rafmacalaba/datause-encoder-data huggingface dataset:
Besides the substantial heterogeneity across sectors, we also find substantial, but also intuitive, heterogeneity across countries. For the CEFTA countries, the initial MFN tariff rate and the initial openness explain quite well the resulting trade effects. For the EU countries and the ROW, the effects are small in all sectors. The obvious reason here is that for the EU countries trade with the CEFTA countries is only a small fraction of their overall exports.

Let us next turn to our border scenario. Note that for the border scenario we also report results for services, which we could not include in our tariff scenario, as tariff data for services are not available. We find substantial increases of total exports for all 6 CEFTA countries in manufacturing, agriculture, and mining, and these increases are larger than in the tariff scenario. This highlights that only relying on tariff data to investigate the effects of the integration between the CEFTA countries and the EU may miss a substantial part of potential effects from trade liberalization. The comprehensive measure of the change in bilateral trade costs based on differences of the border estimates can be useful to quantify these additional gains.

Comparing across sectors, we find the largest increases again in agriculture, followed by manufacturing. Besides heterogeneity across sectors, we again find substantial heterogeneity among countries. Albania, Bosnia and Herzegovina, and North Macedonia are predicted to be the countries with the largest gains. Again, gains for the EU countries and ROW are small.

For services, we also find for some CEFTA countries, namely Albania, North Macedonia, and Serbia, substantial trade effects. The reason that we do not find substantial total export increases for the other CEFTA countries is the very low level of services exports of these countries to the EU.

# **5 Conclusion**

We introduced a simple econometric approach to quantify ex-ante the “deep” impact of trade liberalization and the “hard” effects of protection with the empirical structural gravity model, and we demonstrated the effectiveness of our methods by quantifying the partial and GE impact on trade of the integration of the CEFTA countries with the EU. Our partial equilibrium estimates revealed that, even after controlling for the impact of standard proxies for trade costs (e.g., distance, language, etc.), trade borders within the EU are large and, more importantly for our purposes, that 24
based on excess currency demand or latent variable methodologies (e.g., the MIMIC model). The first advantage is conceptual: what self-employment measures is clear and well defined (although we can argue that self-employment does not comprise all informal activity). In contrast, methodologies based on estimated residuals or derived latent variables render data whose meaning is subject to multiple interpretations. The second advantage is practical: self-employment data are available not only for a cross section of countries but also for several consecutive years per country. Conversely, most other measures are either only available across countries or have very limited timeseries. Needless to say, for the type of research we undertake here both cross-country and time-series dimensions are crucial.

Data on self-employment are obtained from the International Labor Organization, which on its website (http://laborsta.ilo.org) publishes yearly employment statistics for most countries. Self-employment is measured as the percentage of self employed workers with respect to the total active population, and the full dataset contains 783 observations in 93 countries. We drop, however, some observations for the following reasons.

First, we drop countries from Eastern Europe, as self employment in these countries appears to be still in transition to market-economy levels. In particular, selfemployment levels in Eastern Europe remain substantially lower than in non-Eastern European countries, and the gap persists after correcting for their level of per capita GDP and their institutional characteristics. This gap is likely the result of their socialist past, when employment took place exclusively in state enterprises. Since the gap is large and changing over time, including Eastern European countries would bias both the long and short run estimates.

Second, we drop countries that do not have at least four consecutive pairs of observations, which corresponds to the minimum threshold of observations for the short-run regression. This choice is a compromise between having sufficient time-series observa15
1997-1998, 2001, and 2008. Due to sample restrictions, among bond funds we only observe the drop in assets in 2008.

Fund assets may grow because of higher returns of their investments or because of injections to the fund by the underlying investors. In fact, the growth rate of fund ’s total assets, , can be trivially written as where is the (net) return to fund at time , and is the injection to the fund expressed as a fraction of the fund’s initial assets . While injections are not directly observable, we can estimate them. To do so, we compute individual fund returns on a given month using data from Bloomberg and Datastream and obtain injections from the difference between the change in total net assets and individual returns. More formally, where is the gross rate of returns to fund at time , computed as , with being the fund price or net asset value (NAV), adjusted by dividend payments.<sup>19</sup> The evolution of returns and injections for the median fund is shown in Figure 5, Panels B and C, and summary statistics are reported in Table 2. For the median equity and bond funds, both returns and injections experience significant fluctuations. For equity funds fluctuations in fund returns are much more volatile than those in injections (standard deviation of 6.23% and 2.05%, respectively), while for bond funds the volatility of these components is similar (standard deviations of 2.53% and 2.05%, respectively). This is consistent with equity returns being more volatile than those of fixed income securities (Schwert, 1989; Andersen et al., 2007). Both components also exhibit a similar time pattern, which also coincides with that of the growth rate of assets, suggesting that the components do not cancel each other, but instead reinforce themselves. Both returns and injections expand during good times and experience severe contractions during periods of financial turmoil. Across types of funds by target region, the most salient pattern is the large growth in injections to funds specialized in BRICs.

> 19 A fund’s net asset value (NAV) corresponds to the total net assets ( divided by the number of shares ( ). Thus, the ratio of NAV in two consecutive periods correspond to the ratio of the total asset values times the inverse ratio of total shares . The flows into the fund can also be expressed as the increase (decrease) in shares times the value of the share Replacing this in Equation (2), we obtain that the gross returns correspond to the ratio of net asset values. The only caveat to our calculation is that total net assets discount the value of a fund’s liabilities, such as the fees paid to the managers. However, if these fees are proportional to the assets under management they would only bias the levels of the variables but cancel out when computing the returns and flows relative to initial assets.

11
_69_ __bme_ Anna C _Expet A_ Some expert assessments differ substantially from the regression esimates. Consideration of one particular discrepancy-that for diarrhea in the 65 and over age group in Latin America and the Caribbean-is instructive. The regression estimate of the mortality rate was 105 per 100,000, intermediate among developing countries, and the expert assessment was that this should be reduced to 10 for each developing country region. For the reporting countries in Latin America as a group, the rate is actually 85. Since these countries have 53 percent of the regional population, even if no one dies from this cause in the nonreporting countries-e.g., Colomia, Bolivia, Haiti, Jamaica, and Nicaragua-a regional average of 10 is impossible. The regional rate for tiis age group, if the data reported to WHO are credited, cannot be lower than 45 per 100,000.

# At least four explanations can be advanced for such discrepancies.

First, nonreporting countries may in fact be so different that projecting contrasts among reporting countries does not come close to capturing their variation. This explanation may suffice where contrasts are less extreme than in this case, but is insufficient here. No matter how different the nonreporting countries are assumed to be, the recommended regional average cannot be attained.

* Second, the data reported to WHO may be poor, as is certainly the case for some countries, at least with respect to leaving some causes unspedfied. In this case, reports of diarrhea deaths at older ages may also include deaffis for which the underlying cause was different.

* Third, the morbidity surveys on which several of the expert assessments are based may also be inaccurate: through their focused nature they may encourage reports on the particular causes being investigated, they may incorporate associated with underlying causes, they may focus on such groups as the young with inadequate attention to others such as the elderly, or they may be somewhat dated and give earlier rather than current levels.

v Fourth, case-fatality rates, when these are used to convert cases to deaths, may be problematic. They often have a weaker basis tan morbidity estimates, may well be inaccurate, and are generally broad averages that fagl to reflect substantial variability by age, region, severithr of disease, and so on. They are also easy to misapply, since they may have been calculated to cover periods other tan a year (or without a reference period at all) or may relate only to hospitalized cases rather than the entire spectrum of disease, which survey data would attempt to cover.

We cannot assess these possibilities in every case. However, in giving the expert assessments below (Table C2), we do indicate the contrasts with the regression estimates and adjust the assessments to be consistent with estimated mortality levels and in some particulars where they appear dearly too extreme.

## _Diarrhea_

Alternative rates were recommended for the four developing-country regions, based on a substantial number of country surveys and a report from the U.S. nstitute
periods on dummy variables for those five year periods. We include a constant term, exclude the first five year period and add country fixed effects.

**Table 1: Magnitude, Volatility and Persistence of Capital Inflows and Outflows**

|||Median<br>Quarterly<br>Average|Median<br>Standard<br>Deviation|Median Coefficient<br>of Variation|Persistence|
|---|---|---|---|---|---|
|FDI|inflows|0.65|0.63|0.96|0.53|
||outflows|0.14|0.28|1.64|0.26|
|FDI|inflows|0.61|1.31|2.12|0.40|
|non-|outflows|0.32|0.94|1.93|0.21|
|Portfolio|inflows|0.05|0.22|3.15|0.31|
|Equity|outflows|0.02|0.08|3.01|0.3|
|Ptfli Dbt|inflows|0.24|0.60|3.17|0.14|
|oroo e|outflows|0.06|0.22|3.13|0.13|
|Oh fl|inflows|0.32|1.08|2.90|0.43|
|ter ows|outflows|0.20|0.81|3.34|0.18|

_Notes:_ Mean, standard deviation and coefficient of variation are the median across all countries in the sample. Coefficient of variation is the standard deviation divided by the mean. Persistence is the AR(1) coefficient of a fixed-effects panel regression for respective capital flows. Non-FDI flows are the sum of portfolio equity, portfolio debt and private other flows. Data are quarterly from 1990 Q1 to 2015 Q4. All capital flows are expressed as % of annual trend GDP.

The results indicate few changes on the inflow side. Portfolio debt inflows are significantly higher in 2006-2010 than in 1990-1995, but there are no other changes. This is evidence of stability in the volatility of inflows over time.

In contrast, there are significant increases in the volatility of FDI outflows from emerging markets in 2001-10 and again in 2011-15. In addition, we see significant increases in the volatility of “other” (bank-related) outflows after the turn of the century. We obtain the same results at even higher levels of precision (significance) when we regress the coefficients of variation on time trends (t=1 in 1990-95, t=2 in 1996-2000 etc.).

This is a striking answer to our question about trends in volatility trends. Capital inflows into emerging markets are volatile but not increasingly so. What is new is the growing volatility of outflows from emerging markets, bank-related outflows after the turn of the century and FDI outflows after 2005 and especially after 2010. That FDI outflows are a growing source of capital account volatility in emerging markets is not adequately appreciated in the literature, in our view.

Which countries are mainly responsible for this increase in the level and volatility of FDI outflows from emerging markets? Some readers will suspect that China is driving the results. But recall that all such flows in our analysis are scaled by country-specific trend GDP. The countries with the highest share of outward FDI in GDP in the most recent five year period are Chile, Malaysia, Hungary and the Russian Federation – not China. In 2015 the countries with the largest 8
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27
**Table 5: Tests for heterogeneity in the impacts of the movie**

||Education<br>Primary<br>school vs.<br>illiterate|s.e.|Pearlin<br>2 - 1|s.e.|Pearlin<br>3 – 1|s.e.|Caste<br>2 - 1|s.e.|Political<br>2- 1|s.e.|Male -<br>female|s.e.|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Heard|-0.037|0.023|0.028|0.033|0.013|0.039|0.019|0.022|-0.052|0.062|-0.040**|0.021|
|Work score|0.171|0.248|0.406*|0.232|0.367|0.304|0.298|0.296|-0.589|0.768|0.329*|0.182|
|Facilities score|0.225|0.185|0.082|0.178|0.13|0.237|0.038|0.18|-0.819*|0.48|0.212|0.135|
|Perceptions 1|-0.048|0.058|-0.03|0.063|0.013|0.086|-0.095|0.066|-0.172|0.153|0.038|0.047|
|Perceptions 2|-0.004|0.06|0.033|0.066|0.112|0.082|0.02|0.067|-0.305**|0.126|0.019|0.044|
|Perceptions 3|-0.08|0.064|0.104|0.066|0.154*|0.086|-0.104|0.07|-0.089|0.137|0.003|0.049|
|Perceptions 4|-0.048|0.042|0.019|0.039|0.101*|0.06|-0.109**|0.046|0.043|0.071|0.028|0.033|
|Perceptions 5|-0.018|0.059|0.048|0.058|0.180**|0.077|-0.101|0.062|0.021|0.097|0.038|0.044|
|Perceptions 6|0.036|0.061|0.024|0.052|-0.032|0.065|-0.093*|0.054|-0.009|0.088|0.015|0.043|
|Perceptions 7|-0.003|0.096|0.152|0.131|0.096|0.15|0.174*|0.105|0.018|0.212|0.012|0.079|
|Perceptions 8|-0.009|0.061|0.149*|0.077|0.105|0.101|0.014|0.073|-0.12|0.18|0.033|0.055|
|Perceptions 9|0.026|0.057|0.054|0.057|0.131**|0.063|0.024|0.054|-0.171|0.15|0.002|0.042|
|Perceptions 10|0.138**|0.059|-0.003|0.058|0.008|0.082|0.132**|0.06|-0.149|0.116|-0.059|0.046|
|Perceptions 11|0.014|0.016|0.021|0.03|0.035|0.031|-0.012|0.011|0.03|0.077|0.013|0.015|
|Perceptions 12|0.049|0.069|-0.031|0.076|-0.034|0.11|-0.185***|0.055|-0.374***|0.101|-0.001|0.056|
|Perceptions 14|0.102|0.068|0.014|0.07|0.067|0.087|0.186**|0.075|-0.318***|0.117|0.033|0.05|
|Perceptions 15|-0.006|0.051|0.041|0.063|-0.014|0.084|0.072|0.057|-0.061|0.14|-0.004|0.048|
|Perceptions 16|0.001|0.051|0.059|0.063|0.026|0.085|0.019|0.056|-0.064|0.141|0.006|0.048|
|Perceptions 17|-0.109*|0.06|0.069|0.065|0.051|0.085|-0.072|0.066|-0.220*|0.127|0.003|0.046|
|Perceptions 18|0.042|0.053|-0.003|0.045|0.022|0.065|-0.145***|0.052|-0.327***|0.121|-0.011|0.038|
|<br>Perceptions 19|-0.140**|0.06|-0.154***|0.059|-0.170**|0.083|-0.211***|0.063|-0.026|0.139|-0.043|0.046|
|<br>Perceptions 20|-0.086|0.065|0.129*|0.067|0.163*|0.088|-0.121*|0.068|0.028|0.122|0.033|0.049|
|Perceptions 21|-0.08|0.051|-0.013|0.048|0.054|0.075|-0.132**|0.055|0.024|0.127|0|0.041|
|Perceptions 22|-0.051|0.061|0.088*|0.051|0.057|0.07|-0.183***|0.058|0.003|0.112|0.04|0.044|
|NREGpart.post-movie|-0.021|0.019|-0.01|0.034|0.023|0.037|-0.025|0.041|0|0.052|0|0.021|

25
communes are typically resource poor, relying on the provincial or district governments to undertake road rehabilitation or other basic infrastructure construction (Rao et al., 1999). Furthermore, under the project, provinces retained substantial control of project funding. Thus, displacement is unlikely to have been significant at the commune level.

Behavioral responses by aid recipients could also result in road spending being used differently in project areas. For example, when labor inputs are supplied locally and are fixed, project funds may encourage a switch in local road rehabilitation efforts away from more labor intensive techniques (more sealed roads, for example). The outcome may be fewer rehabilitated kilometers but higher quality rehabilitation. There can be a tension with donor preferences here given that the development aid community has emphasized labor intensive earth road rehabilitation methods.

It should be noted that even with full fungibility, one may still see a positive impact on road spending. Fungibility means that one is free to spend on whatever one likes. A pure income effect can still be expected on the demand for all normal goods including roads. This poses a problem for measuring fungibility (Khilji and Zampelli, 1994). Because of a potential income effect of the fungible component of aid funding, we cannot identify the precise amount of fungibility. The joint outcome of fungibility and an income effect can be called “displacement.”<sup>4</sup> If resources are not fungible and a flypaper effect is found instead, the issue of an income effect does not arise.

# **3. The project intervention and participant selection**

The Vietnam Rural Transport Project 1 (RTP1) is a rural roads rehabilitation project aiming to link commune centers to markets and so reduce poverty (World Bank 1996). Communes are the lowest administrative unit in Vietnam, above which are districts and, above them, provinces. Communes have an average of about 1,300 households living in 10 or so hamlets (or villages).<sup>5</sup> The project was launched in 1997 for implementation in 18 poor provinces over 4

> 4 In order to quantitatively isolate fungibility from the income effect we would need an evaluation design whereby some communes got an allocation of untied funds equal to that received under RTP1.  One could then evaluate the effect of that allocation on road spending in those communes and add that amount to the displaced spending to get at the degree of actual fungibility.

> 5 This is based on the 200 communes in our survey, described in the next section.

6
these data to actually estimate equation (1). In practice, available data refer to the same country over different periods of time (as in time series analyses), or to different countries at given points in time, as in cross section analyses, or combinations of the two, as in panel estimations. For example, in the case of time series estimations, although we may reasonably suppose (and test) that rates of income growth (and consumption) be stationary over long periods of time, it might equally be possible that non stationarity be found over shorter time spans. Developing countries, for example, might experience growing rates of growth for a relatively long period. If that were the case, even with perfect insurance we might reasonably expect growth rates in consumption to co-vary more strongly with income growth in case of positive innovations (which partly embody an increase in permanent per capita consumption), and more moderately in the case of negative innovations (which would be largely offset by insurance mechanisms). Consequently, on average we might detect lack of insurance (i.e., a statistically significant coefficient β 1 ) even when insurance is in fact complete, particularly when positive shocks prevailed over negative ones. Likewise, lack of stationarity might be induced by taste shifts, entailing similar consequences. When insurance is severely restricted, on the other hand, the opposite (i.e., detecting more insurance than it is in fact the case) might also realize, as consumption growth would strongly co-vary with negative income innovations, and would be less sensitive to positive ones.

That is why it might be sensible to split the shock variable in two variables, reflecting negative and positive income innovations, and focus on the estimate of coefficient β 1 − , attached to the former.

To sum up, by restricting the coefficients of the two sub-variables to be equal (i.e., if we did not split the shock variable), we might fail to detect full insurance (when present), or make the opposite mistake, of detecting more insurance than it is in fact the case. On the other hand, by splitting the shock variable in any sensible way, nothing would change in the standard, full insurance and stationary case, corresponding to equation (1): both coefficients should turn out to be statistically non significant. Put another way, the procedure followed in the rest of the paper will be without loss of generality, with respect to the maintained theory.

Hence, we will carry out our consumption insurance test by explicitly distinguishing between “negative” and “positive” realization of the shock variable, where the terms “negative” and “positive” will be better clarified in the next section, and the equation to be estimated will take the form:

> 2 In section 3 a methodology to distinguish between negative and positive realizations of the shock variable is proposed.

5