rafmacalaba/datause-encoder-data huggingface dataset:
benchmark prior with _c_ = 1 (Prior 1) leads to very small prior and posterior density values at _g_ 0, so that the ratio can really not be computed with any accuracy. However, given that this is indicative of a very small prior density in the region of the mode of _ly_ ( _g_ ), it is safe to assume the associated Bayes factor would be quite small. The same problem affects other models with the other two datasets (indicated with – in Table 4). As commented in Section 4, the priors that put a lot of weight on regions of _g_ with high _ly_ ( _g_ ) do particularly well in this respect. Thus, the Forte _et al._ , Zellner-Siow, Bottolo-Richardson, hyper- _g/n_ and horseshoe/ _n_ priors lead to the highest Bayes factors while the F-Z, hyper- _g_ and benchmark priors do worse. This is in line with the prior density functions in Figures 1 and 2 and the marginal likelihood in Figure 8. In Table 4 we also include the models where we choose a fixed value of _g_ , in which case we can compute the Bayes factor as the ratio _p_ 3( _g | y_ ) _/p_ 3( _g_ ) evaluated at the fixed value of _g_ (as explained in Section 4). Some entries in the table are missing, as they correspond to _p_ 3( _g|y_ ) = 0 for the fixed value of _g_ ; again, this can be taken as a sign that this value is associated with a very small Bayes factor. So, whereas the data do support choosing _g_ = _n_ , the case _g_ = _k_<sup>2</sup> does not receive much support in terms of Bayes factors.

**Table 4.** Bayes Factors of Different Priors vs Benchmark with _c_ = 0 _._ 01 (Prior 3)

(Random _θ_ , _m_ = 7)

|_i_|Prior on _g_|FLS|SDM|Tobias-Li|
|---|---|---|---|---|
|2|Benchmark _c_= 0_._1|0.3|–|0.4|
|4|Maruyama-George|16.3|10.1|–|
|5|Bottolo-Richardson|23.6|57.2|24.4|
|6|Hyper-_g_, _a_= 3|2.3|8.7|3.0|
|7|Hyper-_g_, _a_= 4|0.7|3.0|1.0|
|8|F-Z, _a_= 2 + 2_/k_<sup>2</sup>|0.1|0.1|0.3|
|9|F-Z, _a_= 2 + 2_/n_|2.4|5.6|0.1|
|10|Horseshoe|11.2|34.6|11.9|
|11|Forte _et al._|53.2|123.2|n.a.|
|12|Hyper-_g/n_|21.8|50.8|1.7|
|13|Horseshoe/_n_|25.9|68.5|7.6|
|14|Zellner-Siow|29.8|60.1|–|
||_g_0|25.5|19.5|19.5|
|15|_g_ =_n_|19.7|37.1|–|
|16|_g_ =_k_<sup>2</sup>|–|0.1|–|

# The SDM data

Another popular growth dataset was introduced by Sala-i-Martin _et al._ (2004) (SDM), who model annual GDP growth per capita between 1960 and 1996 for _n_ = 88 countries using _k_ = 67 potential regressors.

The 50 randomly selected estimation subsamples each have 75 observations, and lead to the boxplots in Figure 9. The results are mostly in line with those for the FLS data, but with larger differences between the priors in terms of posterior model size.

26
# Using referenda to improve targeting and decrease costs of conditional cash transfers<sup>∗</sup>

Jennifer M. Alix-Garcia, Katharine R.E. Sims, and Daniel J. Phaneuf

## JEL codes: Q58; Q28; O13; D04

Keywords: targeting; conditional cash transfers; payments for ecosystem services; stated prefer-

## ence

> ∗Alix-Garcia: Department of Applied Economics, Oregon State University, jennifer.alix-garcia@oregonstate.edu; Sims: Department of Economics, Amherst College, ksims@amherst.edu; Phaneuf: Department of Agricultural and Applied Economics, University of Wisconsin-Madison, dphaneuf@wisc.edu. Acknowledgements: We are grateful for financial support from The World Bank’s i2i program, Mexico’s National Evaluation Agency (CONEVAL), the Mexican National Forestry Commission (CONAFOR) the Carnegie Corporation of NY, and our academic institutions. The data on WTA was collected concurrently with data for a study led by PIs Alix-Garcia, Sims, and the World Bank Development Impact Evaluation (DIME) unit. The Mexican National Forestry Commission and its field offices aided substantially in the implementation of the survey, which was carried out by Chapingo University. Seminars at the University of Wisconsin, Oregon State University, University of Toulouse, Oxford University, the London School of Economics, and the University of California-San Diego provided important inputs for the development of this manuscript. The IRB protocol number is: #2016/04/17.
28 costs. The average wage per worker in the manufacturing sector is about 25 percent of that of China’s and 70 percent of that of Indian workers, and it compares favorably with Vietnam (see Table1).

|**Table 1: Average**<br>|**Monthly Wag**<br>|**e Including B**<br>|**enefits, by In**<br>|**dustry (US dol**<br>|**lars)**<br>|
|---|---|---|---|---|---|
|Sector|**Tanzania**<br>All workers|**Ethiopia**<br>Unskilled-<br>skilled|**Nigeria**<br>Unskilled-<br>skilled|**Vietnam**<br>Unskilled-<br>skilled|**China**<br>Unskilled-<br>skilled|
|_Food_|84|26-141|87-135|78-363|192-442|
|_Garments_|80|26-185|54-85|79-181|237-370|
|_Textiles_|61||71-120|||
|_Machinery &Equipment_|60-151||125-163|||
|_Wood, woodproducts_|67|37-119|67-102|85-259|206-442|
|_Metal and Metalproducts_|124|89-181|82-107|117-233|192-369|
|_Other manufacturing_|168|67-154|87-130|||
|**_Average Manufacturing_**|181|||||

Source: For Tanzania, Annual Survey of Industrial Production and Performance, 2008; for Nigeria, Lin 2012a. Other statistics are from Dinh et al. (2011), Vol II. Note: Since Tanzania Survey does not distinguish unskilled or skilled workers, wages for both unskilled and skilled workers are used for other countries.

For Sub-Saharan Africa, the advent of a multi-polar world may convert the demographic challenges outlined above into new possibilities. Africa’s “youth bulge” represents an unprecedented opportunity to generate inclusive growth and reduce poverty (opportunities): with the rest of the world, especially the developed world, ageing, Africa can be the main supplier of the world’s workforce. At the macroeconomic level, all these unemployed and underemployed young people not only need jobs, but they can potentially create jobs and foster economic growth. Also, increasing population density can be an asset to development (World Bank 2008).

At the sectoral level, Sub-Saharan Africa can take advantage of rising manufacturing wages in China, India, Brazil, and other large, successful middle-income countries by attracting most of the estimated 119 million jobs that will be relocated—provided that it can out-compete other low-income regions of the world by quickly and effectively implementing key strategic reforms that are required to accommodate domestic and foreign investors. Employment in Africa’s manufacturing sector is still at very low levels. There are already many signs of outsourcing in the services sector, as call centers and financial services emerge in countries such as Kenya or Nigeria. In the agriculture sector, changes in global food markets—most notably increased demand and prices—are also likely to bring new opportunities for job creation to Africa.
– Table A.23 Heterogeneous treatment effects on living standards, shocks, and migration

||Had an IGA|at baseline|Married a|t baseline|Consumptio|n at baseline|TCLP pa|rticipation|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Treatment|(2)<br>IGA _×_<br>Treatment|(3)<br>Treatment|(4)<br>Married _×_<br>Treatment|(5)<br>Treatment|(6)<br>Conso _×_<br>Treatment|(7)<br>Treatment|(8)<br>TCLP _×_<br>Treatment|
|**Household living standards**|||||||||
|Total consumption per capita|0.707***|-1.032|1.095**|-0.546|1.018**|-0.511|0.637|0.086|
||(0.272)|(1.392)|(0.548)|(0.630)|(0.494)|(0.588)|(0.732)|(0.839)|
|Food consumption per capita|0.391***|-0.073|0.824***|-0.572**|0.494**|-0.161|0.239|0.060|
||(0.118)|(0.589)|(0.232)|(0.268)|(0.198)|(0.241)|(0.308)|(0.358)|
|Non-food consumption per capita|0.098|-0.358|-0.039|0.150|-0.114|0.254|0.044|-0.007|
||(0.122)|(0.610)|(0.259)|(0.292)|(0.227)|(0.267)|(0.327)|(0.381)|
|Asset index (stock)|0.136***|0.197|0.129*|0.016|0.092|0.061|0.045|0.159|
||(0.039)|(0.186)|(0.078)|(0.090)|(0.080)|(0.091)|(0.095)|(0.115)|
|Asset index (bought since 2019)|0.006|0.021|0.049|-0.055|-0.008|0.022|0.004|0.001|
||(0.043)|(0.148)|(0.088)|(0.098)|(0.093)|(0.105)|(0.073)|(0.097)|
|**Shocks and coping mechanisms**|||||||||
|Economic shock|0.023***|0.037|0.027*|-0.001|0.020|0.006|0.042**|-0.022|
||(0.008)|(0.032)|(0.016)|(0.018)|(0.014)|(0.016)|(0.021)|(0.025)|
|Other shock|-0.009|0.156*|-0.016|0.018|0.043|-0.060|0.060|-0.088|
||(0.019)|(0.087)|(0.039)|(0.045)|(0.038)|(0.043)|(0.049)|(0.058)|
|Extreme coping strategy|-0.034***|0.097|-0.007|-0.030|-0.005|-0.034|-0.057*|0.023|
||(0.013)|(0.064)|(0.027)|(0.031)|(0.025)|(0.029)|(0.034)|(0.041)|
|Other coping strategy|0.007|0.037|0.008|0.001|0.031|-0.029|0.039|-0.037|
||(0.016)|(0.075)|(0.033)|(0.038)|(0.030)|(0.035)|(0.041)|(0.049)|
|**Migration**|||||||||
|Individual migration (since 2019)|-0.001|0.052|0.010|-0.012|-0.007|0.013|0.028|-0.031|
||(0.019)|(0.075)|(0.038)|(0.043)|(0.035)|(0.041)|(0.051)|(0.059)|
|Expected likelihood of future migration|0.032***|0.010|0.018|0.018|0.033*|-0.001|0.048*|-0.021|
||(0.011)|(0.035)|(0.020)|(0.024)|(0.017)|(0.021)|(0.027)|(0.032)|
|**Observations**|1,824|1,824|1,824|1,824|1,824|1,824|949|949|

Notes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

68
# Table 3.4 REGPSSION COEFFMCZDMTS Ml EDUCATEON OF -SPOUSZ OF HEAD OF HOUSEHOLD, WMTH CONSUMPrION OF NISR}ENTS AS DEPENDENT<sup>VAR5ALzS</sup>

|Region/Level<br>of Education|Calories|Protein|Fat|Carbohydrates|Calcium|rron|Vitamin<br>A|Thiamine|Riboflavin<br>|Niacin|**Vitamin**C<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||(qr)|(gr)|(gr)|Isq)|<br>(.q)|mT|_(aE)_|(nq)|(,g)|_(sq)_|
|Java||||||||||||
|Education<br>of<br>Spouseof Head:||||||||||||
|Elementary<br>School|*****|**f**|0.1245|*****|*****|*****|*****|*****|*****|*****|**f**|
|Junior<br>High<br>School|-0.1397|*****|*****|-0.1409|*|*|*****|-0.1264|*****|-S.1303||
|Senior<br>iigh<br>School|-3.'939|-0.1654|||*|-0.1800|*****|-0.2570|*****|-03.802|*****|
|Higher<br>Education|*|||||-0.3482||-0.3920||||
|Outer<br>rslands||||||||||||
|Education<br>of<br>Scouse of Head:||||||||||||
|Elementary<br>SchooL|||0.0699|*|*|*|*|**||||
|Junior<br>High School|-0.1011|*|*|-0.0924|*|-0.0727||-0.0982||-0.0956||
|Senior<br>High School|-0.1445|-0.1022|*|-0.:524|*|-0.1028||-0.1078||0.1199||
|Higher<br>Education|-0.2358|*||-0.2993||-0.2113||-0.26i0||-0.2022||
Policy Research Working Paper 6271

# **Abstract**

This paper investigates the urbanization of the Indian manufacturing sector by combining enterprise data from formal and informal sectors. It finds that plants in the formal sector are moving away from urban and into rural locations, while the informal sector is moving from rural to urban locations. Although the secular trend for India’s manufacturing urbanization has slowed down, the localized importance of education and infrastructure has not. The results suggest that districts with better education and infrastructure have experienced a faster pace of urbanization, although higher urban-rural cost ratios cause movement out of urban areas. This process is associated with improvements in the spatial allocation of plants across urban and rural locations. Spatial location of plants has implications for policy on investments in education, infrastructure, and the livability of cities. The high share of urbanization occurring in the informal sector suggests that urbanization policies that contain inclusionary approaches may be more successful in promoting local development and managing its strains than those focused only on the formal sector. Cities are evolving in India from places of goods production to forges of human capital and coping mechanisms for survival.

This paper is a product of the Economic Policy and Debt Unit, Poverty Reduction and Economic Management 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 EGhani@worldbank.org.

_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 D.4 (continued): Program impacts on self-control skills, 12–13 month survey only

|||||I|TT regre|ssion (N=94|7)||
|---|---|---|---|---|---|---|---|---|
|||Control|Ther|apy only|Cas|h only||Both|
|Outcome||mean|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Attit|udes toward political violence (6 questions)|0.096|-0.143|[.118]|-0.002|[.123]|-0.225|[.119]*|
||Suppose the politician that you support|0.129|-0.020|[.035]|-0.012|[.037]|-0.055|[.035]|
||does not win and there was no cheating.<br>If that politician asks you to protest for||||||||
||him, would you do so?||||||||
||Suppose the politician that you support<br>does not win and there was no cheating.<br>If that politician asks you to loot for him,<br>would you do so?|0.079|-0.015|[.029]|0.009|[.032]|-0.042|[.029]|
||Suppose the politician that you support<br>does not win and there was no cheating.<br>If that politician asks you to loot for him<br>for 200 LD, would you do so?|0.080|-0.011|[.031]|0.028|[.033]|-0.024|[.031]|
||Suppose the politician that you support<br>does not win but there was cheating. If<br>that politician asks you to protest for<br>him, would you do so?|0.204|-0.075|[.042]*|-0.047|[.044]|-0.085|[.044]*|
||Suppose the politician that you support<br>does not win but there was cheating. If<br>that politician asks you to loot for him,<br>would you do so?|0.151|-0.057|[.038]|-0.008|[.039]|-0.072|[.039]*|
||Suppose the politician that you support<br>does not win but there was cheating. If<br>that politician asks you to loot for him|0.130|-0.034|[.037]|0.024|[.037]|-0.056|[.037]|
||for 200 LD, would you do so?||||||||

_Notes:_ The table reports long-run (12–13 month) intent to treat estimates of outcomes that were not a priori specified as of primary interest. We calculate the impact of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. We focus on pre-defined composite measures, typically defined by survey module. The overall summary indexes are the standardized mean of its composite outcomes, standardized. Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1 xxxi
16 Table 3- Continued

|Horizon|Generalized|Variance Decom<br>Percentage of th|position of**Industr**<br>e Variance Associa|**ial Output- Me**<br>ted with Shock|**xico**<br>to:|
|---|---|---|---|---|---|
|(in<br>months)|Terms of<br>Trade|Spreads|Domestic Real<br>Interest Rate|Real<br>Exchange<br>Rate|Output|
|1|5|1|2|4|96|
|4|6|12|_5_|12|73|
|8|5|39|22|43|36|
|12|4|57|37|59|24|
|16|3|56|41|61|25|
|20|4|50|39|56|26|
|24|4|49|37|54|25|

||Generalized V|ariance Decom|position of**Industri**|**al Output- Chile**||
|---|---|---|---|---|---|
|Horizon|Pe|rcentage of the|Variance Associat|ed with Shock to:||
|(in<br>months)|Terms of<br>Trade|Spreads|Domestic Real<br>Interest Rate|Real<br>Exchange<br>Rate|Output|
|1|3|3|13|0|90|
|4|4|13|25|8|62|
|8|3|23|25|18|47|
|12|6|24|27|16|43|
|16|7|25|28|16|40|
|20|7|25|28|16|39|
|24|7|25|29|16|39|
||Generalized Vari|Table<br>ance Decompo|3 (Continued)<br>sition of**Industrial**|**Output- Colom**|**bia**|
||P|ercentage of th|e Variance Associat|ed with Shock to|:|
|Horizon<br>(in<br>months)|Terms of<br>Trade|Spreads|Domestic<br>Real Interest<br>Rate|Real<br>Exchange<br>Rate|Output|
|1|12|2|23|18|79|
|4|16|2|28|22|59|
|8|22|9|35|26|49|
|12|19|12|47|26|38|
|16|18|18|48|28|30|
|20|18|21|43|30|27|
|24|17|23|41|30|26|

||Generalized V<br>P<br>|ariance Decom<br>ercentage of the<br>|position of**Industr**<br>Variance Associat<br>|**ial Output- Peru**<br>ed with Shock to<br>|:<br>|
|---|---|---|---|---|---|
|Horizon<br>(in<br>months)|Terms of<br>Trade|Spreads|Domestic<br>Real Interest<br>Rate|Real<br>Exchange<br>Rate|Output|
|1|14|3|1|6|95|
|4|17|4|12|6|81|
|8|17|6|13|6|77|
|12|28|13|11|13|60|
|16|27|12|12|13|59|
|20|30|13|11|13|55|
|24|30|12|10|11|56|
Panel A refers to original households. Two sources of attrition are considered, as described in section IV.A: (i) localities composition, which means that just a sub-sample of the 506 original localities was surveyed; and (ii) response attrition, which means that some households in the selected localities were not found. In the first part of Panel A (Analysis sample) we show the main results estimated with the ANCOVA for original households for comparative reasons. Next, we show the results if no attrition happened (Original distribution). This results correspond to the household distribution in 506 and we add it for comparative reasons with respect to the original literature about the program. The reweighting factor employed for this result is the one included in column (1) of _Table 3_ . As can be seen, no significant effects on asset accumulation are obtained for this reweighted sample. Afterwards, in the third part of Panel A, we show the results that would be obtained if we only control for the response attrition. That is, the purpose is to replicate the distribution of all the households in the selected 334 localities. The reweighting factor in this case corresponds to column (3) of _Table 3_ . Again, no significant effects in asset accumulation are found. These results were expected since the attrition analysis suggested that no differential attrition between treatment groups was found.

Panel B refers to the new households. For this group attrition seems to be more relevant. Attrition here refers to the impossibility of finding all the individuals in the relevant age groups that exited their households to form a new one (or joined another household). Columns (5) and (6) in _Table 3_ show the attrition analysis for this group. To start with, a small proportion of individuals was found (40%). Second, the attrition analysis shows some slight significant differences between treatment and control (the joint test with the interaction terms has a p- value of 0.075). For this reason, we perform the reweighting strategy using the results from column (6) where the interaction terms were included. This reweighting factor would replicate the household distribution that would result if all the individuals in the relevant age groups that moved out of their original household were found in their new household. The results are shown in comparison to the main ANCOVA results (Analysis sample). As can be seen in the table, the results in asset accumulation are very similar to the resulting analysis with the sample found.

# **VI.B Youths and Intergenerational Transmission**

It is paramount when looking at long-term effects of such an important program to look at the life-cycle as well as at the intergenerational effects, ultimately the long-term success of the policy will be based on the success of the offspring of the originally treated households or the children who directly benefited from the government transfers as well as the other components of the program.

27
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Table 5 Estimated Program Net Impacts and<sup>Costs for the Major U.S. Displaced Worker Demonstration</sup> Projects

||Ne<br>|t impact<br>||Elti mthd|
|---|---|---|---|---|
|Demonstration project|Eamingse|Ul benefltsd|Coste|vauaon eo|
|Downrivera|$111.||Levels not avail. Cost of<br>training more than twice that<br>of JSA|Treatment<br>and comparison<br>groups<sup>randomly drawn</sup><br>from different plants|
|Buffalob|JSA: $134<br>JSAICT: 122<br>JSANOJT:<br>64||JSA: $851<br>JSA/CT: 3,282<br>JSA/OJT: 3,170|Treatment<br>and comparison<br>groups randomly drawn<br>from different plants|
|Texas WAD:<br>Houston|$547|-$204|Tier l: $1,460-2,072<br>Tier l/ll: 2,9814,381|Random assignment<br>of<br>eligible workers to treatment<br>and control groups|
|El Paso|Men: $770<br>Women: 1,148'|-$194<br>-227'|Tier l: $406-702<br>Tier I/lI: 725-1,099||
|New Jersey Ul|JSA: $263<br>JSA/CT: 103<br>JSA/OJT: 278|-$87<br>-81*<br>-170-|CT: $2,723<br>OJT: 1,960|Random assignment<br>of<br>eligible workers to treatment<br>and control groups|
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22