rafmacalaba/datause-encoder-data huggingface dataset:
5

**exchang. controls** or<sup>**other**factors ensure that international</sup> capital mobility is not<sup>high for the country.</sup>

The appatach implies that<sup>a nominal devaluation has real</sup> **effects** tnat are sufficiently<sup>long lasting to be worth pursuing,</sup> **at** least provided expenditure<sup>policy avoids excess demand at the</sup> **same** time. Domestic<sup>prices and wages are assumed to</sup><sup>**be**</sup> **imperfectly flexible**<sup>downward (and in the simplest models</sup> **presented** later, are<sup>actually held constant). There is strong</sup> evidence by now that.<sup>other than in the Latin American chronic</sup> inflation countries, devaluations<sup>do have real effects that last</sup> for several years provided<sup>appropriate domestic credit policies</sup> are **being** followed at<sup>the same time. Thus the evidence does seem</sup> to justify one of the<sup>key assumptions, at least for a period of,</sup> say, two to four years.<sup>Even in the case of high inflation</sup> countries continuous<sup>nominal depreciations may have real effects</sup> in the sense of preventing<sup>real appreciations that would</sup> otherwise take place<sup>2</sup> The alternative nominal<sup>anchor approach is a version of</sup> monetarism, and used<sup>to be known as "international monetarism".</sup> The exchange rate<sup>anchors the domestic inflation rate (broadly)</sup> to the inflation rate<sup>of trading partner countries. Possibly the</sup> 2 On the basis of the real<sup>exchange rate indices calculated</sup> by the IMF it seems<sup>clear that since 1981 real and nominal (trade</sup> weighted) exchange rates<sup>have moved closely together in the case</sup> of many of the countries<sup>in our group. For earlier years there is</sup> strong evidence in<sup>Edwards (1989). But the effects do tend to get</sup> eroded, as evidenced<sup>both in Edwards (1989) and, for example for</sup> Indonesia, in Warr<sup>(1984).</sup>
was within village variation in votes (73% of villages). In both cases, the sample average of the predicted median voter price is around 200 pesos – of similar magnitude to what we find using the leader sample to estimate WTA (discussed below). Importantly, it appears that the leaders broadly vote as their constituents would wish: in 71 and 77 percent of the villages, the leader vote was consistent with the median voter price. The majority of the inconsistencies occur in cases where the leader was rejecting a bid that was higher than the estimated median voter price (overall, 19 to 23% of all votes)<sup>12</sup> . This may be explained by the fact that the leaders may have a better sense of the true total costs of program participation than do the households. It could also reflect the strategic incentive for leaders to signal higher price sensitivity, though based on the high percentage of yes votes we observe among the leaders, it does not seem that this was an important driver of choices.

Table 4: Median voter prices and leader votes

||Mean|5th %tile|Median|95th %tile|
|---|---|---|---|---|
|a. Full sample|||||
|Median voter price|179|3|48|626|
|Leader vote consistent with median voter price|0.77||||
|Leader=yes, offer<median voter price|0.04||||
|Leader=no, offer>median voter price|0.19||||
|Observations|862||||
|b. Sample with within-village vote variation|||||
|Median voter price|203|25|93|759|
|Leader vote consistent with median voter price|0.71||||
|Leader=yes, offer<median voter price|0.06||||
|Leader=no, offer>median voter price|0.23||||
|Observations|625||||

Panel a. shows statistics for the full sample and panel b. for the sample that has variation in voting within the village.

Based on this reasoning, in the remainder of the paper we use the estimates of WTA from the 12The existence of sampling noise around our point estimates of the median voter prices implies a higher proportion of leader votes could be consistent if we compared their votes to confidence intervals, rather than point estimates. One aspect of sampling error that could affect our findings is the potential for unusually large or small community level average offer prices, which would induce some communities to have unusually large or small average yes votes. To examine this, we completed our median voter price analysis on a subsample that excludes communities with average household level offer prices that are larger or smaller than 1.5 standard deviations from the overall average. Our findings using this subsample closely mirror the findings in table 4.

25
much closer on mean monthly temperature. After an increase of 1° C. since 1970, both predict another increase of 1° C. by 2050.

This illustration serves to make one point very clearly: In scenario A2, the GCMs concur that India’s temperature will rise steadily as greenhouse gases accumulate in the atmosphere. They also predict significant impacts on precipitation, but not consistently, and with different implications for extreme events. A wetter India will experience more floods, while a drier India will have more droughts. But in either case, it is critical to note that a changed climate in the full scenario is associated with a changed country. By 2050, India has become a middle income country, whose per capita income, human resources and institutional capabilities give it much greater resilience in the face of weather changes.

The Indian case reflects a more general and under-appreciated feature of scenario A2, as well as the other SRES scenarios: Anticipated future emissions increases reflect the expectation that development will also continue. By implication, many of the countries currently termed Low Income or Lower Middle Income by the World Bank will have departed that status by 2050.

Tables 3a and 3b, calculated from the Hughes’ (2009) projections, provide a striking indication of this shift for 94 countries identified as Low Income and Lower Middle Income by the World Bank in 2000. Table 3a tracks the status of 56 Low Income Countries through the succeeding decades. By 2050, 27 have moved to Lower Middle Income Status or higher. Table 3b provides the same information for 38 countries identified as Lower Middle Income in 2000. By 2050, only one has not advanced beyond that group.

Table 4 provides related information on changes in life expectancy, which are linked to the population projections. Life expectancy information is not available for two of the countries tabulated in Tables 3a and 3b. During the present decade, 14 countries still have life expectancies between 41 and 50 years and 18 have life expectancies between 51 8
(Figure 3.3). The left‐hand panel shows that for non‐exporters the productivity gap between servicified firms and non‐servicified firms initially increased then partially rebounded. When looking at exporters in the right‐hand panel, the TFP gap between the servicified and non‐servicified exporters has grown over time. Goods exporters (without service affiliates) recovered quickly after the crisis, while servicified exporters have struggled to do so.

**Figure 3.3: Firm‐level TFP developments in Turkey (2007‐2014)**

<!-- Start of picture text --> TFP ACF non-exporters TFP ACF exporters<br>2007 2008 2009 2010 2011 2012 2013 2014 2007 2008 2009 2010 2011 2012 2013 2014<br>Goods non-exporters Goods exporters<br>Servicified goods non-exporters Servicified goods exporters<br>1<br>1<br>.995<br>.995<br>.99<br>.99<br>TFP changes .985 TFP changes<br>.98 .985<br>.975 .98<br><!-- End of picture text --> Source: EIS data from Turkey; author’s calculations. Note: TFP based on Ackerberg et al. (2015)

**While most industries show negative “productivity premiums” for firms with service affiliates, some show positive performance.** Underperformance and overperformance can be measured by the extent to which servicified firms have a higher or lower productivity level than non‐servicified firms. This productivity premium is computed for each manufacturing sector and plotted in Figure 3.4 for non‐ exporting (top panel) and exporting (bottom panel) firms. A positive (i.e. higher than zero) productivity premium means that servicified firms in that industry have a higher productivity level than non‐servicified firms. The opposite is true for negative values. The dashed lines separate sectors with positive versus negative productivity premiums. For both exporters and non‐exporters, pharmaceuticals, printing and publishing, other transport equipment, motor vehicles, furniture, and leather show a negative productivity premium. Basic metals, chemicals and electrical machinery show positive premiums, meaning that servicified firms in these sectors are doing better than non‐servicified firms. More industries have negative productivity premiums across both categories.

17
- Rasul, I., & Rogger, D. (2018). Management of Bureaucrats and Public Service Delivery: Evidence from the Nigerian Civil Service. _The Economic Journal_ , _128_ (608), 413–446. 13

- Roelcke, G., Nkya, F. M., Besta, N. A., Mchallo, I., Musyangi, M. E., & Orao, S. (2018). Urban local government strengthening program united republic of tanzania (program-for-results operation): Quality assurance review of the sixth annual performance assessment (assessing the performance during fy 2016/17). 26

- Stern, N. H., Dethier, J.-J., & Rogers, F. H. (2005). _Growth and empowerment: making development happen_ . Cambridge, Mass.: MIT Press. OCLC: 62034045. 1

- Stewart, D. (1795). _Account of the Life and Writings of Adam Smith, LL.D._ . 1

- Tilly, C. (1992). _Coercion, capital, and European states, AD 990-1992_ . Studies in social discontinuity. Cambridge, MA: Blackwell, rev. pbk. ed ed. 4, 7

- Weber, M. (1946). _From Max Weber: Essays in sociology_ . New York: Oxford university press. Open Library ID: OL6498314M. 7

- Weigel, J. L. (2020). The Participation Dividend of Taxation: How Citizens in Congo Engage More with the State When it Tries to Tax Them. _The Quarterly Journal of Economics_ , _135_ (4), 1849–1903. Publisher: Oxford Academic. 4

- World Bank (2005). Project appraisal document on a proposed loan in the amount of us$14.50 million and a proposed credit in the amount of sdr 9.92 million to the republic of Indonesia for the initiatives for local governance reform project. 27

- World Bank (2011). _Conflict, security and development_ . No. 33.2011 in World Development Report. Washington, DC: World Bank. OCLC: 730038754. 5

- World Bank (2012). Program Appraisal Document on a Proposed Credit in the Amount of SDR 167.6 million (US$255 million equivalent) to the United Republic of Tanzania for an Urban Local Government Strengthening Program (Program-For-Results Operation). 2, 4

- World Bank (2014a). International development association program appraisal document on a proposed credit in the amount of SDR 161.80 million (US$250 million equivalent) to the Socialist Republic of Vietnam for the results-based national urban development program in the Northern Mountains Region. 27

- World Bank (2014b). International development association program appraisal document on a proposed credit in the amount SDR 245.6 million (US$380 million equivalent) to the Federal Democratic Republic of Ethiopia for the second urban local government development program. 27

34
|**_Table D2a. Heterogeneity by Men's Beliefs: Married C_**|**_hild Depend_**<br>(1)|**_ent Sample_**<br>(2)|**_and restrict_**<br>(3)|**_ed to those c_**<br>(4)|**_ountries for_**<br>(5)|**_which we inf_**<br>(6)|**_ormation o_**<br>(7)|**_n WVS_**<br>(8)|
|---|---|---|---|---|---|---|---|---|
|||||Ti|me Spent on(|Hoursper day)|...||
||Busines|s closed|||Care and|Domestic|Care and|Domestic|
||<br>(1=Yes|<br>; 0=No)|Paid|Work|<br>Respons<br>(Self/M|<br>ibilities<br>arried)|<br>Respon<br>(Spouse|<br>sibilities<br>/Married)|
||_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|
|School closure due to COVID-19=1|0.745**|-0.210|-7.517|-2.995|6.339|0.071|3.530|1.353|
||[0.32]|[0.21]|[4.98]|[3.39]|[3.99]|[2.38]|[3.12]|[2.92]|
|Agree Men and women should have equal opportunities (Men)|<br>0.290*|<br>0.086|<br>7.900**|<br>0.737|<br>0.950|<br>7.048***|<br>3.865*|<br>-3.131|
||[0.17]|[0.12]|[3.31]|[2.35]|[3.28]|[1.72]|[2.25]|[2.18]|
|School closure due to COVID-19=1 # Agree Men and women|||||||||
|<br>should have equal opportunities (Men)|-0.802**|0.239|8.816|3.462|-6.288|-0.146|-3.803|-1.334|
||[0.35]|[0.23]|[5.46]|[3.78]|[4.32]|[2.68]|[3.42]|[3.24]|
|Constant|<br>-0.212|<br>0.232|<br>7.649**|<br>9.396***|<br>2.426|<br>0.798|<br>5.105**|<br>7.899***|
||[0.22]|[0.17]|[3.89]|[2.89]|[3.28]|[1.94]|[2.42]|[2.62]|
|Observations|5652|13245|3311|7869|5652|13245|4295|10196|
|Adjusted R-squared|0.11|0.09|0.12|0.13|0.13|0.04|0.06|0.06|
|<br>Mean|0.16|0.16|6.80|7.62|6.50|4.05|3.70|6.51|
|Notes:* significant at 10% level, ** significant at 5% level, *** significant at 1% level.<br>[1] School Closure due to COVID-19 is a dummy variable where 1 = schools closed i<br>_and women should have equal opportunities_measured prior to the COVID-19 pande<br>gender equality.<br>[2] OLS pooled regressions include Region of the world and Time Fixed Effects and<br>recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and T<br>[3] Sample is restricted to business owners who are married or living with a partner<br>versus MALE (Columns (2), (4), (6), and (8)).|<br>n country at time<br>mic. Men's Com<br>full set of contro<br>ypes of Depende<br>with any child de|of survey wave.<br>munity Norm is th<br>ls including Firm<br>nts). Academic B<br>pendents aged 1|Men's Individual<br>e country-level a<br>Controls (Sector<br>reak control is in<br>8 years or young|Belief is the propo<br>ggregate of the pe<br>+ Size, Age and R<br>cluded in the regr<br>er in the househol|rtion of men in th<br>rceived social nor<br>ole in Business); M<br>essions but not sh<br>d and is split by ge|e country who agr<br>m as reported by<br>obility Controls (<br>own in the table.<br>nder with FEMAL|eed with the stat<br>men in the samp<br>Movement aroun<br>E (Columns (1),|ement that_men_<br>le towards<br>d retail &<br>(3), (5), and (7))|

60
## **Table 7 Determinants of the Z-Score**

# _ROA_ + _CAR_ The dependant variable is _Z score_ defined as _SROA_

where ROA is return on assets, CAR represents capital assets ratio and SROA stands for standard deviation of return on assets. _Assets_ is the natural logarithm of total assets in US $. _Equity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflation-adjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding is_ the share of non-deposit short-term funding in total deposits & short-term funding. _Fee income sq_ and _non-deposit funding sq_ are the square of fee income and non-deposit funding, respectively. We get the data for these bank level variables from Bankscope. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. We obtain these macroeconomic variables from World Development Indicators (WDI) of World Bank. We estimate all regressions with clustering at country level. For panel A, both the dependent and independent variables are mean value over years. For Panel B, the dependent and independent variables are for year 2004. Standard errors are in parentheses. *, ** and *** denote <u>significance at 10%, 5% and 1%, respectively.</u>

|Panel A|
|---|

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Assets|-1.369|-2.113**|-1.534|-2.020*|
||(0.876)|(0.878)|(1.018)|(1.067)|
|Equity|16.769|9.402|20.678**|24.161***|
||(10.098)|(13.618)|(9.824)|(8.307)|
|Assets growth|16.292*|13.940|15.545|15.702*|
||(9.762)|(9.727)|(9.901)|(9.259)|
|Overhead|-62.909***|-95.542***|-80.516***|-68.815***|
||(12.278)|(31.970)|(17.958)|(16.957)|
|Fee income|-27.171***||-29.537***|2.735|
||(4.193)||(4.709)|(17.816)|
|Non-deposit funding||-2.846|11.091|53.564***|
|||(5.839)|(6.866)|(16.897)|
|Fee income sq||||-34.575**|
|||||(15.869)|
|Non-deposit funding sq||||-56.499***|
|||||(20.237)|
|Inflation|-16.008|-10.527|-5.169|2.926|
||(28.718)|(31.389)|(29.521)|(28.427)|

43
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We present the results from six specifications. Specification (1) includes a single control function for all schools (a quartic in the composite dropout-risk score). Specification (2) adds a set of school fixed effects. While this allows for different intercepts in different schools, it still imposes the restriction that a given change in household socio-economic status (as measured by the composite score) is associated with an increase in the probability of enrollment of the same magnitude across all schools. Conceivably, such an assumption of equal control functions may not do justice to the data. For example, there may be differences in school quality that affect not only whether school enrollment is higher in some schools than in others at all levels of socio-economic status (implying different intercepts across schools), but also the gradients between socio-economic status and enrollment (different slopes across schools). Specification (3), our preferred specification, therefore relaxes this assumption by allowing for school-specific quartics in the dropout-risk score (in addition to the school fixed-effects).

Specification (4) is comparable to (3), but limits the sample to applicants whose score placed them within ten ranks of the cut-off. Specification (5) builds directly on the visual evidence in Figure (1); instead of modeling the control function in terms of the dropout-risk score, we include a quartic in each applicant’s relative rank—literally, the rank-distance from the cut-off. Finally, specification (6) is based on the household survey rather than the school visits.<sup>13</sup> We note that it is unusual to have data from both unannounced school visits and an independently administered household survey to compare results.

Table 2 makes clear that the $45 scholarship had a very large effect on school attendance. The impact varies from 18 to 28 percentage points across the various specifications. Model (3), our preferred specification with school intercepts and school-specific quartic control functions suggests an impact of 25 percentage points. In contrast, we can never reject the null that the impacts of the $45 and $60

> 13 The household data have two potential disadvantages for our purpose. First, the sample is considerably smaller (a sample of just under 3500 applicants, rather than the entire universe of more than 26,500 applicants in the first year of the program). Second, since it is based on reported current enrollment status it is conceivable parents might misreport the school enrollment status of their children, and that this misreporting is itself correlated with whether or not they were offered a scholarship, and of what magnitude. Specifically, the concern is that parents who have received a scholarship are more likely to report that their children are enrolled in school, even if they are not, than are parents who were not offered a scholarship (or who were offered a smaller scholarship); in this case, our estimates of the CSP program effects might be biased upwards. On the other hand, the household survey has an advantage in that it asks parents about enrollment in _any_ school, not just a CSP school.

10
# **1. Introduction**

The unorganized sector in India is exceptionally persistent, despite the efforts by many policy makers to reduce it. For example, the employment share in the unorganized sector of manufacturing rose from 80.3% in 1994 to 81.5% in 2005. A useful comparison point for India is the urbanization rate, which is also a policy priority. During this same period, the urbanization rate of Indian manufacturing rose by 8%, from 33% to 41% (Ghani, Grover, and Kerr 2011). Thus, despite all of the exceptional changes to the Indian economy over the past 20 years and its overall growth, the relative size of the unorganized sector has not declined in the least. While the organized sector has grown over the past two decades, the unorganized sector has kept pace.

Understanding what lies behind this persistence is very important, as international comparisons like Figures 1 and 2 show that India has a relatively large informal sector for its stage of development. (As described in greater detail below, establishments in the unorganized sector in India are unregistered, do not pay taxes, and are generally outside the purview of the state, so this group closely parallels common discussions and definitions of the informal sector.) Hsieh and Klenow (2009, 2013) also describe India’s skewed employment concentration towards small firms that lack strong growth prospects. Informal sectors are frequently associated with high poverty rates, poor jobs, and gender discrimination (e.g., OECD 2009, Kanbur 2011), and evidence for India suggests that the productivity growth for the unorganized sector is not keeping pace with the organized sector (Kathuria et al. 2010).

This account sounds quite negative, and indeed there are many ways in which the Indian economy could function better. This study, however, adds a silver lining to the persistence of the unorganized sector. As we document below, virtually all of the persistence in employment and establishment counts for the unorganized sector from 1994 to 2005 was caused by a rapid increase in the share of women-owned businesses in India. Expansion of the economic role of women is highly desired, and women’s participation in the workforce and business ownership is a metric on which India sorely lags its peers. Most women-owned enterprises enter the unorganized sector at a very small scale, and they are typically based out of the household. Thus, 2