rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>2/87|Celade printout 2/87|
|Hoty See|Apr 48|1 F|UNPVSR<br>4/87 (official<br>est.)|--|-|
|Noncdras|Mar 74|2657 J|CELADE printout 2/87|Celade printout<br>2/87|Celade printout 2/87|
|Hong Kong|Mar 86|5396 F|U.N. 1988 revision (prelim.)|Bank est.|Based on U.N. 1988 revision<br>(prelim.)|
|angary|Jan 80|10709 F|UNPVSR<br>4/87 (official<br>est.)|Based on INED 10/87|Based on UNPVSR<br>4/87|
|celand|Dec 70|205 J|UNPVSR<br>4/87 (official<br>est.)|Based on INED 10/87<br>|Based on UNPVSR<br>4/87<br>|
|ndia|Mar 81|685185 F|Bank projection from census,<br>adjusted for 1.83X undercount|Bank assessment of Sample<br>Registration System data|Based on SwapLe Registration<br>System data|
|ndonesia|Oct 80|147490 F|U.N. 1984 assessment|1987 DHS|Bank assessment of 1985<br>intercensat survey|
|ran, Islamic<br>Rep. of|Nov 76|33709 F|U.N. 1984 assessment|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Iraq|Oct 77|12000 F|U.N. 1984 assessment|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Irelard|Apr 81|3443 F|UNPVSR<br>4/87 (
does the reforms independently of the others (independent policies), a second scenario in which all Western Balkan economies coordinate and implement the reforms at the same time (cross-economy coordination), and a third scenario in which economies also gain EU accession (cross-economy coordination + Full EU accession).<sup>3</sup> According to the Trading across Borders indicators, measured by The World Bank as part of the Doing Business Report, the time in hours to import goods and fulfill the border and documentary compliance is three hours (28%) higher in the six Western Balkan economies than in the high-income OECD countries. Therefore, on the counterfactual, we assume that an economy that implements the package of trade facilitation reforms mentioned above will be able to reduce by three hours the waiting time at the border. As shown by the model estimates, this intervention would be similar to lowering tariffs by around 2%. Figure 4 plots the aggregate effects of reducing border-crossing times. On average, welfare (real income) increases between 1.5%-3.0% in the Western Balkans in the long run. The most significant result is that if there is coordination among them, welfare increases by 8% more relative to the counterfactual in which each economy implements the reforms independently. Similarly, if economies are part of the EU, the gains are amplified by an additional 6%.

Thus, the simulation reveals three important key messages. Firstly, the impact of these trade facilitation reforms on welfare is positive and significant in all Western Balkan economies. Second, if economies adopt a coordinated approach to the trade facilitation reforms, the benefits are 8% larger. Additionally, if the Western Balkan economies become EU members, and assuming that this leads to a reduction in trade costs of 4% and of migration costs of 3% as suggested by Caliendo et al. (2021), the gains associated with the reduction of waiting times at the border are even greater, leading to welfare gains approximately 6%
Figure 44. EACR Decomposition of Credit Card Ownership between countries <!-- Start of picture text --> FINDEX 2011 FINDEX 2014<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>Netherlands Spain Denmark Taiwan, China Netherlands Spain Denmark Taiwan, China<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base. In FINDEX, 141 countries are sorted on Coverage in 2011 and 143 countries are sorted in 2014, and only nearest neighbors in coverage rates are shown. _Sources_ : FINDEX Figure 45. EACR Decomposition of Credit Card Ownership between countries <!-- Start of picture text --> Household (LITS)<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>Latvia Czech France Croatia<br>Republic<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ LITS is a household level data set. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries.

_Sources_ : LITS 47
expand and this will happen when exchange rate changes make Chilean exports relatively cheaper. Given these exogenous changes, a firm will be more likely to take advantage of these export opportunities if it is exposed to those markets. A natural measure of destination exposure in this case is the share of a firm’s exports to that destination in total firm sales. Access to the administrative customs records allows us to do this for the Chilean ENIA firms. Formally, we define the instrument _z_<sup>0</sup> as where _sdij_ is the share of exports of firm _i_ (in industry _j_ ) to export destination _d_ at the initial time period (year 2001), and _rdt_ is the bilateral exchange rate between Chile and country _d_ at _t_ . Hence, _zijt_<sup>0isaweightedaverageoftherealexchangeratefacedbyChileanexporters,</sup> where the firm-specific weights are the initial shares of exports in sales. As in Brambilla, Lederman and Porto (2012), we also interact _z_<sup>0</sup> with initial firm sales (i.e., log sales in 2001) to include any firm advantages in profiting from export opportunities based on firm size. Averages for the value of these instruments are reported in panel E) of Table 1.<sup>2</sup> The statistical validity of the instrument can be checked with first-stage results for the

> 2In all our specifications, we use both _z_ 0 and its interaction with initial sales as instruments. We do this to maintain consistency with the literature that utilizes a similar approach as ours. However, all the results in the paper remain unaltered by a specification that keeps only the average firm-level exchanger rate, _z_<sup>0</sup> , and drops the interaction with initial sales. These results are reported in an Online Appendix available at the authors’ websites.

5
POLICY RESEARCH WORKING PAPER **2399**

# **Summary findings**

> Kaminsky, Lyons, and Schmukler address the trading

> strategies of mutual funds in emerging markets. The data set they develop permits analyses of these strategies at

> the level of individual portfolios.

**A** methodologically novel feature of their analysis: they disentangle the behavior of fund managers from that of investors.

For both managers and investors, they strongly reject the null hypothesis of no momentum trading. Funds' momentum trading is positive: they systematically buy winners and sell losers.

Contemporaneous momentum trading (buying current winners and selling current losers) is stronger during crises, and stronger for fund investors than for fund managers.

> Lagged momentum trading (buying past winners and

> selling past losers) is stronger during noncrises, and stronger for fund managers.

Investors also engage in contagion trading-selling assets from one country when asset prices fall in another. These findings are based on data about mutual funds that represent only **10** percent of the market capitalization in the countries considered. Were it a larger share of the market, finding counterparties for their trades (the investors who buy when they sell and sell when they buy) would be difficult-and the premise that funds respond to contemporaneous returns rather than causing them would become tenuous.

> This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the

> group to understand capital flows to developing countries. The study was funded **by** the Bank's Research Support Budget under the research project "Mutual Fund Investment in Developing Countries." Copies of the paper are available free from the World Bank, **1818** H Street NW, Washington, **DC** 20433. Please contact Emily Khine, room **MC3-347,** telephone 202- **473-7471,** fax **202-522-3518,** email address kkhine@worldbank.org. Policy Research Working Papers are also posted on
provides household-level variations in the exposure to food price shocks, enabling the identification of food price shock impact.

For a household _i_ in province _j_ , we estimate: where ∆lnk is the log of gross investments in agricultural assets (in real term) between 2007 and 2010. Landholding size (production scale) L0, the durable asset (initial wealth endowment) A0, and a vector of household characteristics in 2007 (i.e., the age of the household head, the household’s average years of schooling, and household size) X2007 are included. Depreciation of agricultural assets is not captured. The standard error is clustered at the village level.

In order to obtain causal effects of food price on investments, we use two-year panel data and difference out household-level fixed effects (time-invariant unobserved heterogeneity). We can difference out the risk aversion, which is the covariance term in the RHS of Eq. (5), and farmer’s abilities and tastes which could affect their crop choice. These unobserved factors may be correlated with the production (revenue) weight of each crop in 2007 ( φ 2007 ), which may bias our estimate of the effect of food price on the investment decision.

11
project estimated job losses at the firm level onto labor force survey data for the first round of data (CFUWBES1) and apportion realized outcomes to groups of workers. Then, since risks are inherently a forward-looking concept, we predict job losses into the near-term future using a subsequent round of the CFUWBES (CFUWBES2) and then compare the predictions to real time survey data for Jordan, where such data were available. There is a methodological tradeoff between prediction and inference as it pertains to model selection.<sup>32</sup> However, since our claim is that understanding causes is important to assigning risk levels, and to maintain internal consistency and simplicity, including for usability in other contexts and countries, we elect to use our inference model for prediction.

# 5.1 Projection Method

With our econometric estimates in hand, the next step of our method is to translate model- predicted job losses (“modeled”) from the CFUWBES1 to projected job losses for the population of PFPS workers as identified in the labor force surveys as follows: First, we use the statistically significant factors for job loss reported in Section 4 above (from the Job Loss equation) to predict job losses for firms still in operation as of round 1 of the CFUWBES. For comparison purposes, we also report results using simple CFUWBES survey means (“survey-measured”). For job losses at permanently closed firms, given data limitations we must use a more information-limited method. Ideally, one would augment the econometric modeling of job losses with a firm entry and exit equation. However, using the CFUWBES data, it was not possible to model these dynamics in a manner separate from the selection equation method used because new firms were not sampled, and very few of the questions relevant to the drivers of job loss or jobs at risk were asked of permanently closed firms (in either round).<sup>33</sup> Therefore, we had to adopt a few simplifying assumptions to account for such closures: (1
now dropped to 700 gCO2/kWh. However, the heat rate of JLEC, outperforms ONEE’s older coal plants, such as Jerada, which requires almost twice the quantity of coal to produce the same amount of power. While JLEC has a heat rate of 2,195 kilocalories per kilowatt-hour, the values for ONEE’s Jerada and Mohammedia coal plants, commissioned over three decades ago, are 3,850 and 2,534, respectively.<sup>32</sup> These stable system-wide indicators are underpinned by a growth and diversification of Morocco’s electricity generation mix in at least three ways. First, there are more energy sources today than there were in 1990. The fuel-type concentration index has declined steadily over the years (Figure 4) from 0.48 in 1990 to 0.36 in 2017. In the 1990s, there were three main fuel sources, oil, hydro and coal. Therefore, the fuel-type concentration index reached a peak of 0.57 in 1992 when oil constituted 73 percent of generation (7,087 GWh) and coal contributed about 17 percent (1,668 GWh). The concentration index dipped to 0.39 from 1997 when hydro-electric generation more than tripled to an all-time high of 2,083 GWh. There was another shift in the 2000s, when the generation mix became more concentrated, at 0.63 in 2000. At this time, hydro-electric output collapsed to 718 GWh and coal-fired generation began an upward trend to 8,783 GWh that continued for the next decade. This was in part, due to the drought between 1997 and the year 2000. Over the next decade, other energy resources were added to the mix, including gas, wind, and solar. Gas for instance, contributed 126 GWh in 2004 and 2,003 GWh in 2005 due to imports through regional interconnections with Algeria.<sup>33</sup> In 2014, for the first time, windgenerated electricity surpassed hydroelectric power as the biggest contributor
Policy Research Working Paper 9516

# **Abstract**

This paper presents a novel method for estimating the likely welfare effects of competition reforms for both current and new consumers. Using household budget survey data from 2015/16 for Ethiopia and assuming a reform scenario that dilutes the market share of the telecommunications stateowned monopoly to 45 percent, the model predicts a 25.3 percent reduction in the price of mobile services and an increase of 4.6 million new users of mobile phone services. This reform is expected to generate a welfare gain of 1.37 percent among all consumers. Poverty rates are expected to decline by 0.31 percentage point, driven by a reduction of 0.22 percentage point for current consumers and 0.09 percentage point among new users. Inequality would increase by 0.23 Gini point since better-off consumers are more likely to reap the benefits of greater competition. This method represents a powerful tool for supporting the analysis of competition reforms in developing countries, particularly in sectors known for excluding significant segments of the population due to high consumer prices.

This paper is a product of 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 crodriguezc@worldbank.org and emalasquez@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 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
395)|
|Observations|544|544|544|551|551|551|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|

<mark>Table A3 presents estimated effects of maternal mental health on child vocabulary at age 5, separated by child</mark> gender. We estimate the model selected from LASSO procedure. Selected control regressor set includes mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. * and ** indicate statistical significance at 10% and 5%, respectively. Robust standard errors in parentheses.
relatively low interest rates (a median borrowing rate of 3%, mean rate of 3.8%). This may reflect an unwillingness to take on risk; village funds want to lend from equity, not debt. And if loan demand were to exceed the available supply, 63% of VF committees say they would still lend to all qualified borrowers, but in a majority of these cases they would reduce loans equally, effectively sharing the pain (see Table 14). Faced with excess loan demand, about one VF in seven would refuse some loans; and about one in eight would try to borrow more from the BAAC or another bank in order to accommodate the demand.

|**Table 14. Response of Village Funds to insufficient funds for lending**||
|---|---|
|How does the VF respond if there are insufficient funds relative to loan demand?|% breakdown|
|Still lend to all|2.1|
|Lend to all, reducing loans equally|35.8|
|Lend to all, reducing some loans by more|24.8|
|Refuse some loans|13.6|
|Borrow more from BAAC/GSB/KTB|12.7|
|Other|11.0|

Source: Village Fund Survey, 2010 Almost a third (32%) of village funds claim to have a AAA credit rating, and these funds are slightly more likely to want to borrow funds in order to lend more to their members. Most VF committees (91%) say they announce their financial results publicly.

# _Risk Mitigation_

Although every Village Fund needs to be concerned about managing risk, this is particularly true of VFs that borrow to on-lend. There are a number of possible ways in which risk may be reduced. An altruistic VF will try to minimize the burden of such provisions, consistent with some form of non-bankruptcy constraint. Informal mechanisms probably work better in small than large villages.

The first approach is
forecasts of economic growth.<sup>23</sup> We take the difference in annualized growth rates of survey means and national accounts as our measure of the gap (or ‘error’ in the growth forecast literature). A negative value indicates that survey means grew slower than the national accounts measures.

The distribution of the gap is presented in Figure 4, showing a large variation in differences in growth rates between the two sources. To check for systematic differences, we estimate these differences in growth rates by subgroups of countries, time periods and spell lengths, for each type of survey (income and consumption).<sup>24</sup> The main results are presented in Table 2. Overall, growth rates in surveys are, on average, lower than in national accounts. However, the difference is not statistically significant for the overall sample which pools both income and consumption for all countries and time periods. For the subsamples of lower middleincome countries, as well as the 1990s, survey means grow more than one percentage point slower than HFCE in national accounts on average, a difference that is statistically different from 0 at the 95%-level. The gap in growth rates is also larger when looking only at longer, comparable spells for each country (with four-to-six-year spells).

The gaps in growth rates between surveys and national accounts are larger and more robust when looking at the subsamples consisting of only survey consumption means. Overall, we find that the average growth rate for consumption means in surveys in our full sample is 0.53 percentage points less than HFCE and 0.43 percentage points less than GDP growth. The difference is particularly large in middle-income countries. For income means, the differences in growth rates with respect to national accounts are smaller and mostly statistically insignificant. Notably, we observe a positive gap for high-income countries, where survey means grow faster than HFCE. This is despite the North America region (where both the US and Canada are also considered high income)
, who received a comparable returnee flow from Algeria, Portugal was less developed at the time, and the economy was in a downturn.

Two published studies looked at the impact of these returnees on the local labor market. Carrington and de Lima (1996) used time series econometrics to observe the evolution of the Portuguese labor market before and after 1974. They find a sharp deterioration of labor market indicators after 1974. However, the immigration rate had no impact on the unemployment rate, the employment to population rate or wages except for a small one year lagged effect on the unemployment rate and real wages. Interestingly, these results hold whether the authors control for Portuguese macroeconomic indicators or the Spanish labor market, which had similar characteristics to the Portuguese labor market in the 1970s. Using a separate longitudinal model and focusing on the construction sector, the same authors find a large and significant effect on earnings in this sector. However, the authors’ challenge their own results considering the unobserved heterogeneity that affects the latter model leaving conclusions somewhat open to interpretation. They also argue that the persistence of the effects raises question if the returnees were the cause.

Makela (2017) uses a SCM approach based on comparator countries (see empirical models section) to estimate the impact of the returnees on average annual productivity, wages and the unemployment rate. The author finds a significant negative effect of immigration on all three outcomes. The estimate impact on productivity is around 26 percent in the five years after the shock, the one on wages is from 8 up to 55 percent from 1977 to 1985 whereas the unemployment rate rises by about 2.3 percentage points between 1975 and 1980 but declines by a similar amount between 1980 and 1985. The author also combines the SCM approach with a Difference approach working with Portuguese regions rather than countries and focusing on the agriculture and construction sectors. In addition, a fixed effects and and a generalized synthetic control method are used as robustness tests. All these estimations are consistent in finding a significant negative effect on
# **I. Introduction**

Bangladesh has consistently implemented the Household Income and Expenditure Survey (HIES) every approximately five years since 2000. Before HIES, the Bangladesh Bureau of Statistics (BBS) monitored poverty using a smaller survey limited to collecting expenditure data (Household Expenditure Survey, HES). Four HIES rounds were collected until 2016/17 under the same survey design, data collection processes with no changes in critical questionnaires, like the food and non-food consumption modules, recall periods, and data entry and management (i.e., Pen-and-Paper Personal Interview (PAPI) and Computer Assisted Field-Based Data Entry (CAFE)).<sup>3</sup> Nevertheless, despite maintaining these aspects unchanged, comparability over time was not always fully guaranteed. For instance, the 2016/17 HIES introduced vital changes that could have affected its comparability with previous rounds. Notably, the sample size increased significantly from 12,240 households in 2010 to 46,080 in 2016, enhancing the survey's granularity for poverty estimates at various administrative levels. This expansion necessitated shifting the sampling frame from the Integrated Multiple-Purpose Sample to Enumeration Areas from the 2011 Population and Housing Census. This shift impacted the survey strata and increased the coverage of urban slum areas. Additionally, changes in geographic classifications and the replacement of Statistical Metropolitan Areas with a new Rural/Urban/City Corporation classification affected urban comparability. The larger sample size and revised fieldwork protocols led to more incomplete and inconsistent income data, particularly for wealthier rural households. Despite these issues, the changes are not deemed to significantly alter the overall findings of the poverty assessment (World Bank, 2019).

In the 2022 HIES round, Bangladesh implemented survey design and fieldwork operation improvements that impacted consumption comparability over time.<sup>4</sup> Introducing the Classification of Individual Consumption According to Purpose (i.e., COICOP) increased the number of food and non-food items from 149 to 263 and 261 to 441, respectively. The data