rafmacalaba/datause-encoder-data huggingface dataset:
- 22 -

One difference from the previous estimation result is that the production function with _WORK_ exhibits increasing returns to scale. The degree of returns to scale is estimated at 1.236 with a standard error of 0.122. However, the production system, again, looks highly land-intensive: The coefficient of land, _H_ , is the largest at 0.55 among production factors. Labor is positive and weakly significant, but its elasticity is much smaller than land.

**<u>Table 8. Instrumental variable estimation: Actual work effect</u>**

||Battese's<br>|specificati<br>|on|Small-positiv<br>|e-number<br>|model|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||
|_WORK_*_t_|3.424|(1.306)|***|4.147|(1.470)|***|
|_WORK_|-1.183|(0.685)|*|-1.211|(0.764)||
|_t_|-1.916|(0.717)|***|-2.336|(0.783)|***|
|ln_POT_|0.028|(0.026)||-0.497|(0.179)|***|
|ln_L_|0.131|(0.070)|*|0.160|(0.085)|*|
|ln_H_|0.551|(0.076)|***|1.054|(0.197)|***|
|ln_S_|0.268|(0.086)|***|0.037|(0.037)||
|ln_F_|0.136|(0.043)|***|0.010|(0.006)|*|
|ln_P_|0.192|(0.089)|**|0.043|(0.008)|***|
|ln_TR_|-0.041|(0.031)||0.019|(0.006)|***|
|d (_S_)|1.409|(0.454)|***||||
|d (_F_)|0.500|(0.254)|**||||
|d (_P_)|1.340|(0.982)|||||
|d (_TR_)|-0.610|(0.274)|**||||
|ln_SIZE_|-0.018|(0.066)||0.043|(0.079)||
|d (_MALE_)|0.001|(0.067)||0.022|(0.078)||
|ln_AGE_|0.069|(0.068)||0.139|(0.086)|*|
|ln_EDU_|0.104|(0.036)|***|0.010|(0.005)|**|
|_CONC_|0.114|(0.089)||0.275|(0.106)|***|
|_TOIL_|0.013|(0.065)||0.083|(0.075)||
|_MOTO_|0.188|(0.228)||0.300|(0.269)||
|_BIKE_|0.084|(0.064)||0.125|(0.074)|*|
|_CHIPATA_|0.518|(0.126)|***|0.640|(0.147)|***|
|Constant|3.104|(1.195)|***|9.775|(1.618)|***|
|Obs|1891|||1891|||
|R-squared|-0.456|||-0.408|||
|Wald chi2|1142.21|||639.14|||
|Exogeneity test:|||||||
|C statistic (chi2)|33.342|***||44.399|***||
|Overidentifying restriction:|||||||
|Hansen's J statistic (chi2)|1.328|||5.603|*||
|Marginaleffect of_WORK_|0.949|(0.362)|***|1.149|(0.407)|***|

The dependent variable is the log of total crop production value, _V_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.
Policy Research Working Paper 6267

# **Abstract**

This paper analyzes real estate market dynamics over the past decade in the city of Ahmedabad, India, with a view to improving the living conditions of the large population living in slums. The paper combines census data, the National Sample Survey, and slum household surveys to review the demand side of the market. Satellite photography was used to estimate the production of both formal and informal housing over the past ten years. Analysis of the execution of the development plan for the Ahmedabad region and town planning schemes shows how the system of housing supply has evolved. These analyses are used to assess the feasibility of various approaches to achieving “slum free” cities, the goal of the Government of India’s planned assistance program Rajiv Awas Yojana. The paper concludes that notwithstanding a substantial increase in public housing production in recent years, providing subsidized formal homes from government or through reservations for lower income groups in private developments would take more than a generation just to handle the current slum population—representing one-third of households. Providing basic environmental infrastructure services in existing underserved neighborhoods—a proven approach under the Slum Networking Program—and bolstering infrastructure networks for the city to accommodate increased demand are affordable and feasible. Addressing issues such as rural-urban land conversion and ambiguous land tenure, and allowing flexibility for realistic building standards and increasing maximum floor space standards in certain neighborhoods can help to ensure a growing supply of housing that is affordable for moderate and low-income households.

This paper is a product of the Finance Economics and Urban Department, Sustainable Development Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pannez@worldbank.org or pcannez.clarke.work@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table A.22:** Robustness Checks on Public Goods Provision (Table 7)

||**Public Goods Index**|**Citizen**|**survey**|**Village head survey**|
|---|---|---|---|---|
||_Podes_2021|Access|Quality|Take Action|
||(1)|(2)|(3)|(4)|
|1. Baseline|0.503<sup>_∗_</sup><br>(0.263)|0.073<sup>_∗∗_</sup><br>(0.043)|0.208<sup>_∗∗_</sup><br>(0.102)|0.165<sup>_∗∗_</sup><br>(0.086)|
|2. No controls|0.682<sup>_∗∗∗_</sup><br>(0.276)|0.061<sup>_∗_</sup><br>(0.039)|0.202<sup>_∗∗_</sup><br>(0.098)|0.126<br>(0.098)|
|3. Region fxed effects|0.452<sup>_∗∗_</sup><br>(0.238)|0.054<sup>_∗_</sup><br>(0.040)|0.172<sup>_∗∗_</sup><br>(0.098)|0.117<br>(0.087)|
|4. 3rd-degree polynomial|0.282<br>(0.407)|0.111<sup>_∗_</sup><br>(0.064)|0.271<sup>_∗∗_</sup><br>(0.125)|0.150<br>(0.140)|
|5. 1/2 MSE-opt. bandwidth|0.293<br>(0.477)|0.082<br>(0.085)|0.202<br>(0.189)|0.158<br>(0.157)|
|6. 3/4 MSE-opt. bandwidth|0.442<br>(0.364)|0.077<sup>_∗_</sup><br>(0.063)|0.212<sup>_∗∗_</sup><br>(0.143)|0.157<br>(0.126)|
|7. 2x MSE-opt. bandwidth|0.458<sup>_∗∗_</sup><br>(0.235)|0.036<sup>_∗_</sup><br>(0.039)|0.097<sup>_∗∗_</sup><br>(0.095)|0.124<sup>_∗∗_</sup><br>(0.075)|
|8. Excl. Suharto villages|0.511<sup>_∗_</sup><br>(0.272)|0.067<sup>_∗_</sup><br>(0.043)|0.189<sup>_∗∗_</sup><br>(0.100)|0.171<sup>_∗∗_</sup><br>(0.089)|
|9. Fuzzy RD|0.688<sup>_∗∗_</sup><br>(0.320)|0.110<sup>_∗∗_</sup><br>(0.050)|0.308<sup>_∗∗∗_</sup><br>(0.124)|0.191<sup>_∗∗_</sup><br>(0.097)|
|10. Excl. lame-duck|0.498<sup>_∗_</sup><br>(0.272)|0.055<br>(0.044)|0.175<sup>_∗_</sup><br>(0.107)|0.176<sup>_∗∗_</sup><br>(0.092)|
|Observations|378|8848|8846|443|
|Control mean|0.23|0.78|-0.028|0.76|
|MSE-opt. bandwidth|18.7|15.2|14.9|24.9|

_Notes_ : This table reports robustness checks on Table 7. The first row reports our baseline estimates. In the second row, we remove election year dummies and our control for the survey experiment treatment, which are included in our baseline estimation. In the third row, we include region fixed effects and dummies for pairs of election years (2015-2016, 2017-2018, etc.). The main regions in our sample are Java, Sulawesi, Sumatra, Kalimantan, and NTB-Bali. In the fourth row, we use a 3rd-degree polynomial to construct the point estimator. In the fifth, sixth, and seventh rows, we use a RD bandwidth equal to 1/2, 3/4, and twice the MSE-optimal bandwidth from Calonico et al. (2014), respectively. In the eighth row, we exclude villages with bureaucrats appointed during the Suharto era. In the ninth row, we report fuzzy RD estimates of _γ_ in equation (1). We use 1 (margin _jt >_ 0) from equation (1) to instrument for a dummy equal to 1 if the current village head in our survey sample is a different individual from the incumbent who competed in the last election. In the tenth row, we exclude villages where the current village head is serving in their third term. The bottom panel reports the bandwidth size for the baseline estimation. The dependent variables are identical to those examined in columns 2–4 of Table 7. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses. In columns 1–2, standard errors are additionally clustered by village.

58
those documented by Baird, et al., without any additional costs.

To quantify the benefits here, we must extrapolate (as Baird, et al., do) to earnings impacts of early changes in cognition. For this, I rely on a pair of studies: one that documents an early childhood intervention changing cognitive measures, in a developing country, and a followup that shows changes in wages in adulthood. Grantham-McGregor, et al. (1997) found that an early childhood stimulation intervention in Jamaica increased performance on a variety of measures several years later, when the study participants were 7 to 8 years old. In a follow-up, Gertler, et al., find that this intervention eventually increased wages by 25 percent.

In Table 4, I compare coefficients on four cognitive measures in the Grantham-McGregor, et al. (1997) study to the coefficients in the present study. Taking their ratio, and multiplying by the 25 percent wage increase found to result at age 22 by Gertler, et al. (2014), I arrive at potential extrapolated percentage wage increases listed in the last column. The figures range from 12.5 percent to 83.3 percent: even the smallest of these is quite substantial.

In order to place this in dollar terms, I must also set this in relation to typical earnings. For this, I adopt the approach of Baird, et al.; they estimate net present values of benefits of deworming from a public finance perspective. This task includes calculation of the net present value (NPV) of typical lifetime earnings for an individual in western Kenya, for which they arrive at a figure of $1509.96.<sup>20</sup> Though many of their effects appear to operate through years of schooling and health, here the effects appear to operate through cognition. As such, the simplest calculation does not involve any government-borne costs of additional schooling. The question is, by what fraction will lifetime earnings rise, and how much additional revenue will the government eventually collect as a consequence? Multiplying the NPV of earnings by the most conservative percentage from Table 4, this is an additional NPV of just over $180 in earnings. Scaling that down by the 16.5 percent tax rate to calculate government revenues, I find an additional NPV of government revenue of just over $30 per child benefiting from spillovers.

A simpler and potentially more conservative approach is to consider the effects in terms

> 20 One could, instead, extrapolate lifetime earnings using Kenya’s GNI per capita as reported by the 2014 World Development Indicators. At $860 in current (non-PPP) dollars (World Bank 2014), the relevant steps would yield a larger total figure. I use the Baird, et al., approach to remain conservative.

19
group, we also calculated pensions<sup>for"ten-yearwomen"whoworkedonlytenyears</sup> prior to child-bearing and "full<sup>careerwomen"who hadthesamelaborforce</sup> participation as men. Full career<sup>women give us an indicationof the impactof increasing</sup> age- and education-specific labor<sup>forceparticipationrates.Theabsenceof longitudinal</sup> data meant that we could not<sup>varywagesasafunctionof experience sothelifetime</sup> earnings and pensions of full career<sup>womenare probably understated.</sup> Our representative men and women<sup>are assumedto be singleuntil the median age</sup> of marriage in each country,<sup>and marriedthereafter.They marry within their educational</sup> class, and the average husband<sup>isthree yearsolder than thewife.Thus we do not model</sup> women who remain single<sup>throughout theirlifetimes,becauseof small samplesizeof</sup> single women in some age-educational<sup>cells.Since single women probably have a greater</sup> labor force attachment than<sup>marriedwomen, our simulationsforfull careerwomen may</sup> give us a rough approximation<sup>of their lifetime earningsand benefits.</sup> _Data._ In constructing our<sup>syntheticmen andwomen,weusednational datasets</sup> for urban areas (see Appendix<sup>Bondatasources).Thesedatado not coincide precisely</sup> with groups that are actually<sup>covered by the social security system.Somesocial security</sup> affiliates live in rural areas while<sup>some urban residentsare not coveredby socialsecurity.</sup> In Chile our data cover only<sup>those affiliatedtosocialsecurity,which means they werein</sup> the system at some points<sup>intheirlives.Thishelpsexplainwhy thelaborforce</sup> participation rates of women<sup>appearto be higher in Chilethanin Argentina andMexico,</sup> where all urban workers are<sup>included.These datamayunderstatewagesandworkof</sup> women who were covered by<sup>social security andthereforeoverstatethe pensiongender</sup> differential in Argentina and<sup>Mexicofor thisgroup.AlsoinChilethe wageand work</sup> data primarily cover full time<sup>workers whilein Argentinaand Mexicotheycoverfull</sup> time plus part time workers.<sup>Thus fulltimeequivalentyearsaresmallerand fulltime</sup> wages are larger for urban<sup>working women thanthenumbersgivenfor thelattertwo</sup> countries. We attributed all working<sup>timeascontributingtime, butit is quitelikely that</sup> part of this work is outside<sup>the formal laborsectorand thesocial securitysystem-these</sup> data would then overestimate<sup>lifetimecontributions,especiallyforwomen.For this</sup> reason we may have underestimated<sup>thegender differentialin pensionsfortheaverage</sup> woman stemming from the<sup>privatepillar.However, thisbiaswillprobablydiminish</sup> 13
17 carrying out of O&M through Performance Contracts; (iv) the redefinition of the operating jurisdictions of the various institutions in the water sector; and (v) the establishment of water rights and formation of water markets in project-affected areas.

The NDP reforms can be divided roughly into four heirarchical categories: (1) reforms at the national sector planning level, (2) reforms at the federal (WAPDA) executing level, (3) reforms at the provincial planning and executing level, and (4) reforms at the on-farm drainage level. To keep the analysis manageable, we selected a subset of five reforms which are of greater analytical interest. These reforms are described in Matrix 1 below. The number of players is also relatively large. Groups and individuals affected by the reforms, and in the position to affect the outcome of the reforms, include, for example the Government of Pakistan, its leaders and agencies, WAPDA, provincial governments and their leaders and agencies, local organizations, the media, affected officials, farmers' groups, and ordinary farmers. [A complete list of the players involved can be found in World Bank (1997).] Even within each group, there are either different subgroups or individuals that should be considered separately. For example, different parts and inldividuals in WAPDA may have opposing interests and abilities in affecting the outcome of various reforms. Big farmers and small farmners have different roles and should also be treated separately. With the exception of the farming community, which we divided into "big" and "small" farmers, we categorized all other organizations and groups in the analysis as representing one point of view. The players we considered for the analysis are mentioned in Table 1, and their interaction with the reforms is explained also in Matrix 1.

# How THE PLAYERS COULD AFFECT THE REFORMS

There are a variety of means by which parties may affect reforms. Each party may prefer a subset of means based on their relative effectiveness and cost. We describe the means by which potential players might influence the various reforms in Matrix 2 below. It should be noted that it is possible that two players using similar vehicles to influence the reforms' achievement levels may end up having different actual impacts.
# **_Idle Youth in Mexico: Trapped between the War on Drugs and Economic Crisis_**<sup>**_1_**</sup>

Rafael de Hoyos _The World Bank_ Washington DC Carlos Gutiérrez Fierros _Observatorio de Desarrollo Regional_ Mexico City J. Vicente Vargas M. _Observatorio de Desarrollo Regional_ Mexico City

**Keywords** Ninis; Violence; Mexico; NAFTA **JEL** K42; F16; J24

> 1 The present study is a background paper for the regional study “Out of school and out of work: Risk and opportunities for Latin America’s ninis.” (De Hoyos, Rogers, and Székely 2016). The authors are especially indebted to Laura Chioda, Daniel Lederman, Luis Felipe López Calva, Julian Messina and Halsey Rogers for useful comments and suggestions.
cheaper rents. That is, for these poorest groups, the substitution effect of the tax on travel does dominate over the income effect (see effect (5) above) and this is reinforced by the lessened competition in the housing market which causes lower rents.

Our model borrows features from Anas and Rhee (2006) and its antecedents introduced into urban economics by the first author. This type of model combines the random utility theory of discrete choice contributed and refined by McFadden (1973) and widely utilized in transportation economics since then, with the constant elasticity of substitution utility function for treating a taste for variety in consumption, contributed by Dixit and Stiglitz (1977). In the present context, random utility modeling (and more precisely the nested multinomial logit model) allows us to treat discrete choices such as owning a car or not, and mode by which to commute to work daily. The Dixit-Stiglitz C.E.S. allows us to treat discretionary trip making by a variety of modes, through which goods and services are purchased by the consumer, and the trading off between discretionary trip making and housing consumption.

# **2.1 Consumer preferences and utility maximization**

There are five types of consumers by income quintile denoted by the superscript _f_ . The choices of consumers combine qualitative (discrete) as well as quantitative (continuous) variables in a three-level decision tree as shown in Figure 1. In the top two levels, discrete choices are made and in the bottom level the values of the continuous variables are chosen. At the first (highest) level of the tree (Figure 1), the discrete choice is whether to own a car or not, denoted by _C_ =1, _C=_ 0 respectively. Car ownership entails an annualized acquisition cost and thus reduces the disposable income left for the lower level choices. However, car ownership also imparts satisfaction (which will be measured by idiosyncratic terms in the utility function) and enables faster travel which frees time that can be used to generate additional income. Fuel efficiency of one’s car is also key in the model because together with the travel speed, it determines the fuel cost of traveling by car. At the second level of the tree (Figure 1), the consumer chooses one of the modes for his commute to work, conditional on owning or not owning a car, determined at the first level.

6
Following the implementation of the cap, nominal lending rates of MFIs decreased and interestingly also lending rates by deposit‐taking banks, which were mostly not subject to the cap,<sup>28</sup> converged towards the 18% level. Initial data on MFI lending activity show that the number of borrowers decreased by around 45,000, or 2.5%, between January and August 2017. Over the same period the average loan size increased, suggesting that fewer small loans were disbursed and MFIs tried to reduce costs and increase the efficiency of their lending operations.

**Figure 13: MFI borrowers and loan size before and after the cap**

<!-- Start of picture text --> Number of borrowers Average loan size<br> 1,900,000 10,000<br> 1,850,000 9,000<br> 8,000<br> 1,800,000<br> 7,000<br> 1,750,000<br> 6,000<br> 1,700,000 5,000<br> 4,000<br> 1,650,000<br> 3,000<br> 1,600,000<br> 2,000<br> 1,550,000 1,000<br> 1,500,000 ‐<br>January August January August<br><!-- End of picture text --> _Notes:_ 2017 data _._ Average loan size is expressed in KHR thds. It is calculated by dividing the total amount of MFIS loan outstanding by the number of borrowers. _Source:_ National Bank of Cambodia

# WAEMU: Reduction of the interest rate ceiling

Interest rates in Benin, Burkina Faso, Côte d’Ivoire, Guinea‐Bissau, Mali, Niger, Senegal and Togo, the eight member countries of the West African Economic and Monetary Union (WAEMU), have been capped since 1997. The caps are set as absolute limits with different levels applied to banks and non‐bank financial institutions, mainly MFIs. On 1<sup>st</sup> January 2014, the levels of the caps were revised downwards by the BCEAO. For banks, ceilings went from 18% to 15% and for microfinance institutions they went from 27% to 24%.<sup>29</sup> Before the downward revision real lending rates were substantially higher than the Sub‐Saharan Africa median.

> 28 The cap applies to microfinance deposit‐taking institutions but not to commercial banks.

> 29 http://www.bceao.int/Avis‐no‐003‐08‐2013‐aux.html

28
United States, Jacobson’s work (Jacobson 1989 and Jacobson 1991) shows that payment incentives do lead to declines in absenteeism. However, the welfare impacts are less certain.

Jacobson (1989) documents how a payment incentive scheme led to a decline in teacher absenteeism. Nevertheless, one year later a fact- fi nding mission concludes that: “While the District’s attendance statistics for the past several school years may lead some to conclude that attendance improved (...) the fact fi nder does not believe the record before him established that improved attendance rate (...) raised the quality of teachers or teaching in the District. In fact, the District and Association expressed their agreement that they knew of no way to measure the effectiveness of a sick teacher who came to work to assure receiving a higher share of EIT money vs. that of a sick teacher who stayed home to recuperate while a substitute taught his/her classes (...) I conclude that an attendance based criterion for the 1987/88 EIT distribution simply would not serve to promote the "excellence in teaching" envisioned by the State Legislature and the Governor (PERB 1988: 9-10).” The situation in low-income countries may be very different. Certainly, studies in India (Chaudhury and others 2005) suggest that teacher absenteeism is largely due to shirking rather than illness. Jacobson’s work however, cautions us in extrapolating views from one continent to another. If teachers in Zambia and other Sub-Saharan countries are absent because they shirk and incentive schemes and greater accountability lead both to greater attendance and better performance, then such schemes can lead to better learning outcomes. However, if teachers’ utility functions are altruistic so that most absenteeism is “genuine”, incentive schemes might hurt teacher motivation. This con fl ict between treating teachers as “professionals” who respond to monetary incentives and thinking of them as “dedicated to students’ needs” remains at the center of a contentious debate in the United States. Although research in low-income countries is at a nascent stage, with absenteeism rates approaching 25 percent in some countries, steps towards a deeper understanding are critical.

Our fi ndings also raise a methodological issue. The results obtained on the cohort of children who stayed with the same teacher, do not extend to the entire sample. The policy of retaining the same teacher for the student-cohort was implemented only for larger schools, so non-movers come from more urban schools where the teachers are better (more experienced and better trained), families are richer and parents are more educated. With better access to markets for private tuition and home schooling, we expected the impact of teacher shocks to be lower among the sample of children who are non-movers. Intriguingly we found no impact of teacher shocks on student learning among the movers in our sample. We suggested two reasons for this fi nding. If the 21