rafmacalaba/datause-encoder-data huggingface dataset:
# **111.2 Empirical Specification**

<!-- Start of picture text --> 100%<br>75 _ _-* *E<br>5*% * * * *utilities<br>50%__<br>25%<br>* flflflfl<br>0% X_<br>n a0X 0ss<br>0°C a) O D ° D<br>z -1 -j -i -. -.<br><!-- End of picture text --> Figure 2: Upper Bounds of Bribe Payments to Utilities (as % of Revenues) in the Sample Countries.

Note: The categories are cumulative. Because each enterprise has unique upper and lower bounds, enterprises are classified by upper bounds. For example, an enterprise that reported paying between 0.1% of revenues and 1.9% of revenues in bribes to utilities would be counted only in the 'less than 2.0%' and 'less than 5.0%' percent categories (even if its actual payments - which are not observed - were only 0.2% of revenues).

The dependent variable used in this study is the percent of revenues paid by the enterprise to electricity and telecommunications in the form of bribes.<sup>2</sup> 1 This variable is calculated using answers from two questions in the WBES - the percent of revenues paid per annum in 'unofficial' payments to public officials (including to the employees of electricity and telecommunications companies) and the share of those unofficial payments that were spent 'to get connected to and maintain public services (electricity and telephone)'. The enterprise manager's response to the first question about total unofficial payments was categorical (i.e., 0% of revenues; less than 1%; between 1 and 1.99%; between 2 and 9.99%; between 10 and 12%; between 13 and 25%; and over 25%), while the manager's response to the second question was any number between 0 and 100% (of total 'unofficial payments'). From this information, it is possible to calculate a range for the percent of revenues that each enterprise reported paying to electricity and telecommunications utilities.<sup>22</sup> About 75 percent of enterprises 21 The question refers specifically to power and telecommunications, but does not separate between the two. To encourage honest responses to questions about bribery, and to allow enterprise managers to avoid implicating themselves when answering questions about frequency and level of bribe payments, the WBES asked about bribes paid by 'firms like yours' rather than about the manager's own firm. In the empirical analysis, we assume that the manager was answering the question for a firm similar to the manager's own enterprise in terms of the independent variables.

22 That is, the share to the utilities times the percentage of revenue as unofficial payment. Because the second response could take any value between 0 and 100%, the ranges are distinct for each enterprise.

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27 adjustment required by a successful devaluation. In the subsection that follows we use data on 39 devaluation episodes to implement the first two tests described here.<sup>22</sup> IV.l The Politics of Successful DevaluAtion Prozrams: Some Preliminar EmDirical Evidec The purpose of this subsection is to use a cross country data set to investigate the extent to which political considerations determine the degree of success of 39 devaluation programs.

IV.1.lTh QASJbaL_iE The data set on devaluations used in *ur empirical investigation corresponds to that assembled by Edwards (1989) in his study on real exchange rates in developing countries. The episodes are listed in Table 7, and have been classified into a group (Panel A) of countries that implemented a stepwise devaluation and a group (Panel B) that adopted a crawling peg after devaluing. As can be seen all devaluations are substantial -- of at least 15 percent.

The stated objective of these devaluations, and their accompanying packages, was to help solve the external crises in these countries by reversing the real exchange rate overvaluation -- that is, by generating a real exchange rate depreciation -- improving the current account and improving the net foreign position. Edwards analyzed the degree of econgomi success of these devaluations using a two step procedure. First, he analyzed the evolution of a set of external sector indicators -- the real exchange rate, the current account, and the net foreign asset position -- in 22We don't attempt, however, to directly test the decentralization hypothesis. 23 These are in fact the stated objectives of the IMF programs that were associated with most of these devaluations.
in particular in foreign markets. However, all the results presented above are obtained after controlling for firm fixed effects in all regressions, which helps to mitigate that concern.

# **4. Further Export Performance Premia for Exporter-Importers**

Importing intermediate inputs and importing relatively higher amounts of intermediate inputs are associated with strong export performance for firms in Peru. But it is important to investigate further these premia and consider several dimensions of intermediate inputs that may better capture the degree to which they embody foreign technology and the degree to which they represent increased variety and quality, some of the key mechanisms predicted by the literature for the growth-enhancing role of imports of intermediates. To address this issue we estimate a variant of Eq. (1) where the sample is restricted to exporter-importers only and where the exporterimporter status dummy variable is replaced alternatively by the number of imported HS 6-digit products (in logarithms), the number of imported varieties defined as an HS 6-digit-origin country cell (in logarithms), and a dummy variable for exporter-importers that import more than 50% of their inputs from high-income countries.<sup>6</sup> Table 3 shows the results from these specifications which reveal significantly higher export values, larger numbers of destination markets served, and relatively higher product quality than average for the exporter-importers that import more products, more varieties, and a larger share of imports from high-income countries. For example column (9) indicates that exporter-importers firms with more than 50% of imports of intermediates coming from high-income countries have 29% larger export values than exporter-importers with a lower percentage coming from highincome countries overall across all sectors (this premium is 49% and 34% in the case of agribusiness and apparel respectively.<sup>7</sup> These estimates provide evidence that the foreign technology embodied in imported intermediate inputs and their higher sophistication have a beneficial effect for the export performance of exporter-importers in Peru.

> 6 High-income countries are defined based on the World Bank income classification as of year 2010.

> 7 These effects are obtained as 100*(exp(β)-1) where β is the estimated coefficient.

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Table 3. Length of Railroads under Private Sector Operations

||Obs|Mean|Std.<br>Dev.|Min|Max|
|---|---|---|---|---|---|
|Freight|22|2,910|2,327|184|8,029|
|Passenger (intercity, local)|9|484|773|35|2,270|

Partially due to this scale effect, the transport sector seems to face with some serious problems of effectiveness of PPP contracts. Guasch (2004) shows that renegotiation was especially common in transportation concessions (Table 4). Our sample is broadly consistent with his results; the renegotiation rate is 57 percent of total road concessions. Renegotiation is less likely to occur in railroads; the probability is estimated at 32 percent.

<u>Table 4. Incidence of Renegotiation</u>

||Inciden<br>(% of total co|ce<br>ntracts)|
|---|---|---|
||Guasch<br>(2004)|Our<br>sample|
|Total|41.5||
|Electricity|9.7||
|Transport|54.7||
|Roads||57.3|
|Railroads||32.3|
|Water & sanitation|74.4||

Sources: Guasch (2004); and authors’ calculation.

One possible reason for frequent renegotiation in the transport sector is a lack of competitive nature. The degree of competition for transport is normally relatively limited compared with energy and telecommunications. The other reason is that the transport sector might involve greater uncertainty beyond the reasonable assumptions accounted for in the contract. For instance, the demand forecasts for road traffic may be systematically more difficult those for electricity. It could also be attributed to the possibility that transport transactions might be more vulnerable to corruption and collusion, which would materialize as a renegotiation incidence.

However, the most fundamental issue is that it should take long time for private operators to be motivated to invest in infrastructure (i.e., network) and recover their investments with sufficient revenues. During the long contract periods many exogenous and endogenous factors would change. Interestingly, however, the rail franchising experience in U.K.—which is a pioneer in this area—shows that aggregate revenue 8
**Table 10:** The effect of visa provision on the bilateral migration stocks: (i) controlling for the depth of PTAs, (ii) placebo test using migration unrelated provisions.

|Dep var:||B|ilateral Stoc|k of Migrant|s||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Visa|0.138***<br>(0.034)|0.128***<br>(0.034)|||||
|Movement of capital|||-0.047|-0.042|||
||||(0.035)|(0.034)|||
|Money laundering|||||-0.008|-0.005|
||||||(0.044)|(0.044)|
|RTA Depth|0.003***|0.003***|0.005***|0.005***|0.004***|0.004***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|
|Schenghen||0.134***||0.141***||0.143***|
|||(0.036)||(0.036)||(0.036)|
|GDP diference (ln)|0.001|-0.000|-0.000|-0.002|-0.001|-0.002|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Estimator|PPML|PPML|PPML|PPML|PPML|PPML|
|Country-Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Pair Fixed Efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Sample period|1990-2020|1990-2020|1990-2020|1990-2020|1990-2020|1990-2020|
|Observations|63,803|63,803|63,803|63,803|63,803|63,803|
|Pseudo R-squared|0.99|0.99|0.99|0.99|0.99|0.99|

Note: Dependent variable is the stock of immigrants in country _j_ originating from _i_ . Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
light density comes from the National Oceanic and Atmospheric Administration.

Table 2 presents descriptive statistics for all the main variables in our analysis. Our sample includes 1,080 observations that correspond to the 30 municipalities that represent cities of most in Colombia and 36 months.<sup>10</sup> We focus on capital departments capital cities that host about two-thirds of all Venezuelans in our sample (e.g., using the locations reported in the RAMV data) in order to reduce possible biases in crime reporting due to internal conflict that still persists in rural areas (Iba ̃nez et al., 2017). Yet, as a robustness test we also show that our main results hold for different levels of geographic aggregation B. A. (such as aggregating the data by department-month level) in Appendix Panel A of Table 2 illustrates the total number of crimes B the ones re- reported; panel ported _by Venezuelans_ in rates per 100,000 individuals. The rate of all crimes reported by Venezuelan migrants in our sample is 0.79 per 100,000 individuals (panel B). The most common crimes reported by Venezuelans is theft (0.44 per 100,000 individuals). These numbers are quite low, as expected—at the time, Venezuelans represented at most three percent of Colombia’s population, with heterogeneity in terms of locations, naturally. Yet, they have enough variation to exploit for our exercise. Indeed, given the high standard deviations relative to the mean, significant variation is evident across crime reports, cities, and months. Similar trends are observed for crimes perpetrated by Venezuelans (Panel C), where theft is also the most common crime.

In terms of the treatment variable, panel D of Table 2 shows that for the average city in our sample, there were 4,839 Venezuelans who received the PEP migratory status. That number, however, ranged between 13 and 35,729 across all cities in the sample. In proportion to the local population, the number of PEP migratory status awarded was, on average, 217.98 per 100,000 inhabitants. While the number of observations in this table is 10Colombia has 32 departments; the panel only includes those 30 cities for which there were consistent monthly crime reports and control variables. The cities excluded did not had available data for all periods and the data was of low quality.

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Table 6. Women’s Social Status After Marriage Post the Gujarat Riots of 2002

||(1)<br>Controlled by<br>Husband|(2)<br>Emotional<br>Violence|(3)<br>Physical<br>Violence|(4)<br>Makes Health<br>Decisions|(5)<br>Makes Decisions<br>about Visits|
|---|---|---|---|---|---|
|Post X Riot|0.003|0.028<sup>_∗∗∗_</sup>|0.045<sup>_∗∗∗_</sup>|-0.001|-0.019<sup>_∗∗_</sup>|
||(0.004)|(0.003)|(0.005)|(0.006)|(0.007)|
|Observations|55133|55162|55162|70700|70700|
|R-squared|0.135|0.0557|0.124|0.0643|0.0760|
|Control Average|0.474|0.118|0.280|0.740|0.730|
|District Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|

_Note_ : This table reports coefficients from specification 1. Standard errors are clustered at the state level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

Table 7. Women’s Economic Status After Marriage Post the Gujarat Riots of 2002

||(1)<br>Employed in<br>Last 12 months|(2)<br>Controls own<br>Income|(3)<br>Makes Household<br>Purchase Decisions|
|---|---|---|---|
|Post X Riot|-0.017<sup>_∗∗∗_</sup>|-0.022<sup>_∗∗∗_</sup>|-0.050<sup>_∗∗∗_</sup>|
||(0.002)|(0.007)|(0.004)|
|Observations|423957|16043|70700|
|R-squared|0.0208|0.108|0.0687|
|Control Average|0.871|0.817|0.717|
|District Fixed Efects|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|

_Note_ : This table reports coefficients from specification 1. Standard errors are clustered at the state level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

29
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[) Sporadic post-2010 [_] Systematic post-2010
long after the science had emerged in Britain and France. Indian associations pioneered statistical survey methods to expose peasant poverty.

# _Religion, nationalism and social aspiration_

By the later nineteenth-century as Carey Watt (Watt, 2005) has shown, India possessed an extraordinarily sophisticated range of civil society bodies, dedicated to the generation and diffusion of information, public education, national independence and social reform. One feature of these associations that should be mentioned here is the importance of religious organization in the generation of social capabilities. This has been noted in modern development literature. For much of the nineteenth and twentieth centuries the major ‘development work’ done in India – outside the reluctant and under-funded operations of the colonial state – was carried out by bodies representing new ‘religions of mankind’, Hindu, Christian, Muslim, Parsi and others. Neo-Hinduism was essentially ‘this-worldly’ in Weberian terms, though ironically Weber’s misunderstanding of India and later economic historians’ misunderstanding of Weber have occluded this point. These religious movements included the Arya Samaj (Aryan society) that worked on behalf of peasant communities (Datta, 1999) and established itself among the Indian plantation workers in Mauritius and the West Indies, attempting to educate them and improve their working conditions. Even earlier the Brahmo Samaj (Divine Society) seeking to rid Hinduism of what it saw as polytheism established schools for the poor and initiated campaigns to improve the lot of secluded Indian women and widows. At a rough calculation, various religious societies employed up to a 100,000 full-time or part-time employees by the end of the nineteenth century. Even moderate nationalist movements such as the Servants of India Society drew on traditions of this worldly religion, in that they drew on earlier ideas of sacred service ( _seva_ ) and transformed them into a practice of public service. These volunteers, with their larger number of aids and helpers, were active in educational and charitable activities across the country.

_Contesting colonial government_ 21
in developed countries, whereas the literature on developing countries remains anecdotal, descriptive or based on analyses of small qualitative samples or household surveys, with all their limitations.<sup>14</sup> This paper tries to address this gap by using harmonized census data from a select group of Latin American and African countries.

# **3. Data and Methodology**

# **_3.1. Data_**

The data used in this paper are available through the Integrated Public Use Microdata Series-International (IPUMS-I) initiative led by the Minnesota Population Center at the University of Minnesota. It includes nationally representative samples of country censuses (using the household as the sample unit) that correspond to 10 percent of the census in each country. Although the censuses belong to different years, they are harmonized for comparability across countries and over time.<sup>15</sup> To study the impact of gender and ethnicity on education outcomes, we limit our analysis to censuses collected since 2001 that include a proxy for respondents’ ethnicity. The literature on self-reports of ethnic background indicates that race, color and other indicators of selfidentification are “fluid” (i.e. tend to vary across time, place, economic status, and even the interviewer’s race or color), which reduces their reliability over time or across countries (Telles and Paschel 2012).<sup>16</sup> In contrast, language-based indicators of ethnicity are less malleable and easier to interpret than race or color, both over time and across contexts. For this reason, we focus on countries in which the linguistic information included in the census is unambiguous and

> 14 For example, Carr (2013) argues that household surveys are more prone to underrepresenting traditionally excluded populations compared to censuses (e.g. people who live in fragile or disjointed households, slum populations, those who live in areas posing security risks, among others). Surveys can also be more prone to issues of measurement error and sampling bias.

15 For more information about the IPUMS-I database, see https://international.ipums.org/international/about.shtml.

16 Telles and Paschel (2012) find that the relationship between wealth and racial self-identification is fluid and varies across Latin America. In Colombia, for example, increases in wealth are associated with greater tendencies for identifying oneself as white; in Brazil and Costa Rica, they are associated with polarization, as wealthier respondents are more likely to self-identify as black or white rather than of mixed race. Their paper also provides a good overview of the literature on this topic.

9
- Pastore, C., M. Gazzano, and M. Pinotti. 2012. “Por Que a Produção Industrial Não Cresce Desde 2010?” Mimeo.

Reis, J., and T. Farole. 2012. “Trade Competitiveness Diagnostic Toolkit.” World Bank, Washington, DC.

Rodrik, D. 2007. _Industrial Development: Stylized Facts and Policies; Industrial Development for the 21st Century_ . New York: UN.

Salter, W. 1959. “Internal and External Balance: The Role Price and Expenditure Effects.” _Economic Record_ 35 (August): 226–38.

29
_14_ _regime._ The two regimes have different cash flow patterns, but have the same marginal effective tax rate and the same incentive effect on saving. _A key characteristic which is common in the two regimes is that the investment income ofpension assets is exemptedfrom taxes._ This tax provision has been a key reason that voluntary pension plans have been widely used and that pension funds have become major institutional investors across countries. By contrast, Indonesia taxes pension funds' investment income.

The table below shows the tax rules applicable to different types of investment income earned by pension funds in selected countries with well-developed pension systems.

# **Taxation of Pension Plans' Income and Capital Gains Cross-Country Comparison**

||Bank<br>Deposits|Listed Loc|al Securities|Open-Ended<br>and Unliste|Mutual Funds<br>d Securities|Prope|rties|
|---|---|---|---|---|---|---|---|
||Interest|Int & Div|Cap Gain|Int & Div|Cap Gain|Rental|Cap Gain|
|**Canada**|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|
|U.S.|NoTax|NoTax|No Tax|No Tax|NoTax|NoTax|No Tax|
|U.K.|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|
|Chile|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|
|**Singapore**|NoTax|No Tax|No Tax|NoTax|NoTax|NoTax|NoTax|
|**Malaysia**|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|NoTax|
|**Indonesia**|NoTax|NoTax|NoTax|_Fully Taxed_|_Fully Taxed_|_Fully Taxed_|_Fully Taxed_|

Notes: Only approved (qualified) plans are considered. For Indonesia, plans with pre-1992 approval also get tax exemption for property income.

Sources: Davis (1993), and information supplied by Malaysia's Employee Provident Fund and Singapore's Central Provident Fund.

The failure to grant tax-free status for pension plans is obviously motivated by the intention to protect tax revenue. The implications of this practice, however, may not be well understood. One consequence of the selective taxation as practiced in Indonesia is to reduce the safety of pension assets, as investment managers cut the allocation of assets with taxable earnings and thereby limit the benefits of diversification. In most other countries, the cost-benefit analysis of this question has pointed conclusively in one direction - not to tax pension plans. This choice is an acknowledgment of the need to limit the adverse effect of income-based taxation on saving, particularly long-term saving. Furthermore, the tax revenue foregone by not directly taxing pension plans is more than offset by revenue gains elsewhere, including increased taxable pension benefits and increased business activities which are financed by pension assets.

**_Retirement Age_** For the purpose of making public policy, setting an appropriate retirement age is one of the most crucial decisions. The choice made has major implications for the attractiveness of pensions, both for the sponsor and the worker, as well as the old age security of the retirees. Currently, the standard retirement age in Indonesia is 55. In other ASEAN