rafmacalaba/datause-encoder-data huggingface dataset:
# **II. Data Description**

We focus on U.S. equity mutual funds that are established to purchase assets around the world. Funds that focus on both debt and equity are excluded from our sample, even though they do invest a significant share of their portfolios in foreign stocks. We use two types of data in our empirical analysis: data on mutual fund holdings and data on firm attributes.

Mutual fund holdings are available from Morningstar, a company that collects mutual fund data. We analyze monthly reports from March 1992 (when they became available) until June 2004. While some mutual funds provide monthly reports, most do so quarterly or semiannually (depending on the reporting SEC rules at the time). Given this heterogeneity in the release of new information, we construct our database with the last reported portfolio information for each fund in any given year. For example, our sample of mutual fund holdings for 2003 contains portfolio data for the Fidelity Worldwide fund, with portfolio holdings as of October 2003, and portfolio data for Scudder global fund, with portfolio holdings as of December 2003. We collect detailed information on portfolio holdings: stock names, amount invested in each stock by each fund, and country of origin of these holdings on a yearly basis.<sup>14,15</sup> The U.S. mutual fund industry is organized by splitting funds according to their investment scope. In particular, funds are classified into five distinct categories: world funds,

> 14 Some families sell the same portfolio to investors under different names depending on their fee structure and minimum investment requirements. We consider these different funds (with identical portfolios) only once; i.e., we do not treat them as separate funds as Morningstar does. For example, Fidelity Advisors Funds contain the following Latin America funds with the same portfolio: Fidelity Advisors Latin America A, Fidelity Advisors Latin America B, and Fidelity Latin America T.

> 15 We classify holdings according to the country of origin of the company issuing the stock, independent of whether these assets are traded in domestic or international markets.

16
ly insulated from Liberal Party pressures. The Bureau of the Budget within the Ministry of Finance was also an island of innovation, urging reforms of the budgetary process.

In April 1959, the EDC economists drafted a Three Year Plan which was to be the foundation for later planning efforts. The plan spurred reforms in the government's sVatistical capabilities and became an important educational device for all those immediately concerned. The fate of these reforin efforts, and particularly the Three Year Plan. was decided by politics both within and outside the bureaucracy The semi-independent status of the EDC, the growth of MOR and the plans of the reformers encountered stiff bureaucratic resistance. More importantly, the economic bureaucracy as a whole had little autonomy from pressures emanating from the Part), the private sector and the executive itself. The Liberal Party had a powerful say in personnel administration, leading to frequent changes at the ministerial level (Table 1).

Table 1 Turnover Among Economic Cabinet Ministers, 1948-1960

|Ministry<br>----------------------------------------------------------------|Number of|Ministers|Average Term<br>__-----------_|
|---|---|---|---|
|Agriculture<br>Commerce|16<br>11|<br>|9 months<br>13 months|
|Finance|9||14 months|
|Reconstruction|4||13 months|
|-------------------------------------------------------------------|||__--------|
|(Source:<br>Calculated from<br>|Frank M.<br>|Landers, Technical<br>|Assistance in Public<br>|
|Administration: USOM/Korea|, 1955-196|7 [Seoul: USOM/Korea|, 19671, p. 60).|

Conditions only worsened as the political situation deteriorated in the late 1950s. Preoccupied with political problems, Rhee reviewed the Three Year Plan only twice during the two year period of its formulation. The Liberal Party shelved the final draft of the plan for a year in 1959, approving it only a few days before the student revolution that ended Liberal Party rule.

10
have been drastically reduced over the period as have their contribution to the variance. On the other hand, the Central region, Volta, Ashanti, and Upper Eastern have increased the premium, but not so much in increasing the share of explaining the variance of expenditure per single person, except Ashanti, which explains 10 percent variance in 1992 but nearly none in previous years.

Table 9 Parameter estimates of expenditure per adult equivalent and _S, s_

||Parameter|estimates||Contribution<br>explained|to variance||
|---|---|---|---|---|---|---|
|Independent Variable|1988|1989|1992|1988|1989|1992|
|1) Household size|-0.04*|-0.04*|-0.04*|23.0|18.3|37.2|
|2) Education of household head||||12.6|23.9|15.7|
|Some primary|0.01|0.02|-0.04||||
|Completion of primary|-0.09|0.08|-0.01||||
|Some middle|0.02|0.07|-0.04||||
|Completion of middle|0.10*|0.21 *|0.10*||||
|Some secondary|0.17*|0.24*|0.09**||||
|Completion of secondary|0.23*|0.46*|0.22*||||
|Higher education|0.37*|0.65*|0.45*||||
|3) Sector:|||||||
|Export farming|-0.005|0.08|-0.04|3.4|2.2|7.4|
|Food crop farming|-0.07*|-0.03|-0.08*|0.1|0.0|0.6|
|Nonfarm|0.01|0.03|0.07*|0.3|0.0|0.3|
|Informal|-0.04|-0.005|-0.01|-0.5|-0.3|-0.5|
|4) Sex of household head<br>male=1,female=0|0.01|0.04|-0.08*|0.5|0.5|-1.8|
|5) Region:|||||||
|Western|0.48*|_0.53*_|0.13*|12.9|6.6|_-1.5_|
|Central|0.05|0.37*|0.29*|-0.8|-1.3|4.2|
|Greater Accra|0.48*|0.52*|0.19*|24.9|16.3|3.8|
|Eastern|0.23|0.39*|0.23*|-0.3|-1.3|0.3|
|Volta|0.05*|0.24*|0.19*|-0.8|-3.5|-1.2|
|Ashanti|0.21*|0.45*|0.35*|-1.2|2.4|13.1|
|Brong-Ahafo|0.33*|0.79*|0.20*|1.0|19.5|-2.9|
|Northern|0.22*|0.24*|0.19*|-1.9|-3.5|-3.8|
|Upper Eastern|-0.05|-0.18*|0.35*|0.9|4.9|-1.6|
|6) Experience of hhd head|0.01*|0.01*|0.0008|-16.7|-36.8|_5.5_|
|7) (Experience of hhd head)y|-0.0002*|-0.0003 *|-0.0001 *|32.1|48.2|13.8|
|8) Employment ratio|0.15*|0.15*|0.16*|2.7|3.3|6.7|
|9) Female employed|-0.13*|-0.02*|-0.10*|7.8|0.4|4.6|
|_R-square_|**_0.27_**|**_0.29_**|_0.19_|_0.27_|_0.28_|**_0.18_**|

Note: '*' indicates that the parameter is statistically significant from zero at 5 percent level, and '* *' at 10 percent level. GLSS2 (1989) data is adjusted. The adjusted data from GLSS3 yield similar results, which are presented in the Appendix A.

23
subject countries (i.e. those not named in the petition). As noted previously, only a subset of countries supplying the U.S. market is actually named in a petition. The ability to compare the evolution of import values provides a perspective on why the subject countries are likely to be singled out. In particular, we are looking for any changes that are pronounced enough to potentially motivate the domestic industry to file an AD petition. Further detail is provided by making additional comparisons based on changes in unit values and imported quantity. This additional level of detail helps to provide a narrative in terms of the source of shocks since positive or negative correlations in price and quantity map into distinct sources of shocks (i.e. demand shocks are typically associated with a positive correlation while a negative correlation is more closely aligned with supply shocks).

To address the question of why these products and not others, we turn to the comparison between subject and non-subject products. The architecture of the U.S. import statistics provides a natural basis for the comparison, as it identifies very good candidates for inclusion in the group of reference commodities. Products within the same HS-4 category as the subject products are treated as the relevant reference group.<sup>20</sup> (The reference group contains only those products that were not included in a petition over the entire period of our analysis.) In the analysis of the reference products, the set of subject countries is the same as with subject products. Shocks that hit national economies within broader HS-4 category can then be observed as common movements across subject and non-subject commodities. Hence, differences in the evolution of subject and non-subject products in terms of value, price and quantity will provide insight into the question of why only certain products were targeted and not others.

# **4.2 Decomposition**

Since our inference is based on comparisons we adopt a decomposition methodology developed by Hummels and Klenow (2005) that facilitates comparisons both across groups and over-time.<sup>21</sup> The decomposition technique involves two steps. First, the trade data within each month is summarized in terms of aggregates representing subject countries/products and the appropriate comparison group. Second, a log-linear regression is used to summarize common movements in the aggregates. Our regression technique is designed to isolate common, within commodity movements in the de-

> 20As an example, the 2001 petition involving mussels from Canada included two HS-10 products (‘farmed mussels’ and ‘other mussels’) within the larger HS-4 category covering mollusks and aquatic invertebrates other than crustaceans that are fit for human consumption. There were 29 HS-10 products within the HS-4 category that were non-subject products, including HS-10 products that track imports of oysters, cuttlefish, octopus, snails and clams.

> 21Other applications of this methodology include Hillberry and McDaniel (2002), Bernard et al. (2007) and Hillberry and Hummels (2008).

10
targeted to populations in greatest need and tailored to local conditions, thereby promoting the efficiency and impact of program efforts.

The emergence of standardized cross‐sectional household surveys such as the Demographic and Health Surveys (DHS) Program,<sup>19</sup> which now routinely include georeferencing (i.e., latitude and longitude coordinates), provides a new opportunity to understand subnational geographic variations in a wide range of socioeconomic, health and demographic indicators, including stunting. A recent DHS survey has been undertaken in Tanzania<sup>15</sup> and, like nearly all others, is configured to provide representative estimates of stunting rates at the first subnational unit level (regions in Tanzania), but not below. However, geospatial methods have been established that can use the georeferenced survey data alongside relevant spatially gridded covariate data to reliably estimate DHS indicators at much finer levels of spatial disaggregation.<sup>20,21</sup> The use of georeferenced survey data with geospatial models to generate interpolated surfaces is perhaps most established in the field of infectious diseases.<sup>22,23,24,25,26</sup> In recent years, these methods have also been developed and applied to a broader set of social, demographic, and health contexts. This includes the use of both DHS and World Bank Living Standards Measurement Survey data, among other sources, to create high‐resolution poverty maps for a range of Sub‐Saharan African countries.<sup>27,28,29,30</sup> Most recently, the method has been applied to mapping water and sanitation indicators in Nigeria based on the 2015 Nigeria Water and Sanitation Survey.<sup>31</sup> This study describes the use of such an approach to generate a new high‐resolution map of stunting prevalence in Tanzania.

# **Methods**

# _Data_

Data on stunting were obtained from the Tanzania DHS 2015‐16.<sup>15</sup> This consisted of a two‐stage cluster randomized survey design and a final sample of 9,846 children under the age of five for whom anthropometric and age data were obtained and stunting rates for those children computed.

Geospatial covariates (spatial gridded data for which values are known at all 1×1km pixels across Tanzania) were also included in the model, as they may partially explain the observed spatial variation in stunting rates and thus allow more accurate predictions at locations without DHS survey data. An initial set was identified based on previous performance in predicting poverty, water and sanitation indicators and other related metrics in Sub‐Saharan African countries.<sup>32,33</sup> These included satellite‐ derived measures of vegetation density (Enhanced Vegetation Index, EVI), temperature (Land Surface Temperature, LST), land wetness (the Tasselled Cap Wetness index, TCW), and land brightness 3
Policy Research Working Paper 10887

# **Abstract**

How redistributive are fiscal policies in China? This paper applies the standard fiscal incidence analysis to data from the China Family Panel Study 2018 to study the effect of government taxes and spending on inequality in China. The analysis includes fiscal elements, such as personal income tax, contributions to social insurance, value-added tax, consumption tax, cash transfers, contributory pensions, and spending on education and health, and accounts for 63 percent of total revenues and 43 percent of total government spending. Consistent with previous studies, the paper finds that fiscal policy in China continues to redistribute quite effectively, achieving inequality reduction of about 10.3 Gini points, placing China around the median of upper-middle-income country peers on the level of redistribution achieved by fiscal policy. Not unlike several other countries where similar analysis has been done, most of the inequality reduction achieved by China is through education and health spending. Findings from the paper further suggest that while the fiscal system delivers more to those who need the most support, the heavy burden of user fees— relative to disposable income—may prevent some families from accessing needed health care services and imply high costs of raising children. In addition, there is room for the progressivity of the overall package to be enhanced. In particular, the fiscal system could make a greater dent in inequality by collecting more from those who could afford to pay more and leaving more money in the pockets of those who need it the most. This could be done by increasing the share of fiscal revenues collected through progressive taxes such as personal income tax and increasing the level of cash-based social benefits (such as residents’ pensions and transfers).

This paper is a product of the Human Development Practice Group and the Poverty and Equity 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 mlugo1@worldbank.org, nlustig@tulane.edu, vmontalvatalledo@worldbank.org, stiwari@worldbank.org, and ywang18@tulane.edu.

_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 the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Selected countries<br>.<br>+<br>yo +<br>=w<br>Dcr, + +<br>aBu2 + . + i ++<br>Oo 4<br>> +<br>co<br>So<br>o fe LA} 5 oO oO Cc 12<br>#¢ $8 # 3 38 & 2 &<br>@ = £ G 5 a i ie<br>oO > e uw se) a ec<br><= is Oo &<br><!-- End of picture text -->
The parameter space is defined by the following 21 parameters: I partition the parameter space of the benchmark model in two groups: Ω1 and Ω2. In the first group, parameters are fixed based on previous results from micro studies or data. In the second group, parameters are estimated to match selected empirical moments, including the division of the labor force among unemployment, informal and formal-sector employment, employment and unemployment duration, and selected moments of the wage distribution.

Let Ω1 be defined as: The parameter values are chosen with a quarter as the implicit unit of time.

Table 8 summarizes the value of the fixed parameters based on data or micro studies.

The real interest rate in Colombia in 2003 was approximately 7.8 percent,<sup>72</sup> so _r_ is chosen to match the corresponding quarterly rate. Price indexes of tradable and non-tradable goods are used as proxies for the sectoral prices _PF_ and _PI_ , respectively.

The parameters _μy_ , _σy_<sup>2arechosentocoincidewiththecorrespondingempiricalmoments.73</sup> I choose the rate of arrival of the productivity shock _λ_ equal to 0.04 times per quarter. I use our micro data to test if the estimate falls within an admissible range. Using the separation rates of the formal and informal sectors, _hFF_ and _hII_ , I can express _λ_ as follows: Using duration data, I estimate that a formal-sector job lasts on average 6.89 years (27.5 quarters) while an informal-sector job lasts 7.69 years (30.77 quarters). Assuming that employment duration follows an exponential distribution, I estimate formal and informal-sector mean separation rates of 0.036 and 0.032, respectively. Given that 0 _≤ HF_ ( _RFU_ ( _y_ ) _| y_ ) _≤_ 1 and 0 _≤ HI_ ( _RIU_ ( _y_ ) _| y_ ) _≤_ 1, I have that 0 _._ 032 _≤ λ < ∞_ and 0 _._ 036 _≤ λ < ∞_ , , so assuming 0.04 shocks per quarter is within an admissible range in the model.<sup>74</sup>

The opportunity cost of leisure, _b_ , is set equal to 1. In Colombia there were no unemployment benefits in this period, so one must think about what the monetary compensation for the unemployed should be, relative to the average wage, or the average ‘replacement rate’. According to HKV2005, such a rate is 0.7 for European countries where benefits are relatively high, and 0.2

> 72Source: IMF, International Financial Statistics.

> 73 I use educational attainment as a proxy for worker types.

> 74 In ANV2009 _λ_ is set to 0.5 using one year as a unit of time, while in Hornstein, Krusell and Violante (2005)

> (hereafter HKV2005) _λ_ is set to 0.1 using one quarter as a unit of time.

27
18 otherwise. As shown in Figure 3 and in the first row of table 10, ∗ displays considerable mm variation, allowing us to estimate a proper demand function. After obtaining the parameters in PP (2), the consumer surplus lost if the HUS removed is the consumer surplus loss when prices go from ∗ to . mm mm PP Alternatively, the HUS can be thought of as a demand shifter. The price elasticity of PP demand cannot be estimated due to insufficient variation in prices, but we can constrain it to be equal to a plausible value, and amend equation (2) to: (3) ββ̅, . mm mm mm mm mm mm Note that in equation (3) the log of the gas HUS enters as a separate regressors, and not (llBB QQ −ββ̅ ∙llBB PP ) = αα<sup>′</sup> + γγ<sup>′</sup> ∙llBB hB ii + xx δδ′+ λλ∙llBBG BBH HH + εε as part of income, since the gas HUS can only be applied to the gas bill and is not cash that can be spent on anything else—at least not during our study period.<sup>19</sup> In other words, we have no reason to believe that households would consider the gas HUS as additional income. If this approach is adopted, then the change in consumer surplus is simply the difference between the consumer surpluses with and without the HUS. In what follows, we set =-0.16, the price elasticity of demand in Alberini et al. (2020) based on gas-using households in Uzhhorod in ββ̅ Western Ukraine. In addition to OLS, equation (3) lends itself to quantile regressions to examine whether λ is different for light and heavy users of gas.

# **5. Results**

# _5.A. Effect of HUS on Energy Expenditure Burden_

> 19 In May 2019, the HUS was monetized, i.e., it became a cash payment and was no longer directly applied to the bills. Unfortunately, we only have annual expenditures, income and HUS for 2019, and the quality of the 2019 HBS is doubtful, which means that we are unable to assess the effect of the monetization using the HBS data.
**Table 1: Gross Preprimary Enrollment Rates, 2000**

|_Country_|_Gross preprimary_<br>_enrollment_|_Gross preprimary_<br>_enrollment: GDP-_<br>_adjusted_<br>_(weighted)_|_Gross preprimary_<br>_enrollment: GDP-_<br>_adjusted_<br>_(unweighted)_|
|---|---|---|---|
|Argentina|60.4|-22.6***|-19.6***|
|Barbados|80.3|-7.7|-2.6|
|Bolivia|46.3|11.5***|14.9***|
|Brazil|61.5|7.1|4.5|
|Chile|77.5|12.3*|10.9**|
|Colombia|37.0|-6.5|-5.8**|
|Costa Rica|90.8|30.7***|29.5***|
|Cuba|108.7|||
|Dominican Republic|35.1|-11.4**|-11.5***|
|Ecuador|69.6|32.4***|35.0***|
|El Salvador|43.9|-0.6|-0.2|
|Guatemala|51.1|10.2***|11.6***|
|Honduras|21.3|-12.1***|-9.3***|
|Jamaica|82.0|29.2**|29.9***|
|Mexico|75.3|4.9|3.4|
|Nicaragua|26.8|-5.9**|-1.9|
|Panama|45.2|-14.3**|-16.2***|
|Paraguay|28.9|-10.0|-8.2***|
|Peru|59.5|15.9***|16.3***|
|Venezuela|51.6|-14.5**|-15.8***|
|**Latin America and**|**61.1**|**4.3**|**10.9**|
|**Caribbean**||||

_Note_ : Regressions with Huber-White corrected standard errors. * Significant at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level. Sample size is 144 countries. _Source_ : World Bank databases.

28
<!-- Start of picture text --> ARGENTINA: SAVING AND i, /ESTMENT,<br>*s- 989.<br>30<br>2.5<br>20<br>10 -tO~~~~~~17 0 S^g<br>8 ~~~5<br>N~~~<br>S 0<br>-5<br>-10<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>(Annual)<br>0 Invlstnlt O wonetic toWing * Impor - exports<br><!-- End of picture text -->
relationship creates a positive bias in the impact of (some) aid on ERM.<sup>4</sup> To correct for this or other possible sources of endogeneity, we instrument for aid using two-stage least-squares. Exogenous instruments include initial (1995) levels of infant mortality, population (with data from the _World Development Indicators_ ), and a set of regional dummy variables.<sup>5</sup> Higher infant mortality is an indicator of low development and greater need for aid as likely perceived by donors. Population reflects national prestige interests of donor countries: donors tend to spread their aid across many recipients to establish a presence, just as they have an embassy in every country. This donor behavior results in higher aid levels (on a per capita basis, or as a share of national income) for smaller recipient countries.

Table 3 (column 1) reports results for the first-stage regression, with aid/GNI averaged over 1999-2005 as the dependent variable. The excluded instruments infant mortality and population are both significant predictors of aid levels, and in the expected directions. The regional dummies are jointly significant at the .05 level. Collectively, these instruments explain a sizeable fraction of the variation in aid. As seen by comparing the two columns in Table 2, the other second-stage regressors explain 50% of the variation in aid in the absence of the exogenous instruments. With their inclusion, 67% of the variation in aid is explained.

Fuel exports are also potentially endogenous to ERM. For example, distortionary tax policies could reduce incentives to invest in manufacturing or services, reducing GNI

> 4 However, _allocations_ do not always translate one-to-one into aid _commitments_ , and commitments in turn do not always translate one-to-one into _disbursements_ .  Aid is measured as actual disbursements in the empirical analysis.

> 5 Regions follow the World Bank’s groupings: Sub-Saharan Africa (the omitted category), East Asia and Pacific, Eastern Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, and South Asia.

11