rafmacalaba/datause-encoder-data huggingface dataset:
Another important recommendation that stems from the results of this study is that promoting remittances can have a positive impact for the poor. Lowering the cost of remittances, both nationally and internationally, and supporting the productive use of remittances can benefit the well-being of Cambodian households. Some alternatives include promoting monetary transfers using mobile technology, developing financing products linked to remittances and introducing matching programs. These actions can act as multipliers of the positive effects of remittances on agricultural investment observed in the results of this study.

# **References**

- Aakvik, A. (2001): “Bounding a Matching Estimator: The Case of a Norwegian Training Program,” _Oxford Bulletin of Economics and Statistics_ , 63(1), 115–143.

- Abadie, A., and G. W. Imbens (2006): “Large Sample Properties of Matching Estimators for Average Treatment Effects,” _Econometrica_ , 74(1), 235–267.

- (2008): “On the Failure of the Bootstrap for Matching Estimators,” _Econometrica_ , 76(6), 1537–

- 1557.

- (2011): “Bias-Corrected Matching Estimators for Average Treatment Effects,” _Journal of Busi-_

- _ness and Economic Statistics_ , 29, 1–11.

- Abdoulaye, T., and J. H. Sanders (2005): “Stages and Determinants of Fertilizer Use in Semiarid African Agriculture: the Niger Experience,” _Agricultural Economics_ , 32(2), 167–179.

- Acosta, P. (2011): “School Attendance, Child Labour, and Remittances from International Migration in El Salvador,” _Journal of Development Studies_ , 47(6), 913–936.

- Acosta, P., P. Fajnzylber, and J. H. Lopez (2008): “Remittances and Household Behavior: Evidence from Latin America,” in _Remittances and Development: Lessons from Latin America_ , ed. by P. Fajnzylber, and J. H. Lpez, pp. 133–169. The World Bank.

- Adams, R. H., and A. Cuecuecha (2010): “Remittances, Household Expenditure and Investment in Guatemala,” _World Development_ , 38(11), 1626–1641.

- Alvarez, R., and R. A. Lopez (2005): “Exporting and Performance: Evidence from Chilean Plants,” _Canadian Journal of Economics_ , 38(4), 1384–1400.

- Amuedo-Dorantes, C., and S. Pozo (2011): “New Evidence on the Role of Remittances on Healthcare Expenditures by Mexican Households,” _Review of Economics of the Household_ , 9(1), 69–98.

- Arnold, J. M., and K. Hussinger (2005): “Export Behavior and Firm Productivity in German Manufacturing: a Firm-Level Analysis,” _Review of World Economics_ , 141(2), 219–243.

33
!~~~~~~~~~~~~~~~~~~~ Using tA2.2) and the fact that the equilibrium<sup>growth rate nR</sup> obtained In section III Is such that: <!-- Start of picture text --> 1- A - 7 (nR)<br>l +¢tn 4d) o R xIUn<br>0 aI61 2 n<br>I o(n +d) -U<br><!-- End of picture text --> we can conclude that n- a n R and also that U(tr1 - I OlGO + d) a I + ffnR + d)- UR Thus U(r) a UR . Q.E.D 23
# B

Is not necessarily warranted. For example, studles In India, Malayela, and the PhIlippines show that there Is a significant lev'il of competition In Informal markets and relatively llttle monopoly. In the PhilIppInes, monopoly profits amounted to loss than 4 percent of the total sum lont.<sup>6</sup> In many Instances, the hlgh rates of Interest charged by Informal lnders reflect primarily the hlgh costs and risks Involved In lending to small farmers, although cases of exploitation exist.

10. The general validity of the promiso that credit shortages Inhib5t adoption of new technology Is questionable because many Inputs and tochnologles are divisiblo and can be adopted In a gradual manner, so little capital Is needed Initlally. Poor marketing networks, Input supplies and distorted product prices are often more crucial constralnts on technology adoption than lack of credit. Unless the new technology reQuires substantlal up-front outlays, wlth returns spread over tknu, It Is not obvious that credit Is the best mechanism to encourage adoptlon of now technoblos. Studles indicate that even small farmers have a signlficant savings potential, and are capable of mobilizing resources when profitable opportunitles for Investment exist.<sup>7</sup> This potential justifles institutlon building to facilitate Intermediatlon, but not necessarily Infusion of external funds. Rather, a large Inflow of external funds, especially when made avallable at subsidized rates, suppresses both savings and the growth of viable commercial 6 lHarrf, 'Monmy and Commodie: Their InteractioI **n** a Rural Indian Setng', In von Plschk **e** t al., Rural Financial Market In DevOlno Countre. Johns Hopkins Uniesit Pre", 1963.

K Slnh, *Strc ot Intr Ras on Consumpdon Loans In an Indian Vllage' In von Plshke et al., cited above, 1983. R.J.G. Web, _-An_ Input Credit Progrm for Small Famer In Wet Mla^y' In von Piechke et at. dt above, 1983. Technical Board of Agricultural Credit 'A Study on te Informal Rural FInnci Markts', Manila, 1981.

7See for example, 'Review of Rural Fian Seices in Sub-aharan Africa', Agriculture Division of Africa Technical Cepartment, 1889 or A. Wgue, Mobiliation of rura savings in developing countris: the case of Mai', Ph.D. Dissertaton, Univery of Cincimati. 1988; or C. _Cuvas._ 'Saving and Loan Cooperative InRural Areas of Developing Counties, Roent Peifomance and Potential', in &avinas and Deoent No. 1.188.
Thus, we have no shortage of explanations for the low commitmen **t** o city social overhead investment during the First Industrial Revolution. But if Britain underinvested in city social overhead, then ws should see it in the form of high social rates of return to such investment. Reformists of that time certainly made the case that the cities were crowded and filthy, and that sickness and death were highly correlated with city ugliness, but to clinch the case for public intervention the reformers had to show clearly that public health investment would yield favorable returns as well as to show which projects should have been favored first. The reformers could not show this in 1842 and modern historians have difficulty showing it today. Indeed, we are not even sure what was driving mortality trends across the 19th century, including the contribution of sanitary reform to those trends. Nonetheiess, we do have some suggestive hints.

Thomas NcKeown (McKeown, 1976; NcKeown and Record, 1962) guessed that public health investments introduced by the sanitary reformers accounted for about a quarter of the mortality decline in the second half of the nineteenth century, but even that brilliant guess has been challenged in subsequent debate. As Fogel (1986, Table 9.1, p. 440) has recently shown, the British standardize **d** eath rate declined by 21 points between 1700 and 1980, from 28 per thousand to 7 per thousand, about half of which took place before 1911, and a third in the six decades following 1850. NcKeown argued that improved nutriticn and sanitation investment were the principal factors accounting for the decline, especially the former (another key wage good for the poor which was rising in relative price prior to the late 1840s, as we have seen). In fact, NcKeown and Record (1962, p. 120) suggested that in the second half of the 19th century investment in public health accounted for about a quarter of the decline while the rise in nutrition accounted for about half. Decrowding explained none of the decline, since it failed to take place.

-76-
5 Results Table 3 presents results on the effectiveness of the information intervention. Column 5 shows an index of knowledge, which is the sum of the number of correct answers to 10 questions regarding maternal health and nutritional issues. The index is our preferred measure of knowledge because it deals with the fact that we study many outcomes, and without any type of correction we will be likely to find some statistically significant improvements purely by statistical chance. Levels of knowledge in the control group are still low at endline, as there has been essentially no change from the baseline level of the knowledge index. Women in both the information only and information plus cash group do significantly better at endline, suggesting that the information sessions led to improved knowledge. Women in the information plus cash intervention on average answer 1 additional question correctly, which is an improvement of 17% above the control group mean, and is also significantly higher than women in the information only group. Columns 1-3 in table 3 present three of the individual items which make up the index, and column 4 shows the number of beneficial food items that a mother lists when given an open ended question on what a child should eat. All are consistent with the information intervention improving knowledge, particularly so for women in the information plus cash group.

It is somewhat surprising that women who participate in the same session on information experience different gains in knowledge. It is possible that women who also receive cash are more invested in the sessions as they believe cash is important to making some of the behavioral changes, such as appropriate supplemental feeding for young children. Alternatively, it is possible that women in the information plus cash group simply participate more in the information sessions, especially since receiving the cash transfer was conditional on attending the meeting. Column 4 of table 4 shows that women in the information session are significantly more likely to have attended a village meeting in the last month than women in both the control group and the information only group. Note that this is only suggestive evidence as the previous month the question refers to takes place after the intervention.<sup>12</sup> Column 1 of table 4 shows that expenditures in the past month were not significantly higher in the information plus cash group, though in column 3 we

> 12Additionally, at the midline survey, when the question refers to a month during which the intervention is taking place, there is no statistical difference in attendance across treatment groups. However, issues of attrition during the midline survey mean that this finding is not completely reliable.

12
# **Table 11: IV estimates for all dependent variables, land inheritance**

||(1)<br>Log expend-iture<br>|(2)<br>|(3)<br>Non-farm<br>|(4)<br>|(5)<br>Farm|(6)<br>Same residence as|
|---|---|---|---|---|---|---|
||per capita|All|Heads|Non-heads|employment|parents|
|Inherited land|0.0417|0.142**|0.101|0.142*|-0.0368|-0.0325|
||(0.145)|(0.0592)|(0.0905)|(0.0778)|(0.0437)|(0.0598)|
|Father in farming|0.142***|0.0260|-0.00304|0.0536*|0.0163|-0.0656***|
||(0.0546)|(0.0218)|(0.0301)|(0.0275)|(0.0195)|(0.0207)|
|Mother in farm.|-0.0147|-0.0231|-0.0374|-0.0203|0.117***|-0.0120|
||(0.0581)|(0.0236)|(0.0333)|(0.0308)|(0.0247)|(0.0237)|
|Father in non-farm|0.331***|0.0583**|0.0456|0.0782**|-0.0111|0.0514**|
||(0.0586)|(0.0238)|(0.0347)|(0.0315)|(0.0168)|(0.0246)|
|Mother in non-farm|-0.0865|0.109***|0.165***|0.0982***|-0.0182|0.0960***|
||(0.0599)|(0.0249)|(0.0399)|(0.0313)|(0.0154)|(0.0255)|
|Father‘s schooling|0.173**|-0.0182|0.0217|-0.0476|0.0179|-0.00635|
||(0.0683)|(0.0260)|(0.0391)|(0.0364)|(0.0158)|(0.0299)|
|Mother‘s schooling|0.275***|-0.0138|-0.00311|-0.0103|0.0151|0.0447|
||(0.0858)|(0.0380)|(0.0603)|(0.0485)|(0.0253)|(0.0366)|
|Constant|12.07***|0.0324|0.350|0.000805|0.541***|0.598***|
||(0.361)|(0.108)|(0.224)|(0.135)|(0.117)|(0.180)|
|Observations<br>|3,499|3,524|1,519|2,005|3,524|3,524|
|R<sup>2</sup>|0.494|0.234|0.278|0.270|0.272|0.214|

Linear IV coefficients for (2)-(6). Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. The regressions exclude own education, whether fostered and age at first marriage; other controls are as in Table 5.

37
**Table 1: Accessibility rates by pre‐war region.**

||**Percentage of**<br>|**population in**<br>|
|---|---|---|
|**Pre‐war region**|**accessibl**|**e areas**|
||**Urban areas**|**Rural areas**|
|Awdal|100%|94%|
|Bakool|35%|21%|
|Banadir|87%|96%|
|Bari|99%|92%|
|Bay|86%|46%|
|Galgaduud|88%|50%|
|Gedo|100%|43%|
|Hiraan|44%|28%|
|Lower Juba|92%|9%|
|Lower Shabelle|28%|33%|
|Middle Juba|0%|0%|
|Middle Shabelle|98%|77%|
|Mudug|100%|76%|
|Nugaal|100%|100%|
|Sanaag|100%|100%|
|Sool|89%|98%|
|Togdheer|100%|98%|
|Woqooyi Galbeed|100%|96%|
|Overall|89%|48%|

Source: Authors’ calculations Low accessibility in south and central Somalia motivated the imputation of poverty in inaccessible areas using geo‐spatial information (section 5). The accessibility map was incorporated into the sampling frame to draw EAs only from accessible areas. The resulting sample was thus representative of the entire Somali population within secure areas.

# 2.2. Sampling frame and sample selection

The sampling frame for wave 2 of the SHFS is the exhaustive list of sampling units for every stage in the multi‐stage selection process (denominated according to the stage of selection, i.e. primary sampling units (PSUs) in the first stage, secondary sampling units (SSUs) in the second stage, and so on) employed in the survey’s sampling strategy. Sampling units are listed separately by stratum. Each sampling unit must have information concerning the population residing in it to allow for selection proportional to size (United Nations Statistical Division, 2005). In the absence of a recent population census, no readily useable enumeration areas and population estimates existed. To overcome these challenges the SHFS drew from a variety of data sources and GIS techniques to create a population sampling frame, strata boundaries, and a comprehensive list of enumeration areas.

## _Strata boundaries_

In line with stratification at the intersection of administrative regions and population type, the following GIS data sets were combined to spatially demarcate strata boundaries:

- (i) Pre‐war region boundaries;

- (ii) IDP settlement boundaries;

7
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comparison of informal firms to India. We compare the reported constraints on firm growth for rural and informal sector firms with those of formal sector firms, which were used in the previous analysis in the paper. The informal sector survey of India includes only manufacturing firms.

For the formal/rural comparison, we pool the rural sector firms for the three countries. For both the formal/rural and formal/informal comparisons, the comparison group are the firms in the Enterprise Surveys for the relevant set of countries, which we call “formal”. Only manufacturing firms in the Enterprise Surveys are included for the comparison with informal firms.

We do two exercises for the formal/rural and formal/informal comparisons. The first is to compare the levels of the reported constraints in the two groups of firms (Tables 16 and 18. The second is to examine whether there are any differences between formal and rural or informal sector firms in how constraints are ranked (Tables 17 and 19).

In the left hand panel of Table 16 (specification 1), we show the unconditional mean evaluations of each obstacle for formal sector firms and informal or rural sector ones. To facilitate comparison between the formal and informal/rural sectors, we show the results for small formal sector firms (dropping from the samples those firms with more than 20 employees). The column headed “Diff?” shows whether there is a statistically significant difference between the mean for the small formal and the informal/rural sector firms.

The right hand panel of Table 16 (specification 2) includes a size control for firm characteristics interacted with an informal or rural dummy (plus country-survey fixed effects). This allows the two groups of firms being compared (formal vs. informal and formal vs. rural) to have their own coefficients on the size control. We can interpret the constant or the constant plus the dummy as means conditioned on size.

The benchmark firm is the same as in the rest of the analysis except that it is centered at average employment of 5 persons, which is more appropriate given the size distribution of informal and rural firms. In the comparison of small formal vs. rural 35
εi = Random error term

# **3.2 Estimation**

We estimate (1) by probit, using the 13,997 observations with non-missing values for all model variables. Table 3 reports their joint distribution by quintile for household asset ownership and mother’s years of education. The sample populates all cells of the table, although observations are relatively sparse for extreme pairs (e.g. [asset quintile 5, mother’s education none]; [asset quintile 1, mother’s education post-secondary]).

**Table 3:  Intake of** animal-source foods **regression sample, DHS 2000-2014**

|**Household**<br>**Asset**<br>**Quintile**|**0**|**M**<br>**Primary**<br>**(1-6)**|**other’s Y**<br>**Middle**<br>**(7-9)**|**ears of Educ**<br>**Secondary**<br>**(10-12)**|**ation**<br>**Post-**<br>**Secondary**|**Total**|
|---|---|---|---|---|---|---|
|**1**|**1,351**|**749**|**135**|**28**|**7**|**2,270**|
|**2**|**1,736**|**2,623**|**1,260**|**290**|**28**|**5,937**|
|**3**|**404**|**1,106**|**1,092**|**444**|**67**|**3,113**|
|**4**|**128**|**456**|**706**|**496**|**164**|**1,950**|
|**5**|**38**|**85**|**222**|**268**|**114**|**727**|
|**Total**|**3,657**|**5,019**|**3,415**|**1,526**|**380**|**13,997**|

We report our results in Table 4, excluding estimates for the 191 grid cell dummy variables. To prevent total collinearity, our specification drops dummy variables for mother’s education [0 years], household asset quintile 1, survey year 2000 and survey month January. For each category (education, etc.), estimates should therefore be interpreted as increments from partial effects for the excluded variable levels.

We find high significance for the variables in each category. The effect of mother’s education increases steadily by schooling level, while the impact of asset ownership increases rapidly for quintiles 2 and 3 and more slowly for quintiles 4 and 5. We approximate the age effect with a log-cubic 8
# **Gender and Public Goods Provision in Tamil Nadu’s Village Governments**

**Kiran Gajwani** and **Xiaobo Zhang**<sup>*****</sup>

Keywords: Local governance, public goods provision, gender, India JEL codes: H4, O1 Sector Boards: GEN, PSM

*Kiran Gajwani (corresponding author) is a Lecturer/Advisor in the Economics Department, Harvard University, Cambridge, MA.  E-mail: kgajwani@fas.harvard.edu.  Xiaobo Zhang is a Professor at the National School of Development, Peking University, China and a Senior Research Fellow at the International Food Policy Research Institute, Washington DC.  E-mail: x.zhang@cgiar.org.  We thank C. Ramasamy, S. D. Sivakumar, Uma Krishnasamy, and the entire Tamil Nadu Agricultural University research team for their hard work in organizing and carrying out the data collection. We are very grateful to Ravi Kanbur for his comments and assistance throughout the preparation of this paper. Our sincere thanks also go to Nancy Chau, Stefan Klonner, Jordan Matsudaira, Ganga Shreedhar, David Newhouse, Sudha Narayanan, seminar participants at IFPRI, Cornell University, and the IFPRIKiel Workshop on Government Decentralization Research, and Stephen Chong, Kerry Papps, Paul GoldsmithPinkham, and Will Dobbie for their technical assistance with Stata.  Any errors are our own. This work was supported by the International Food Policy Research Institute.
# **Figure 3. Labor market outcomes over the 2000s in Moldova**

Labor force participation rate (%) Unemployment rate (%) <!-- Start of picture text --> 70 12<br>65 10<br>60 8<br>55 6<br>50 4<br>45 2<br>40 0<br>total male female total male female<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: WDI and ILO, KILM data.

**Figure 4. Employment by sector, thousands**

<!-- Start of picture text --> 1600<br>1400<br>297.20 303.70<br>1200 303.60<br>301.10 302.60<br>325.80 323.40 316.40<br>1000 315.80 315.50 316.60 314.30<br>800<br>600<br>400<br>200<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Agriculture Manufacturing<br>Construction Trade, hotels/restaurants, transport<br>Finance, real estate/business Others<br><!-- End of picture text --> Source: ILO, KILM data.

5