rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 5: **Effects of ASP Social-Emotional Learning on Social-Emotional Skills and Emotion Regulation** _(Intention-to-Treat Estimates)_

||SES<br>Index|Fluid<br>Intelligence|Fluid<br>Intelligence<br>after<br>Negative<br>Stimuli|Fluid<br>Intelligence<br>after<br>Positive<br>Stimuli|Arousal|Valence|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Psychology-based curricula|0.003|-0.037|0.051|-0.027|-0.014|-0.245*|
||(0.013)|(0.088)|(0.086)|(0.094)|(0.117)|(0.129)|
||[0.845]|[0.700]|[0.600]|[0.830]|[0.910]|[0.050]|
|Mean pure club group|0.000|0.000|0.000|0.000|0.000|0.000|
|MDE|0.233|0.219|0.221|0.217|0.233|0.230|
|Observations|755|733|733|733|622|622|
|R-squared|0.043|0.045|0.032|0.030|0.034|0.024|

_Notes:_ This table shows the estimated impacts of the ASP’s social-emotional learning on social-emotional-skills (SES) and emotion-regulation outcomes. _Psychology-based intervention_ is a dummy equal to 1 if the student was enrolled in a school that was
# **Figure 2.  The gender gap in account ownership persists**

<!-- Start of picture text --> 100%<br>80%<br>High‐income economies<br>60%<br>Developing economies<br>40%<br>20%<br>0%<br>2011 2014 2017<br>Share of adults with an account<br><!-- End of picture text --> _Source_ : Global Findex database. The lines represent the share of male and female adults aged 15 and above who own an account at a financial institution or through a mobile money provider.

In some ECA countries, the gender gap is much larger than the developing country average of 9 percentage points (table 2and figure 3). The largest gap is in Turkey, where 83 percent of men but just 54 percent of women have accounts (figure 4). The gender gap is about three times as large as the average gap in developing countries. The income gap in account ownership is about 21 percentage points in Turkey, much higher than the global average. Younger adults are also less likely to own an account than older adults (see box 1). Such countries include Armenia (15 percentage points), Kosovo (17 percentage points), Tajikistan (10 percentage points), and Turkmenistan (10 percentage points).

**Table 2. Gender gap in account ownership varies by country**

||_2_|_011_|_2_|_014_|_2_|_017_|
|---|---|---|---|---|---|---|
|_Country_|_Men_|_Women_|_Men_|_Women_|_Men_|_Women_|
|_Central Asia_|22|22|39|39|46|43|
|Kazakhstan|40|44|52|56|57|60|
|Kyrgyz Republic|4|4|18|
|||
|Share of government 2002 (%)|<br>.053<br>(.018)|||||||||
|Share of industry 2002 (%)|<br>.113<br>(.075)|||||||||
|Share of service 2002 (%)|<br>.174<br>(.096)|||||||||

_Sources_ : Author’s calculation based on the VHLSS 2002, VHLSS 2004 and the _Enterprise Survey_ data. _Note_ : Between parentheses are the standard deviations.

51
consideration. The variation in consumption observed in the poverty map reflects variations in the household characteristics as observed in the 1999 Census, and so from this perspective, the poverty map might be seen as referring to the Census year. On the other hand, the parameter estimates for the prediction model of the welfare are based on the household characteristics observed in the 2002 survey. Given that the "zero" stage exercise ensured that only variables with the same meaning and distribution across the two datasets were selected for the regression, it made be inferred that the procedure is imputing 2002 consumption into the 1999 census. Thus the map can be argued to be presenting a picture of the 2002 spatial distribution of poverty.

In reality the map presents a picture of poverty in neither 1999 or 2002. During the period between1999 and 2002 there has been both a reduction in fertility, which in turn has had an impact on household size and composition, and extensive internal and external migration. Hence both the imputed welfare regression (which comes form the household budget) and the spatial distribution of the population (which comes from the census) are likely to have changed between the two years. Thus it is best to interpret the map as providing a guide to the spatial distribution of welfare at the end of the twentieth century i.e. over the period 1999-2002.

7
employment occurred in export-oriented sectors.<sup>20</sup> As labor reallocation appears to occur across broad economic sectors, analyses which focus only on the formal industrial sector seem to be incomplete in capturing the whole impact of trade shock. The VHLSS 2002 and 2004 data used for analyzing wage movements in this study have an advantage relative to firm-level data as they allow the researcher to extend the analysis to all of Vietnam’s economic sectors.

# 3. THE IMPACTS OF THE BTA ON WAGES AND THE SKILL PREMIUM

# (a) Estimation strategy

This section investigates empirically how the exogenous trade shock resulting from the BTA translated into changes in wages for skilled and unskilled workers. Whereas this paper is inspired by the basic insights from the H-O-S model, its analysis does not follow the strict version of the H-O-S theory. Instead, I relax some of its assumptions so that the conceptual framework is consistent with Vietnam’s “real world” data and my empirical design. First, whereas the StolperSamuelson theorem links changes in relative output prices to changes in relative wages, it is difficult to model this linkage directly since price data are not readily available. Thus, I take an indirect approach assuming that the impacts of changes in product prices induced by trade are “revealed” in changes in labor demand.

Second, this paper extends the idea of the H-O-S theory to Vietnam’s provinces; i.e., it is assumed that Vietnam’s provinces have different specialization patterns given heterogeneity in endowments and difference in initial industrial development, and that the impacts of trade shocks translate differently at the province level. The model is designed to capture general equilibrium impacts of the trade shock within a province: it is assumed that labor is mobile across sectors and that factor prices tend to be equalized within province. In regard to labor mobility across

> 20 Discussing a similar situation, Anderson and Dimon (1999) report evidence that export oriented production in _maquiladoras_ (across-border export processing plants) created formal sector job opportunities for
55**|**21**|**29**|**25**|**26**|**30**|**27**|**11**|**30**|**18**|**20**|**27**|**474**|
|**Supported**<br>**Projects**|**10**|**4**|**11**|**2**|**7**|**4**|**6**|**6**|**1**|**5**|**4**|**1**|**0**|**0**|**0**|**0**|**61**|
|**RATIO**|15.6%|16.7%|19.3%|20.0%|12.7%|19.0%|20.7%|24.0%|3.8%|16.7%|14.8%|9.1%|0%|0%|0%|0%|12.9%|

Source: DBT BIPP database; asterisks denote special calls (see text for details).

To mid-2011, BIPP has deployed $36 million, of which $13 million in grants and $23 million in soft loans, with a debt-to-grant ratio of roughly 2 to 1. Public BIPP funding has leveraged an additional $66 million in private investment by the recipient enterprises in the approved 61 projects as their core contribution, for a total investment of $102 million across approved projects. It is noteworthy that under a shorter span of time (two-and-a-half years versus fiveand-a-half years
Sugar|27.5|27.7|105.3|186.7|84.1|90.0|160.2|242.2|24.5|76.5|
|Services|22.5|22.5|0.0|0.0|15.0|15.0|0.0|0.0|0.0|0.0|
|Textiles|26.7|27.3|3.5|4.1|15.5|15.9|4.3|4.9|7.0|7.6|
|Vegetables and fruits|14.0|14.8|8.9|17.3|33.6|35.9|5.0|7.1|0.8|1.0|
|Vegetable oils and fats|20.6|21.0|4.1|6.1|75.8|80.4|5.1|5.7|2.0|2.2|
|Wearing apparel|30.3|30.4|3.5|4.1|11.7|12.5|9.8|9.9|5.4|6.1|
|**Simple Average over sectors**|18.8|19.4|16.6|26.4|31.7|35.2|32.4|44.0|5.2|8.5|

_Source: Authors’ estimates based on MAcMapHS6v2.1_ The entries in Table 1 highlight some important points. The first is that the τ<sup>e</sup> is weakly above the initial weighted-average tariff, τ<sup>R</sup> , but can be substantially above its level. The final row of the table, which presents the simple
to make assumptions about the relationship between the error terms and . Specifically, we assume that these are, on average, positively correlated over time. In the presence of ii1 εε household fixed effects and persistence of shocks, this does not seem to be an unreasonable assumption. ii2 εε Where panel data are available, the correlation coefficient can be estimated directly, and indeed Dang et al. (2014) report values between 0.39 and 0.66 for the six countries they investigate.<sup>10</sup> We do not make ρρ further assumptions about the shape of the distribution of the error terms; in that sense, these estimates are non-parametric.

> 9 This age range is chosen so that formation of new households and dissolution of existing households should be at a minimum. Moreover, it ensures that in round 1 heads will generally have obtained their maximum level of education.

> 10 Dang et al. estimate a value of 0.50 for Indonesia (1997-2000) and values of 0.58 – 0.66 for Vietnam (all pairs of 2004, 2006 and 2008) (Dang et al. 2014, p. 120).

10
, revenues accounted as expenses, and capital income.

23 See Mintz (1994, p. 1483). The whole debate on the flat tax is a good example. In the May-June 1996 issue of the magazine "Challenge", for instance, Roberts/Sullivan criticize the flat personal income tax proposal on the grounds that "under the flat tax, many of the wealthiest individuals would pay absolutely no tax. For example, a family whose income consists solely of millions of dollars of dividends, interest, and gains from the sale of stock held for investment would pay zero tax." (p. 25).

24 See Sorensen, 1994, for a simple numerical example. See also Mintz, 1994.

25 The survey is based on a random sample of all 4.1 million income tax returns filed by Hungarian taxpayers in 1994. Declared household income amounted to HUF 1,714.6 billion. The TARKI study was commissioned by the Ministry of Finance and financed by a PHRD grant from the World Bank supporting the Govemment's public finance reform.
equivalent to 0.73 and 0.85 Canadian college graduates, respectively.

We revisit the wage e¤ect of immigration on average wages when the skill levels of immigrants and nationals are quality-adjusted. Di¤erences in the quality of education is likely to be a source of ’brain waste’(see Matoo et al, 2008). Hence, our adjustment consists in multiplying the number of college graduates originating from a given country by the relative productivity index computed for that country, and consider the remaining fraction as less educated workers. In the previous example a college graduate from Angola is considered as a combination of 0.73 college graduates and 0.27 non-college graduates in the rich country. This method has two main limitations. First, as our adjustment factor is based on Canadian data, it su¤ers from a selection bias. Indeed, Moroccan migrants to Canada are more than likely to have higher skills than Moroccan migrants to France. For this reason, we also provide a second simulation with a correction based on the squared of the Canadian index. Coming back to our example above, one college graduate immigrant from Angola or Portugal accounts for 0.51 or 0.72 units of highly skilled workers, respectively. Second, while our benchmark non-adjusted measure implies that immigrants’human capital is equivalent to that of natives (as if all migrants were trained in the host country), our adjusted measure implies that all immigrants were trained in their birth country. Reality is obviously somewhere in between. However our only objective here is to explore whether a correction for education quality can modify our predictions, hence an extremely negative assumption implies simply that our estimate is a lower bound of the wage e¤ect.

Our third correction in the accounting of foreign-born highly educated is based on the US wage of immigrants from each country of origin (in year 2000). We calculate the weekly wage of college educated immigrants from country j as a linear combination of the wages of US-born college educated and non college
Policy Research Working Paper 9456

# **Abstract**

Based on their objective economic situation and comparing with their peers, individuals form perceptions of their economic position in a society. Data from the three waves of the Life in Transition surveys of European countries show that these perceptions systematically deviate from the rankings obtained using consumption levels. People position themselves in the middle ranks in larger numbers than those who are in the middle ranks according to their consumption levels. Correspondingly, many people who objectively are classified in the top, richest, or bottom, poorest, ranks subjectively feel that they are in the middle class. This puzzling “bunching in the middle” is the focus of this paper. Explanations are tested and discarded that consider subjective perceptions as misperceptions or the result of other mistakes due to data limitations (such as tail bias). The paper concludes that rather than reflecting a subjective assessment of the distribution of welfare, subjective rankings reveal subjective economic well-being. The paper show that monetary consumption is a strong predictor of subjective economic well-being, but that the latter is influenced by many other factors, including economic security, proxied by employment status or other measures of human capital, such as health and education. These findings have policy relevance, since redistribution measures aiming at simply protecting consumption levels may not be sufficient to restore the economic well-being provided by having fulltime secure types of employment.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, mlebrand@worldbank.org and itorre@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of
>37|37|3,738|<br>3,738|3,738|
|R-squared|0.29|0.41|0.40|0.30|0.33|0.25|0.25|0.37|0.27|0.30|
|meanoutcome|36.5|3.4|-0.27|-0.28|-0.19||96.4|4.5|-0.17|-0.22|-0.09|
|<br>p-valueonregion-specifictrends<br>|[97]<br>|[.52]<br>|[.55]<br>|[.49]<br>|<br>[.98]<br>|<br>[49]<br>|[.57]<br>|[.31]<br>|[.94]<br>|[.78]<br>|
|<br>p-valueonRegion5-6v.Region3-4||[.85]|<br>—[.91]|[.35]|[.68]|[.82]|[.96]|[87]|[.97]|[.96]|[.60]|

Outcomes for Grade 12 exam shown. All regressions use pre-treatment years only, with time rescaled within region relative to first year of girls scholarship program. All regressions include linear trend in this rescaled time and calendar year fixed effects. Sample for level and log of test takers is district panel, and includes district fixed effects. Sample for test scores is students, and includes age, age squared, and school fixed effects. Standard errors in parentheses, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of
supply (caused by a shift of US farmers from cotton acreage to cereals production for bio fuel). The increase in cashew nuts price was also due to a decline in supply in the world market following unfavorable weather conditions in Vietnam and Brazil<sup>12</sup> .

# **4. Data and Descriptive Statistics**

The Tanzanian dataset used for the empirical analysis was obtained as part of the first ever cross-country exporter-level customs transaction database that is being collected under the Export Growth and Dynamics Project by the Trade and International Integration Unit of the World Bank’s Research Department (Freund and Pierola, 2011). The novel dataset contains daily information on shipments of Tanzanian exporters between 2003 and 2009. The original source of this dataset is the Tanzania Revenue Authority<sup>13</sup> . Each daily observation contains information on arbitrary<sup>14</sup> firm ID, product information (at HS-6 digits), date, destination (ISO3), for shipment value in US dollars<sup>15</sup> and net weight. In light of merging this dataset with other data, it is very important to ensure that the product codes belong to a single classification<sup>16</sup> .

In order to obtain the final dataset used for this analysis, daily transactions are aggregated (summed) by firm-product-destination-year quartets (i.e. exports of coffee by a Tanzanian firm to Germany in 2003). This paper focuses on annual exports for several reasons: first, to avoid dealing with seasonality in both daily and monthly data, and also because most firms do not export a given product to a given market in every day or month (not even in every year); second, the empirical analysis of this paper studies the relationship between firms' export prices and destination characteristics, some of which are available yearly (i.e. GDP). As a consequence, using a daily or monthly dependent variable (price) could generate
the countries without reported crises are known to have rather dysfunctional controlled banking systems, but ones which have managed to remain solvent or nearly so.

_Gini coefficients before and after crises_ (a) Long-term effects Looking first at long-term effects, it is of interest to compare Gini coefficients measured before any crisis hit, with those measured a sufficiently long interval (say ten years) after any crisis was reported as being in progress.

Because of the frequency and protracted nature of banking crises, there are relatively few datapoints in either category. Thus, confining ourselves to the data in _Povcal_ (and excluding transition economies<sup>21</sup> ), we obtain just 23 pre-crisis Gini observations, with a range from 0.26 to 0.63, a mean of 0.439 and a standard deviation of 0.102. As for “long-after” post-crisis observations, i.e. those 10 years or more after the last recorded crisis date, there are also 23 of these, ranging from 0.29 to 0.74, with a mean of 0.476 and

> banking crises – complicates the interpretation.  Also note that, contrary to other results in the literature

> (e.g. Beck et al., 2004) financial deepening is found by Lopez to worsen inequality.

> 21 For whom the strong, known, dynamics of increasing measured inequality will surely swamp any crisis effects.

13
substantially—except for the short-term creditors. The long-term prospects for the debtor country are likely to worsen as are those for the remaining private creditors.

Returning to the 1980s, some might argue that Baker to Brady was the right sequence: as Cline (1990) noted, the U.S. financial system was saved. But at what cost and to whom? According to an FDIC study (FDIC 1997), the four largest debtors, Mexico, Brazil, Venezuela and Argentina, owed $37 billion to the eight largest US banks, which amounted to 147 percent of their capital and reserves. Suppose the banks had to take a 25 percent haircut on these loans. This would have amounted to $9.25 billion and wiped out some 37 percent of these banks‘ capital. However, even their total exposure, $37 billion, was less than 1 percent of U.S. GDP in 1985. Different and less costly strategies were conceivable that would have benefited millions of poor people in Latin America who eventually bore the brunt of its lost decade even as official intervention kept the banks going. Borrowing access by the debtor countries would probably have been disrupted; but such a hardening of their budget constraint was needed to address the underlying fiscal and political economy problems, as the country studies in Sachs (1990) bring out.

Similarly, suppose that the official community had persuaded Russia to float the ruble in May 1998, backed it in seeking a haircut for private creditors and given it an official rescue package at the risk-free rate. The situation would still have been difficult and the U.S. probably would still have had to bail out the systemic hedge fund operated by Long Term Capital Management (LTCM) which fell victim to the contagion from the Russian default (see Dungey et al. 2006) but the problem would have been smaller, as noted by Rogoff. Including the swap, dollar-denominated public debt had increased by $16 billion or 8 percent of post-crisis GDP over the previous 10 weeks
sampled 6,000 households from 500 primary sampling units (PSU) nationwide.<sup>2</sup> The survey collected considerable locational information from households: GPS locations, names of villages and estimated average travel time and distance to markets, hospitals, banks, schools, and vehicle roads. To ensure the accuracy of the GPS data, this study employs data from three successive years from 2016 to 2018. For the purposes of this analysis GPS locations that fall outside the boundary of the listed village were eliminated from that year’s data set. This process narrowed the data set to 6,250 households from 6,367. If the GPS of more than a year fall within the boundary, we compute the household location as the geographic average of the GPS data.

Table 7 summarizes the mean and median time to the facilities based on HRVS result and the developed model (regular and monsoon). To comparing the modeled remoteness with the survey results, households with travel times over 8 hours are dropped, expecting that people will not walk over 8 hours per day and will take a long rest overnight, where the time for resting is not captured in the model. Roads are most accessible to households, while banks are least accessible. The final model results for the location of a household GPS point were spatially joined to the HRVS data and regressed against reported times to validate the final results. Figure 7 plots the regression results.

**_Table 7: Comparison of model times to Reported travel times (HRVS)_**

||**Ro**|**ad**_(excludin_|_g path)*_|**M**|**edical Faci**|**lities**||**Bank**||
|---|---|---|---|---|---|---|---|---|--
Policy Research Working Paper 9911

# **Abstract**

The Philippines was among the most infected countries in East Asia at the onset of the COVID-19 outbreak. This study analyzes how international trade on various margins was affected by the country’s own lockdown policies and those of trading partners. Using a monthly series of product-by-country data for the period from January 2019 to December 2020 and an event study design, the paper shows that domestic lockdown measures did not affect international trade but external lockdowns affected both ex- ports and imports. The introduction of lockdown measures by trading partners affected imports more than exports, leading to 7 and 56 percent monthly average drops in export and import values, respectively. Restrictions on internal movements and international travel controls in partner countries were responsible for the drop in exports. The slump in imports was because of workplace closure, stayat-home requirements, restrictions on internal movement, and international travel controls by trading partners of the Philip- pines. Intermediate goods were the key driver of the drop in imports following foreign lockdowns, reflecting supply disruptions in backward global value chain participation. At the same time, exports of intermediate goods were more resilient to the lockdown policies. Finally, both exports and imports were more affected at the extensive margin than the intensive margin, as lockdown measures hindered interactions among people, in turn reducing the potential of businesses to create new relationships and launch new products in foreign markets. Overall, diversified and geographically dispersed suppliers can help countries adjust better to future disruptions.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at amontfaucon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about