rafmacalaba/datause-encoder-data huggingface dataset:
________________________________________________________________________________________________________________________________

|Country|Year|Variable||||||In|dustry||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|___________|_______|___________________|DA<br>______|DB<br>______|DC<br>______|DD<br>_______|DE<br>______|DF<br>_______|DG<br>______|DH<br>_______|DI<br>______|DJ<br>______|DK<br>______|DL<br>______|DM<br>_______|DN<br>_________|
|Spain|1990|Participation rate 40.9|53.1|71.1|54.2|49.5|NA|79.2|64.6|54.8|63.3|83.3|78.4|76.9|55.0||
|||Export intensity 19.8|18.2|40.2|21.0|14.2|NA|17.5|13.7|21.5|24.7|27.1|22.6|27.0|22.0||
|||Number of firms 225|145|38|24|99|NA|120|48|104|158|102|125|108|100||
||1999|Participation rate 63.5|66.1|64.7|71.4|67.5|NA|92.1|73.9|60.7|75.4|85.1|84.7|86.9|76.3||
|||Export intensity 19.2|24.6|35.8|19.0|20.8|NA|31.0|29.1|35.0|32.2|37.0|34.3|46.7|26.3||
|||Number of firms 159|112|34|21|83|NA|89|69|84|142|87|98|107|80||
|Sweden 1997|Particip|ation rate 57<br>95|78|91|75|100|99|95|86|73|87|88|90|93|||
|||Export intensity|10|47|38|43|40|29|58|45|30|48|44|59|55|33|
|||Number of firms 299|113|18|337|504|8|147|215|117|792|611|395|244|209||
||2004|Participation rate 58|100|80|84|74|100|97|95|87|76|90|90|91|90||
|||Export intensity|15|59|73|40|48|67|65|42|18|52|50|62|43|27|
|||Number of firms 329|75|10|309|451|11|145|222|101|859|574|366|273|182||
|UK|1995|Participation rate 57.7|80.2|84.8|64.3|47.5|80.0|90.2|82.7|70.3|80.0|88.4|87.1|80.7|77.8||
||Export i|ntensity<br>20.1|28.2|31.9|33.4|26.5|14.1|35.9|22.5|34.1|28.0|39.2|38.9|34.3|25.7||
|||Number of firms 366|258|33|14|632|15|386|260|145|799|380|667|192|446||
||2004|Participation rate 46.9|78.8|81.3|25.0|43.0|69.6|86.2|74.5|57.9|77.4|85.8|83.2|67.1|68.9||
|||Export intensity|14.0|26.6|47.7|3.9|24.3|35.5|40.3|25.7|32.0|30.5|38.8|41.4|32.0|28.8|
|||<br>Number of firms 392|189|16|8|632|23|348|267|133|646|324|625|213|4||

________________________________________________________________________________________________________________________________ Note: Results are for firms with at least 20 employees. For a definition of industries see appendix 1. Participation rate is the percentage share of exporting firms. Export intensity is the average percentage share of exports in total sales for exporting firms. See table 1 for more information on the samples.

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## **Figure 2: Global extreme poverty since 1980**

<!-- Start of picture text --> Percentage of population living on less than $1.90 a day<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1980 1990 2000 2010 2020 2030<br><!-- End of picture text --> Source: Global Monitoring Report 2015/16 (World Bank) Note: Numbers until 2012 are estimated and numbers for 2015 and 2030 are projections.

# **Post 1990 —Poverty reduction goes global**

Between 1965 and 1990, poverty reduction was almost exclusively an East Asian phenomenon. Since then, it has gone global. The next quarter century saw continued progress in East Asia led this time by China, but more than a dozen countries in South America, South Asia, and Africa also began to rapidly reduce the ranks of people living in extreme poverty. The most successful ones, such as Vietnam, relied on the proven 2.5‐ point plan: a strategy that emphasized economic growth generally initiated by improvements in agriculture, big investments in education, health and family planning, and well‐chosen social assistance programs.

Along the way, there has been innovation. Most noticeably, development specialists have discovered ways to design large‐scale social assistance programs in ways that reinforce policies to foster labor‐using growth and investments in human capital. There have been numerous experiments, and some of them have been successful. The especially promising experiments are new ways to provide cash transfers, such as _Progresa_<sup>8</sup> in Mexico and _Bolsa Familia_ in Brazil, extending unemployment insurance to rural workers through programs such as the National Rural Employment Guarantee Scheme in India, and social assistance for the very poor such as China’s _Dibao_ and Ethiopia’s Productive Safety Net Program. The costs of these programs range from 0.5 to 5 percent of GDP.

What some countries have also experienced is the effect of natural disasters—of droughts and disease, and storms and tsunamis. A major contributor to Ethiopia’s success in cutting the extreme poverty rate by more than half may just be good fortune: clement weather conditions during the last decade. Bangladesh has been trying to help

> 8 Mexico’s _Progresa_ program was later renamed _Oportunidades_ and, more recently, _Prospera._ For simplicity, its original name is used throughout this paper.

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Two other sets of comparisons between the rural and urban areas are worth looking at in order to better interpret the results presented in the following sections. First, Oportunidades participation rates are much lower in urban areas. Two factors explain this: (i) the percentage of eligible households is smaller, and (ii) the take-up rate among eligible households is smaller. On average, rural communities have between 30 percent and 70 percent of the households enrolled in the program, while the urban communities have at most 17 percent (Table 2).

Second, due to the geographic targeting of the program, localities incorporated later in time tend to be relatively better-off. Using the Housing and Population and Count data we analyzed a broad set of socioeconomic variables, including dwelling characteristics (floor and roof materials, access to basic services such as running water and electricity), asset ownership, literacy rates, school attendance, and access to health insurance. Within rural areas, both the mean and the median (not shown) of each characteristic improve in communities that were incorporated later. In contrast, urban communities that received the program earlier look similar to those incorporated later. This is consistent with the fact that the geographic targeting of the program was done only in rural areas.<sup>9</sup>

# **4. Identification strategy**

The identification strategy exploits the combination of three elements: (i) the evidence that nutritional interventions have only modest effects on child height after the first few years of life; (ii) the fact that Oportunidades was gradually expanded over the years; and (iii) the panel dimension of MxFLS. Based on the first two, program exposure is defined as a function of the age

> 9 Even though by looking at some socioeconomic indicators urban localities do not look, on average, better-off as they are incorporated later in time, the expansion was still not random. Therefore, we still think it is reasonable to keep working with the locality classification introduced earlier which uses year of entry to define locality groups.

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<!-- Start of picture text --> With face value debt reduction<br>(2000-2019)Chi Rescheduling only<br>(1975-1999)Paris Club Rescheduling only<br>(000.2019) With face value debt reduction Rescheduling<br>(1970-1999)Privat Rescheduling only<br>Private a<br>(2000-2019) With face value debt reduction Rescheduling<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text -->
Table 7: Relationship Between Mean TVA and Log Salary for Public and Private School Teachers

||(1)<br>Log Salary<br>Public|(2)<br>Log Salary<br>Public|(3)<br>Log Salary<br>Public|(4)<br>Log Salary<br>Public|(5)<br>Log Salary<br>**PRIVATE**|(6)<br>Log Salary<br>**PRIVATE**|
|---|---|---|---|---|---|---|
|_MeanTVA_||-0.007|-0.028|-0.044|0.111**|-0.011|
|||(0.014)|(0.025)|(0.036)|(0.046)|(0.049)|
|_Female_|-0.036***|-0.035***|N.A.|N.A.|-0.413***|-0.287***|
||(0.013)|(0.013)|||(0.043)|(0.047)|
|_Local_|-0.052***|-0.051***|-0.049|-0.019|-0.178***|-0.043|
||(0.019)|(0.019)|(0.032)|(0.044)|(0.029)|(0.035)|
|_SomeTeacher Training_|0.518***<br>(0.141)|0.518***<br>(0.141)|0.392***<br>(0.140)|0.838***<br>(0.317)|0.165***<br>(0.045)|0.127***<br>(0.040)|
|_HasBAor Better_|0.255***<br>(0.019)|0.255***<br>(0.019)|0.263***<br>(0.028)|0.213***<br>(0.043)|0.334***<br>(0.045)|0.282***<br>(0.042)|
|_Had >_3_Yearso f Expin_2007|0.063<br>(0.042)|0.064<br>(0.042)|0.120*<br>(0.064)|0.117<br>(0.099)|0.020<br>(0.029)|0.058*<br>(0.031)|
|_TemporaryContract_|-0.354***<br>(0.032)|-0.355***<br>(0.032)|-0.327***<br>(0.059)|-0.310***<br>(0.091)|||
|_Age_|0.058***|0.058***|0.063***|0.039|0.016**|0.022***|
||(0.015)|(0.015)|(0.020)|(0.029)|(0.007)|(0.008)|
|_Age_<sup>2</sup>|-0.0005***|-0.0005***|-0.001**|-0.0002|-0.0002**|-0.0002*|
||(0.0002)|(0.0002)|(0.0002)|(0.0003)|(0.0001)|(0.0001)|
|_MeanTeacher Knowledge_||||0.034<br>(0.027)|||
|Mean Salary|6,987|6,987|6,987|6,745|1,403|1,403|
|Fixed Effects|District|District|School|School|District|School|
|Adjusted R Squared|0.616|0.615|0.662|0.707|0.459|0.768|
|Number of observations|1,383|1,383|1,383|919|807|807|
|F|108.304|96.471|35.025|14.450|38.522|16.157|
|Clusters|471|471|471|469|294|294|

This table reports estimates from regressions of log mean teacher salaries in public (columns 1-4) and private (column 5 and 6) schools on teacher characteristics, including mean TVA (columns 2-6) and average teacher test scores across subjects (column 4). The association between female and log salaries in the public sector cannot be credibly estimated in the presence of school fixed effects because the public sector is not co-educational. Very few public schools (29) are observed with both male and female teachers over the course of the sample. All regressions include either district or school fixed effects, and standard errors are clustered at the school level.

39
Jappelli, T. and M. Pagano, 2002, Information sharing, lending, and defaults: Cross-country evidence, _Journal of Banking and Finance_ 26, 2017-2045 Jarl Kallberg and Gregory Udell, 2003, "The Value of Private Sector Business Credit Information Sharing: The U.S. Case," Journal of Banking and Finance 27(3), 449-469 Kaminsky, G. and C. Reinhart, 1999, The twin crises: The causes of banking and balance-ofpayments problems, _American Economic Review_ 89, 473-500 Kaminsky, G. and S. Schmukler, 2003, Short-run pain, long-run gain: The effects of financial liberalization, _NBER Working Paper_ 9787 Krugman, P., 1999, Balance sheets, the transfer problem, and financial crises, in P. Isard, A. Razin and A. Rose, eds., _International Finance and Financial Crises_ , Kluwer Academic Publishers: Boston

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- Miller, M., 2003, Credit reporting systems around the globe: the state of the art in public and private credit registries, in Miller, ed., _Credit reporting systems and the international economy_

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Rajan, R., 1992, Insiders and outsiders: The choice between informed and arm’s-length debt, _Journal of Finance_ 47(4), 1367-1400 27
bursements are usually spread out over many quarters at different stages of the project. Actual disbursements may deviate substantially from the originally planned disbursement schedule; for instance, disbursements may be accelerated in response to natural disasters or delayed in the face of civil conflict. However, the amount of aid disbursed in a given quarter is largely the result of project approvals made in previous quarters, which creates variation in disbursements that is arguably exogenous to contemporaneous shocks.

Following Kraay (2012, 2014), we build an instrument by predicting quarterly disbursements for each project based on the initial commitment and the average disbursement schedule across all other projects implemented in the same sector and the same geographical region. Summing over predicted disbursement at the project-level, we predict aggregate disbursements for each country and quarter.<sup>10</sup> We never use predicted disbursements for the commitment quarter as an instrument since it suffers from the same potential endogeneity as the actual disbursements. In the most rigorous tests, we only use predicted disbursements related to projects approved at least 3 quarters before as an instrument to strengthen the case for exogeneity.

## **2.3 Other variables**

We collect information about events that may be associated with simultaneous changes in aid disbursements and cross-border capital flows: Wars from the PRIO Armed Conflict Dataset; Coups from Powell and Thyne (2011); Natural disasters from the International Disaster Database; Financial crisis from Laeven and Valencia (2012); Petroleum rents and financial sector development from World Development Indicators (WDI). We also collect information on country characteristics that may mediate the effect of aid disbursements on haven deposits: Corruption from Worldwide Governance Indicators (WGI); Disclosure requirements for members of parliament from Djankov et al. (2010); Capital account openness from Chinn and Ito (2006); Political regime characteristics from the Polity IV Project. We document these variables in more detail and provide summary statistics in Table A2 in the Online Appendix.

# **3 Empirical strategy**

To assess whether disbursements of aid are accompanied by money flows to havens, we estimate the following baseline model:

> 10The analysis is limited to projects that were approved after 1984.

8
price of investment goods, the real rate of interest, whether or not a law permitting private entry has been passed, and whether or not a regulatory body for the sector exists in the country. We include a time trend and sector dummies (omitted sectors are railways and gas) and pool the data over all years, sectors and countries covered. The estimation accounts for the panel structure of the data by using country fixed effects.

We report results of regressions in which both the investment price and the real rate of interest are included although including the real rate of interest leads to the loss of some 200 observations. This is because comparable real interest rate data was not available for some countries during the earliest years covered in this analysis.<sup>5</sup>

# _Detenninants of private sector investment_

The results of the primary regressions are presented in Models I-I[ in Table 2. Model I is largely consistent with intuition. The overall relationship of private investment and public investment is one of substitutability. As might be expected, private investment is positively related to past period real GDP, indicating that richer economies generate higher private investment flows. A 1% increase in previous period real GDP is associated with an increase of 4.6% in private investment levels. We use lagged real GDP as the explanatory variable to reflect the potential causal relationship between GDP and private investment.<sup>6</sup>

> 5Dropping the real rate of interest from the regressions and using only the investment price led to higher significance of the latter and no changes of note in the signs or significance of the coefficient estimates on the other variables.

> **6** Since the dependent variable is private investment in different infrastructure sub-sectors, and not total investment in the economy, the issue of reverse causality from investment to income or GDP is less likely to be a problem. We expect the effect _on_ GDP of private investment to be very attenuated and, given that infrastructure investment has long gestation lags, to act only over time.

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performance in replicating the business cycle patterns with respect to investment. By including credit-constrained firms in this framework, one can examine the role of financial development further. Extending the model with borrowings by firms will help in understanding how increase in households’ access to finance affects consumption-smoothing behaviour when production and demand for resources are subject to firm’s access to finance.

Finally, the open economy framework, following Aguiar and Gopinath (2007), assumes that consumers transact an internationally traded bond, which is the source of capital flows in the economy. A bulk of literature has explored macroeconomic effects of the interaction between financial openness and domestic financial development through the firm borrowing channel (Aghion et al. 2004, 2010). However, a wide array of capital control measures existing in India (Patnaik and Shah 2012) restricts access of Indian households and firms to foreign capital. Again, India liberalised current accounts at a faster pace than capital accounts. Hence an open economy framework, capturing trade liberalisation following Mendoza (1995) and Kose and Yi (2006), may help in improving the fit of the model in the open economy framework.

Further, differentiating between agricultural and nonagricultural goods in the consumption basket may help to capture the effects of structural shifts away from agriculture to nonagriculture on the post-reform stylised facts.

REFERENCES Agenor, P. R., C. J. McDermott, and E. S. Prasad. 2000. “Macroeconomic Fluctuations in Developing Countries: Some Stylised Facts.” _The World Bank Economic Review_ 14: 251–285.

Aghion, P., G. M. Angeletos. A. Banerjee. K. Manova. 2010. “Volatility and Growth: Credit Constraints and the Composition of Investment.” _Journal of Monetary Economics_ 57 (3): 246–65.

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-74-

matching procedure for the three countries according to the A-B-M method for a medium-sized industry like the radio and TV receivers industry took about 20 man-hours of work.

For smaller, simpler industries the maximalist approach remains the most appropriate method.

Tables 5.2 and 5.3 show that the number of matches decreases significantly when we move from the maximalist to the A-B-M approach in the case of motor vehicles, from 26 to 6 for the Brazil/USA comparison and from 19 to 5 for the Mexico/USA comparison.

Table 5.1 summarizes the range of PPPs for both approaches. These PPPs are ratios of 1975 unit values in Brazil and Mexico to 1977 US unit values. For both the Brazil/US and the Mexico/US comparison, the A-B-M approach yields a distinctly smaller range of PPPs than the maximalist approach, and average PPPs which are not too different.

TABLE 5.1 Range of PPPs and Weighted Average PPP in the Motor Vehicle Industry (adJusted for quality differences), Brazil/US and Mexico/US

> Brazil (1975) - US (1977) Mexico (1975) - US (1977) Range of Average PPP Range of Average PPP PPPs weighted at PPPs weighted at quantity weights quantity weights USA Brazil USA Mexico Maximalist Approach 0.78-19.57 5.90 5.71 4.32-33.22 12.25 11.94 ABM Approach 3.65- 8.43 5.69 5.55 8.11-33.22 12.06 11.88

Source: tables 5.2 and 5.3
central government administrative records of which villages won the prize. The program studied occurred in 2004, our endline data were collected in 2005, and the first village in our sample ever to do so won the prize in 2006. We received data on prize winners in July 2012, indicating which villages in Ahmednagar had ever won the prize by that time.

In the treatment group, 9 of 30 villages have won the clean village prize; in the control group, 3 of 30 villages have won the prize. This 20 percentage point difference is statistically significant with robust regression _t_ , Mann-Whitney, and Fisher exact tests with p-values of 0.054, 0.055, and 0.052, respectively. Because these prizes were awarded several years after our experiment ended, because they involve several investigations by various agents, and because during the time period studied prizes were ultimately approved by the central government in Delhi rather than the state government, we consider it to be very unlikely that the prize outcomes were manipulated to create the appearance of an effect of this experiment. Therefore, we interpret this finding, that treatment group villages were more likely to go on to win the prize than control group villages, as independent confirmation that the experiment happened and caused an improvement in sanitation.

# **3.4 An effect on children’s height**

Did the program increase children’s heights? Table 3 presents regression evidence from Ahmednagar that it did. The table reports results from 16 specifications in order to demonstrate the robustness of the finding. Results are collected into two panels, corresponding with regression equations (1) and (2), respectively:

- **Panel A:** Double difference (Ahmednagar only, treatment _×_ time), midline and endline separated, that is, treatment and control villages were compared only using Ahmednagar data, comparing the differences over time between the two groups,

- **Panel B:** Double difference (Ahmednagar only, treatment _×_ time), midline and endline collapsed into “after,”

17
economies, mostly European Union member states, improved later, but in 1998 a financial crisis hit the Russian Federation and affected most of the Commonwealth of Independent States. However, since the early 2000s, these economies have grown, improving their citizens’ living standards and reducing poverty rates: Their average real per capita income rose from US$5,903 in 1998 to US$8,411 in 2005, moving 50 million people out of poverty (World Bank, 2008).<sup>4</sup> Earlier, and partial, reforms, created some early winners and losers in some countries; later and more comprehensive reforms even more gainers and losers (Mikhalev et al., 2003)—people in these countries have diverse personal mobility experiences, which we examine in this paper to learn how these mobility experiences affect support for redistribution.

We define support for redistribution, the dependent variable, as an that indicator equals one if an individual thinks that the state should be strongly involved in “reducing the gap between the rich and the poor”. As measures of downward mobility, we use a section on labor market and life histories in LITS I, which includes a set of questions on mobility experiences such as whether an individual had to accept wage cuts in a particular year from 1989 to 2005, whether she had to sell some of her household assets, and whether she had to respond to shocks by cutting down food consumption.<sup>5</sup> First, we construct three measures of mobility by intensity for each individual and each of the years: (1) whether an individual experienced any type of shocks, (2) whether an individual experienced labor market related shocks only, and (3) whether an individual responded to shocks by taking extreme measures. Then, we summarize each of the measures by adding up the number of years an individual had experienced downward mobility during the seventeen-year period.<sup>6</sup> (The measures are, therefore, the number of years an individual experienced downward mobility by intensity.)

> 4 The figures are in constant US$ equivalent of purchasing power parity.

> 5 See LITS (2006) for more details of this survey. We exclude Mongolia and Turkey to focus on the 27 former socialist countries in Eastern Europe and Central Asia; we also exclude mobility experiences in 2006 to make sure we examine the effects of past mobility experiences only.

> 6 See Appendix 1 for the details on how we construct all variables.

4