rafmacalaba/datause-encoder-data huggingface dataset:
|Cohort|Coefcient|Standard error|Treated|N<br>Control mean|
|---|---|---|---|---|
|**A. Aver**|**age treatme**|**nt efect on the **|**treated **|**by cohort**|
|2003|-1.291|(0.083)|604|2.188|
|2008|-0.386|(0.053)|87|2.188|
|2010|0.092|(0.103)|1,850|2.188|
|2015|0.168|(0.055)|839|2.188|
|2017|0.384|(0.185)|123|2.188|
|2019|-0.133|(0.074)|54|2.188|
|2020|0.049|(0.051)|1,502|2.188|
|2021|0.193|(0.044)|1,234|2.188|
|**B. Aver**|**age treatme**|**nt efect on the **|**treated **|**(all cohorts)**|
|All|-0.107|(0.053)|7,574|2.188|

**Table S4** : **Average effects of AP management on log(bird species).** This table displays average treatment effects on the treated (ATTs) derived from the regression described in SI A.2.5. The dependent variable in this regression is the log(number of unique bird species observed) per birding trip. Panel A displays all ATTs returned from summarizing the regression object by the calendar year in which protected areas were transferred to AP (“cohort”). The command in R, summary(regression object, agg = “cohort”), returns ATTs for 8 of 12 possible cohorts, perhaps due to insufficient data or variation in the data for the remaining 4 cohorts. Panel B displays the ATT across all parks managed by AP. The Coefficient displays a given ATT and the Standard error column displays the ATT’s standard error. The Treated N column is the number of observations in the given AP cohort (Panel A) or across all parks managed by AP (Panel B). The total number of observations in the regression is 145,200. The Control mean column is the mean log(number of unique bird species observed) across all control parks.

35
estimate. The second approach uses achievement test scores measuring cognitive ability and employs them as additional controls in the earnings function. The third approach uses sources for exogenous variation in educational attainment, such as institutional changes in the schooling system in the form of changes in compulsory schooling laws, abolition if fees etc, as well as other “natural variations” (for example, school construction projects) affecting the schooling decision, to estimate a causal return to education effect using instrumental variable estimation. This is the approach followed in this paper.

Most of the estimates of the return to education based on “natural experiments” report a higher return to education (rather than a lower one), compared to OLS-based estimates of the return to education (see, for example, Angrist and Krueger 1991; Kane and Rouse 1993; Card 1995; Harmon and Walker 1995; Meghir and Palme 2005; for developing countries, see Duflo 2001; Patrinos and Sakellariou 2005; Sakellariou 2006). The dominant explanation for these seemingly counter-intuitive results is that institutional changes in the school system (such as compulsory schooling laws) affect the schooling decision of a subset of individuals who, otherwise, would not have pursued a higher level of education and not the average individual. Furthermore, individuals affected by such reforms tend to have a higher return to education than the average individual.

To incorporate cognitive ability, we rely on the Chilean data from the International Adult Literacy Survey (IALS). The IALS was created to generate comparable literacy profiles across national, linguistic and cultural boundaries. The IALS data have been used in several studies, with little evidence on Chile, the only country in the survey outside Europe and North America.

2
Note that the ICRG corruption measure has a relatively poorer coverage such that it is available for only 89 out of 125 countries in our sample. The Control of Corruption measure is available for all the countries and CPI is available for 118 countries.

# 2.4.2 _Financially constrained firms_

The ES also asks firms if they applied for a loan during the last year. If they did not, they were asked to choose the main reason for not doing so from the following list: No need for a loan as the firm has enough internal funds; application procedures were too complex; interest rates were not favorable; collateral requirements were too high; loan size and maturity was insufficient; or did not think it would be approved. For firms that did apply for a loan, the ES asks if the loan application was rejected, still pending, approved partially, or approved in full. Using this information, we first define a firm as financially unconstrained if it either did not apply for a loan because there was no need to do so (has enough internal funds) or if it applied for a loan and the loan amount was approved in full. The remaining firms are classified as financially constrained. They include, for example: firms that applied for a loan, but the application was rejected either fully or partially; firms that did not apply for a loan because they did not have the required collateral, etc. Formally, we define a dummy variable equal to 1 if the firm is financially constrained as defined above and 0 if otherwise.

As with corruption, we use average values of the financially constrained dummy variable where the average is taken over all other firms in the country-industry cell excluding the firm in question ( _Financially Constrained_ ). In our baseline sample, the mean value of the _Financially Constrained_ variable equals 0.47 and the standard deviation equals 0.20. Thus, about 47 percent 15
**Figure 3. Prevalence of Smoking by Relative Educational Level in the United States, Age 25 and Above, 19402000**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>1950 1964 1970<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Under average education Above average education<br>1940194219441946194819501952195419561958196019621964196619681970197219741976197819801982198419861988199019921994199619982000<br>Percentage of individuals smoking<br><!-- End of picture text --> _Note_ : From smoking histories constructed from the 1978, 1979, 1980, 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994, 1995, 1997, 1998, 1999 and 2000 National Health Interview Surveys. The information about the dangers of smoking diffused gradually: 1950, consensus in medical journals, 1964, first Surgeon General’s Report, 1970, clear health warning on packages. Each individual is classified as above or under the average educational achievement in each year for individuals who were aged 25 in that particular year.

**Figure 4. Prevalence of Smoking by Education Category in the United States, Age 25 to 60 at the Time of the Survey, 1945-2000**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>1950 1964 1970<br>20%<br>10%<br>0%<br>Less than High School High School Some College College and above<br>1945194819511954195719601963196619691972197519781981198419871990199319961999<br>Percentage of individuals smoking<br><!-- End of picture text --> _Note_ : From smoking histories constructed from the 1978, 1979, 1980, 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994, 1995, 1997, 1998, 1999 and 2000 National Health Interview Surveys. Only individuals aged less than 60 at the time of the interview were selected. The information about the dangers of smoking diffused gradually: 1950, consensus in medical journals, 1964, first Surgeon General’s Report, 1970, clear health warning on packages.

28
- 29 -

# Mble 3: OIGSA - 00Q4C FP NDMZ, ADh T, AND RTH, 197086 (kual average, fin rea amper tsm unless otheuAse nted)

|||iport-led<br>Goth|Coffee<br>BOcM|GwthP|acesion|Miu|sbnt w|ith Grwth|
|---|---|---|---|---|---|---|---|---|
|||1970-75|1976-0|1981-83|1983|1984|1985|1986 a/|
||N?dr1coffee prioe<br>**lodax**b/|84|133|88|89|98|99|135|
||Notcapit7d<br>inflor<br>(USW S(|284|543|1,699|1,014|710|1,576|1,000|
||<br>)grthrate|2.1|3.6|3.3|2.3|4.7|3.0|2.0|
|_II._|_O.ce_<br>Mic||||||||
||-al<br>.wh<br>rate<br>Ind=<br>d/|89|85|75|75|82|107|<br>112|
||ttW baegrowth|23|35|18|14|18|27|28|
||<br>lic<br>setor<br>deficit/<br>G3P ratio|3.0|5.5|6.8|7.6|7.6|3.7|1.0|
||Importse//GP<br>ratio|15|15|17|16|14|16|17|
|_'II_|_._<br>_T-_||||||||
||gmW<br>th|5.7|5.5|1.3|1.6|3.2|2.9|5.0|
||port growth el|6.9|7.4|-5.2|-11.9|9.8|13.9|25.0|
||<br>Iort grath e/|2.2<br>|11.1<br>|3.2<br>|-8.4|-7.8|0.8|1.0|
||Inflatin<br>rate|17.1|24.5|24.9|19.8|16.4|24.0|20.0|
||Qrentacommt/GP|-2.9|1.2|-6.5|-7.3|-5.3|-3.9|0.0|
||rato||||||||
||permas (mtof||||||||
||imQorts)|2.7|8.1|7.9|5.0|3.1|3.5|5.5|
||1abe-esrvigy<br>ratio<br>_/_|<br>22.7|14.7|31.1|36.9|37.2|38.2|34.0|

s:re: bilald Iistry of ftnsne, Dr and Wbrld Blak etntea, ald (GM Oatl*x a/ preLiu y. Note tiut the significait 1Mroumne estimted ln the belu of psyum~t In 1966 wa patly tim malt of tih coffee boa-; b/ 1970-100.

pitd acowt balsm, Iludling errors and outsaio.w. t caonlatim are bed on a trweighted _carrqenc_ baket, with 1975 - 100. An incem _Is_ dcf£md here as a depqration of the peso.

G **ood** anl nonfactor sezviin f/ A a proportimn of exports of good. and ewvices.

Da - of erly 1987.
-7-

rate for remittancec (if any) and the official exchange rate in the home country. The first and last factors studied by Swamy directly reflect the effect of the special incentive schemes introduced by labor-exporting countries to enhance IWR through official channels. Swamy tests the model using data from Greece, Turkey and Yugoslavia over eighteen years and found that remittances were not significantly affected by any of the above factors. Instead, Swamy's results show that about 90 percent of the variations in remittance flows are explained by the stock of workers abroad and their earnings.

Swamy's broad conclusions are supported by Straubhaar (1986), who builds a simple model to examine the remittances of Turkish workers in Germany. The model is tested through a reduced form equation in which the flow of remittances is a function of the deviation of the official exchange rate from the one defined by a purchasing power parity equilibrium between Turkey and Germany, the difference between expected real rate of returns to investment in the home and the host country, the stock of Turkish workers in Germany, and their wages. In Straubhaar's model all variables are expressed in variations. Like Swamy, Straubhaar also found remittances to be significantly affected by the stock of workers and their income, but not by the other variables.

Kateeli and Glytsos (1986) incorporate risk factors in an intertemporal model of exchange of factors, more specifically an exchange of abundant unskilled labor for scarce capital. The inflow of capital takes the form of remittances while the migrant is abroad, and human capital upon his return. Katseli and Glytsos generate a budget constraint for the migrant where the return on the portfolio is a function of his wealth, the relevant interest rates (foreign and domestic) and the exchange rate variability. The budget constraint and the risk return preference schedule jointly determine the share
countries. As a result, even within the group of FSU countries there is a wide range of variation in the behavior of the elite and the degree of institution building during the transition period. While both the presence of significant resource endowments and an entrenched political elite turned out to be detrimental to the emergence of secure property rights and rule of law, the effect was reinforced by the presence of both.

A case in point is Armenia, a landlocked country with few natural resources that gained independence from the Soviet Union in 1991 while having a conflict with neighboring Azerbaijan over the territory Nagorno Karabakh. Despite a long socialist tradition as a member of the FSU, the war with Azerbaijan led to a strong nationalist movement under the leadership of Levon Per Petrosian, which gained power in the first parliamentary elections. Under his government, Armenia initiated important reforms, such as land and housing reforms, a first step to the establishment of property rights. Reforms were accompanied by improvements in the rule of law and control of corruption. Compared to its neighboring countries, Armenia’s has shown strong institution building and economic performance. In fact, Armenia’s GDP per capita growth over the period 1992 to 2002 has been the one of the highest among all transition economies. While the war with Azerbaijan may have played an important role in shifting the balance of political power from the communists to the nationalists, it seems unlikely that this alone can explain subsequent reform and economic performance. Azerbaijan was involved in the same war, but the communists retained power and economic growth during the transition period was substantially lower than the average across transition economies.<sup>10</sup> Furthermore, why would one expect the nationalists to extract fewer rents than the former nomenclatura did? A key difference between the two countries is the endowments of natural resources, Armenia having relatively

> 10 While economic growth in Azerbaijan started to pick up in 1996, growth occurred mainly in oil-related activities that were controlled by the state and the political elite (World Bank 2002).

13
<!-- Start of picture text --> J ts~~~~olc,: Rowrh md Exea Af -<br>-~~~I<br>Human Resouroz<br>WPS 649<br><!-- End of picture text --> This paper Is a product of the Human Resources Division, Technical Department, Latin America and the Caribbean Regional Office. Copies are available free from the World Bank, 1818<sup>H Street NW,</sup> Washington, DC 20433. Please contact Barbara Diallo, room 14-196, extension 30997 (12 pages).

Clinic-based data on malnutrition are the most readily available for following malnutrition levels and trends in most countries, but there Is a bias Inherent in clinic-babed estlirtes of malnutriton rates.

Grosh, Fox, and Jackson compare annual clinic-based malnutrition data and those from four household surveys in Jamaica. The clinic data give lower estimates of malnutrition than - the survey data in all four cases signiflcantly so In three.

The size of the bias was variable over time, so the clinic data were not a good Indicator of either levels or trends In ntfltition status.

**TMe** PRE Woridng Papea Series disseminates the findings **of** work **under** way in the Bank's Policy, Research, **and Extemn** Affairs Complex. **_An_** objective of theseries is **to** get these findmgs **outquicidy,** even **if presentio**<sup>ae less than fully polished.</sup> The findings, intepretations, and conclusions in these paperS do not necessarily **represent** official **Bank** policy.

Produced by the PRE Disseminadon Center
the fitted BSA RER, the real exchange rate is under-valued, in that domestic prices, valued in US dollars at the official nominal exchange rate, are lower than what they would be expected to be given the country’s per capita income. Conversely, a country’s real exchange rate is overvalued if its real exchange rate is higher than its fitted BSA RER. Obviously, the mean of overand under-valuations (in logs) for all 151 countries in each of the four sub-periods is zero.

# **Table 8 Estimates of Balassa Samuelson Adjusted Real Exchange Rate Under (-) and Over (+) valuation in the Former Soviet Union Countries: Five year averages 2000-18**

||2000‐4|2005‐9|2010‐14|2015‐18|
|---|---|---|---|---|
||%|%|%|%|
|Armenia|‐17.5|‐3.9|‐8.4|‐7.0|
|Azerbaijan|‐61.9|‐51.9|‐28.4|‐59.7|
|Georgia|‐15.7|‐6.0|‐7.2|‐13.6|
|Kazakhstan|‐83.9|‐44.2|‐18.9|‐39.7|
|Kyrgyz Republic|‐33.9|‐19.5|‐8.4|‐5.7|
|Tajikistan|‐32.2|‐13.1|‐2.7|‐23.2|
|Turkmenistan|21.9|23.1|‐11.5|‐23.7|
|Uzbekistan|‐37.1|‐38.3|‐20.1|‐14.1|
|Belarus|‐52.2|‐33.9|‐37.9|‐44.5|
|Moldova|‐50.4|‐31.0|‐4.6|‐3.9|
|Russian Federation|‐33.0|‐17.7|‐13.8|‐28.4|
|Ukraine|‐55.6|‐25.0|‐20.7|‐34.7|
|FSU countries*|‐37.6|‐21.8|‐15.2|‐24.9|
|Remittance receiving FSU countries|‐30.0|‐14.7|‐6.3|‐10.7|
|FSU Hydrocarbon exporters|‐38.8|‐25.8|‐18.6|‐33.1|

*Excludes the three Baltic States

A positive number denotes an under-valuation of the RER, a negative number an over-valuation Source: authors’ calculations using data from WDI The estimated BSA RER over or under-valuations for each of the eight FSU countries in CASC along with other FSU countries are reported in table 8.<sup>5</sup> Each of the four remittance receiving countries in CASC had an undervalued BSA RER in each of the four sub-periods. In all four countries, the undervaluation of the real exchange rate was at its largest during 2000-04, but the trends since then varied. The Kyrgyz Republic experienced a consistent decline in

> 5 We did not compute real exchange rates for the three Baltic states because of difficulties arising from a break in the data series when these countries adopted the euro.

12
6 countries and import goods, services, and production factors (capital, technology) that are not available within the region.

In historical retrospect, trade and economic contacts have been fundamentally established with and shaped bv extraregional markets. As a result, neighboring markets are often unknown and the infrastructure within a regional group is generally underdeveloped, which Is a major barrier to enhanced intraregional trade. In 1989, intraregional exports by the RIDC totalled $40.5 billion or 1.34 percent of world exports, and less than 5 percent of intraregional trade in the EC (Table 2). Even compared to total South-South trade, the RIDC's share amounts to only 31 percent, with about 70 percent being interregional trade flows. Only ASEAN's and LAIA's intraregional exports ($22.6 billion and $9.3 billion in 1989, respectively) deserve some attention as volumes of international significance. On examining the share of intraregional trade in total trade for the groups selected, ASEAN's 18 percent figure stands out, while that of CACM, CARICOM, and LMIA is in the 10 to 13 percent range. In all other groups this figure is far below 10 percent, and in some cases even below 5 percent. Compared with the same indicator of more than 60 percent for the EC, the decisive role of extraregional to intraregional trade is indisputable.

# 2. The Training Ground Theory

This theory starts out from the hypothesis that the international competitiveness of developing countries can be gradually improved by relying on the regional market in the first phase of industrialization. Free trade among members and usually high (common) external tariffs on extraregioral imports temporari y protect infant industries and provide, at the same time, suff1icently large markets for future development. Entrance into the world's market may be
important when forcing the model to include dummy variables at the regional level, the level for which the sample survey is considered to be representative. Forcing the inclusion of regional dummies in the model selection process generally increases the predictive accuracy of the model, by controlling for fixed characteristics of the region. In addition, including regional dummies enables model selection algorithms such as LASSO and stepwise to prioritize variables that best explain within-regional variation for inclusion in the model. Finally, since these algorithms use the sample to determine how many variables to select, using more disaggregated predictors ensures that a richer and more accurate predictive model is selected. The household model may also slightly benefit from predicting a continuous welfare variable, rather than discarding information—about how close a household is to the poverty line—by first converting it to an estimated headcount poverty rate, although it is not clear that this difference is important empirically.

Table 3 shows empirical comparisons of accuracy, as measured by R<sup>2</sup> , from selected evaluations. R<sup>2</sup> is shown because it is commonly reported and it tends to track closely with Spearman rank correlation, which is in turn useful for evaluating targeting performance. In most cases, unfortunately, the evidence reported below is based on a single real-life survey. Only in Mexico, to our knowledge, is there simulation evidence comparing area and household-level models using geospatial auxiliary data. In each case, models are selected using LASSO and regional dummies are included.

_Table 3: R_<sup>_2_</sup> _by method and in or out of sample, relative to validation data_

|Country|Indicator|Sample|Validation<br>data|In-sample||Out-of-samp|le|
|---|---|---|---|---|---|---|---|
|Model||||Household-<br>level|Area-<br>level|Household-<br>level|Area-<br>level|
|Burkina<br>Faso|Headcount<br>poverty|Single<br>survey|Census-based<br>EBP<br>estimates|0.76|0.56|0.21|0.26|
|Sri<br>Lanka|Non-<br>monetary<br>poverty|Single<br>survey|Census|0.77|0.71|N/A|N/A|
|Tanzania|Non-<br>monetary<br>poverty|Single<br>survey|Census|0.77|0.78|N/A|N/A|
|Mexico|Headcount<br>poverty|Single<br>sample|Census-based<br>EBP<br>estimates (in-<br>sample)|0.74|0.63|0.49|0.44|
|Mexico|Labor income<br>poverty|Design-<br>based<br>simulation|<br>Intracensus|0.89|0.88|0.64|0.56|

Source: Edochie et al. (forthcoming), Masaki et al. (2022), Newhouse et al. (2022). The results for design-based simulation for Mexico reported in the bottom row are based on area-level predictors while all other rows are based on sub-area level predictors.

In general, the household model predicts more accurately than the area-level model in contexts where they have been directly compared. The one notable exception is out-of-sample areas in Burkina Faso.

17
Seale, J., A. Regimi and J. Bernstein (2003) “International Evidence on Food Consumption Patterns“, _Technical Bulletin_ 1904. USDA. October.

Wodon, Q., C. Tsimpo, P. Backiny-Yetna, G. Joseph, F. Adoho and H. Coulombe (2008), “Measuring the potential impact of higher food prices on poverty: summary evidence from West and Central Africa”, _mimeo_ , May, World Bank, Washington D.C.

World Bank (2008a), “ _Rising food prices: policy options and World Bank response”_ , background note for the development committee, April, Washington D.C.

World Bank (2008b), “Guidance for responses from the human development sectors to soaring food price”, _mimeo_ , May, Washington D.C.
27 inspection of this variable for countries that are not in the traded<sup>group is</sup> instructive. In this group countries that might be judged to have<sup>good</sup> repayment prospects have larger outstanding debt to private<sup>creditors: near</sup> 16 billion 1986 U.S. dollars for Korea and near 6 billion<sup>1986 U.S. dollars</sup> for Greece. In contrast, countries with relatively bad<sup>repayment prospects,</sup> such as Ethiopia, and Trinidad and Tobago, also appear in<sup>this group. Debt to</sup> private creditors are .08 billion 1986 U.S. dollars, and .19<sup>billion 1986 U.S.</sup> dollars for Ethiopia and Trinidad and Tobago respectively.<sup>One plausible</sup> explanation for these observations is that the transaction<sup>costs incurred in</sup> trading private debt on the secondary market prevents trading<sup>for countries</sup> with a small amount of private debt.

Table 2 presents some summary information concerning che secondary<sup>market</sup> discounts for the 23 countries whose debt was traded at a discount in the market over the 1986-88 period. The discounts presented in this<sup>table are</sup> calculated using bid prices. The mean discount for all countries over the period 1986-88 is 52.27 (with a standard deviation of 25.56).

The variable of interest is the degree of concentration of debt in the hands of the large international banks relative to small banks. The model suggests that we incorporate information on all banks that have outstanding loans to a country. In our empirical analysis, however, we<sup>will be restricted</sup> to data for U.S. banks only, since bank exposure data for individual countries are available only for U.S. banks in a systematic way. Since<sup>U.S banks have</sup> historically been the major players in the market, however, one<sup>may argue that</sup> not having other countries' banks may not be a major defect.

Another issue raised in creating a measure of concentration across countries is how the large banks should be selected. Should the set<sup>of large</sup>