rafmacalaba/datause-encoder-data huggingface dataset:
ratio, and is the labor force participation rate (labor force to working-age population ratio). The variables tt 0, h , and are exogenous and evolve at ρρ 0 h the following annual growth rates: tt tt tt tt tt , respectively. Throughout AA , NN , ωω ρρ AA NN ωω ρρ the model, exogenous variables are indicated with a bar index notation (as in tt tt tt tt tt ). gg̅ , gg̅ , gg̅ , gg̅ , gg̅ **The natural resource sector.** The setup of the natural resource sector builds gg̅

**The natural resource sector.** The setup of the natural resource sector builds on Hansen and Gross (2018) and Arezki et al. (2017) in being a Cobb-Douglas function of proven reserves (R) and physical capital (K), with decreasing returns in both R and K (equation (2)).<sup>6</sup> This production function has the desired property that the first reserves are relatively easy to extract – for example, being close to the surface—but later reserves require more and more capital (or technology) to generate the same output as firms are forced to drill further underground or in less accessible locations.<sup>7</sup>

accessible locations.<sup>7</sup> As countries produce multiple commodities, the natural resource sector, , is further disaggregated into non-renewable resource industries (e.g., RR oil, natural gas, copper, gold, and others). As shown in equation (2), the output of NN ii∈{1, ... , NN} resource industry , , is produced using reserves and physical capital ii ii ii in that industry, tt tt−1 tt−1 ii QQ RR KK � � (2) 1−γγii γγii ii ii ii ii where QQtt = AA is the TFP in industry tt�KKtt−1 �RRtt−1 —which grows at exogenous rate , 0 < γγii < 1 and ii∈{1, ... , NN} — and is ii AAii the share of resource rents in industry tt (then, is the capital income share). tt ii AA ii gg̅ γγ Capital and reserves are state variables determined in the previous year ii . ii 1 −γγ The dynamics of reserves in each industry obey the following law of motion, tt−1 (3) ii ii ii ii tt tt−1 tt tt RR = RR −QQ + DD� , ii∈{1, ... , N}

> 6 Hansen and Gross (2018) and Arezki et al. (2017) also include labor in the production function, but its share is very small (0.13); so for simplicity we exclude it.

> 7 Alternatively, can be interpreted as a quality-adjusted index of reserves, that takes into consideration geological factors such as ore grade (for minerals) or the composition of hydrocarbons ii tt (for petroleum and natural gas). As the highest quality mines and oil fields tend to be explored first, RR further extraction and depletion reduces the quality of the remaining reserves, scaling down the industry marginal product of capital (see Cochilco 2017). However, our default calibration of the model is not adjusted for the quality of reserves.

7
# **Tamil Nadu:**

# **_Issues raised_**

About half of the _gram sabhas_ recorded in Tamil Nadu (45 of 89) had a revealed presence of SHG members, making Tamil Nadu the most active out of the four states in terms of SHG members’ vocal participation in the _gram sabha_ .<sup>16</sup> The top-six issues raised by all women and by SHG women in TN converge with some differences in the frequency with which they were raised. These issues are civil works<sup>17</sup> (20.31%, 28.11%) and water (14.95%, 15.79%), the top two priorities, followed by health & sanitation (9.77%, 6.18%); jobs & entrepreneurship (8.99%, 14.88%); finance to women for economic enterprise (8.36%, 11.62%); and civics & participation (6.82%, 7.05%). Thus, there is a greater degree of similarity than dissimilarity. Rank ordering these top-six issues for all women and SHG women reveals that ‘health and sanitation’ appears at number three versus number six for the two categories of women. Other than this difference, the rank ordering of these issues is quite similar. It is noteworthy that ‘jobs and entrepreneurship’ and ‘finance’ ranked within the top-six for both categories of women. This can be speculatively ascribed to the indirect influence of SHGs and of the state’s focus on financially supporting SHGs with loans for income generation, which may have made even non-SHG-affiliated women interested in possibilities of income generation. Other issues received comparable support from all women and SHG women: Education (5.78%, 5.07%); Entitlement and Ration (3.54%, 4%); Transportation (2.74%, 2.84%). Looking beyond this broad commonality, we can uncover some subtle but important differences. Table 1.0 represents the percentage distribution of issues raised by all women and SHG women.<sup>18</sup> 26
Imports contribute to innovation through access to more varieties to produce output. This increases their productivity and results in higher profit. Using data from Indian firms, Goldberg et al. (2008) provide empirical evidence on how imported intermediate goods increase new product innovation in the economy.

Since both exports and imports are likely to lead to more innovation and growth, firms that perform both activities should be more likely to perform better than the ones who perform only one of the activities. In the reduced form model introduced in the empirical section, I test these relations. More specifically, I test the hypothesis whether firms that are engaged in global markets either through importing or exporting grow faster and innovate more than the firms who participate only in the domestic markets.

# **3 Data**

For the analysis, I use plant<sup>10</sup> level data collected through the World Bank’s Enterprise Surveys. The surveys cover a rich set of developing countries from different regions of the world. In the survey of each country, a random sample of firms is selected that is representative of the manufacturing industry. The sample of firms is stratified by size, and location. A total of 27,754 firms from the manufacturing industries of 43 developing countries are used in the analysis. The surveys conducted in 2002, 2005, and 2008 cover Eastern European and Central Asian countries (ECA surveys). The 2006 survey covers the Latin American and Caribbean countries (LAC survey)<sup>11</sup> . Table 1 shows the number of firms included in each survey. Details of the observations from countries are given in Table 16 in the Appendix. In addition to the cross-sectional data, there are 2,911 firms from 31 countries that were surveyed twice in three years. The number of firms included in this panel is given in Table 2.

**Table 1 Survey Summary**

||Percent<br>|
|---|---|
|ECA 2002|23|
|ECA 2005|34|
|ECA 2008|18|
|LAC 2006|25|
|Total|27,754|

> 10 Although, in the surveys unit of observation is plant, I use firm in the rest of the paper. In the LAC 2006 and ECA 2008 surveys, firms were asked whether they are a part of a larger firm. 89% of 6919 firms in LAC survey and 90% of 5063 firms in ECA 2008 survey who answered to this question own a single plant. The multi-plant firms make 34% of total employment in LAC region in 2006 and 20% of total employment in ECA region in 2008.

> 11 Data in the surveys refers to the last fiscal year when the survey was conducted (i.e. data in 2008 survey is from fiscal year 2007).

9
**Table A5. Transition matrices for intra-generational mobility in Moldova, upper bound estimates 1999 to 2004**

|**1999-2004**|**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|
|---|---|---|---|---|
|**<$2.5**|**34.1**|32.8|9.9|1.3|
|**$2.5-$5**|5.1|**6.6**|3.4|0.6|
|**$5-$10**|1.3|2.3|**0.7**|0.1|
|**$10+**|0.7|0.8|0.3|**0.0**|

Source: Calculations based on HBS data.

Note: Results presented are for the upper-bound mobility estimates using the synthetic panel methodology by Dang et al (2011).

**Table A6. Transition matrices for intra-generational mobility in Moldova, upper bound estimates 2006 to 2011**

|**2006-2011**|**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|
|---|---|---|---|---|
|**<$2.5**|**0.7**|12.9|8.8|0.4|
|**$2.5-$5**|0.7|**20.3**|21.1|1.4|
|**$5-$10**|0.1|9.8|**16.0**|1.5|
|**$10+**|0.0|1.9|3.9|**0.5**|

Source: Calculations based on HBS data.

Note: Results presented are for the upper-bound mobility estimates using the synthetic panel methodology by Dang et al (2011).

**Table A7. Transition matrices for intra-generational mobility in Moldova, lower bound estimates 2006 to 2009**

|**2006-2009**|**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|
|---|---|---|---|---|
|**<$2.5**|**16.5**|6.3|0.0|0.0|
|**$2.5-$5**|0.9|**34.1**|8.4|0.0|
|**$5-$10**|0.0|1.1|**22.9**|3.4|
|**$10+**|0.0|0.0|0.3|**6.0**|

Source: Calculations based on HBS data.

Note: Results presented are for the upper-bound mobility estimates using the synthetic panel methodology by Dang et al (2011).

**Table A8. Transition matrices for intra-generational mobility in Moldova, lower bound estimates 2009 to 2011**

|**2009-2011**|**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|
|---|---|---|---|---|
|**<$2.5**|**11.2**|3.4|0.0|0.0|
|**$2.5-$5**|0.2|**36.4**|5.0|0.0|
|**$5-$10**|0.0|0.9|**34.1**|1.4|
|**$10+**|0.0|0.0|0.9|**6.5**|

Source: Calculations based on HBS data.

Note: Results presented are for the upper-bound mobility estimates using the synthetic panel methodology by Dang et al (2011).

40
comparison of debt management performance in each group finds it does not correlate to income: those classified as low income exhibited the strongest debt management performance (as defined by meeting with the DeMPA scores of at least C) and those classified as lower an upper middle income about the same (Figure 4). On average the first group met the minimum requirement in 36 percent of the dimensions assessed and surpassed it, with a score A or B, in 9 percent: the comparable figures for small states in the upper middle income group were 32 percent and 12 percent, respectively.

**Figure 4: Debt Management Capacity – Comparison by Income Group**

<!-- Start of picture text --> 0 20 40 60 80 30<br>25<br>Low<br>20<br>A<br>Lower-middle 15<br>B<br>10 C<br>Upper-middle<br>5<br>0<br>D C and above Low Lower-middle Upper-middle<br><!-- End of picture text --> Source: DeMPA reports for 17 sample small states<sup>24</sup>

# **How Does the Debt Management Capacity of Small States Measure Up?**

A summary of how small states in the sample perform in each dimension of debt management is discussed below and illustrated by the data in Figure 5.

In six dimensions of debt management 10 or more small states, two thirds of those assessed, met or surpassed the minimum requirement for:

- coordination with fiscal policies, specifically accurate and timely forecasts on total debt and debt service under alternate scenarios;

- coordination with monetary policy, in particular the imposition of limits on direct access of resources from the Central Bank;

- managerial structure for borrowings and debt-related transactions;

- existence, coverage, and content of the legal framework;

> 24 For analysis purpose, Maldives is classified as lower-middle-income country. Maldives was classified as lowermiddle-income country when the DeMPA was conducted, which status was changed to upper-middle-income for FY12 based on 2010 GNI.

14
equipment. They undertook interviews with users of the facility, visiting a randomly selected ongoing class in each school for close observation and interviewing a randomly selected patient in each health facility. Finally, they interviewed the director responsible for each facility.

In total, data were collected from 3,379 households in 735 villages, 610 primary schools, 504 health facilities, and 1,496 children’s exams were conducted.<sup>9</sup> These data provide us with 171 outcome variables (definition and summary information can be found in Appendix E). To avoid over-rejection of the null hypothesis due to multiple inference (Anderson 2008), we committed, in advance, to a mean effects approach. That is, we reduced the effective number of tests we conduct by combining the individual measures into eight family outcomes (Kling et al. 2007), of which the individual components were pre-registered.

In Appendix F, we show that the randomization procedure was successful in ensuring substantive balance across treatment arms. This study's empirical strategy is therefore straightforward. We compare mean outcomes between treatment and control areas, and – for those areas that participated in the parity lottery – between gender parity and non-parity areas. These analyses provide unbiased estimates of the average treatment effect (Rubin 1974). Specifically, we estimate (for individual-level outcomes) an equation of the following form: Where _i_ indicates the individual, _j_ indicates the village, and _k_ the lottery block, and _T_ is the treatment status (i.e., assignment to the Tuungane program). We use lottery bin fixed effects to control for average differences in observable or unobservable predictors across lottery bins, and we cluster our standard errors at the village cluster level.

# **Results**

Panel (a) of **Figure 2** presents results for the eight outcome families. Appendix G provides results for the outcome measures individually.<sup>10</sup> Each circle is a point estimate and bars (ticks) present 95% (90%) confidence intervals. The dependent variables are standardized. We report sample

> 9 Further details related to the data sources and attrition can be found in Appendix D. Attrition took place for a number of reasons, including inaccessibility of some regions for security reasons, as well as the loss, damage, and theft of tablets. Rates of attrition of these sources are balanced across treatment groups. 10 To create the family indices we took the following decisions (similar to Humphreys et al., 2012). We do not impute data for missing observations. When a unit has individual outcomes missing, the family index is constructed based on the remaining non-missing observations. Finally, when a family index is based on individual outcomes from different levels, we conduct the analysis at the village level. Results related to the outcome families mirror those from the individual outcomes.

7
# **3.2 Empirical Facts**

In this section, I discuss three empirical findings that show a negative relationship between informality rates and the accessibility of formal jobs in Mexico City: 1) informal workers are more sensitive to commuting costs and spend less time commuting; 2) informal workers are located in areas in which they have poor access to formal employment; and 3) informality rates decrease with transit improvements that connect informal workers to formal employment.

## **3.2.1 Cross-sectional Variation**

**_Finding 1:_** _Informal workers spend less time commuting and work closer to their home relative to formal workers._

To reach the first finding, I use the 2015 Intercensal Survey. With this data, I observe the residence and workplace and average commuting time of each worker at the municipality level. Exploiting cross-sectional variation, I compare the average commuting time and the workplace decisions of informal vs. formal workers. I adopt the standard definition of informality based on the contractual relationship of the worker, I also restrict the sample to individuals who worked the week before the census interview. I run the following linear probability model to test whether informal workers spend less time commuting: where _yi_ is a dummy variable that takes the value of 1 if individual _i_ commutes to a different municipality than the one in which he/she resides, whether he/she works in the central business district (CBD) of Mexico City, or whether their average commuting time is within some window of time (i.e., 16 to 30 minutes); _Xi_ is a vector of individual characteristics that includes age, education, gender, relationship to head of household, and a dummy variable indicating whether the individual has an African or indigenous background; _γl_ ( _i_ ) and _γn_ ( _i_ ) are origin and destination fixed effects; _γm_ ( _i_ ) is a transportation mode fixed effect to compare informal vs. formal workers that use the same transportation mode; and, _εi_ is the error term of the regression.

Figure 2 depicts the point estimate and confidence interval of a linear probability model. I relate the probability that the average commuting time of a worker is within some window of time with a dummy variable that takes a value of 1 if the worker is informal. As the figure shows, informal workers spend less time commuting than formal workers. For instance, the first bar shows that informal workers are more likely to work from their home relative to formal workers by 13 percentage points. Similarly, informal workers are more likely than formal workers to spend 15 minutes commuting. On the other hand, formal workers are more likely to spend 30, 60, or 120 minutes commuting.

To provide more evidence of this result, table B1 in the Online Appendix reports the results for 10
**Figure 6. Brain drain and LDC's growth** **_(with 2_**<sup>**_nd_**</sup> **_order polynomial trend)_**

<!-- Start of picture text --> 0.04<br>Pakistan<br>Indonesia<br>Guatemala Honduras<br>China<br>0.02<br>India<br>0.00 Brazil Paraguay Colombia<br>0 Thailand Venezuela2 PeruArgentinaUruguay4 Bolivia Egypt 6Chile 8 10 Mexico 12 14 16 18 20<br>Ecuador<br>Costa Rica<br>Dominican Rep Nicaragua<br>-0.02<br>Philippines<br>South Korea<br>-0.04<br>Net effect on annual GDP growth rate (in %)<br><!-- End of picture text --> <!-- Start of picture text --> Emigration rate of tertiary educated workers (in %)<br><!-- End of picture text -->

# 3.5 Remittances

Migrants’ remittances constitute another channel through which the brain drain may generate positive indirect e¤ects for source countries. It is well documented that workers’ remittances often make a signi...cant contribution to GNP and are a major source of income in many developing countries. Remittances impinge on households’ decisions in terms of labor supply, investment, education (Hanson and Woodru¤, 2002, Cox Edwards and Ureta, 2003), migration, occupational choice, and fertility, with potentially important aggregated e¤ects. This is especially the case in poor countries where capital market imperfections (liquidity constraints) reduce the set of options available to members of low-income classes.

The literature on migrants’ remittances shows that the two main motivations to remit are altruism, on the one hand, and exchange, on the other hand.<sup>14</sup> Altruism is primarily directed towards one’s immediate family, and then decreases in intensity with social distance. By contrast, in principle, no such proximity is required in the case of exchange; the exchange-based theory of remittances posits that remittances simply ”buy” various types of services such as taking care of the migrant’s assets (e.g., land, cattle) or relatives (children, elderly parents) at home. Such transfers are typically observed in case of a temporary migration and signal the migrants’ intention

> 14See Rapoport and Docquier (2004) for a comprehensive survey of the theoretical and empirical literature.

24
We can thus recover the change in the factor adjustment cost from data on the change in the ratio of input expenditures. This requires no production function estimation. It is a double difference within a firm across inputs over time. Importantly, z can be interpreted as the τ from Hsieh and Klenow (2009), where τ’s are distortions that raise the marginal product of one input relative to the other input. This can be generalized to the case of k variable inputs, in which changes in k-1 relative adjustment costs over time can be estimated.

Note that the price mark-up μ does not appear in equations (4) to (6). This method cannot be used to estimate the price mark-up - or, more generally, any distortion that affects all inputs proportionally (such as the ‘input-neutral implicit tax’ in Hsieh and Klenow, 2009)). As equation (3) shows, estimating the mark-up and the level of the adjustment cost would require production function estimation.

# 3.2 Empirics: Estimating trends in factor adjustment costs from firm-level panel data

The main objective of our descriptive analysis is to apply equation (5) to firm-level data to estimate how factor adjustment costs have changed over time in Indian states.

v Suppose there are k variable inputs, with zist being the adjustment cost of input v (relative to input 1) for a firm. Rearranging Equation (5), θi1 where the ratio of output elasticities, log v , is by assumption fixed over time. θi Now suppose that This equation specifies that the adjustment cost consists of a firm-level fixed component Avis, annual shocks which depend on a set of initial firm characteristics Xi, and an idiosyncratic firm-specific shock eistv . The initial firm characteristics could include, for example, the state in which the firm is located, firm size, sector and age.

We do not observe log (zistv ), but can infer the α′s and β’s – the changes over time –from the ratio of input 1 αit expenditure shares log v ~~,~~ as described in equation (7). Combining equations (7) and (8), we arrive at our αit baseline regression specification: We are mainly interested in how the adjustment cost varies over time by state. As specified in equation (8), we regress the ratio of firm-level input shares on firm fixed effects and on year dummies interacted with state dummies. The firm fixed effect absorbs the time-invariant output elasticity ratio and all time invariant components of the adjustment cost. Thus, the regression identifies how the adjustment cost has changed relative to the baseline year, and how that change varies by state.

We are also interested in how the adjustment cost varies over time for firms of different size and age. This 7
between banks for those opportunities coincided with lower spreads. However, the shift out of government securities was much swifter for some banks than others, and most banks increased their holdings of those securities from 2004 to 2005. In addition, yearly average spreads in Figure 1 mask wide variation across banks and our statistical analysis below indicates that the drivers of changes in spreads differ across bank ownership types. For these reasons, the reduction in government debt issuance does not provide a complete explanation of the evolution of spreads over this period.

**Figure 1**

<!-- Start of picture text --> Interest Spread<br>20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Source: Data are from Central Bank of Kenya Statistical Bulletin, June 2008. The lending rate is the weighted average of commercial banks’ interest rates on loans and advances. Similarly, the deposit rate is weighted average rate paid by commercial banks on savings deposits. For 2006 and 2007, we use the December figures. For 2008, we use the June figures (latest available).

Further indication that the decline in spreads is not wholly attributable to macroeconomic stability and improved management of government debt comes from simple arithmetic decompositions of interest spreads. In the decomposition exercise we follow the method used in Beck and Fuchs (2004), subtracting the interest rate paid for deposits from the interest rates charged on loans. To calculate the weighted average interest rate charged on loans, we add the 11