rafmacalaba/datause-encoder-data huggingface dataset:
Operational constraints limit the number of randomized treatments we can implement. We complement experimental evidence with measured correlations between insurance purchase decisions and household characteristics, and household self-reports about demand for insurance.

# _A. Correlates of insurance purchase_

Similar to the analysis presented above, we simply regress a dummy for whether the household purchases insurance on a set of household characteristics drawn from the surveys conducted in Andhra Pradesh and Gujarat in 2006. Results are presented in Table 8. As far as possible, similar variables from the two survey areas are defined in a consistent way for this analysis, to allow a comparison of coefficient estimates.

Wealth is positively correlated with insurance purchase, especially for the Gujarat sample, consistent with other evidence on the role of liquidity constraints, likely to be more binding for poorer households. Second, variables presented in Table 3 measuring households’ ability to answer probability, math and insurance questions (measured by the variables “financial literacy”, “probability skill” and “insurance skills”) are in general positively correlated with insurance purchase decisions, consistent with a hypothesis of limited cognition or imperfect information about the product.

Third, prior experience with the insurance product and vendor is positively correlated with insurance purchase. These are measured in a number of ways: by whether the household purchased insurance in previous years, whether the household is familiar with the insurance vendor, whether the household has other types of insurance, and, for Andra Pradesh, whether the household’s village had experienced positive rainfall insurance payouts in 2004 and 2005.

Finally, and surprisingly, higher risk aversion is _negatively_ correlated with insurance purchase in both the Andhra Pradesh and Gujarat samples. This replicates a finding of Giné et al. (2008) using an earlier 2004 sample. Giné et al. (2008) show that this apparently perverse result is concentrated amongst households without knowledge of BASIX or of insurance, suggesting that uninformed risk-averse households are unwilling to experiment with the insurance product, given their limited experience with it. Again, this appears consistent with our other evidence that limited trust and/or understanding of the product reduces insurance demand.

These results extend the experimental evidence presented earlier and, where applicable, appear consistent with the experimental findings. They are also generally consistent with the 21
evidence shows that certain characteristics of the survey such as how the questions are structured in the survey, the ordering and the wording used, the format of the interview, or the quality of the translation, all significantly influence the outcome of survey responses. For example, whether the question is formulated in a positive way or in a negative way is more likely to translate into a positive or a negative answer. In addition, Schwarz (1999) describes how closed vs open response formats or the rating scale can also bias the response obtained.

Further sources of bias in subjective measures include context, cultural differences, and social desirability. The context in which questions are formulated is likely to influence answers, mainly due to the framing of the respondents. For example, Kaplan and Pathania (2010) show how perceptions‐based indicators used to measure the quality of the business environment are highly correlated with the business cycle. Using data from the World Bank Enterprise Surveys (ES) the authors show how perceptions about the business environment worsen during periods of higher economic growth and, therefore, how changes in perceptions are not a good measure of changes in the business environment. Cultural differences are also relevant to how individuals understand and respond to survey questions. This is because aspects of cognition that affect response behaviors can differ across cultures (Johnson et al., 1997; Harkness et al. 2002). Village‐level characteristics, such as ethnic heterogeneity and social participation, for example, have been shown to influence perception measures of corruption (Olken 2009). Furthermore, there is a large literature showing how social desirability affects the quality of subjective measures. One example is the reluctance of individuals to express subjective opinions perceived as non‐desirable such as racial attitudes (Bertrand and Mullainathan, 2001).

A final important element that affects the bias in subjective measures is the reference or recall period (Schwarz, 1999). The time reference for the question or the recall period in the formulated question impacts the quality of the answer, and has been shown to impact both objective and subjective measures. Das et al. (2012) show that the length of the recall period, comparing a monthly recall with a weekly recall, had a large impact on reported morbidity, doctor visits, sickness, or reported use of self‐medication in India. Bertrand and Mullainathan (2001) show that in some cases subjective answers can change over time due to the fact that the individual answering the question may not have established any preferred attitude.

# **2.3 Methodological problems in innovation surveys**

The sources of bias identified in the literature on subjective measurement are particularly relevant for innovation surveys given the subjective nature of many of the questions used in innovation surveys. The 7
84% of the net change in employment in the unorganized manufacturing sector from 1994 to 2005 is due to the increased role of women-owned businesses. Had women’s ownership rates remained in proportion to their levels in 1994 relative to male-owned firms, the unorganized share of manufacturing employment would have fallen to 79.3% by 2005, instead of increasing to 81.5%. While this counterfactual difference is small in absolute terms, it would represent meaningful progress towards reducing informality. We also show district-level regressions that indicate that this link is present when exploiting variations across districts within Indian states and accounting for many traits of districts (e.g., size, income levels, education levels, population growth rates). Places where women have increased most their business ownership have also had stronger persistence in the unorganized sector. The relationship is evident, but also significantly weaker, in the services sector. The relationship holds in simple instrumental variable estimations that combine the initial industry composition of a district with the national change for each industry in the women-owned business share.

The second half of the paper then evaluates whether or not the women who are leading these new unorganized sector firms have better prospects than they did before they entered the manufacturing sector. Our data do not permit us to do this at the level of the individual business owners, but we develop suggestive evidence from aggregate survey tabulations. We first note that the female-owned businesses are typically household based and that they are smaller and less productive than male-owned businesses. Moreover, the growth in the female ownership rate is associated with deteriorations in these ratios. That is, the additional entry is increasing the raw gap between male- and female-owned establishments. This is a simple consequence of most of these new entrants occurring among small, household-based establishments. Yet, even at the small entry sizes, these female-owned establishments appear to make an important step forward in economic participation. Using household-based surveys from the Census, it appears that these new entrants are moving out of unpaid domestic work.<sup>2</sup>

> 2 With further development, it may be that paid employment positions emerge in the organized sector that are superior to owning a small business in the unorganized sector. We cannot address this issue in this study. We only depict how the steps that women are taking with respect to business ownership in the unorganized sector are better than other opportunities available to the women at the time of their choices.

4
to look into the underlying mechanisms, which may potentially coincide with the opportunity costs children face by continuing school, instead of working, or be related to the pace of completing school related to the first enrolment age and school quality.

Figure 12: Regional Level Share of Children of School-going-age Not in School a) b) <!-- Start of picture text --> Urban Not in School at School going Age Deprivation - Rural Not in School at School going Age Deprivation -<br>by Geopolitical Zone - GHS by Geopolitical Zone - GHS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE<br>Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS<br>Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW<br>c) d)<br>Urban Share Not in School at School going Age Rural Share Not in School at School going Age<br>- by Geopolitical Zone - MICS - by Geopolitical Zone - MICS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW<br>MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

Comparing state level severe education deprivation rates in Figure 9 with state level non-enrollment rate in Figures 13 and 14 shows that high shares of education deprivation do not necessarily imply high dropout rates, the difference between the shares displayed in Figures 10 and 13/14 for those who ever attended school. Rather it seems to suggest, that while children in some states may be less severely deprived of education, it does not inform about the duration of schooling for those non-deprived children, leaving quality all aside. Overall, however, state level school non-enrollment rates in Figures 13, 14 and 15 largely display the same urban rural and north-south pattern as estimates of severe education deprivation in Figures 9 and 10 though with even higher inter-state discrepancies. The highest share of children of school going age not in school is found in Bauchi, where 68 percent of rural girls and 66 percent of rural boys are out of school, meaning only roughly one third of rural children is attending school. It is worth noting though that there is a large inter-state discrepancy between ever having been to school and 26
<!-- Start of picture text --> 0.68 0.67<br>0.00 0.02-/<br>t2 gistance from local market in hours (log scalel .12 3lstance from local mlarkeet in hours (log scalel<br>C Inherited wealth C Inherited wealth<br>o 0<br>.12<br>9.40 - \-0.38 -\<br>.12 istance frbm local market in hours (log scale1 .rFo distance town in ho2urs<br>Distance from local market Distance from towns<br>Figure 15. Inherited wealth, food expenditures, and distance<br><!-- End of picture text -->
- **McCormick, Barry and Jackline Wahba** , “Overseas work experience, savings and entrepreneurship amongst return migrants to LDCs,” _Scottish journal of political economy_ , 2001, _48_ (2), 164–178.

- **McKenzie, David** , “How effective are active labor market policies in developing countries? A critical review of recent evidence,” _The World Bank Research Observer_ , 2017, _32_ (2), 127–154.

- , “Identifying and spurring high-growth entrepreneurship: Experimental evidence from a business plan competition,” _American Economic Review_ , 2017, _107_ (8), 2278– 2307.

**and Christopher Woodruff** , “What are we learning from business training and entrepreneurship evaluations around the developing world?,” _The World Bank Research Observer_ , 2013, _29_ (1), 48–82.

- **and Dean Yang** , “Evidence on policies to increase the development impacts of in-

- ternational migration,” _The World Bank Research Observer_ , 2015, _30_ (2), 155–192.

- **, Steven Stillman, and John Gibson** , “How important is selection? Experimental vs. non-experimental measures of the income gains from migration,” _Journal of the European Economic Association_ , 2010, _8_ (4), 913–945.

- **Mesnard, Alice** , “Temporary migration and capital market imperfections,” _Oxford economic papers_ , 2004, _56_ (2), 242–262.

- **Naud ́e, Wim, Melissa Siegel, and Katrin Marchand** , “Migration, entrepreneurship and development: Critical questions,” _IZA Journal of Migration_ , 2017, _6_ (1), 5.

- **Peri, Giovanni** , “Do immigrant workers depress the wages of native workers?,” _IZA world of Labor_ , 2014.

- **Portes, Alejandro and Jessica Yiu** , “Entrepreneurship, transnationalism, and development,” _Migration Studies_ , 2013, _1_ (1), 75–95.

- **Sieff, Kevin** , “The Stay Here Center,” Washington Post 2019. `https: //www.washingtonpost.com/news/world/wp/2019/04/19/feature/this-schoolaims-to-keep-young-guatemalans-from-migrating-they-dont-know-itsfunded-by-the-u-s-government/` , [Online; accessed 21-August-2019].

- **Williamson, Jeffrey G** , “Regional inequality and the process of national development: A description of the patterns,” _Economic development and cultural change_ , 1965, _13_ (4, Part 2), 1–84.

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# **3.2. Services have become more productive**

Revolution in ICT technologies has made services more productive. In some countries, the real sources of growth in output per worker have emerged in services than manufacturing. Table 1 shows output per worker and total factor productivity growth sectorally over the period of 19902006 for India and China. Both countries have experienced relatively high TFP growth in services. However, India‘s growth experience has been leveraged by service TFP growth whereas China‘s in Industry. The two countries have taken different growth paths, and India‘s growth experience has shown that over 7 percent annual growth can be achieved by a developing country on the foundations of service productivity growth. With lack of disaggregated data on employment by sectors for developing countries, we plot the service output per worker for selected advanced economies in Figure 4 which suggests a rising trend.

**Table 1. Sources of Growth in Output per Worker in India and China: Total Economy and Major Sectors, 1990-2006,(average annual percentage change).**

||Total|Economy|Agri|culture|Ind|ustry|Ser<br>|vices|
|---|---|---|---|---|---|---|---|---|
||1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|1990-<br>2000|2000-<br>2006|
|**India +**|||||||||
|Output Per Worker|4.1|4.5|1.3|1.4|3.3|2.7|4.9|4.6|
|TFP|1.8|2.1|0.7|0.9|0.6|1.6|3.1|1.9|
|**China***|||||||||
|Output per Worker|8.9|9.0|4.6|4.2|10.2|10.4|4.7|6.0|
|TFP|4.5|4.4|1.8|2.2|6.9|6.4|1.1|1.8|

_Source_ : + India from Bosworth, Maertens (2010). * China from Bosworth, Collins (2006). Note: Latest time period for China is 2004.

8
The cluster component of the error term, _ηc_ , is drawn from a normal distribution with mean 0 and variance, _σ_ ˆ _η_<sup>2.Thedatageneratingprocessofthevariancetermis</sup> assumed to follow a gamma distribution.

The household component of the error term, _εch_ , is drawn from a normal distribution with mean 0 and the variance predicted by equation (6). The variance term is calculated by applying the parameters from the source data (equation 5) to the 2014-15 data (equation 6).

̃ The 100 simulated values, _β_<sup> ̃</sup> , _α_ , _ηc_ and ̃ _εch_ are used to obtain a vector of 100 simulated ̃ welfare estimates, _ych_ . The mean of the 100 simulations gives the point estimate of household expenditure. The standard error of the poverty estimate is calculated using Rubin’s rules (Rubin (2004)) which takes into account the variation both within and across simulations. We use the STATA package SAE developed by (Nguyen, Corral, Azevedo, and Zhao (2017)) to carry out the estimation and the Monte Carlo simulations.

# **B Variable Selection**

The process described above requires a set of right-hand side variables to estimate equation (1). We elected for a model selection process that emphasizes transparency and simplicity, while addressing concerns that excessive multi-collinearity could jeopardize the model’s out-of-sample predictive accuracy. Multicollinearity, which occurs when one or more right-hand side variables are highly correlated with the remaining right-hand side variables, leads to imprecise coefficient estimates. Coefficient estimates that are imprecise, especially for important drivers of predicted welfare change such as the coefficient on the linear time trend, will reduce the accuracy of the predictions. In addition, models with greater multicollinearity among the regressors are fragile to minor changes in the model’s specification such as removing particular variables.

We start with the set of candidate variables listed in Tables 3 and 4, which are common to both surveys. For each variable, we also take district means, in order to improve the accuracy of the prediction and their standard errors. Finally, a subset 13
and **by** the Swiss franc. This means that for narrow bands we are able to verify the announced exchange rate regime.

We also examined the verifiability of regimes self-declared as free floating in several Latin American and East Asian countries in the 1990s **-** Brazil, Mexico, Peru, South Korea, and Thailand. Even though there are different ways to verify floating regimes, we followed the same methodology used for bands. We tested whether supposedly floating exchange rates are correlated with major exchange rates. Our tests do not show significant evidence against the hypothesis that the exchange rates of these countries are indeed floating, with the exception of Peru and part of the post-Tequila period in Mexico. In these cases, we find some evidence that the exchange rate is in fact moving along with some weighted combination of strong currencies. This appears to agree with the conclusions reached **by** other researchers. In sum, we fail to reject freefloating regimes, although cross-currency correlations tend to reject free-floating **-** even when governments do not intervene. Whether these findings can be extended for long periods of free floating, after the high volatility in the aftermath of crises has vanished, is a question for future research.

The analysis in the main text was complemented **by** means of Monte Carlo tests reported in Appendix 1 assessing the effects of bandwidth and number of currencies in the basket on the time needed to verify exchange rate bands. On the whole, the results agree with the above findings. As expected, when the range of variability of the exchange rate is relatively large, the number of observations needed to verify the regime increases considerably with the width of the band. The number of observations needed to differentiate the crawling basket from a random variable in at least half of the samples is

**_35_**
**F ig u r e 7 .5 :  T o p 2 0 S E N A T E R e c ip ie n t s o f A G P A C M o n e y . 1 9 9 9 - 0 0 E le c t io n C y c le .**

|R O H D E W IN E|
|---|
|R V T J E F F O R D S<br>D IA [ A G ] H A R K IN|
|D N E [ A G S u b C m M IP P , P P C ] K E R R E Y|
|D G A M IL L E R|
|R M S L O T T|
|D N E N E L S O N|
|D C A F E IN S T E IN|
|D N D [ A G S u b C m F C R R , M IP P ] C O N R A D|
|R N Y L A Z IO|
|R F L M C C O L L U M|
|R D E R O T H|
|R N V E N S IG N|
|R M O A S H C R O F T|
|R D C [ A G C h a ir ] L U G A R|
|R V A A L L E N|
|R M I A B R A H A M|
|R P A [ A G S u b C m F C R R , R N G L ] S A N T O R U M|
|R M N G R A M S|
|R M T B U R N S|
|R W A G O R T O N|
|$ 0<br>$ 5 0 , 0 0 0<br>$ 1 0 0 , 0 0 0<br>$ 1 5 0 , 0 0 0<br>$ 2 0 0 , 0 0 0<br>$ 2 5 0 , 0 0 0|

41
Figure 5: Unweighted model-based estimates vs weighted model-based estimates (under the unit context model with weighting following Guadarrama et al., 2018) at target area level <!-- Start of picture text --> Burkina Faso Chad Guinea<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1 0 .25 .5 .75 1 0 .25 .5 .75 1<br>Mali Niger<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1 0 .25 .5 .75 1<br>Unweighted model estimates<br>Weighted model estimates<br><!-- End of picture text --> Table 4 presents the median, over target areas, of the coefficient of variation estimated for the model-based versus the direct estimates. Our preferred measure of uncertainty for the direct estimates is based on the Horvitz-Thompson approximation, calculated using the R SAE package, with the sum of the sample weights for each area used to approximate the domain size.

Table 4: Median cve for direct and model-based estimates.

|Country<br>|Burkina<br>Faso<br>|Chad<br>D<br>|Guinea<br>irect survey es<br>|Mali<br>timates<br>|Niger<br>|
|---|---|---|---|---|---|
|Sampled<br>areas|0.435|0.271<br>M|0.370<br>odel-based es|0.415<br>timates|0.425|
|Sampled<br>areas|0.167|0.115|0.121|0.164|0.155|
|Non-<br>sampled<br>areas|0.2084|0.268|0.220|0.229|0.229|

18