rafmacalaba/datause-encoder-data huggingface dataset:
the specification in Equation 1 using the matched sample, the coefficient βB recovers the causal impact of the program while βL recovers the impact of the secondary market on non-beneficiaries in lottery villages.

Column 8 of Table 7 reports for completeness the results using the index of agricultural outcomes using the sample of 427 households with data in both the baseline and endline samples. The point estimates are comparable to those in column 7 using the full sample and somewhat less precisely estimated due to the lower power. Panel A of Table OA7 in the Online Appendix reports the individual items of the index for the sample of 427 households with baseline and endline data. Overall, the results in Panel A of Table OA7 in the Online Appendix are comparable to those in columns 1 to 6 of Table 7.

Column 9 of Table 7 reports the impact of NAIVS on agricultural outcomes using the matched sample. Compared to the results on column 7, we see that the point estimates are smaller in magnitude and not statistically significant. This suggests that the impacts detected in column 7 are mostly driven by the exante differences between beneficiaries and non-beneficiaries in non-lottery villages, rather than the NAIVS program.

Panel B of Table OA7 in the Online Appendix report the individual items of the agricultural index for the matched sample. Column 4 suggests that beneficiaries only increase total fertilizer use by 7.6 Kg from a base of 18.3 Kg among non-beneficiaries in non-lottery villages. This increase is small and not statistically significant. Since roughly the same proportion of beneficiaries in the matched sample report receiving the vouchers as in the full sample (column 3 of Table 4) we conclude that the increase in fertilizer use due to the NAIVS is small. Using maize yields and input use we estimate a nutrient use efficiency of 8.2 Kg of maize per Kg of nitrogen from a base production of 383 Kg of maize.<sup>11</sup> This estimate is consistent with the response rates from other evaluations of ISPs in Sub-Saharan African countries in the range of 8–24 kg of maize per kg of nitrogen applied reviewed in Goyal and Nash (2017). In contrast, evidence from “researcher-managed” farm trials in East and Southern Africa produced estimates ranging from 18 to 40 kg of maize per kg of nitrogen (Tscharntke et al. 2012; Vanlauwe et al. 2011). Given the prevailing fertilizer and farmgate maize prices in our study area, while the estimates in the range of 18–40 kg of maize per kg of nitrogen would almost always result in highly

> 11 We find the same nutrient use efficiency of 8.2 Kg of maize per Kg of nitrogen when using the matched sample.

25
24 Figure 3: Changes in nominal assistance coefficients for rice and wheat,<sup>a</sup> high-income and developing countries, and world exporters and world importers, 1972-74, 1984-86, and 2005-08 (percentage changes)

- (a) High-income and developing countries

<!-- Start of picture text --> 80<br>Rice Developing countries<br>60 High‐income countries<br>40<br>20<br>0<br>‐20<br>‐40<br>‐60<br>1972‐74 1984‐86 2005‐08<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>Wheat Developing countries<br>60 High‐income countries<br>40<br>20<br>0<br>‐20<br>‐40<br>‐60<br>1972‐74 1984‐86 2005‐08<br><!-- End of picture text -->

- (b) World exporters and world importers

<!-- Start of picture text --> 50<br>50 Wheat<br>Rice Importers Importers<br>30 Exporters<br>30 Exporters<br>10<br>10<br>‐10 ‐10<br>‐30 ‐30<br>‐50 ‐50<br>1972‐74 1984‐86 2005‐08 1972‐74 1984‐86 2005‐08<br><!-- End of picture text --> Source: Authors’ compilation from Table 5.
<!-- Start of picture text --> Per Capita Expenditure Food share in Total Expenditure<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>.5<br>.5<br>0<br>0<br>-.5<br>-.5<br><!-- End of picture text --> 85
of services.<sup>15</sup> Through this regression, it is possible to obtain ad valorem equivalents of the regulatory barriers in the countries of their sample.

Our methodology defines a mapping from the World Bank database to the scoring matrices of the Australian authors. In this manner, we score the regulatory regimes of the 11 sectors in the 103 countries according to the Australian authors’ criteria. We adopt the World Bank terminology and call these scores Services Trade Restrictiveness Indices (STRIs).

Our methodology assumes that the international regressions estimated by the Australian authors to get AVEs applies to our 103 countries, and this allows us to convert our STRIs into AVEs. The estimates and documentation are available in Jafari (2014c).

Since our model contains the eight business services sectors of the GTAP database, not all 11 of the World Bank data set, we had to aggregate fixed line and mobile telecommunications into communications; road and rail transport into transport not otherwise classified; and legal and accounting and auditing services into business services, not otherwise classified. In addition, since our model contains ten regions, we had to aggregate regions according to our mapping in appendix A. The results for our six African regions are in tables 4a to 4f. Jafari (2014d) explains the methodology and data used in these aggregations to arrive at the ad valorem equivalents of the sectors and regions of our model.

**Kenya and Tanzania.** We refer the reader to Balistreri, Jensen and Tarr  (2011) for a summary of the key institutional and policy issues in telecommunications, banking, insurance and transportation in Kenya; and to Jensen, Rutherford and Tarr (2010) for Tanzania.  We note, however, that there have been substantial changes in the services regulatory regimes in both countries in the past few years. Probably the most significant development has been the failure in both countries of the private company commissioned to regenerate freight train traffic. Cargo traffic on the roads is very overloaded, as the volume of freight traffic vastly exceeds the capacity of the facilities.

> 15Warren estimated quantity impacts and then using elasticity estimates was able to obtain price impacts.

12
Table 3: Impacts on Firm Survival

||(1)<br>Business was operating<br>at endline<br>(Yes 1; No 0)|(2)<br>No longer operates<br>a business<br>(Yes 1; No 0)|(3)<br>Started<br>a new business<br>(Yes 1; No 0)|(4)<br>Opened business<br>in a new sector<br>(Yes 1; No 0)|(5)<br>Opened business<br>in the same sector<br>(Yes 1; No 0)|
|---|---|---|---|---|---|
|Treatment (0/1)|0.165**|-0.0893|-0.121|-0.129**|0.0139|
||(0.081)|(0.065)|(0.073)|(0.061)|(0.031)|
|Psychometric score above median (0/1)|0.165**|-0.185***|0.0449|0.0373|-0.0201|
||(0.080)|(0.065)|(0.067)|(0.056)|(0.029)|
|**ANCOVA**|✗|✗|✗|✗|✗|
|Observations|124|124|124|124|124|
|Control mean|0.667|0.197|0.197|0.152|0.0152|
|_R_<sup>2</sup>|0.066|0.082|0.045|0.049|0.011|
|Randomization inference p-value|0.072|0.21|0.096|0.022|0.71|
|Sharpened q-value|0.15|0.19|0.15|0.12|0.35|

Notes: Outcome 1 takes the value of 1 if the WEDP business was operating at the time of the endline survey (September 2021). Outcome 2 takes the value of 1 if respondent stated that they closed their original business, and their main economic activity is not another business they own. Outcome 3 takes the value of 1 if respondent reported starting a new business regardless of their WEDP business’s operational status. Outcome 4 takes the value of 1 if respondent stated their new business is in a different sector from their original WEDP business. Outcome 5 takes the value of 1 if respondent stated their new business is in the same sector as their original WEDP business. Psychometric scores in the IE sample ranged from 370 to 452, and the median score used a cut-off is 383 (note that 370 was the qualifying score to receive an uncollateralized loan). Includes a control for number of children in the households that was imbalanced between treatment and control at baseline. Observations represent number of individuals. Robust standard errors are in parentheses. * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
percent of the total sample size met the literacy requirements (Table 2). Still, maternal education has the largest impact with a 12 percentage point increase in the likelihood of a child being literate if he or she has a mother who completed primary school or above, compared to a mother with no education. Having improved sanitation and treating water at home increase the probability of child literacy by 5 and 7 percentage points, respectively.

Arsenic contamination has a significant negative influence on a child’s performance in the learning domain (Table 5). In the first two specifications, a shift from no contamination to some contamination leads to a 3 percentage point decrease in the likelihood of a child meeting the learning criteria. Factors that positively influence child learning include breastfeeding, age, maternal education, and household size. Improved sanitation has a small and significant impact with a 1 percent association. Breastfeeding has a substantial effect with a 5 percentage point increase in the likelihood of being on track in the learning domain.

Table 6 reports a negative association between physical development and arsenic contamination. The magnitude of the correlation increases from 2 percent in specification (1) to 3 percent in specification (2), indicating a greater negative impact on physical development at higher levels of contamination. Improved water has an unexpected negative association with physical development. This might be attributed to poor water quality despite improved coverage.

Finally, social development is also found to be negatively affected by arsenic contamination (Table 7). In specification (1), a shift from no arsenic to some contamination (WHO standard) leads to a 6 percentage point decrease in the likelihood of a child being on track for social development. The magnitude is greater (8 percentage points), when a Bangladesh standard is used in specification (2). This change is further illustrated in specification (3), where the association triples from a 3 percentage point decrease with medium exposure to a 9 percentage point decrease at high exposure, with no arsenic as the reference category. Other factors that play a role in social development include maternal education, child’s age and sex. A female child is more likely to be socially developed compared to a male child. In addition, a mother’s completion of primary education can lead to 3 percentage point increase in the probability of a child’s social development, with no education as the base category.

# 5. Discussion

The results of this paper complement other findings from epidemiological studies on arsenic and cognitive development. This paper adds some noteworthy advancement to the literature base. One is that arsenic developmentally hinders children as young as age 3, highlighting the importance of 12
- 11 -

by the district's size. The price variables are the aggregate price index based on the internat<sup>4</sup> onal commodity prices, an all India price index of fertilizer, the district-level urban wage income, and the PACS rate of interest. The agroclimate variables include annual rainfall and permanent characteristics such as soil moisture capacity, length of rainy season, exceesive rainy months, irrigation potential, number of cold months and flood potentials. For a detailed discussion of these variables see Binswanger, Khandker and Rosenzweig, (1988).

The data for the CBs and the CCBs are published by the Reserve Bank of India in Banking Statistics. The National Bank for Agriculture and Rural Development (NABARD) of India has kindly provided unpublished data on the PACS and the LDBs which were collected by sending questionnaires to the State headquarters of these institutions.4 Note that the CCBs primarily advance credit to agriculture via lending to the PACS and the LDBs. Thus, rural credit is defined in this paper as the amount of institutiorial credit advanced to the rural sector by the CBs and the credit advanced to agriculture by the PACS and the LDBs. Rural credit thus reflects both the subsidized agricultural credit advanced by the PACS, LDBs and CBs and nonsubsidized nonagricultural credit advanced by the CBS. The variable such as subsidized agricultural credit cannot be constructed because districtlevel data on agricultural credit advanced by the CBs are not available. Since moiey is fungible, it is the effect of rural credit that is perhaps important to look at. However, I report the effects of credit advanced by the cooperative sector (i.e., PACS and LMBs) to compare with those of rural credit advanced by the banking system (i.e., PACS, LDBs and CBs). The mean and standard deviation of variables involved in this paper are presented in table 1.
The cost shares of Mode 1 services and skilled labor are the highest in the Indian services sector. While the unskilled labor share is larger than the skilled labor cost share, unskilled labor’s cost share is greatest in the agriculture sector.

The services sector is also responsible for 46 percent of India’s total output, and is thus the largest industry in the Indian economy. 2.6 percent of this output is exported as Mode 1 services, and is worth US$ 10.12 billion in the benchmark database. In contrast, export values of the agriculture and manufacturing sectors are three to seven times greater than Mode 1 exports, despite those industries’ shares of total output being less than that of the services sector.

# **5. Analysis**

The analysis is based on a simulation of the historical changes in India’s services trade and how they may have affected the factor incomes in India between 2001 and 2006. The initial database used in this analysis has 2001 as a base year. So, by introducing specific technological improvements into the global trade of Mode 1 services, the Indian services trade will then be simulated to reflect the trade patterns of 2006. Specifically, Indian services exports will be increased by 400 percent, while Indian Mode 1 imports will be increased by 280 percent. These are the approximate changes in Mode 1 exports and imports over the period 2001‐2006.

These sharp increases in Mode 1 service trades may be made possible through technological improvements that would allow more services to be internationally traded at lower costs. These technological improvements can be conceptualized as being any technological advancement that makes it easier to deliver these services<sup>12</sup> . For example, they may take the form of improvements in satellite based communication or greater internet bandwidth, which would allow electronic data to be transferred from India to other countries at faster speeds, lower costs, and greater reliability.

> 12 While this paper uses technological improvement as an abstract agent representing the multitude of forces that would have affected the services trade. As NASSCOM‐McKinsey (2005) suggest there are several other forces whose synergy may have contributed to the rising trade, including technological improvement, demand increases from outsourcing countries, and large pool of skilled Indian labor that could be easily mobilized to the services industry. Policy reforms such as the liberalization of an overvalued exchange rate would have also removed constraints to the imports of inputs necessary for export growth.

13
||No employment|Full time fixed<br>term employment|Part time open<br>term employment|Part time fixed<br>term employment|Self Employment|
|---|---|---|---|---|---|
|Full time fixed term<br>employment|14.8|354.1|2.8|35.9|14.1|
|Part time open term<br>employment|15.7|5.4|2822.6|78.9|17.0|
|Part time fixed term<br>employment|23.7|121.7|308.7|4748.3|9.6|
|Self Employment|49.3|61.0|29.0|90.4|7610.2|
|||**2005-2 **|**006**|||
||No employment|Full time fixed<br>term employment|Part time open<br>term employment|Part time fixed<br>term employment|Self Employment|
|Full time fixed term<br>employment|11.3|155.2|4.7|29.3|8.0|
|Part time open term<br>employment|12.9|4.7|1689.0|48.8|12.5|
|Part time fixed term<br>employment|21.8|89.0|334.1|4399.0|8.9|
|Self Employment|50.4|35.3|18.9|42.4|5101.3|
|||**2006-2 **|**007**|||
||No employment|Full time fixed<br>term employment|Part time open<br>term employment|Part time fixed<br>term employment|Self Employment|
|Full time fixed term<br>employment|15.3|272.7|7.0|70.4|11.6|
|Part time open term<br>employment|13.1|16.1|1694.2|148.5|11.7|
|Part time fixed term<br>employment|58.3|177.0|159.0|9063.2|20.4|
|Self Employment|75.7|95.7|14.3|74.3|9351.2|
|||**2007-2**|**008**|||
||No employment|Full time fixed<br>term employment|Part time open<br>term employment|Part time fixed<br>term employment|Self Employment|
|Full time fixed term<br>employment|10.3|158.7|4.5|49.5|7.2|
|Part time open term<br>employment|9.4|10.6|1000.9|99.9|10.3|
|Part time fixed term<br>employment|39.6|168.6|242.2|10896.8|35.7|
|Self Employment|80.5|90.9|23.9|206.3|9095.8|
|Notes: Baseline resul<br>employment,All coef|ting category – ful<br>ficients in RRR form|l time open term em<br> ,All coefficient statis|ployment, baseline s<br>ticallysignificant.|tarting category – f|ull time open term|
|Source: Author‘s calc|ulations based on LF|S data sets from year|s 2003-2008.|||

The results presented in Table 5 are not surprising. At first the relative risk of transition to the same state as in previous year is always much (even thousands in some cases) higher than to full time/open term employment. Such high coefficients result from very low probability of transition from full time/open term contract to any form of contract and also from low probabilities of transitions from any other state to full time/open term contract.

In addition, the relative risk of transition to non-employment increases for any type contract other than a full time/open term one. What is most interesting is that this relative risk is the highest for the self-employed. Partially it is a result of very low probability of transition from self-employment to full time/open term employment, but on the other hand it also means that if one treats the open term/full time employment as the ―ideal‖ state, engagement in selfemployment is in relative terms the ―worse‖ possible option with the highest risk to end up without any employment next year instead of reaching the goal. So it proves the earlier observations drawn from the analysis of actual labor market transitions (Table 4).
**Figure 6: China maintained its edge in Textiles and rapidly closed the gap with South Korea in Wearing Apparel**

<!-- Start of picture text --> RCA, 321 Textiles RCA, 322 Wearing apparel, except footwear<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>BRA CHN BRA CHN<br>IND KOR IND KOR<br>Source: UN Comtrade - concordances between SITC and ISIC Codes Source: UN Comtrade - concordances between SITC and ISIC Codes<br>RCA, 383 Machinery, electric RCA, 353 Industrial chemicals<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>BRA CHN BRA CHN<br>IND KOR IND KOR<br>Source: UN Comtrade - concordances between SITC and ISIC Codes Source: UN Comtrade - concordances between SITC and ISIC Codes<br>RCA, 371 Iron and steel RCA, 382 Machinery, except electrical<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>BRA CHN BRA CHN<br>IND KOR IND KOR<br>Source: UN Comtrade - concordances between SITC and ISIC Codes Source: UN Comtrade - concordances between SITC and ISIC Codes<br>RCA, 384 Transport equipment RCA, 311 Food products<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>BRA CHN<br>BRA CHN<br>IND KOR<br>IND KOR<br>Source: UN Comtrade - concordances between SITC and ISIC Codes<br>Source: UN Comtrade - concordances between SITC and ISIC Codes<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>2.5<br>2.5<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>5 1.5<br>4<br>1<br>3<br>2<br>.5<br>1<br>0 0<br>1.5 5<br>4<br>1<br>3<br>2<br>.5<br>1<br>0 0<br><!-- End of picture text --> 18
P a g e <u>|</u> **42**

The housing market in Ahmedabad ( _<u>Figures 10 and 11</u>_ <u>), as might be expected,</u> shows a very different pattern.

- 1) Prices are much lower overall. The lowest priced unit is 30 times less expensive than its equivalent in Mumbai.  A number of informal solutions are available around INR100,000, and all informal housing prices are below INR 400,000, putting the highest priced unit of informal housing at 8 times less than the equivalent in Mumbai.

- 2) There is practically no overlap in price of informal and formal housing. Only subsidized EWS housing and units provided by the Housing Board overlap with both the prices and floor area of the informal sector.  This lack of overlap is due to the relatively lower premium paid for well located slums and the lower costs of land overall.

- 3) Like Mumbai, in the formal sector the price per square foot is tightly correlated with distance from the city center (crossing of Ashram Rd and Gandhi Bridge)  while this is not the case for slums. Ahmedabad’s slums are relatively small pockets distributed close to the city center. Analysis of transport patterns as in the transport survey should be done to confirm the preference for short commutes in Ahmedabad’s slums.

- 4) Unlike Mumbai, Ahmedabad is far less spread out. The furthest distance from the city center for formal housing in Mumbai is roughly six times higher than in Ahmedabad, for slums, it is three times higher.

- 5) There is an overlap between the floor size at the low end of the formal market and the high end of the informal market, which is not the case in Mumbai. Again, this indicates less pressure to economize on expensive land to live in a good location.

- 6) _<u>Figure 11</u>_ suggests that in Ahmedabad  an adequate formal alternative for slum housing would be priced between INR 1500 and 2000 per square foot and would be located not farther than 6 km from the city center. Such a formal dwelling could be attainable based on the implicit price of land in slum areas today. However, the standards for a formal home should not increase the unit size or consumption of land unnecessarily.

42