rafmacalaba/data-use-mentions-extended huggingface dataset:
with water and sanitation, holding constant either the marginal probability, the wealth-index effect or the combination of the other non-nutritional variables in Table 4. This suggests that other factors besides the health environment may well be playing a more important role, including intra-household inequalities.

# **6. Augmented regressions**

Introducing other household-level factors may enhance power for predicting individual outcomes. There may also be a problem with the weights used in constructing the wealth index; for example, the index may not adequately adjust for economies of scale in consumption. Finally, adding basic individual-level variables such as age and marital status for women may enhance targeting capability.<sup>31</sup> To test these conjectures we augment wealth with such household- (and individual-) level variables. The augmented regressions can be expected to perform similarly to the widely-used PMT method based on the predicted values of regressions calibrated to survey data (Section 2).

To motivate the augmented regressions, we can start by thinking of a simple regression of nutritional outcomes on the wealth index: ���� � �� � ����� � ���� (4) where ���� is the nutritional indicator for individual � in household � in country � and ��� is the household wealth index. Call this Model 1. Since the expected value of nutritional status tends to improve with wealth (the aforementioned wealth effect) rankings in terms of the predicted values from these regressions are very similar to those we have seen already. Model 2 augments (4) to contain household-level variables ���, giving: The vector ��� includes the separate components of the wealth index (essentially to allow a re-weighting of the index), as well as other household-level variables such as size and composition, and characteristics of the head. Dummies for survey month and region of residence are also entered as controls. Finally, Model 3 adds the observable individual-level variables, ����: For the incidence of
# **1. Labor Force Survey and Vietnam Household Living Standard Survey**

This paper uses two main sources of data: the Labor Force Surveys (LFS) of 2007 and 2009, and the Vietnam Household Living Standard Surveys (VHLSS) of 2006 and 2008.

The Labor Force Surveys of 2007 and 2009 were collected by the GSO. The 2007 LFS was conducted in the month of August among about 170,000 households using a fairly short questionnaire. The GSO implemented a newly improved questionnaire in 2009. The sample size of the 2009 survey was about 18,000 households. Households were selected randomly in two stages from the list of 15% sample enumeration areas of the 2009 Population and Housing census. All usual residents of the selected households were interviewed and enumerated. The idea is to have such LFS on a more frequent (potentially quarterly) basis. These LFS data are not directly comparable with the data from the previous period, since the methodology has changed significantly (i.e. levels cannot be compared between the two surveys; see ILO, 2010a).

The VHLSS is a household survey that contains information on demographics, education, health, labor market status, consumption, assets, dwelling and non-labor income. The surveys collected information through face-to-face interviews with household heads and key commune officials. These surveys have been conducted every two years by the GSO since 2002. The sample size consists in 2006 and 2008 of about 45,000 households (about 36,000 households in the income survey and about 9,000 households surveyed on both income and expenditure) in about 3,000 communes/wards which were representative at national, regional, urban, rural and provincial levels. A subset of households that were interviewed in 2006 were re-interviewed in 2008. These are clearly identified in the 2008 dataset, and we are able to use information on 4086 such households.

The labor market information collected in LFS and VHLSS is not directly comparable. Each dataset has its own reference period and questions. For
determinants of _SAH_ influence work effort and hence personal incomes. To help alleviate these concerns we estimate regressions in which personal income is dropped. We also test sensitivity to dropping the labor force activity variables, given the endogeneity concerns.

# **3. Setting and data**

Russia experienced a severe deterioration in overall health status in the 1990s — dubbed a “health crisis” by Field (1995). Male life expectancy fell from 65 years in 1988 to 59 years in 2001, while for women it fell from 74 years to 72 years (Heleniak, 2002).<sup>8</sup> This was in a period of overall economic contraction and rising inequality (Lokshin and Popkin, 1999), the latter stemming in large part from the diverse labor market outcomes of the period (Milanovic, 1999). There is also evidence of considerable “churning” in living standards, with both gainers and losers at any given level of living (Lokshin and Ravallion, 2000). There are many ways these economic changes could impinge on health. Deteriorating public health services, combined with reduced purchasing power over private health inputs, can be expected to take a toll on health status, and probably more so for the poor. Other channels include the psychosocial stresses of the transition from a time (prior to 1989) in which there was relatively little economic insecurity at the individual level to a period of much uncertainty. New opportunities emerged, but so did new stresses on individuals, who naturally had different abilities to respond to those opportunities (Gavrilova et al., 2002; Brainerd and Cutler, 2005).<sup>9</sup> Using a nationally representative survey for Russia in 1995, Cockerham (2000) found no statistically significant

> 8 Field (1995) reports a number of other signs of worsening health in the early 1990s.  Further evidence and (sometimes differing) interpretations of Russia’s “health crisis” can be found in Ellman (1994), DaVanzo and Adamson
dierence-in-dierence estimation of the payment response to the withholding rate increase, and nally examine the mechanisms of the withholding impact.

# 6.1 First Stage and Impact on Card Use

As discussed in section 3.3.2, a reform in August 2011 revised the sales tax withholding rate schedule. Panel A in Figure VII shows that the reform lead to a large increase (approxiately a doubling) of the average withholding rate, as intended. This also conrms the high compliance of card companies with the government-assigned withholding rates. The graph displays small jumps every semester, when the withholding rates are revised by the tax authorities and communicated to the withholding agents. The graph also displays a gradual decrease in the withholding rate after the initial postreform jump, suggesting that a number of rms obtained a lowering of their withholding rate, probably by making a revision request to the tax authorities.<sup>45</sup> To understand the extent of deviation between the assigned and realized withholding rate, we predict each rm's withholding rate based on its past tax returns and the withholding rate schedule in Table II. As panel B shows, the predicted rate tracks the realized rate closely, for rms for which we can observe both rates, but the realized withholding rate is always slightly higher. This is consistent with the fact that rms can request a lowering of the withholding rate from the tax authorities, for instance if they have losses for several consecutive months.

Panel C and D investigate the possibility that the reform may have discouraged rms from ling their sales declaration, or using their credit/debit card machine, which would have limited information reporting and withholding by card companies. Both the number of sales tax declarations and of credit/debit card reports (D153 informative declaration by card processing companies) led with the tax authorities is steadily increasing and smooth at reform time. Similarly, the share of sales tax declarations that can be matched with at least one credit/debit card report and vice-versa does
Dropped|0.051<br>(0.278)||0.452<br>(4.873)|*|
|Constant|1.2715<br>(3.831)|*|0.709<br>(0.812)|-0.561<br>(-2.049)|*|-1.543<br>(-7.349)|*|

Source: Authors' estimates based on ENIGH 1996 survey. * Significant at the 5% level T-stat in parenthesis.

19
27 (eq. (9)) but now the dependent variable is the primacy rate in year _t_ (116 x 7 = 812 obs.). The variables of interest are again MFGSERV ( _t_ ), DEINDU (change 1980- _t_ ), and NRXGDP ( _t_ -1) (or FMXGDP and AGXGDP). In Cols. (3)-(6), we consider either three lags or four lags of the variables of interest (i.e., two or three extra lags in addition to the contemporaneous lag) and show the overall correlations across the various lags. We control for the urban share in _t_ , include controls for populations in _t_ , and cluster standard errors at the country level. Again, we find no significant correlation.

**City-Level Results.** We use the Functional Urban Area (FUA)-level data of the GHSL database to study whether the _largest cities_ , not just the _largest city_ , grow differently than other cities depending on the country’s “type”. We have population estimates for 7,422 FUAs c. 1975, 1990, 2000 and 2015 in 115 sample countries. We regress the log growth of their population between 1975 and 2015 – log (pop. + 1) 2015 - log (pop. + 1) 1975 – on the (country-level) variables of interest and their interactions with a dummy for whether the FUA is the capital/largest city (2015; we call this dummy “top 1”). We also consider the capital city and the two (top 2) or five (top 5) largest cities, or the capital city only (top 0) as well as add the controls of the long-difference regressions at the country level.

As seen in Web Appx. Table D.6, there are no significant correlations. However, point estimates suggest that the top cities in countries specialized in agricultural exports indeed grow slower relative top cities in industrialized countries. Yet, differences are not significant
**Figure 3. Value Added by Sector, Russia and Peers, 1990–2013**

<!-- Start of picture text --> 100<br>90<br>32 35<br>80 44 39 46<br>70 53 55 51 56 57 60<br>67 65 69 70<br>60<br>50 41<br>40 26 48 46<br>26<br>30 44 25<br>39<br>40 38<br>20 28 32 25 36<br>27 29 28<br>10 17 15 23 18<br>0 8.1 4.6 5.6 6.4 3.3 5.7 10 3.9 2.4<br>1990 2000 2013<br>Agriculture Industry. Services<br>p e rc e n t<br>B R A C H N IN D R U S Z A F B R A C H N IN D R U S Z A F B R A C H N IN D R U S Z A F<br><!-- End of picture text -->

**Figure 4. Growth in Value Added by Sector, Russia and Peers, 2000–13**

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>BRA CHN IND RUS ZAF<br>Agriculture Industry Services<br>Value added % grow th<br><!-- End of picture text --> Source: World Development Indicators. Notes: BRA = Brazil, CHN = China, IND = India, RUS = Russia, ZAF = South Africa.

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- d. Listened to educational programs on radio

- e. Session / meeting with lesson teacher (tutor) f. Other (specify)

A key advantage offered by HFPS data is that it is based on self-reporting (by parents) of whether a particular child was learning during the period of school closures, and if so, what the learning modality was. To our knowledge, this is the only source of cross-country comparable household-level data on actual modalities of learning engagement of children in surveyed households throughout the period of school closures for a large set of developing countries. A second advantage is that the above information can be paired with household characteristics such as parental education and location (rural/urban), to estimate how learning losses were distributed between different socioeconomic groups within a country. While the extent of learning loss for each child must still be estimated using assumptions, the estimate is informed, crucially, by the type of learning engagement that the child participated in, as observed from the HFPS data. In comparison, Neidhofer et al. (2021) are not able to use any observational data on the education of children during the pandemic in their survey sample. Instead, they rely on national level information such as the duration of school closures, mitigation measures (e.g., availability of offline and online learning modalities) that were deployed, and group averages for indirect measures such as internet coverage among different socio-economic groups, to simulate the differences in learning losses of children from different types of households.

While HFPS data offer many advantages that this paper utilizes, it also has a number of limitations that the reader should be aware of. In particular, in light of the mode of data collection, the data is representative of the phone-owning population in each country, which excludes households without access to a phone. Second, in some countries the data was collected by Random Digit Dialing (RDD) while in others the sample was based on a pre-pandemic household survey that collected information on the head of the household
37|4.76|0|0.00|0.00|0|0.00|0.00|21|
|Slovenia|1|0.21|8.33|0|0.00|0.00|11|2.75|91.67|0|0.00|0.00|12|
|Slovak Republic|1|0.21|12.50|0|0.00|0.00|7|1.75|87.50|0|0.00|0.00|8|
|United Kingdom|22|4.58|12.29|118|43.70|65.92|10|2.50|5.59|29|16.96|16.20|179|
|Total|480|100.00|36.34|270|100.00|20.44|400|100.00|30.28|171|100.00|12.94|1,321|

Notes: Authors’ elaboration on Eurostat data. By-category percentages are defined on column totals. By-country percentages are defined on row totals. High- (not high-)income regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than (lower than) the 90% of the sample average. High- (low-)growth regions are those recording a long-run growth rate of GDP per capita between the years 2003 and 2017 equal to or greater than (lower than) the 50% of the sample average.

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Policy Research Working Paper 5538

# **Abstract**

In the aftermath of the global financial crisis of 2008–2009, there has been an increased interest in the role of small and medium enterprises in job creation and economic growth. However the lack of consistent indicators at the country level restricts extensive crosscountry analyses of lending to small and medium enterprises. This paper introduces a new dataset to fill this gap in the small and medium enterprise data landscape. In addition, it provides the first set of results of analyses with this new dataset, predicting the global small and medium enterprise lending volume to be $10 trillion. The bulk of this volume, 70 percent, is in high-income countries. On average, small and medium enterprise loans constitute 13 percent of gross domestic product in developed countries and 3 percent in developing countries. Note that although a unique small and medium enterprise definition does not exist, differences in definitions across countries are not statistically significant in explaining the differences in small and medium enterprise lending volumes.

This paper is a product of the Financial Access Team in Consultative Group to Assist the Poor, Financial and Private Sector Development. 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:// econ.worldbank.org. The author may be contacted at nmylenko@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 and its affiliated organizations, or those of the
**Assumption 2:** The predictors for poverty imputations should be key determinants of the intertemporal variations in household expenditure and be stable over time. Large shocks between surveys may compromise the model’s stability by changing what variables are meaningful predictors of household expenditure, and/or their respective coefficients. Violations to this assumption can go in two directions: first, poverty correlates might not change immediately after a big shock, such as dwelling conditions or household asset ownership; and second, some variables present a natural evolution over time and, if included, can mask the true evolution of well-being.<sup>11</sup> The large economic and social disruptions in Afghanistan since the IE-LFS survey was collected raise concerns about the validity of the survey-to-survey imputation model. However, the empirical assessment shows that models using the SWIFT-Plus approach can project poverty rates accurately even after a large economic or climate shock.

The estimation approach mitigates these risks by including dummies for consumption of certain food and non-food items as they respond to shocks quickly **.** The inclusion of consumption dummies in the model allows for a more direct measure of the current expenditure levels of households in the presence of structural changes in the economy. The SWIFT-plus models used to impute poverty in the AWMS R3 include several of these variables as they are to be more “reactive”, although the size of the shock— especially in urban areas—could also imply sufficient changes in terms of assets. Lastly, to limit concerns related to the change in the relation between expenditure and poverty correlates, we excluded labor market variables as changes in economic structure are likely to have had a significant effect on returns.

Out-of-sample tests using the IE-LFS 2021 data confirm the relative precision of model-based estimates and their superior predictive capacity compared to asset-only models. As an additional test, a model was calculated excluding consumption dummies with data from the IE-LFS 2021 survey to assess the predictive performance of the models. The urban and rural models were further tested for out-of-sample performance using the
22 Marra, M., P. Pardey and J. Alston (2002), ‘The Payoffs to Agricultural Biotechnology: An Assessment of the Evidence’, _AgBioForum_ 5(2): 43-50. Downloadable at - <u>http://www.agbioforum.org/v5n2/v5n2a02 marra.pdf</u> OECD (2006), _Producer and Consumer Support Estimates, OECD Database 1986-2004_ . At www.oecd.org/document/54/0,2340,en_2649_33727_35009718_1_1_1_1,00.html Qaim, M. and D. Zilberman (2003), ‘Yield Effects of Genetically Modified Crops in Developing Countries’, _Science_ 299: 900-02.

Science Council (2005), _Science for Agricultural Development: Changing Concerns, New Opportunities_ , Rome: Science Council of the CGIAR, December.

Shui, S. (2005), ‘Policies Toward the Chinese Cotton Industry: the Commodity Chain Analysis Approach’, mimeo, Commodities and Trade Division, FAO, Rome, February.

Sumner, D.A. (2005), “Boxed in: Conflicts between U.S. Farm Policies and WTO Obligations”, Trade Policy Analysis No. 32, Cato Institute, Washington DC, 5 December.

Sumner, D.A. (2006), “Reducing Cotton Subsidies: The DDA Cotton Initiative”, in _Agricultural Trade Reform and the Doha Development Agenda_ . K. Anderson and W. Martin eds., Ch. 10, New York: Palgrave Macmillan.

van der Mensbrugghe, D. (2005), ‘LINKAGE Technical Reference Document: Version 6.0’, The World Bank, Washington, DC, _mimeo_ , and at www.worldbank.org/prospects/linkagemodel WTO (2004a), _Decision Adopted by the General Council on 1 August 2004_ , WT/L/579 (July Framework Agreement), Geneva: World Trade Organization, August 2.
A) and absolute GDP growth forecast errors (Panel B) from private forecasters. However, same-year growth forecast data from private sector forecasters are available only since 2015 and for a smaller set of countries. On average, between 2015 and 2020, the average same-year forecast error from the private sector is 1.7 percentage points, while the average same-year absolute forecast error is about 2.4 percentage points (Appendix Figure A1), not too far off the GEP’s figures. The forecast error and absolute forecast error are also largest for MENA: about 2.8 and 3.4 percentage points on average respectively.

The timing of forecasts also matters. Appendix Figure A2 using same-year GDP growth forecasts from January, April, June and October from the WEO shows that forecast errors for the year tend to fall in magnitude and increase in precision as the forecasting month shifts from January to October. As the forecasting date approaches the end of the year, forecasters have more information and can thus more accurately forecast growth for that year. Utilizing a longer time series, Appendix Figure A3 visualizes the IMF’s April and October GDP growth forecast errors from 1990 to 2020 and finds that the October forecast errors are generally lower.

The longer the forecast horizon, the larger the forecast errors and absolute forecast errors. Appendix Figure A4 shows the GEP’s one-year and two-year ahead GDP growth forecast errors and absolute GDP growth forecast errors. Two-year ahead forecast errors and absolute forecast errors are larger than oneyear ahead ones. In turn, one-year ahead ones are larger than same-year ones. This feature holds even when we impose the same sample size for different horizons (see Appendix Table A1). This finding is consistent with prior results reported by Ho and Mauro (2016).

# III.2.Data Capacity and Transparency

The main variable of interest in our paper, data capacity and transparency, is proxied by the World Bank’s Statistical Capacity Index (SCI). The overall SCI score is based on a diagnostic
BMI marry much older men, increasing the probability of widowhood. In either case, the ―impacts‖ on well-being predate a husband‘s demise. The key issue is then whether women who become widows were already poorer, in which case there may be no causal effect of widowhood per se.

This second problem can be studied further. In the absence of longitudinal data that can identify economic resources prior to widowhood, the best test for such selection problems is to examine individual characteristics that are correlated with living standards but clearly pre-date widowhood and even marriage. If widows are primarily poor women that married poor and hence less healthy men then we would expect this to be reflected in lower average levels of education and height, measures which are typically highly correlated with poverty but largely pre-determined to marriage. Poorer women might also be expected to marry younger.

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