rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6. EVALUATING POLICY IMPACT AND COMPARING ALTERNATIVE METHODS OF MEASURING RESTRICTIVENESS**

This section has three related objectives: to illustrate that services policy matters for outcomes that we care about; to show how restrictiveness of policy may be inferred econometrically, as discussed in Section 3.3; and to demonstrate that our simple STRI produces as economically sensible results as more complex methods of measuring policy restrictiveness.

While collecting and publishing information on regulatory measures affecting trade in services is the primary purpose of this database, we are ultimately interested in evaluating the economic impact of such policies. Due to the idiosyncratic nature of individual services sectors, a full-fledged analysis is clearly beyond the scope of one paper, and for the purposes of this section we assume policy restrictiveness to be exogenous data.<sup>30</sup> We will, however, present two examples of the impact of services trade policies: on mode 3 flows, i.e. inward foreign investment, and on access to services. The value of foreign investment is a uniform metric across heterogeneous sectors and thus is a natural dependent variable for a broad cross-sectoral investigation in Section 6.1. In contrast, ̳performance‘ assumes a different meaning in each sector. In Section 6.2, we use the example of bank lending to illustrate how services trade policies affect access to financial services as measured by domestic credit extended to the private sector.

## **6.1. Cross-sectoral analysis based on FDI flows**

We assess the impact of policy barriers on FDI using a comprehensive dataset on global mergers and acquisitions (M&A) spanning the period 2003-2009.<sup>31</sup> The dataset consists of

> 30  In their study of telecommunications and transport sector policies, Borchert et al. (2012) go further and account for the endogeneity of policy choices.

> 31  It would be of interest eventually to take into account the impact on greenfield investment as well but data concerns prevent us from incorporating greenfield flows
Policy Research Working Paper 10073

# **Abstract**

This paper estimates the monetary value of financial risk reduction associated with membership in Ghana’s National Health Insurance Scheme, based on recent national household survey data. The paper compares the risk premiums for distributions of out-of-pocket healthcare expenditures with and without insurance and find that the difference is small. This does not mean that the National Health Insurance Scheme has no value to members. Indeed, the findings show that the insured pay significantly less for healthcare than the uninsured on average. But that average reduction does not translate into a reduced spread of consumption net of out-of-pocket healthcare expenditures. Thus, the benefit of the National Health Insurance Scheme is entirely a transfer benefit, not a reduction in financial risk.

This paper is a product of the Social Protection and Jobs 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 draju2@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 Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
in education. The human capital embodied in the labor force can then be seen as a function of education. Even accepting education as a valid proxy for investment in human capital, the relationship between education and human capital needs to be correctly specified.<sup>8</sup> The current consensus relies on a log-linear specification between earnings and years of schooling first formulated by Mincer (1974), to express the human capital per worker as an exponential function of the years of schooling, _h_  _e_  ( _N_ ) , where the function  ( _N_ ) represents the efficiency of a unit of labor with _N_ years of schooling relative to one with no schooling (for reviews see Krueger and Lindahl, 2001; Woessman, 2003). We follow common practice and use  ( _N_ )  _re N_ , where _re_ is the rate of return to education.

We made different assumptions regarding the returns to education. Our benchmark is _re_ =8.5 percent return on years of schooling as in Arrow et al. (2010) and Klenow and Rodriguez-Clare

> 7 This is also true for aggregate natural resource stocks; the simple correlation between GNI per capita and the natural resource stock per capita for the countries in our sample is 0.44.

> 8 For example, Klenow and Rodriguez-Clare (1997) show that using secondary school enrolment rates as proxies for human capital in Mankiw, Romer and Weil (1992) exaggerates the role of human capital in explaining differences in output per worker, as secondary enrolment varies more than other (preferable) measures of human capital. The results of Mankiw Romer and Weil (1992) are not robust to expanding the human capital estimator to include primary and terciary education or to using an alternative human capital estimator based on Mincerian regressions.

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for example on the negative impacts of parental migration on child school enrollment and mental health status, on the negative impacts of spousal migration on the mental health of left-behind wives, suggest that the observed associations are not merely spurious but warrant further investigation. These correlational studies also highlight areas of concern for policymakers and can inform the design of support programs for left-behind populations, even as more definitive causal evidence is being developed.

**Left-Behind Children.** According to the data from the Child Questionnaire of CFPS (2016), rural children under 16 spend less time, 1.6 to 2.6 months on average, with their parents than urban children, except for infants and toddlers under 2 (Table 2). Media attention on split families has focused on “the left-behind children,” with anecdotal reports of abuse, mental health issues, and behavioral problems due to insufficient care and protection (e.g. Zhang 2018). These stories raise concerns about the well-being of rural children and challenge a view of migration solely as an income-enhancing option for rural families.

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We are more eclectic in our approach to future productivity levels, which is the other key determinant of the net foreign asset position. For the calibration model, the relevant variable is π which measures ROW's productivity advantage. In our first two scenarios we assume that relative productivity growth is equal to its historical average growth rate. Measured relative TFP from our crude development accounting exercise was 1.2 in 1980, and 1 in 2000. Extrapolating this annual average growth rate in relative productivity of 0.8 percent per year into the future gives us a value of π=0.87 in 2025. We note that this is a fairly conservative estimate of TFP growth for China. In our development accounting exercise average TFP growth for ROW between 1980 and

> 10 The PWT includes a more or less arbitrary, but not unreasonable, downward adjustment in its estimates of China's GDP growth, reflecting a variety of concerns about the quality of Chinese national accounts data.  See Young (2003) for a very thorough discussion of the issues.  The PWT reports data only through 2000 for China, we extrapolate to 2005 using (unadjusted) constant local currency growth rates.

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prevalence of polygyny are invariant to the agricultural yields reported by households.

Despite the inclusion of all the controls mentioned above, not accounting for internal migration might be seen as another potential threat to the identification of the resilience effect of polygyny. Indeed, it might be argued that internal migration creates a pathway for men who want to have several wives to self-select into communities with a high polygyny prevalence. However, in rural Mali— the setting of our empirical analysis— migration is a rare event. Our data indicate that a mere 0.349% of household heads migrate internally. This evidence has at least two important implications for our identification strategy. First, it assuages concerns about the potential presence of a selection bias, as men are unlikely to self-select into communes with a high polygyny prevalence. Second, even if drought were to determine migration endogenously, this would not affect our estimation results.

We use data from two rounds of Mali’s _Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages (EAC-I)_ , in combination with polygyny data from the _Fourth General Census of Population and Housing_ (2009) and meteorological data. Mali is a bride price country (Corno et al., 2020), with the second highest polygyny prevalence in the world at 34% (Kramer, 2020). These facts make Mali a suitable setting for our hypothesis testing.

We find that drought occurring in the current year and the previous year substanreduces in all affected communes. This result is evidence that rural tially crop yields households in Mali are highly dependent on rainfed agriculture for their livelihoods. Importantly, we find that polygyny—a marriage practice known to undermine women’s health (Bove and Valeggia, 2009; Bove et al., 2014) and increase violence against women (Ebrahim and Atteraya, 2020)— enhances household resilience to this adverse effect a year later following the drought shock. This result is robust to various alternative specifications of the interaction between drought and polygyny.

Exploring the pathways of this resilience
, such as the incidence of crime or the effectiveness and predictability of the judiciary. Overall, countries with well-designed institutions are less likely to have bad regulations.

> 8 The World Bank’s Doing Business dataset will be updated and extended every year, which will allow panel data analysis in the future.

> 9 In the following, the period 1994-2003 will be referred as the current period, whereas 1984-1993 is the previous period.

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R&D.<sup>9</sup> Data cover about 22,000 firms operating in the manufacturing and services sectors in the short section in the ES and 11,150 firms in the innovation follow‐up survey (see Table A2 in the Appendix for more details on the sample by country).

> 8 “During the last three years, has this establishment introduced any new or significantly improved organizational structures or management practices?” for management and “During the last three years, has this establishment introduced new or significantly improved marketing methods?” for marketing.

> 9 Organizational innovation is not included in the analysis that follows. The section of questionnaire dealing with organizational innovation is very different compared to the other section and the analysis of the results would require a separate approach.

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methods—the Probit Model and the Logit Model— which are more restrictive and model poverty status (poor and nonpoor) instead of consumption expenditure.

Once the parameters are estimated, the welfare indicator, which is household consumption per capita, is obtained as follows: The imputed poverty rate and its variance are then estimated as: These poverty estimates are unbiased estimates of the parameters of interest (Dang et al., 2017) and outperform in terms of prediction accuracy those proposed by the proxy means testing literature, which typically omits the error terms υ1 + ε1, leading to biased estimates of the welfare indicator.

# **Data**

In its mandate to protect displaced persons in host countries, the UNHCR collects data to track refugees and other populations of interest, better monitor these populations, and deliver assistance and services. In the framework of this study, we use three sets of data collected by the UNHCR (Table A2). The first one is the ProGres data set _,_ which is the UNHCR’s registration system covering all refugees or asylum seekers requiring assistance. The ProGres data set is a live instrument that is continuously updated as new refugees/asylum seekers arrive or existing refugees contact the UNHCR. The data we use were extracted at the end of December 2017. This set of data contains socioeconomic variables (such as household size, marital status, gender, age, country of origin, and region of residence) but no consumption or expenditure data. This data set can be considered as the “census” of refugees.

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Even for tangible assets, there is controversy and disagreement over the appropriate depreciation rates. The question becomes much more difficult for intangible assets, since there is no physical ̳wear and tear‘ to be observed. CHS admit that the rates of depreciation they assume are based on limited information. For better or worse, these rates have since been used in a number of CHS-type studies. In general, they are significantly higher than commonly-used rates of depreciation of tangible assets, reflecting the view that knowledge-intensive capital becomes obsolete relatively fast.

# **2.2 Methodology and data sources for Brazil estimation**

In measuring intangible investment in Brazil, we use the same categorization of intangibles as CHS, and to the extent possible, the closest Brazilian counterpart to the US data source originally used in CHS.<sup>13</sup> As in CHS, our preferred method is to build up the expenditure estimates from primary firm level survey data. The main surveys we use are the Annual Survey of Services (PAS), the Annual Survey of Industry (PIA), the Annual Survey of Commerce (PAC), the Annual Survey of Construction (PAIC) and the Survey of Technological Innovation (PINTEC), all conducted by the Brazilian Institute of Geography and Statistics (IBGE). Like CHS, in case firm survey data are not available, our preferred alternative is to use official data sources such as National Accounts data, and wage and employment data from the Ministry of Labor and Employment.

Since many of our estimates are based on firm surveys which do not capture the government and household sectors, for the sake of consistency we have excluded government and household investment when calculating aggregates for all investment categories. This is largely consistent with CHS, who focus on business intangible investment.

Reflecting differences in data availability across the US and Brazil, some of our sources differ significantly from the CHS original, though they attempt to measure the same underlying economic phenomenon. The Appendix lists our source for every intangible asset class and
## SELECTION OF THE SERIES

# _Reference Series_

The reference variable is the benchmark that indicates fluctuations in the economic activity, and is the variable to be forecast. Usually chosen is the Industrial Production Index, IIP, which has the advantage of being a monthly reported variable, available for many countries, and measures the real sector of the economy. For Lithuania this was the variable selected and was seasonally adjusted with a multiplicative Xll -Census procedure. 5

# _Composite Indicator Series_

As previously mentioned, the essential feature taken into account for selecting a component of the CLI is that it leads the reference series with a similar cyclical profile. Other salient features are: the consistency of the lead of the indicator over the reference cycle at turning points, the absence of extra or missing cycles, smoothness, freedom from excessive revisions by the authorities, timeliness in delivery from the authorities, and availability of a long run of data of satisfactory reliability with no breaks. Monthly series are preferred.

All series are analyzed using their trend-eliminated version. The analysis proceed by performing a graphical inspection of the cyclical profile of the series, reinforced by a turning point analysis (see Annex 4), with special attention to mean or median leads at tuming points. A cross correlogram of the series completes the initial review.

The series analyzed for Lithuania come from the Bank of Lithuania website, the IMF-Intemational Financial Statistics, and the MEI Database via the OECD's OLISnet service. From these databases approximately 130 series were selected for graphical inspection. Many were rejected because of incomplete or insufficient data (less than two years) or cyclical pattems seemingly inconsistent with that of the reference variable (IIP). Twenty-six variables passed this first filter, among them: measures of employment, finance, interest rates, prices, and trade. The seasonally adjusted version of these _5_ See below for details, p.6.

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as well as for idiosyncratic risk factors due to the diverse lending practices and risk profiles of individual banks. It should be emphasized that each component of the proposed stress testing framework presently constitutes a separate research agenda in the literature. The objective of the proposed methodology is thus not to improve on current frontier models of the individual components, but rather to provide policy makers and practitioners with an integrated, flexible and policy relevant tool that can be readily implemented. We illustrate the usefulness of the proposed methodology for policy decision making with an empirical application to a set of Eastern European banks and an ensuing discussion of the results in regards to their potential implications for supervisors.

The remainder of the paper is structured as follows. Section 2 gives a conceptual outline of the proposed stress testing methodology. Section 3 discusses the generation of the macroeconomic scenarios. Section 4 shows how that the systemic and idiosyncratic risk factors are constructed. Section 5 describes the computation of credit risk exposures and exposures at default. Section 6 discusses the main stress test outcome indicators. Section 7 contains an empirical application of the proposed methodology using data of Eastern European banks. We also conduct a brief sensitivity analysis and discuss how to implement the proposed methodology under limited data availability. Section 8 concludes the study.

# **2. Conceptual Outline of the Proposed Stress Testing Approach**

We begin by providing a conceptual overview of the main segments of the proposed macroprudential stress testing methodology, which we summarize visually in a flow dia. gram depicted in Figure 1 The first segment of the stress testing methodology consists of the construction of three macroeconomic scenarios. These are as follows: 8
, these factors alone suggest very large capital flows – far beyond what is observed empirically. But introducing a realistic amount of concern about international default, based on historical experience, results in equilibrium net foreign asset positions close to what is observed in the data. In this model, a country’s net foreign asset position will depend on its per capita wealth and its productivity level relative to the rest of the world. We calibrate the model with the current Chinese data and examine China’s expected net foreign asset position. Our baseline calibration predicts that China should have a net foreign asset position of negative 17 percent of its wealth, in contrast to the small positive position that it has in reality. We show how sensitive this result is to reasonable changes in key parameters.

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occupation from CPS computer supplements

> 6 Special thanks to David Autor for providing us with concordances that provided a consistent coding scheme of industries and occupations for the period. The CPS occupation and industry codes were reclassified in 2003 to correspond to the North American Industrial Classification System, which made it difficult to compare data before and after the change.

> 7 We are unable to distinguish between arms-length trade between firms (“outsourcing”) and trade in general. Note, however, the BEA sample includes all foreign firms where a US parent holds at least a 10% equity stake. This relatively low threshold implies that firms closely aligned will be included in our analysis. It is worth noting, however, that pure firm outsourcing where no equity stake is taken will be classified as trade and not offshoring.
> the private rental sector is a pivotal source of supply for “housing starters” (Figure 5). These are young single households or couples between the ages of 16 and 29 - and young families between the ages of 30 and 44 with at least one child.<sup>48</sup> This is consistent with the pattern in market economies.<sup>49</sup> Poland has the highest share among housing starters with 24 percent, followed closely by Russia, Serbia, and Armenia. Even in Romania, with the smallest overall private rental sector share, 12 percent of starters are recorded as private renters. The high Lithuanian figure of 34 percent could

> 46    Municipalities in Russia control access to land and continue “spot zoning,” which forces developers to relinquish some new dwellings to municipalities in exchange for land access and necessary zoning.

> 47  The urban-rural income divergence is very large in these countries—especially Poland, Romania, and Russia in this study—and thus must be cancelled to arrive at meaningful results. Hence, we report only data for urban areas.

> 48  Owing to availability constraints, data for Lithuania are added under a different definition, which yields a lower age average within each category.

> 49  See Chiuri and Jappelli (2000).

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