rafmacalaba/datause-encoder-data huggingface dataset:
sewing clothes, making food for sale, carpentry and construction, sale of non-food items such as handcrafts, transportation of people or goods in own vehicle, repair of artifacts or machinery, domestic service (wash, iron or cook for a fee), or other activities done on your own. We define micro-enterprise as participation in any of these activities. No micro-enterprise activities were collected in the 1997 baseline survey, and because of lack of comparability (changes in the categories and time frame considered), we do not include the 2003 round.

For the purpose of analysis, we classify households according to the amount of land used and agricultural asset ownership at baseline. We distinguish between: (i) households with no agricultural assets, (ii) landless farms (households reporting no land use but animal ownership), (iii) smaller landed farms (households using at least 3 ha of land for agricultural, grazing or forestry purposes, regardless of animal ownership), and (iv) bigger farms (households using more than 3 ha, regardless of animal ownership). Around one tenth of the sample has no agricultural assets, 31% of households are landless but have some animals, 45% have smaller farms and 13% have bigger farms. We check for balance between treatment and control households within farm size categories in Table 3A in the Data Appendix.

With random assignment of communities to treatment and control groups, we would expect that ownership of farm assets at baseline would be equal between the two groups. Then, any subsequent changes could be attributed to the program. In effect, when we compare agricultural assets of treatment and control households in the TOT sample prior to intervention, we find no statistically significant differences in mean values (Table 4 Panel A).

During the experimental period (October 1998 through November 1999), a simple comparison of the mean values for each dependent variable between households in treatment and control communities gives us an estimate of the program impact (see Table 4 Panel B). We report the results both for whether the household had any of the asset type and for the amount of the asset. We observe significant increases in the likelihood of draft and production animal ownership for all households and in particular for households with no agricultural assets (increases in production 11
15 educational capital in GDP. Guessing a plausible value for this number is more complicated than finding the capital share, since it is not published in the national accounts, but we can nevertheless make plausible guesses. The physical capital share is typically around .4 for developed countries and somewhat higher in LDCs, a figure which is roughly consistent with a variety of evidence; the estimates from national accounts24, the estimates from regression parameters and from rough calculations that depend on rates of return and capital output ratios25. If the share of physical capital is .4 the total wage share must be .6. If the total wage share is .6, how much of that total wage share is due to educational capital?

One way of making this calculation for the share of wages due to human capital is simply to use the ratio of the minimum wage to the average wage. Mankiw, Romer, Weil (1992) use the historical ratio of average to minimum wages in the US to estimate that half of wages are due to human capital26. A similar calculation based on the distribution of wages in Latin America estimates the human capital share between 50 and 60 percente

**2'** From the OECD national accoum **t** s he average share of capital across the 12 countries is 40.3. The difficulty with estinating capital share in non-OECD countries is that "proprietor's income" is typically all attributed to capital, which means in poor countries that all of self-employment (including subsistence agriculture) ends up in capital share, which is therefore much higher than believable. Reasonable assumptions about the allocation of proprietor's income between labor, land and capital can make the existing naional accounts shares consistent with a 40 percent capital share (Pritchett, 1995).

25 For instance if the capital-output ratio (K/Y) is 2.5 and the rate of return to capital is 16 percent then the share of capital rKJY is 40 percent.

26 This is the calculation that MRW use, that the ratio of the average wage in the US to the minimum wage has hovered around 2. Since the wage share w*L can be decomposed into a share due to raw labor w, and a share due to human capital, wL - W0 lJwL or I - wJw is the share due to human capital.

27 Using data on the distribution of worker's earnings (World Bank, 1993) we take the ratio of the average wage of wages up to the 90th percendle (to exclude the effect of the very long tails of the earnings distribution) to the wage of those workers in either the 20th or 30th percendle (to proxy for the wage of a person with no human capital). The estimates of huntan capital share of the wage bill are 62 and 47 percent respectively. If the top tenth percentile is included (so I take the ratio of average wages to 20th or 30th percentile) the esdmates of human capital share are even higher, 74 and 63 percent respectively. While these are considerably higher than other estimates, these are estimates of all human capital, not just educational capital.
<!-- Start of picture text --> Proposals in this paper Other proposals<br>Proposal > Business-as- Equal percapita ResponsibilityHistoric Abilitytopay 80-20 Cuts Preservingfuture AdvisaryGerman Kanitkar Bossettiand Jacoby et,<br>Country usual emissions development | Council aio) 2/ Frankel al, val<br>opportunities ff2009) 1/ (2009)<br>China 295.2 174.8 99,2 150.5 162.2 295.2 148.0 262.0 2939.1 181.4<br>USA 283.3 40,1 2.5 44 133.7 67.9 35.0 675 212.2 78<br>Russia 65.8 18,7 La 6.1 40.1 65,8 15.0 22.3<br>India* 108.1 150.4 285.7 2785 579 108.1 133.0 210.0 78.6 99,8<br>Japan 43,2 16,8 2.1 2.6 28.0 -10.4 14.0 16,2 15<br>Germany 33.3 10.8 1d L6é 13,1 -8.0 3.0<br>Canada 25.3 44 0.3 0.6 13.2 -6,1 W49 8<br>Indonesia 31.8 30.0 43,1 38,9 13.2 31.8 23.0 4i7 31.0<br>Brazil 24,2 25,3 22.8 12.4 10.9 24,2 21.0 23.6<br>Eu** 148.5 36.5 7.6 10.1 86,1 “12.8 14.0 a2,7 153.0 43,5<br>All countries in<br>sample 1413.6 704.0 704.0 704.0 704.0 704.0 730.0 1440.0 1220.6 1011.1<br>1f From Table 5.3-2 of the paper<br>2/ From Table 11 of the paper<br>3/ Corresponds to their full compensation, equal allocation scanario<br><!-- End of picture text -->

||||Proposals i|n this paper||||Other pr|oposals||
|---|---|---|---|---|---|---|---|---|---|---|
|Proposal ><br>Country|Business-as-<br>usual|Equal per<br>capita<br>emissions|Historic<br>Responsibility|Abilitytopay|80-20 Cuts|Preserving<br>future<br>development |<br>opportunities|German <br>Advisary<br>Council <br>ff2009) 1/|Kanitkar <br> aio)2/|Bossetti <br>and<br> Frankel<br>(2009)|Jacoby et,<br>al,val|
|China|295.2|174.8|99,2|150.5|162.2|295.2|148.0|262.0|2939.1|181.4|
|USA|283.3|40,1|2.5|44|133.7|67.9|35.0|675|212.2|78|
|Russia|65.8|18,7|La|6.1|40.1|65,8|15.0|22.3|||
|India*|108.1|150.4|285.7|2785|579|108.1|133.0|210.0|78.6|99,8|
|Japan|43,2|16,8|2.1|2.6|28.0|-10.4|14.0|16,2||15|
|Germany|33.3|10.8|1d|L6é|13,1|-8.0|3.0||||
|Canada|25.3|44|0.3|0.6|13.2|-6,1||W49||8|
|Indonesia|31.8|30.0|43,1|38,9|13.2|31.8|23.0|4i7||31.0|
|Brazil|24,2|25,3|22.8|12.4|10.9|24,2|21.0|23.6|||
|Eu**|148.5|36.5|7.6|10.1|86,1|“12.8|14.0|a2,7|153.0|43,5|
|All countries in<br>sample|1413.6|704.0|704.0|704.0|704.0|704.0|730.0|1440.0|1220.6|1011.1|
|1f From Table 5.<br>2/ From Table 11 <br>|3-2 of the paper<br> of the paper<br>||||||||||
|3/Correspondst|otheirfullcompe|nsation,equ|alallocationsc|anario|||||||
_2_ _Mexico: The 1997 Pension Reform_

# **_TABLE OF CONTENTS_**

|**INTRODUCTION**|**3**|
|---|---|
|**I. MEXICO'S TRADITIONAL PAYG SYSTEM: FEATURES AND NEED FOR REFORM_**|**_3_**|
|The Pension System Before 1997|3|
|The Rationale for Pension Reform|_5_|
|**II.THE 1992 SAR**EXPERIENCE:<br>**HOW**PENSION**REFORM SHOULD**NOT**BE**DONE|8|
|The Second Pillar: Individual Retirement Accounts in the SAR|8|
|Lessons from the SAR Experience|9|
|**-II.**THE 1997 PENSION**REFORM**<br>1|**11**|
|Context for Reform|11|
|Private Management of Retirement, Old Age and Severance Funds|12|
|The Rules of Pension Fund Administration_|14|
|Disability and Life: IMSS and Insurance Companies Living Together<br>_19||
|Looking South|19|
|**IV. ESTIMATION OF THE FISCAL COSTS OF THE REFORM**|20|
|What is the Bill? A Summary of Results|20|
|Fiscal Costs: Assumptions and Scenario Analysis|24|
|V. KEY CHALLENGES<br>**TO THE REFORMED SYSTEM**|32|
|Correcting Design Issues|32|
|Confronting Implementation Challenges_|36|
|**CONCLUSIONS**|**40**|
|**BIBLIOGRAPHY**|**41**|
Table 6: Evidence from Intermediate Inputs Data

||(1)<br>Pr(switch)|(2)<br>Share Imp. Inp.|(3)<br>Avg. Inp. Price|
|---|---|---|---|
|Input PSI|-0.000|-0.001|-0.054|
||(0.003)|(0.004)|(0.045)|
|Import tarif|0.153***|-0.170***|-6.835***|
||(0.043)|(0.057)|(0.709)|
|Observations|138,464|138,464|138,464|
|R-squared|0.354|0.830|0.704|
|Plant-input FE|yes|yes|yes|
|5-digit ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI) applied to inputs, on plant’s probability of switching to domestic inputs (col. 1), share of imported inputs (col. 2) and average input price (col. 3) in the years between 2008 and 2015. _Input PSI_ is a dummy taking value 1 if a NTM is applied on an intermediate input _m_ at time _t_ . The coefficients are obtained by the estimation of Equation 2. All specifications include controls for other NTMs and import tariffs applied on plant’s inputs, plant-input fixed effects, 5-digit industry-year fixed effects and region-year fixed effect. Standard errors are robust to heteroskedasticity and clustered at the plant level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

25
themselves are averaged to arrive at the overall Ease of Doing Business ranking.<sup>8</sup> The DB respondents consist primarily of locally‐recruited attorneys familiar with the relevant laws that form the basis for the DB summary measures. In contrast with GII, DB is primarily focused on collecting _de jure_ as opposed to _de facto_ information, and so we cannot distinguish the DB indicators along this dimension as we do for GII.

Our next step is to identify outcome variables of interest corresponding to these two datasets of potential policy interventions. Our objective in doing so is to try to identify outcomes sufficiently close to the indicators themselves that a policymaker might reasonably consider trying to affect this outcome through policy reforms that would be identified and captured by changes in the individual indicators. In the case of GII, Global Integrity is very explicit that the goal of the GII is to provide guidance on specific indicators to be improved (recall quotes in introduction). Global Integrity also encourages users of the GII to view it as _“...a powerful variable with which to explore other key development indicators — economic growth, income distribution, health and education rates, and other key socio‐economic indicators.”_ While ultimately there surely are links between the dimensions of governance captured by GII and these very broad outcomes, we set ourselves the more limited goal of assessing the links between the many disaggregated variables in GII and more proximate outcomes related to corruption itself. After all, according to Global Integrity the GII “... _represent one of the world's most comprehensive data sets providing quantitative data and analysis of anti‐corruption mechanisms and government accountability in diverse countries around the globe._ ” Global Integrity goes on to very sensibly note that the relationship between the GII and corruption is unlikely to be perfect, cautioning that: _“...users should not necessarily interpret high scores on the Global Integrity Index as reflective of countries where there is no corruption. Instead, those results should simply be understood to reflect circumstances where key anti‐corruption safeguards exist and have been enforced, which while one would hope reduces corruption may not eliminate it entirely. In simple terms, corruption can and will occur even where societies have implemented what are understood to be ideal reforms.”_ Based on this, we think it is reasonable to investigate the links between the many disaggregated GII measures and direct proxies for corruption itself. Of course, corruption is very difficult to measure directly, and the vast majority of empirical measures of corruption are based on the perceptions of

> 8 There are missing values for some countries on some of the DB variables.  We follow DB’s practice of assigning the lowest possible rank to such observations prior to averaging ranks across indicators. The overall DB ranking is a simple average of the ranking on the 10 subcategories.

9
- Lee, D.S. 2009. Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects. _Review of Economic Studies_ , **76** , 1071–1102.

- Lent, M.V. 2018. Goal Setting under Uncertainity: A Field Experiment on Rigid and Flexible Goals. _Working Paper_ .

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- Locke, E.A. 1968. Toward a Theory of Task Motivation and Incentives. _Organizational Behavior and Human Performance_ , **3(2)** , 157–189.

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- Martin, A.J. 2011. Personal best (PB) approaches to academic development: Implications for motivation and assessment. _Educational Practice and Theory_ , **33** , 93–99.

- Martin, A.J., & Elliot, A.J. 2016a. The role of personal best (PB) and dichotomous achievement goals in students academic motivation and engagement: A longitudinal investigation. _Educational Psychology_ , **36(7)** , 1285–1302.

- Martin, A.J., & Elliot, A.J. 2016b. The role of personal best (PB) goal setting in students’ academic achievement gains. _Learning and Individual Differences_ , **45** , 222–227.

- Martin, A.J., & Liem, G.A.D. 2010. Academic personal bests (PBs), engagement, and achievement: A cross-lagged panel analysis. _Learning and Individual Differences_ , **20(3)** , 265–270.

- Michelmore, K., & Dynarski, S. 2017. The Gap within the Gap: Using Longitudinal Data to Understand Income Differences in Educational Outcomes. _AERA Open_ , **3** , 1–18.

-

- MOEVT. 2017. _Zanzibar Education Development Plan II 2017/18 2021/22_ .

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16
little between , and (ranging from 0.03% under to 1.2% under ). However, negative shared prosperity would increase the number of poor substantially in mm= 0% mm= 1% mm= 2% mm= 2% South Asia. For Sub-Saharan Africa, the differences between the shared prosperity premiums are large, mm= 0% with the 2030 poverty rate ranging from 15% to 33.4%.

**Figure 9: Number of poor people for selected regions (at $1.25, 2005 PPPs, 10 year historic growth rates)**

<!-- Start of picture text --> East Asia & Pacific South Asia Sub-Saharan Africa<br>2010 2015 2020 2025 20302010 2015 2020 2025 20302010 2015 2020 2025 2030<br>year<br>m=0% m=-1% m=-2% m=1% m=2%<br>Graphs by Region<br>500<br>400<br>300<br>200<br>100<br>Number of poor people (million)<br>0<br><!-- End of picture text --> Note: Results are from simulations using 10 year historic growth rates.

Figure 10 presents the results from a pure redistribution scenario in which we allow for different shared prosperity premiums while holding mean per capita income fixed. It shows that even without any growth in the mean, substantial progress in poverty reduction would be possible, under and . Under the most positive shared prosperity premium, global poverty is estimated at less than mm= 1% 8% in 2030. However, with negative growth among the bottom 40%, we see a substantial increase in mm= 2% global poverty, with a headcount rate close to 25% in 2030 (for ). Of course, zero growth in the mean is an unlikely scenario. However, the results from this scenario illustrate the effects of mm= −2% differential growth incidence for the bottom 40%, abstracting from growth in the mean. It highlights the importance of boosting shared prosperity for the welfare of the poorest, independent from growth.

16
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**Table 8: Earnings and Selection into the Private and Public Sectors of Wage Employment: 20000**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Public|Private|Log|Selection|
||sector log<br>earnings<br>in 2000|sector log<br>monthly<br>earnings|monthly<br>earnings<br>in 2000|into public<br>sector<br>in 2000|
|||in 2000||(Probit)|
|Female|-0.180|-0.465|-0.321|-1.745|
||(2.21)*|(4.44)**|(4.78)**|(2.01)*|
|Married||||0.200|
|||||(1.38)|
|Log age|-0.978|-0.023|-0.194|2.798|
||(1.46)|(0.08)|(0.73)|(0.95)|
|Log age squared|7.745|0.497|1.872|-22.031|
||(1.60)|(0.23)|(0.98)|(0.99)|
|**Education (reference=no formal s**|**chooling):**||||
|Grades 1 to 10|-0.304|0.217|0.047|4.217|
||(2.22)*|(1.67)|(0.49)|(2.68)**|
|Preparatory school|0.089|0.411|0.343|2.952|
||(0.64)|(2.65)**|(3.21)**|(2.96)**|
|Some tertiary education|0.350|1.344|0.763|8.257|
||(2.35)*|(6.27)**|(6.11)**|(2.74)**|
|**Own occupation**|||||
|**(reference=unskilled worker):**|0.755|0.862|0.842|1.572|
|Professional or technical|(5.90)**|(4.03)**|(7.46)**|(4.13)**|
||0.334|0.601|0.461|2.564|
|Admin or clerical|(2.83)**|(3.76)**|(4.68)**|(3.15)**|
||0.319|0.499|0.434|1.249|
|Skilled production worker|(2.57)*|(4.56)**|(5.20)**|(2.29)*|
|Public2000|||0.401||
||||(3.76)**||
|public sector wage premium2000||||7.618|
|||||(2.54)*|
|Genresidualpublic2000|||-0.135||
||||(1.91)||
|Selectivity term 1|-0.070||||
||(0.83)||||
|Selectivity term 2||0.072|||
|||(0.55)|||
|Constant|-9.554|3.470|0.877|38.011|
||(1.09)|(0.92)|(0.26)|(0.94)|
|city dummies|Yes|Yes|Yes|Yes|
|ethnicity dummies|Yes|Yes|Yes|Yes|
|religion dummies|Yes|Yes|Yes|Yes|
|Observations|291|289|580|580|
|R-squared|0.46|0.51|0.47||
|Log likelihood||||-297|
|Pseudo R-squared||||0.26|

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1% 36
Table 10: Effect of delayed transfer on planned vs. unplanned expenditures

||(1)<br>Fo|(2)<br>od|(3)<br>non-|(4)<br>durables|
|---|---|---|---|---|
||Planned|Unplanned|Planned|Unplanned|
|1-day delay|-345.572|49.913|585.623**|59.427|
||(507.704)|(69.217)|(258.329)|(41.551)|
|8-day delay|12.099|-182.968**|8.179|-29.253|
||(554.377)|(56.217)|(241.477)|(26.274)|
|Observations|472|472|472|472|
|Mean of dependent variable in Immediate group|4990.51|343.08|2058.43|72.63|
|SD of dependent variable in Immediate group|5244.06|620.51|2470.42|280.68|
|R-squared|0.204|0.298|0.241|0.061|
|P-value: one day delay = 8 day delay|0.515|0.000|0.033|0.027|

Categorization into planned and unplanned purchases is based on respondents’ answers during itemized elicitation of expenditures. For each item respondents were asked if they had planned to purchase the item before they arrived at the market or store, or if the decision was made on the spot. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 Table 11: Effect of payment delay on temptation spending

||(1)<br>Defn<br>Alcohol a<br>Amount|(2)<br>ition 1:<br>nd tobacco<br>Share|(3)<br>Defn<br>D1 + sug<br>Amount|(4)<br>ition 2:<br>ars and fats<br>Share|(5)<br>Def<br>D2+ pr<br>Amount|(6)<br>nition 3:<br>epared foods<br>Share|
|---|---|---|---|---|---|---|
|1-day delay|-52.016|-0.002|17.218|0.002|-18.558|-0.003|
||(41.260)|(0.004)|(52.260)|(0.005)|(63.391)|(0.006)|
|8-day delay|-17.146|-0.002|43.432|0.005|-13.346|0.001|
||(35.168)|(0.003)|(44.932)|(0.005)|(54.080)|(0.006)|
|Observations|472|471|472|471|472|471|
|Mean of dependent variable in Immediate group|119.49|0.01|277.92|0.02|451.63|0.04|
|SD of dependent variable in Immediate group|502.15|0.03|591.82|0.04|747.27|0.05|
|R-squared|0.297|0.152|0.312|0.068|0.350|0.082|
|P-value: one day delay = 8 day delay|0.352|0.850|0.631|0.637|0.935|0.588|

All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 <u>per</u> USD. Robust standard errors in <u>parentheses.</u> * _<u>p <</u>_ 0 _._ 1, ** _<u>p <</u>_ 0 _._ 05, *** _<u>p <</u>_ 0 _._ 01.

37