor|ginv‐<br>fbor|pwe|remit|
|---|---|---|---|---|---|---|---|
|Recurrent receipts||||||||
|Direct taxes|5.0|5.0|6.8|5.0|5.0|4.7|4.8|
|Social contributions|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Activitytaxes|0.3|0.3|0.3|0.3|0.3|0.3|0.3|
|Commoditytaxes|5.7|5.7|5.7|5.7|5.7|5.6|5.7|
|Tariffs|1.4|1.4|1.4|1.5|1.5|1.5|1.5|
|Export taxes|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Domestic transfers|1.8|1.8|1.8|1.8|1.8|1.8|1.9|
|Foreign transfers|0.9|0.9|0.8|0.9|0.8|0.8|0.8|
|Total|15.1|15.1|16.9|15.2|15.2|14.8|15.1|
|Recurrent spending||||||||
|Consumption|11.7|11.7|11.5|11.9|11.5|11.4|11.7|
|Domestic transfers|1.6|1.6|1.5|1.6|1.5|1.5|1.6|
|Foreign transfers|0.1|0.1|0.1|0.1|0.1|0.1|0.1|
|Total|13.4|13.4|13.1|13.6|13.1|13.0|13.3|
|Savings*|1.8|1.8|3.8|1.6|2.1|1.8|1.8|
|Investment|4.8|4.8|6.7|7.1|6.7|4.6|4.8|
|Surplus**|‐3.0|‐3.0|‐3.0|‐5.5|‐4.6|‐2.9|‐3.0|
|Financing||||||||
|Net domestic financing|1.5|1.5|1.5|3.9|1.5|1.5|1.5|
|Net foreign financing|1.5|1.5|1.5|1.6|3.1|1.4|1.5|
|Total|3.0|3.0|3.0|5.5|4.6|2.9|3.0|

*Savings is the difference between the totals for recurrent receipts and recurrent spending.

**Surplus is the difference between savings and investment.

Source: Authors’ calculations based on simulation results.

‐63‐