rafmacalaba/datause-encoder-data huggingface dataset:
implements local development should also be able to manage implementation that fits the local culture. Sub-national governments are more likely to have a cultural match with the “symbolic public goods” (Rao, 2008) being created at the local level by facilitators and thus mobilize collective identities with greater ease.

To test these ideas we use a natural experiment from the National Rural Livelihoods Mission in India that separated blocks in each state into centrally or sub-nationally managed blocks known as Resource Blocks and Intensive Blocks. One method featured external facilitators who were actively backed by the state. The other featured local women who were actively backed by local civil society. Both arms of the program aimed to organize women into SHGs that would then link to the state to receive credit.

We examine this in the state of Rajasthan using mixed methods to contrast two approaches of organizing poor rural women within the confines of a single poverty alleviation program. At first glance, the two strategies seem to accomplish similar goals in that they are both able to form a comparable number of groups with similar financial performance. But there are some important differences in what the groups actually _do_ . Groups formed by outsider facilitators are better linked to the state through participation in the local Gram Sabha and other poverty alleviation programs. But they are less likely to engage in collective action on issues related to public service delivery, less likely to actually engage with local politics and less likely to be connected to other civil society organizations. These differences have important long-term implications for the groups themselves as well as for the overall success of the program. In addition, central management with external facilitators is extremely expensive. Given all these reasons, in the case of Rajasthan, local, sub-nationally managed facilitation would have been optimal for the project.

Finally, central management is particularly inappropriate for the state of Rajasthan, which has a long history of civic activism having nurtured important national initiatives such as the Mahatma Gandhi National Rural Employment Guarantee Scheme, and the Right to Information Act. Particularly relevant to the NRLM’s mandate, it has several NGOs with long histories of facilitating the formation and functioning of women’s Self-Help Groups. Thus, sending SHG facilitators from the far-away state of Andhra Pradesh, who were not native speakers of the local language, to form women’s SHGs seems a particularly egregious case of central overreach and cultural mismatch. Centrally managed Resource Blocks may make more sense in states that lack Rajasthan’s civic history where they could, indeed, be a resource for building internal capacity in the state. But, in Rajasthan, it makes little sense.

19
INTRODUCTION IX Figure 1. Increase in Population Average annual increase (millions) <!-- Start of picture text --> By 5-year period By 2S<year period<br>1900.95 2010045 201O013 20225 2050.75 2100-25<br>Period<br><!-- End of picture text --> Figure 2. Comparison of Projections 1990-91 Edition and 1992-93 Edition <!-- Start of picture text --> 110> = --- = MDC, 1990-91<br>1. - MDC, 1992-93<br>LDC, 1990-91<br>70a , LDC, 1992-93<br>3O<br>260 240 21'00 2140<br>2000 2040 2000 2120 2160<br>Year<br><!-- End of picture text -->
|**Discrete dependent variable regressions of crises in potentially affected countries on the lagged uncertainty there**<br>Step 2, Effect of uncertainty in potentially affected countries, B, on the occurrence of crises there.<br>A dummy of stock market crises is the dependent variable. Countries A are excluded from the group of other countries, B.<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>pooled probit,<br>all crises, all<br>countries<br>pooled probit,<br>all crises,<br>emerging<br>markets<br>pooled logit,<br>all crises, all<br>countries<br>pooled logit,<br>all crises,<br>emerging<br>markets<br>linear<br>probability,<br>all crises, all<br>countries<br>linear<br>probability,<br>all crises,<br>emerging<br>markets<br>0.373*<br>0.356*<br>0.634*<br>0.617<br>0.079***<br>0.051**<br>(0.191)<br>(0.201)<br>(0.367)<br>(0.384)<br>(0.015)<br>(0.022)<br>-0.005<br>-0.016<br>0.000<br>-0.025<br>-0.005*<br>-0.006<br>(0.036)<br>(0.037)<br>(0.070)<br>(0.073)<br>(0.003)<br>(0.004)<br>0.800<br>0.665<br>1.464<br>1.263<br>0.093<br>0.081<br>(0.800)<br>(0.873)<br>(1.412)<br>(1.567)<br>(0.084)<br>(0.109)<br>0.127<br>0.139<br>0.161<br>0.170<br>0.002<br>0.009<br>(0.161)<br>(0.173)<br>(0.330)<br>(0.348)<br>(0.010)<br>(0.018)<br>0.486***<br>0.468**<br>0.931***<br>0.896**<br>0.045***<br>0.079***<br>(0.183)<br>(0.201)<br>(0.352)<br>(0.374)<br>(0.012)<br>(0.023)<br>0.361*<br>0.491**<br>0.752*<br>0.904**<br>0.079***<br>0.156***<br>(0.217)<br>(0.236)<br>(0.411)<br>(0.437)<br>(0.016)<br>(0.030)<br>0.689***<br>0.720***<br>1.359***<br>1.396***<br>0.071***<br>0.132***<br>(0.122)<br>(0.133)<br>(0.232)<br>(0.247)<br>(0.010)<br>(0.018)<br>0.738***<br>0.723***<br>1.447***<br>1.406***<br>0.071***<br>0.117***<br>(0.112)<br>(0.122)<br>(0.215)<br>(0.232)<br>(0.009)<br>(0.016)<br>-0.011<br>0.023<br>0.028<br>0.115<br>0.004<br>0.010<br>(0.142)<br>(0.154)<br>(0.279)<br>(0.299)<br>(0.009)<br>(0.016)<br>0.880<br>1.430<br>-0.896<br>-0.260<br>-0.479<br>-1.093<br>(7.286)<br>(7.921)<br>(15.007)<br>(16.047)<br>(0.343)<br>(0.677)<br>country effects<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>Constant<br>-2.739***<br>-2.774***<br>-4.940***<br>-4.927***<br>0.009<br>0.030<br>(0.522)<br>(0.552)<br>(1.056)<br>(1.101)<br>(0.029)<br>(0.045)<br>Observations<br>2515<br>1634<br>2515<br>1634<br>3792<br>1853<br>R-squared<br>0.18<br>0.23<br>Standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%<br>lag of uncertainty<br>lag of mean growth expectation<br>lag of stockmarket volatiltiy<br>lag of crisis in Thailand<br>lag of crisis in Turkey<br>lag of 10 year US gov bond yield<br>lag of crisis in Argentina<br>lag of crisis in Brazil<br>lag of crisis in Mexico<br>lag of crisis in Russia|
|---|

Table 3: Step 2: Effect of uncertainty on the probability of a crisis, without any interaction.

23
# **Table 6:  Agreement Ratio for Changes in WGI Estimates of Governance, 1998-2008**

||**Sample**|**A**<br>**Agree**|**LL CHANGE**<br>**No Change**|**S**<br>**Disagree**|**Agree / (Agree**<br>**+ Disagree)**|
|---|---|---|---|---|---|
|**Voice and Accountability**|199|2.3|0.6|1.2|0.66|
|**Political Stability**|189|2.7|0.7|1.0|0.73|
|**Government Effectiveness**|194|2.6|1.0|1.1|0.69|
|**Regulatory Quality**|194|3.1|0.7|1.4|0.70|
|**Rule of Law**|194|3.2|1.8|1.7|0.66|
|**Control of Corruption**|194|2.6|1.3|1.1|0.70|
|**Average**|**194**|**2.8**|**1.0**|**1.3**|**0.69**|
|||**SIGNIFIC**<br>**Agree**|**ANT CHANG**<br>**No Change**|**ES (90%)**<br>**Disagree**|**Agree / (Agree**<br>**+ Disagree)**|
||**Sample**|||||
|**Voice and Accountability**|22|3.9|0.1|0.7|0.87|
|**Political Stability**|27|4.1|0.1|0.4|0.93|
|**Government Effectiveness**|29|3.7|0.7|0.6|0.87|
|**Regulatory Quality**|18|5.4|0.4|0.1|0.99|
|**Rule of Law**|17|5.9|1.1|0.8|0.88|
|**Control of Corruption**|13|4.6|0.6|0.6|0.90|
|**Average**|**21**|**4.6**|**0.5**|**0.5**|**0.90**|

36
Kırdar, M. G. 2009. “Explaning Ethnic Disparities in School Enrollment in Turkey.” _Economic_ _Development and Cultural Change_ 57 (2): 297–333.

Kırdar, M.G., Dayıoğlu M., and İ. Koç. 2010. “The Effect of Compulsory Schooling Laws on Teenage Marriage and Births in Turkey.” Koc University-TUSIAD Economic Research Forum Working Paper 1035.

- Lavy, V. 1996. “School Supply Constraints and Children's Educational Outcomes in Rural Ghana.” _Journal of Development Economics_ 51: 291–314.

- Lee, D. S., and T. Lemieux. 2010. “Regression Discontinuity Designs in Economics.” _Journal of Economic Literature_ 48 (2): 281–355.

Lang, K., and D. Kropp. 1986. “Human Capital Versus Sorting: The Effects of Compulsory Attendance Laws.” _Quarterly Journal of Economics_ 101 (3): 609–24.

Lochner, L., and E. Moretti. 2004. “The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports.” _American Economic Review_ 94 (1): 155–89.

- Mejia D., and M. St-Pierre. 2008. “Unequal Opportunities and Human Capital Formation.” _Journal of Development Economics_ 86: 395–413.

Ministry of National Education (MONE). 1989–2006. _National Education Statistics_ , Ankara. Munich D., Svejnar J., and K. Terrell. 2005. “Returns to Human Capital under the Communist Wage Grid and during the Transition to a Market Economy.” _Review of Economics and Statistics_ 87 (1): 100–23.

Orazem, P. F., and E. M. King. 2008. “Schooling in Developing Countries: The Roles of Supply, Demand and Government Policy.” _Handbook of Development Economics_ 4: 3475–559.

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40
|R-squared|0.167|0.167|0.137|0.180|0.170|
|---|---|---|---|---|---|
|<br>||C) Rural/u<br>|rban differences f<br>|or women<br>||
|Policy|0.425***|0.494|0.523***|0.719***|0.820***|
||[0.076]|[0.417]|[0.015]|[0.216]|[0.072]|
|Policy * female|0.713***|0.552|0.756***|0.757**|0.811*|
||[0.148]|[0.478]|[0.099]|[0.271]|[0.261]|
|Composite female|1.137***|1.046***|1.279***|1.476***|1.631***|
||[0.141]|[0.271]|[0.114]|[0.156]|[0.194]|
|N|9,088|9,088|3,277|5,787|2,303|
|R-squared|0.307|0.307|0.307|0.313|0.318|

_Source_ : Authors’ calculations based on 2003 and 2008 Turkish Demographic and Health Survey.

49
# **1. Introduction**

Governments and NGOs around the world promote and offer a variety of direct support to small businesses, with the provision of business training one of the most common services provided. Better business practices are strongly associated with better firm performance across a range of countries in both the cross-section and over time (McKenzie and Woodruff, 2015), and so the hope is that teaching these practices will help firms grow. However, a key concern around public or subsidized provision of business training is whether any growth of trained firms comes at the expense of their competitors (Rotemberg, 2014). This concern is particularly apparent when working with microenterprises in rural markets in developing countries, where it is easy to believe that if firms are all selling similar products in a small market, any extra sales made by trained firms must come from competing away these sales from neighboring untrained firms. Conversely, if better management practices are best thought of as a technology (as in Bloom et al., 2015), then there might be positive spillover benefits as untrained firms copy the practices adopted by the trained firms and also become more productive. Furthermore, if training leads firms to expand the variety of products being offered and the customer service provided, the overall market size may increase, particularly in underdeveloped marketplaces.

We conduct a randomized experiment in 157 rural markets in Kenya to test how business training (the International Labour Organization (ILO)’s _Gender and Enterprise Together_ program) affects the profitability, growth and survival of female-owned businesses, and to evaluate whether any gains in profitability come at the expense of other business owners. We work with a large sample of 3,537 firms, and use a two-stage randomization, first randomizing at the market level, and then randomizing the offer of training to individuals within treated markets. A year and a half after the training has taken place, half of the sample assigned to training was then offered a subsequent mentoring intervention intended to test whether additional group-based and in-person support strengthens the impacts of training. Four rounds of follow-up surveys with low attrition are used to measure impacts at one and three years after training.

Firms assigned to training are 3 percentage points more likely to survive after three years, earn 18 percent higher sales, and make 15 percent higher profits. Their owners have better mental health and a higher subjective standard of living. These gains are greater at three years than at one year 2
- 30 -

Finally, the potential difference in demand between male and female minibus users is examined. The Chow test statistic is estimated at 1.48, which is significant at the 5 percent level. Thus, the demand structure is systematically different between male and female passengers ( **Figure 24** ). The main results are shown in **Table 6** . Male passengers seem to be more sensitive to prices. The price elasticity is only significant for male minibus users. Male passengers are also particularly sensitive to travel time. The time elasticity ( _TIME_ ) is only significant for males. These may be related to the difference in trip objectives. The male demand is more influenced by their trip purposes, e.g., work, business and schooling.

For female users, the demand is more related to nonessential trip purposes, such as personal trips and family visits. Despite our prior expectation, there is no gender-specific impact related to safety or comfortability. The household ownership of vehicle particularly matters to females. If a vehicle is owned, the demand for public buses is reduced significantly.

**<u>Table 6. Demand functions differentiated by gender</u>**

|||Pooled model:||Separate|regression:||
|---|---|---|---|---|---|---|
|||Without OD dummies<br>Coef. Std.Err.|With OD dummies<br>Coef. Std.Err.|dFEMA<br>Coef.<br>|LE=1<br>Std.Err.|dFEMALE=0<br>Coef. Std.Err.|
|lnP*(dFEMA|LE=0)|-0.057 (0.023) **|-0.054 (0.024) **|||-0.049 (0.027) *|
|lnP*(dFEMA|LE=1)|0.003 (0.019)|0.008 (0.019)|-0.005|(0.023)||
|Trip purpose:|Work|0.319 (0.060) ***|0.262 (0.059) ***|0.044|(0.119)|0.407 (0.058) ***|
||Business|0.158 (0.086) *|0.115 (0.083)|-0.078|(0.153)|0.258 (0.093) ***|
||School|0.345 (0.060) ***|0.294 (0.060) ***|0.091|(0.121)|0.406 (0.062) ***|
||Shopping|0.114 (0.060) *|0.060 (0.059)|-0.165|(0.118)|0.216 (0.060) ***|
||Personal|0.033 (0.061)|-0.016 (0.061)|-0.270|(0.121) **|0.173 (0.066) ***|
||Family visit|<br>-0.023 (0.062)|<br>-0.070 (0.061)|<br>-0.272|<br>(0.119) **|<br>0.032 (0.070)|
||Hobby|0.092 (0.084)|0.037 (0.085)|-0.383|(0.127) ***|<br>0.239 (0.098) **|
|lnTIME||-0.031 (0.010) ***|-0.034 (0.011) ***|-0.020|(0.015)|-0.049 (0.016) ***|
|dTRF||-0.056 (0.039)|-0.070 (0.041) *|-0.124|(0.059) **|-0.038 (0.056)|
|lnTRF||<br>0.066 (0.025) ***|<br>0.065 (0.025) ***|<br>0.050|<br>(0.034)|<br>0.080 (0.039) **|
|dSUBU||<br>0.008 (0.018)|<br>0.015 (0.021)|<br>0.049|<br>(0.028) *|<br>-0.036 (0.032)|
|dBOTH||-0.079 (0.032) **|-0.063 (0.034) *|-0.026|(0.043)|-0.107 (0.057) *|
|dCUA||-0.021 (0.015)|0.005 (0.062)|0.027|(0.091)|-0.053 (0.089)|
|dUNSAFE||<br>-0.035 (0.019) *|<br>-0.034 (0.020) *|<br>-0.036|<br>(0.027)|<br>-0.040 (0.031)|
|dUNCOMF||0.006 (0.018)|0.005 (0.018)|0.011|(0.026)|-0.003 (0.026)|
|dFEMALE||-0.423 (0.165) ***|-0.434 (0.170) **||||
|dCAR||-0.069 (0.031) **|-0.062 (0.032) *|-0.102|(0.051) **|-0.015 (0.043)|
|dMOTO||<br>0.015 (0.017)|<br>0.013 (0.018)|<br>-0.015|<br>(0.028)|<br>0.029 (0.025)|
|dBIKE||-0.033 (0.020)*|-0.031 (0.020)|-0.045|(0.032)|-0.014 (0.028)|
|Obs||3,133|3,133|1,651||1,482|
10 cash book form) were commended and posed for photographs and an honorable handshake with the Permanent Secretary of Health in a special ceremony.

At the fifth week of the study, an additional incentive program began in the Control group facilities, concluding the experiment. The randomized design permits us to attribute any differences in performance over these four weeks (post-baseline) to the Social Recognition scheme.

# _Data_

To measure performance, we gathered a weekly dataset on the quality of the cash book record-keeping of the accounting staff. The scoring “checklist,” which assessed the quality of the records, focused on whether the cash book had been filled out at all prior to arrival; how complete its various sections were; and how consistent it was with other documentation (including other forms and paper receipts); Based on the answers to these questions, the checklist produced a weekly score for each facility.<sup>4</sup>

# _Implementation Details_

Enumerators already making weekly visits to facilities for the larger PETS/RT project were trained on the incentive intervention. Each enumerator visited on average 4-5 facilities each week, which could include both sample facilities from the Control group as well as the Social Recognition group.

Facilities in the Social Recognition group were expected to display their Certificate of Excellence in a prominent spot at the facility, so that their weekly performance (or the lack of it) would be visible to all. The assumption here was that staff would be motivated to work better at filling out the cash book form because of the desire to earn stronger community approbation

> 4 For details on the exact questions, see Appendix A. Note that one of the questions, question 8 links the main “cash book” form to a different form which records dates and quantities for any drug shipments that the facility receives, whether locally purchased or regionally distributed; this link allows an enumerator to check both forms (cash book and drug purchases) for consistency on at least the arrival of drugs in the facility on a given day.  The distribution of scores is shown in Appendix Figure A3.
We adopted the following specification of the gravity equation from SST (2006).<sup>6</sup> (6) yi = exp(xiβ) + εi =exp(xiβ).ηi = exp(β0 + β1x1i + β2x2i).ηi where ηi ≡1+[εi/exp(xiβ)]; x1 is a binary dummy, designed to mimic variables such as border dummies, that equals 1 with probability 0.4; x2 is a standard normal variable to mimic the behavior of continuous explanatory variables such as the log of distance or income; and the data are randomly generated using β0=0, β1= β2 =1. Following SST, we assumed that ηi is log-normally distributed with mean 1 and variance σi<sup>2</sup> .

To assess the sensitivity of the different estimators to different patterns of heteroskedasticity, we used Santos Silva and Tenreyro’s four cases: Case 4: σi<sup>2</sup> = (exp(xiβ))<sup>-1</sup> +exp(x2i) ; V(yi׀x) = exp(xiβ) + exp(x2i).(exp(xiβ))<sup>2</sup> where Case 1 involves an error term that is homoscedastic when estimated in levels; Case 3 is homoscedastic when estimated in logarithms; Case 2 is an intermediate case between 1 and 3; and Case 4 represents a situation in which the variance of the residual is related to the level of a subset of the explanatory variables, as well as to the expected value of the dependent variable. To help interpret such a large number of results, we also present, in Appendix Table 4, measures of the average absolute bias for each coefficient across the four cases and the average absolute bias across both coefficients, together with averages of their standard errors.

We consider two broad types of data-generating-process (DGP): one based on the Eaton-Tamura model and another based on the Heckman model. We also examine a special case of the Heckman DGP, including an additional variable for the number of products traded, proposed by HMR (2008), whose robustness to heteroscedasticity bias does not appear to have been examined on data sets with known parameters, although SST (2014) raise concerns about potentially serious bias. These DGPs have solid economic foundations and can generate data sets with the required fractions of zero trade values. We considered using the SST (2011) DGP but its economic underpinnings and statistical properties were less obvious to us than was the case for the three more conventional limited-dependent

> 6 These equations correspond exactly to the SST (2006) specification for equations (14) and (15).

13
pendent central banks, as a result of a flatter Phillips curve, it is not clear that is optimal to have zero sacrifice ratio (or vertical Phillips curve). It could be possible that precisely because the sacrifice ratio is high, the inflation rate is low. Actually, this issue boils down to what is the optimal slope of the Phillips curve in the presence of dynamic inconsistency, issue that has not been resolved.

The evidence for developing countries is more scarce, but recently Cukierman, Kalaitzidakis, Summers and Webb (1993) have provided a detailed analysis of the relationship between CBI and macroeconomic performance in developing countries. Analysis of CBI among developing countries show that indices of "legal independence" bear little relation to the actual independence of central banks. For this reason CWN have computed the rate of turnover of governors of central banks. This index seems to perform better in proxying actual independence. In developing countries the correlation between turnover rates and legal independence is very low.

An additional problem in developing countries is that inflation rates are highly variable, including several cases of extreme inflation. CWN suggest to look at 7r/(1 + -7r), which corresponds to the rate of depreciation of real balance.<sup>2 7</sup> Figure 5 presents the simple correlation between turnover rates and inflation for a sample of developing countries. The figure shows a strong positive correlation, but it also reveals that this is to a large extent due to Argentina, the country with the highest turnover rate of central bank governors, and the highest inflation rate. One could argue that Costa Rica may be an outlier too since it has relatively low inflation and hligh turnover.

Table 5 presents a more formal statistical analysis between CBI and inflation. For the whole sample of countries legal independence does not significantly affects inflation. Only when the subsample of OECD countries is considered the results coincide with those of CWN that show legal independence and not turnover rates are significant determinants of inflation. In contrast, the regressions show that for developing countries turnover rates are positively associated with inflation, that is, the higher the rate of turnover the higher the rate of inflation. Several regressions analyze the case of outliers, in particular Argentina. In the whole sample, the coefficient declines from 0.37 to 0.21 but is still significant. In contrast, and contrary to CWN, the coef-

> **2 7 At the** end **of a** period with inflation equal to **7r** real **value** of an **initial $1** is **1/(1** + **7r).** Thus the **depreciation is - 1/(1 + 7r).** One can **also** look **at** the **log** of the inflation **rate.** For convenience **I** will **call** indistinctly **inflation to** both **7r and** ir/(l + 7r).

30