rafmacalaba/datause-encoder-data huggingface dataset:
all of rural India. In general, Ahmednagar and Nanded look similar to one another, while Nandurbar appears poorer and has a larger Scheduled Tribe population. Ahmednagar has better sanitation coverage than Nanded and Nandurbar, but these figures are difficult to interpret because they are from after the program studied was implemented. These districts were chosen because Ahmednagar and Nanded district officials requested early implementation of the TSC at a state level workshop in 2002; a state official selected Nandurbar so that a particularly poor district would be included.

# **2.3 What did happen: An experiment in one district**

Although the government of Maharashtra originally planned to implement an experiment in three districts, in fact, it ultimately only implemented the experiment in Ahmednagar. In this district, the program was indeed implemented in 30 villages randomly selected out of 60 eligible for the treatment or control groups. As table 1 shows, Ahmednagar has better average sanitation coverage than the other two possible districts, and is otherwise similar to Nanded and less poor than Nandurbar.

Due to some confusion (later resolved) about whether the experiment would be implemented in all three planned districts, the World Bank had already contracted with a survey organization to collect data in all three districts. Therefore, the data collection continued in all three districts, as originally planned.

– – This change of government plans and seemingly unnecessary data collection presents an unusual econometric opportunity in the analysis of a field experiment. One important conclusion is already clear, even before any statistical analysis: there are important limits to the ability of the Indian state to translate decisions from high-ranking officials into activities, programs, and services in villages.<sup>9</sup>

> 9For this reason, Pritchett (2009) has described India as a “flailing state”: the head cannot send signals to the hands and feet. While important for sanitation policy, which has yet to reach many of the rural areas where open defecation remains nearly universal, constraints on effective implementation exist for all health related policies. For example, problems of absenteeism and low-quality of publicly provided health services are widespread, as well.

9
and Bangladesh. The figure shows that in these other countries, registration also typically occurs over the size range considered in our study – not with the smallest microenterprises.

The Bangladesh survey asked firm owners what they saw as the main advantages of being formal. Owners give responses which are similar to those in the Sri Lanka survey: links to bank financing, better reputation for the business, a lower chance of being fined, and the ability to operate visibly at a large scale without fear of being caught. Smaller and more informal firms in Bangladesh are more likely to say they see no potential benefits and all firms say the main disadvantages were paying taxes and having to deal with the cost and process of registering (McKenzie, 2010). These same channels also appear in discussions of the costs and benefits of formalizing in different Latin American countries (Perry et al, 2007; World Bank, 2009).

As a result, it seems reasonable to believe that our results are informative outside Sri Lanka about the number of firms at the margin that will be induced to formalize by relatively small changes in the costs and benefits, and also about the characteristics of those firms. Our results suggest that taking the costs of registering from the levels in Sri Lanka to zero induces few firms to formalize. However, increasing the benefits further (in our case by paying firms) induces more firms to formalize. This is consistent with recent cross-country panel data, in which Klapper and Love (2010) find that only changes in business environment reforms which involve more than a 40 percent reduction in costs are associated with changes in firm entry.<sup>11</sup>

# **7. Conclusions**

Prior to the intervention, owners of unregistered firms were either ignorant of, or vastly overestimated the costs of registration. We might therefore have expected that simply informing firms about the costs of registration would be sufficient to induce registration. But we find that information and household-based nationally representative labor survey. The Bangladesh survey data come from a census of 55,817 firms in randomly selected sampling areas from 19 districts conducted by the World Bank in 2009-2010.

> 11 Our results are also consistent with evidence from an unpublished parallel experiment in Lima, Peru which randomly encouraged firms to obtain a municipal license (Jaramillo, 2009; Alcázar et al, 2010). About one-quarter of firms offered information and reimbursement of direct costs obtained the municipal license. One-third of those who didn’t register reported problems with other regulations such as zoning, consistent with our finding that land issues prevent many firms from registering. A follow-up survey 18 months after baseline failed to find any significant impact of registration obtaining the municipal license on firm size, access to credit, or profitability, but their power to detect an effect is low. Moreover, since banks and the government typically require a tax license rather than municipal license as proof of formality in many countries, there may be less ex ante reason to expect a positive impact on firms of municipal registration.

21
Migrant households with a short-term contract are affected by higher school fees (row 4), while those with a permanent or long-term contract are not (not shown).

Next, we divide the sample by two subjective indicators: whether the household head is planning to stay in the city for a long time, and whether the household head is depressed based on Center for Epidemiological Studies Depression Scale (CES-D10) questions.<sup>42</sup> Estimation results, however, are similar for these different groups, even though the depressed migrant workers appear to be more impacted by changes in school fees (with the estimated coefficients being larger than those in Table 3, column 3) (row 6).

We then compare the impacts for migrant households with more than one child and migrant households with only one child. The estimation results show that households with only one child (row 7) are more likely to be affected by school fees. Migrant households with both spouses in the city (row 8) or with only one spouse (not shown) are both sensitive to school fee changes. The same result holds for both employees (not shown) and the self-employed (row 9).

Overall, the estimation results in Table 6 suggest that the vulnerable groups (including the poor, the uninsured, those without permanent contracts, and to some extent, the depressed) are more sensitive to changes in school fees. These results are consistent with our theoretical results. _School fees and child migration during the economic crisis_ Our theoretical results suggest that child migration would increase in response to the reduced school fees in 2009 (Propositions 1 and 3), but would decrease during the economic crisis in this same year (Corollary 3.3). Which effect would dominate child migration? Estimation results using the 2009 wave of the RUMiC survey show that the non-IV estimates (Table 7, row 1) are negative, and are between 60 percent and twice larger in absolute magnitude than those for 2008 (bottom of Table 2). Since the upward biased non-IV estimates

> 42 We recode the answers to the questions about depression such that higher scores imply a more intense state of depression. We define a person as depressed if the summation of his/her scores is greater than 22.

31
- , “Rural reforms and agricultural growth in China,” _American Economic Review_ , 1992, _82_ (1), 34–51.

- **Lu, Feng** , “Three grain surpluses: Evolution of China’s grain price and marketing policies,” 1999.

- **Naschold, Felix** , “The poor stay poor: Household asset poverty traps in rural semi-arid India,” _World Development_ , 2012, _40_ (10), 2033–2043.

- , “Welfare dynamics in Pakistan and Ethiopia Does the estimation method matter?,” _The Journal of Development Studies_ , 2013, _49_ (7), 936–954.

- **Quisumbing, Agnes R. and Bob Baulch** , “Assets and poverty traps in rural Bangladesh,” _The Journal of Development Studies_ , 2013, _49_ (7), 898–916.

- **Rozelle, Scott, Albert Park, Jikun Huang, and Hehui Jin** , “Bureaucrat to entrepreneur: The changing role of the state in China’s grain economy,” _Economic Development and Cultural Change_ , 2000, _48_ (2), 227–252.

- **Sadoulet, Elizabeth, Alain de Janvry, and Benjamin Davis** , “Cash transfer programs with income multipliers: PROCAMPO in Mexico,” _World Development_ , 2001, _29_ (6), 1043–1046.

- **Shao, Yun, Xiangtao Fan, Hao Liu, Jianhua Xiao, S. Ross, B. Brisco, R. Brown, and G. Staples** , “Rice monitoring and production estimation using multitemporal RADARSAT,” _Remote Sensing of Environment_ , 2001, _76_ , 310–25.

- **Sicular, Terry** , “Redefining state, plan and market: China’s reforms in agricultural commerce,” _The China Quarterly_ , 1995, _144_ , 1020–1046.

- **Tang, Shengxiang, Li Ding, and Alain Bonjean** , “Rice production and genetic improvmeent in China,” in Zhonghu He and Alain Bonjean, eds., _Cereals in China_ , D.F., Mexico: CIMMYT, 2010, pp. 15–34.

32
- 4 -

In the African context, poor road and logistics conditions are a common bottleneck to increase intra-regional trade (Buys _et al_ ., 2006, Broadman, 2007).<sup>4</sup> In addition, inadequate market information flows and high illiteracy among market operators are hampering livestock trade integration (Williams _et al_ ., 2006). Table 2 summarizes the key results related to a trade-infrastructure linkage in these recent studies.<sup>56</sup>

||Table 2<br>|. Recent Selected Studie<br>|s on Infrast<br>|ructure and Trade in Africa<br>|
|---|---|---|---|---|
|Source|Methodology|Area|Industry|Main result relevant to infrastructure and trade 1/|
|Buys_et al_.<br>(2006)|Gravity model|Sub-Saharan Africa|All|The elasticity of bilateral trade with respect to the<br>paved road share is estimated at 1.52.|
|Williams_et al_.<br>(2006)|Marketing cost<br>analysis|West Africa|Cattle|The share of transportation and handling costs in final<br>market prices is about 0.01, meaning that a transport<br>cost saving could lower the market prices and improve<br>competitiveness slightly.|
|Broadman<br>(2007)|Comparison of<br>freight transport<br>rates|Ethiopia, Ghana, Kenya,<br>Malawi, South Africa,<br>Tanzania, Uganda, and<br>selected Asian countries|All|The elasticity of total trade with respect to freight costs<br>is estimated at 1.26.|

1/ Author’s estimation based on the original studies.

Figure 1. Trade Value and Freight Transport Rate in Selected African Countries <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>y = -0.0915x + 9.673<br>4 R 2 = 0.2275<br>2<br>0<br>0 5 10 15 20 25 30 35<br>Freight transport rate (percent)<br>ln(Marchandise exports and imports (US$ million))<br><!-- End of picture text --> Source: Author’s recalculation from IMF _Balance of Payment Statistics_ .

4 It is indicated that despite geographical proximity, freight costs from South Africa to Angola are as expensive as those from China to Angola.

> 5 In Buys _et al_ . (2006), the road quality is measured by the weighted average of the share of paved roads, per capita GDP and governance.

6 The Broadman’s result is recalculated from a simple regression of the sum of merchandise exports and imports on the freight transport rate for the same sample countries. Data are extended to 2005, and the year-country observations are pooled in the regression. The coefficient is estimated at -0.091 with its standard error of 0.025 (Figure 1).
But there is still too little research examining the particular mechanisms linking finance and inequality. Part of this is a data problem. Although Beck, Demirguc-Kunt and Martinez Peria, (2007a, b) and World Bank (2007) have recently started to construct systematic crosscountry data on the use of financial services, the general lack of data on access to financial services has limited the degree to which researchers can assess the channels through which finance affects inequality. Besides the data problems, more research is needed on finance and inequality. For example, more randomized experiments are likely to shed further light on the direct impact of access to finance on households and microenterprises. But these types of studies cannot capture important economy-wide, indirect effects that seem to be of first-order importance. Rigorous evaluations of natural policy experiments are particularly useful, but there aren’t enough of them and researchers cannot simply implement more. General equilibrium models help in illustrating potential patterns and examining the impact of different policy changes, but difficulties of capturing all the complexities of real economies in a tractable model reflect the inherent boundaries of this methodology. Using cross country data, both at the macro and micro level, helps in assessing the combined direct as well as indirect effects of financial development, but these studies are subject to considerable identification problems. Since different methodologies all suffer from different problems, a complete picture of the financeinequality relationship can only be painted with a broad brush of complementary methodologies.

We conclude with two observations about financial sector policies. First, there is also startlingly little research on how formal financial sector policies – such as bank regulations or securities markets laws – affect inequality. Given the accumulated body of theoretical and empirical research on the central -- though frequently underappreciated -- role of finance in explaining economic inequality, this is a serious gap. Given the enormous welfare implications at stake, there are potentially high returns to theoretical and empirical research on the impact of financial regulations on economic opportunity, the intergenerational persistence of relative incomes, and the distribution of income.

Second, compare finance and redistributive policies. Many theories motivate redistributive policies as a mechanism for de-linking an individual’s opportunities from parental wealth. One problem with redistributive policies, however, is that they create disincentives to work and save, though researchers debate the actual magnitudes of these disincentive effects.

40
place in 1996, the baseline period in our analysis.<sup>8</sup> To the extent, however, that these reforms have long-lasting effects on farmers, the observed dynamics in figures 1 to 4 will capture them.

There is another element that favors the role of the reform as a major determinant of cotton dynamics. An important observation is that the increase in cotton income shares is larger at the bottom of the distribution of per capita expenditure, i.e., among poorest households. If macroeconomic and aggregate shocks, with magnitudes that affect all households simultaneously, are size-neutral, then we shouldn’t expect differences in the impacts at different points along the income distribution. This indicates, at least, that the relative changes in cotton dynamics are mostly generated by the marketing reforms. For instance, the larger increase in shares at the bottom of the distribution can be due to expanded access to seeds and fertilizers among the poor.

Finally, it is important to have in mind that cotton prices have continuously declined during this period. From 1996 to 1998, in particular, the real price of cotton in international markets has declined by as much as 20 percent.<sup>9</sup> There are two implications for our analysis. First, the dynamics in figures 1 to 4 show that cotton shares could have been even higher, in the face of the reform, had cotton prices been higher or remained stable. In other words, it would be reasonable to expect larger increases in cotton shares due to the cotton reforms if we could control for the change in cotton prices. Second, the decline in the real prices of agricultural products has been similar for other commodities such as maize, the major alternative crop in

> 8Similarly, the collapse of Zimbabwe has had significant impacts on the Zambian rural economy. It is observed, for instance, that tobacco has been increasingly adopted in Zambia, mainly due to the migration of neighboring peasants from Zimbabwe. However, the Zimbabwean crisis took place in recent years, after 1998, and should not affect our analysis.

> 9The decline around the long term trend seems to be, however, lower. See Food Security Research Project (2000).

12
assuming all providers are present at least 95% of the time. Countries that are higher than the 45 degree line are countries where our estimates suggest that having drugs available (Panel A) and reducing absenteeism (Panel B) would significantly improve the probability of care readiness that meets minimum quality standards. Essential drugs are ORS or rehydration therapy for diarrhea; antibiotics for pneumonia; ACT for malaria; and oxytocin for postpartum hemorrhage. Child care includes two conditions (diarrhea, pneumonia). Neonatal & maternal care includes two conditions (post-partum hemorrhage and neonatal asphyxia). Data are from clinical vignettes, unannounced visits, and visual inspections from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Sierra Leone (2018), Tanzania (2016), Togo (2014), and Uganda (2013). ISO 3-digit alphabetic codes are: KEN (Kenya), MDG (Madagascar), MOZ (Mozambique), NER (Niger), NGA (Nigeria), SLE (Sierra Leone), TZA (Tanzania), TGO (Togo), UGA (Uganda).

64
**Table 9 Headcount Index of Poverty among Farm Households, 1985-1993**

|_(poverty line = 75,000 CFAF per year in 1985 CFAF)_||
|---|---|
|Socioeconomic Group<br>1985<br>1988|1993|
|Export crop farmers<br>.086<br>.210|.394|
|Food crop farmer<br>.150<br>.197|.492|
|Source: 1985 and 1988: Grootaert (n.d.).||

1993: Household Poverty Survey.

Note: Poverty indices for 1985 and 1988 are calculated using Grootaert (1995) deflators, rather than the INS deflators. See Appendix B for a discussion of the difference in the two sets of deflators.

**Table 10 Official Export Crop Producer Prices**

|_Campaign_||||_Consumer_<br>_Price_|_Real_|_Producer Price_<br>_(1984/85 = 10_|_Index_<br>_0)_|
|---|---|---|---|---|---|---|---|
|_Year_|_Cocoa_|_Coffee_|_Cotton_|_Index_|_Cocoa_|_Coffee_|_Cotton_|
|1984/85|375|385|115|104.7|100.0|100.0|100.0|
|1985/86|400|200|115|111.1|100.5|48.9|94.2|
|1986/87|400|200|115|118.5|94.3|45.9|88.4|
|1987/88|400|200|115|126.6|88.2|42.9|82.7|
|1988/89|400|200|115|128.0|87.2|42.5|81.8|
|Jul-89|250|||||||
|1989/90|200|200|115|126.7|44.1|42.9|82.6|
|1990/91|200|100|100|128.9|43.3|21.1|70.6|
|1991/92|200|100|90|134.5|41.5|20.2|60.9|
|1992/93|200|170|90|138.3|40.4|33.4|59.2|
|1993/94|200|170|90|||||
|Jan-94|240|220|105|||||
|Mar-94|290|275|105|173.7|46.6|43.0|55.0|
|Sep-94|315|530||||||
|1994/95||||||||
|Feb-95|315|650|160|198.1|44.4|89.2|73.5|
|Sep-95|315|695|170|||||

Note: Campaign year is from October to September. CPI series is on a calendar year basis.

**Table 11 Staple Food Prices, Deflated by CPI** _(1993=100)_

||||_Yams_||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|_Year_|_Assawa_|_Bete_|_Flori_|_Kingle_|_Kponan_|_Plantain_|_Manioc_|_Maize_|_Millet_|_Rice_|
|1987|151.5|117.5|128.6|127.0|153.8|120.3|143.3|128.1|111.5|138.1|
|1988|134.8|102.4|119.5|130.2|132.9|119.8|120.0|130.2|135.3|121.2|
|1989|125.9|103.2|110.5|118.2|121.2|105.6|105.4|116.8|122.9|119.4|
|1990|129.2|106.2|114.6|108.7|131.3|119.7|109.8|116.2|114.1|116.2|
|1991|113.3|92.9|97.5|106.2|107.6|97.0|108.1|118.2|119.9|111.6|
|1992|98.4|85.7|88.9|84.2|101.5|97.7|103.5|96.4|108.0|104.7|
|1993|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|1994|86.1|78.5|81.5|81.2|87.2|87.8|84.2|76.1|82.3|84.6|
|1995|85.4|74.6|78.6|78.8|90.0|100.5|79.0|85.0|68.6|96.7|

Note: Price data obtained the office d'Aide A la Commercialization des Produits Vivriers. Price data covers 10 markets in C6te d'Ivoire and is collected monthly. Mean price for each commodity was obtained by regressing logarithm of the price on dummies for years, markets, and month of observation. Nominal prices were deflated by the CPI series shown in Table 10.

12
# **Spatial Misallocation, Informality, and Transit Improvements: Evidence from Mexico City**<sup>_∗_</sup>

Román David Zárate<sup>_†_</sup> World Bank _Keywords_ : Informality, allocative efficiency, urban transit infrastructure.

> _∗_ I am extremely grateful to my advisors Ben Faber, Cecile Gaubert, and Andrés Rodríguez-Clare for their continuous support and guidance in this project. I also want to thank my discussants Clare Balboni, Gabriel Ulyssea, and Alejandro Molnar, and seminar participants at the Dallas Fed, McGill University, the World Bank, ITAM, Universidad de los Andes, Universidad del Rosario, PUC Rio, Vancouver School of Economics, Nottingham University, the Macroeconomics Conference at Oxford University, the Online Urban Economics Seminar, the NBER SI in Urban Economics, the Cities and Development Workshop, Hitotsubashi University, and the 2021 ASSA meetings for very useful suggestions. I also want to thank David Atkin, Kirill Borusyak, David Card, Andrés González-Lira, Marco González-Navarro, Patrick Kennedy, Chris Severen, Joaquín Klot, Isabela Manelici, Pablo Muñoz, Mathieu Pedemonte, Darío Tortarolo, Nick Tsivanidis, Jose P. Vásquez, Román Andrés Zárate, and Isabel Hincapie for very helpful comments and discussions. I thank the National Institute of Statistics and Geography (INEGI) and especially Dr. Natalia Volkow for granting me access to the data. Financial support from the Clausen Center at UC Berkeley is gratefully acknowledged. The views expressed are only those of the author and they do not reflect the views of INEGI.

> _†_ World Bank, DEC TI. E-mail: rzaratevasquez@worldbank.org. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.