rafmacalaba/data-use-mentions-extended huggingface dataset:
-43-

# Adjustment of Comparisons of Gross Value of Output to a Value Added Basis

In order to avoid double-counting in aggregating the individual industry results, it is desirable to measure value added rather than gross output. This requires separate comparisons of output and inputs separately. Unfortunately, the Brazilian and Mexican production censuses do not give figures for individual inputs at the product level, and the product detail given for "materials consumed" in the US census cannot be related to output of individual commodities. This problem can be met only by adjusting the gross output comparisons by the value added - gross output ratios for the countries, as explained below.

A second important point is that there are differences in the definition of value added in the three countries involved in our comparison (see also chapter II above). In the United States manufacturing _ensus, only inputs directly related to the production process (i.e. raw materials, energy consumption, and packing expenses) are reported. Information on overheads and general expenses, which cannot be allocated directly to a product group is not given. So the "US census concept" of value added is gross of these non-allocable inputs, and is therefore a grosser concept than used in the national accounts.

In the Brazilian census the standard concept of value added ("valor de transformacao") is the same as in the US Census. However, at the level of major industry groups, of which there were 24 (including mining) in Brazil, enough detailed information is provided to permit derivation of a rConcept of value added compatible with that in the national accounts, which we have used in chapter IV below (see also chapter II for a discussion of this point).

In the Mexican census a distinction is made between direct inputs ("materias primas y auxiliares consumidas") and other costs ("otros insumos"). The first category is smaller than US or Brazilian. census inputs so the Mexican census concept of value added is different from that in the USA
The program does not keep track of the number of beneficiaries who are women. One program impact survey found that approximately 50 percent of surveyed borrowers were women. The program attracts many women because loans with relatively short terms, frequent repayments, and high interest rates are most appropriate for petty traders. In Indonesia. women make up a large share of the people engaged in this occupation.

For banks participating in this program, geographic outreach varies significantly. Some banks engage only in lending to channeling groups (Model 3), and service clients who are no more than 6 kilometers from one of their branches. Other banks serve groups much further away by relying on the more indirect lending to credit groups (Model l), or to NGOs that onlend to credit groups (Model 2). As indicated above, one BPR was using Model 2 to serve clients located one hour or more away from the bank by car.

_Productivity._ The PHBK program has very high overhead costs, as reflected in its high unit costs and subsidy dependency index. PHBK's national-level staff is large and spends a very significant amount of time and money supporting regional initiatives. Similarly, regional staff spend a large amount of time working with NGOs and banks. However, the program is rapidly becoming more cost effective as it is increasingly able to benefit from econornies of scale. and as the shift to the relatively simple model of banks' lending to channeling groups (Model 3) reduces ongoing support requirements.

_Reasons for Evoluition int Program Performance._ Program rnanagers attribute PHBK's troubled start and improved performance to financial sector and program-specific circumstances. The program experienced particular difficulties in 1991/92 and 1992/93. This was a troubled period for the entire Indonesian financial sector, which experienced significant liquidity problems and escalating arrears rates. Program volume was also adversely affected and took some time to recover after the program stopped providing subsidized liquidity credits to participating banks in 1992.

Program managers attribute initial, high program costs and arrears rates to inadequate
boys in terms of absolute mobility in the developing world in the coming decade(s). In the highincome world girls have already overtaken boys on average in terms of absolute mobility. We do not observe notable gender gaps in relative mobility.

The remainder of the paper is organized as follows. Section 2 describes our methodology and presents our measures of intergenerational mobility. Section 3 describes the Global Database of Intergenerational Mobility. Section 4 presents the trends and patterns in intergenerational mobility over time, across the world, and by gender. Section 5 analyzes correlates with intergenerational mobility. Section 6 presents robustness checks while Section 7 concludes.

# **2. Intergenerational mobility in education**

Intergenerational mobility is interpreted here in two distinct but related ways, which we will refer to as absolute mobility and relative mobility. What we term absolute versus relative mobility may differ slightly from how these terms are used in other studies, in part because this terminology has been used loosely (for a recent review of different measures see Jäntti and Jenkins (2015) and Fields and Ok (1996, 1999)). Table 1 summarizes the measures of absolute and relative mobility used in this paper, which will be explained in more detail in this section.

The estimation of intergenerational mobility between parents and children requires specifying which parents and children one is referring to. Unless otherwise specified, all children are considered in the estimation of mobility. Whenever parental education is referred to, the maximum education attained by the parents is used.<sup>4</sup> Our motivation for using maximum parental education is three-fold. Firstly, it provides a more accurate measure of the parental human capital that can be transferred to the parents’ children. Secondly, it arguably provides a more accurate proxy of the household resources that can be invested in the human capital growth of the children. Thirdly, maximum parental education is probably a more stable measure to the extent that it is more robust to the expansion of female educational attainment and the increasing correlation of educational
three countries, the reduction in the FGT2 achievable with a naïve scheme, in combination with the most disaggregated poverty map, is fairly sizeable. Broadly, the reduction in the FGT2 on the basis of this scheme is similar to the impact with the optimal scheme at one level of aggregation higher. For example, in Ecuador, combining the parroquia-level poverty map with our naïve scheme (in the base case of a low budget and low poverty line) yields an estimated national level FGT2 of 0.0178, marginally higher than the 0.0177 attainable with a canton-level poverty map and an optimal targeting scheme. Similarly, in Madagascar, firaisana-level targeting with the naïve scheme yields a national FGT2 estimate of 0.0135 which can be compared to the FGT2 of 0.0138 achievable with the fivondrona-level poverty map and optimal targeting. Again, in Cambodia, commune level targeting with the naïve scheme yields an estimated FGT2 of 0.010 which is equal to that achievable with optimal targeting at the district level.

The performance of the naïve scheme becomes much less appealing when combined with a higher budget and higher poverty line. Indeed, there are cases where the impact on the FGT2 of targeting with this naïve scheme is less pronounced than with a non-targeted uniform lump-sum transfer (see for example column 2 in Tables 5a-5c and Figures 1 and 2).

# _Perfect targeting_

How well does optimal geographic targeting at the lowest level of spatial disaggregation permitted by our poverty maps compare to the hypothetical case where we have information on the poverty status of every individual household in the country? We can answer this question in a straightforward manner by noting that the cost of eliminating poverty under the assumption of perfect targeting (i.e. it is possible to observe the precise welfare level 26
ment arm in the intervention and then we cross randomized and assigned the participants into two groups in the midline survey - see Table A1.<sup>15</sup> Each of these groups was asked to complete the survey through an alternative mode, as follows:

- **Group 1 — CATI — 300 individuals:** This group received a call from a live person asking them to complete a phone-based survey (CATI). At the outset, they were informed about the monetary incentive (US$ 2.50 in Tigo Money) to complete the survey. On average, participants received 3,7 (3,4) calls per day over three days – including the picked up survey call. Enumerators were trained to conduct surveys on sensitive topics. Following recommendations by Glazerman et al. (2020), we developed a contact-attempt tracking log for each participant.

- **Group 2 — Self-completion — 299 individuals:** This group received WhatsApp messages<sup>16</sup> inviting them to complete an online survey. The message included a link to the self-completion survey with a note on the incentive to complete the survey (US$ 2.50 in Tigo Money) and the deadline for completion (three days). Tigo provided free access to the survey. On average, participants received 2.9 (2.6) calls per day over three days from trained enumerators who reminded them to complete the self-completion survey. Thus we made the same effort to reach participants in each group. These contact attempts were tracked in the same individual log as Group 1.

To credibly identify the relative convenience of the two modes, we not only randomly assigned individuals to one of the two groups but also ensured that aside from the survey mode itself, both groups were treated equally. The instrument was the same, all individuals received the same monetary incentive to complete the survey, both groups had three days to complete it, there were no costs of completing the survey by
Fifth, table 2 demonstrates that there appears to be little relationship between violence and food access in both regions controlled by the internationally‐recognized government and the rest of the country. Specifically, column (6) re‐estimates the baseline specification but interacts the violence measure with an indicator equaling one if the respondent resided in regions not controlled by the internationally‐ recognized government. Only one of the coefficients is marginally significant (relationship in regions not controlled by the internationally‐recognized government), and the upper bound of the confidence interval for both estimates corresponds to a very small decrease in access to food in response to a doubling of fatalities in a month.

Sixth, it is possible that violence is causing a much larger decline in access to food, but food assistance targeted to violence‐prone regions is mitigating its effects. However, table 2 demonstrates that this explanation is not likely. The WFP survey began inquiring about whether the household is receiving food assistance beginning in 2017, and column (7) includes this in the baseline specification. The estimate is qualitatively identical with the addition of food assistance to the specification.

And lastly, it is not likely that the lack of a correlation between violence and food coping strategies is being completely driven by internal displacement. The primary concern is that those who were worst affected by the conflict simply left the governorate before the next month’s survey to re‐locate where it was safer, and this could obscure the identification of the correlation. However, the WFP data collect information on the displacement status of households, and the date of displacement and the governorate from where the household moved. In the WFP survey to which we have access to the unit‐level data (November 2017), only 8 households (of more than 2,422) reported to have been displaced from their original governorate the month before. And this is despite the fact that there was significant violence in October 2017, including 6 governorates that had more than 100 fatalities each.

# **
affluent areas. Similarly, searches for “jobs” and “unemployment” are negatively correlated with the prevalence of total income declines in the first wave, suggesting that areas hit less hard by the crisis, in terms of the prevalence of income declines, may have been more likely to search for unemployment insurance or new jobs. More generally, the strong correlations in the analysis of the first wave partly reflect the role of precrisis regional characteristics that are correlated with both the real-time indicators and the intensity of the shock.

The correlations with mobility data are more intuitive, as workplace mobility was negatively correlated with the prevalence of wage income declines. This reflects the fact that the mobility data is measured as a change from a pre-crisis baseline. Google searches for “job postings” are positively correlated with wage income declines, as one would expect. Google searches for

> 12 Each survey wave is associated with a month, and the real-time indicators for that month were used.

18
a different domain. To use the terminology of Gilbert and Kravis (1954, p. 79) we are dealing here with "common" products which have a similar function across the countries, but which vary in quality.

Our unit value specification was particularly poor in the case of motor vehicles, largely because of census confidentiality rules. The census information was therefore supplemented in this case by using information on output and consumer price structures from trade sources. Automotive News provides figures furnished by trade associations from trade sources which are rather reliable. The procedures are described in a note in the Statistical Appendix. Producer prices would have been preferable to consumer prices, but the US producer price index is based on information for only a limited number of models, and is as confidential as the census itself. Our method of handling the problem produced a reasonable though not an optimal adjustment for quality. In any case we would stress that our approach is not inferior to that of ICP for this particular industry. As the ICP approach is a multilateral one, its products have to be representative in a global sense. ICP III used passenger car models which were characteristic across its 34 countries, and its comparison for Brazil/USA and Mexico/USA was based largely on Japanese and European models which were quite unrepresentative of the situation in these three markets.
evolution is much slower than that of _PALE_ 1: from 1990 to 2019, _PALE_ 0 _._ 5 increased by 44% as opposed to 101% for _PALE_ 1. Indeed _θ_ = 1 implies that one year spent in poverty is equivalent to one year spent dead. When instead one assumes that a year spent in poverty is equivalent to half a year lost to death, life expectancy has more weight in PALE. As the progress on life expectancy have been much slower than those against poverty, _PALE_ 0 _._ 5 growth is slower.

Note that PALE can not be directly decomposed into each of its components. However, it is possible to decompose its growth into the contribution of that of each of its component. Indeed, the growth rate of _PALEθ_ can be decomposed as follows: where _εLE_ = 1 and _εH_ = (1 _−−θHθH_ )<sup>representtheelasticitiesof</sup><sup>_PALEθ_tolifeex-</sup> pectancy and poverty, respectively. Figure 6 shows the share of the growth of _PALEθ_ explained by changes in life expectancy, from 1991 to 2019. First, note that the choice of _θ_ = 1 is conservative: the contribution of life expectancy to PALE is on average 16 percentage point smaller when _θ_ = 1 than when _θ_ = 0 _._ 5. Second, irrespective of the precise value given to _θ_ , the contribution of life expectancy is growing over time. Life expectancy contributes on average to 17 (resp. 32) percent of PALE’s growth from 1991 to 2004, as opposed to 34% (resp. 51%) from 2005 onwards. Even though mortality has not decreased as much as poverty, the changes in mortality still play a substantial role in the trend of _PALEθ_ .

17
, the rate of IDP return increased only marginally following the signature of the LPA in Zummar (likely influenced by the timing of the LPA and the fact that many IDPs had returned already.) Prior to signature, 95,124 IDPs had returned, and this increased to 97,410 after 12 months. Context indicators did not show significant change, with IOM’s Return Index showing similar rates of concern over revenge attacks and ethnoreligious tension pre- and post-signature.

80 According to IOM’s DTM Master List, the rate of IDP return increased only marginally following the signature of the LPA in Tal Aafar (likely influenced by the timing of the LPA and the fact that many IDPs had returned already.) Prior to signature, 169,686 IDPs had returned, which increased to 172,098 after signature. Context indicators such as blocked return, social cohesion and movement restriction improved according to IOM’s Return Index 11.

16
# 5. Corporate Stress Test Results

**We focus on the sub-sample with non-missing ICR and QR, which tends to contain larger, more leveraged corporates.** Of these, 9,656 firms in 86 countries do not have missing ICR and QR data (of which 56 countries have five or more firms). Together, these firms represent US$ 15.3 trillion in total assets and US$ 4.31 trillion in debt. Firms that have non-missing ICR or QR data tend to be larger compared to firms that have missing data (total assets of the median firm are US$ 84 million vs. US$ 45 million) and they are also more leveraged (median debt to assets is 17.7 percent vs 9.4 percent).

**By the end of 2019, about 15 percent of debt was associated with firms that experience ICR challenges. Progressive shocks to corporate earnings result in a non-linear increase in total debt at risk according to the ICR.** Exhibit 5 presents D as a function of the magnitude of negative corporate earnings shocks. In the absence of any earnings shock, 16 percent of corporate IICCII debt has an ICR less than 1 and is thus considered “at risk”. This suggests that interest expenses DD are relatively small compared to EBIT for most firms. However, debt at risk increases non-linearly with the magnitude of earning shocks. For example, a 10 percent shock to earnings increases the fraction of debt at risk from 16 percent to 19.5 percent, a 19 percent increase. However, a 40 percent shock to earnings almost leads to a doubling of debt at risk. In the case of a 50 percent earnings shock, 52 percent of all corporate debt is considered at risk, an increase of 220 percent.

**Before the pandemic, relatively weak QRs were a fairly common phenomenon. As a result, total debt at risk according to the QR is less responsive to progressively larger shocks than for ICRs.** Exhibit 6 presents
# 1 Introduction

For years, governments have been actively attracting foreign direct investment (FDI) in the belief that it creates jobs, boosts productivity, and increases wages (World Bank, 2020). However, the existing literature documents both positive and negative effects of FDI on productivity (Aitken & Harrison, 1999; Lu et al., 2017). The ambiguity of the FDI effects might not be surprising since previous research mostly focused only on single-country cases, and the heterogeneity across countries and sectors has been ignored. In this paper, we propose a key source of heterogeneity that can reconcile the mixed empirical results. This paper defines FDI spillovers as the effect of FDI on labor productivity. We find that country variation in global value chain (GVC) participation affects the sign and the size of FDI spillovers and gives rise to the seemingly contradicting results in different countries.

Specifically, this paper sheds light on the following questions. First, if a production process is a series of country/sector ‘nodes’ joined together in a value chain, how does FDI improve sector and firm productivity through spillovers? What is the role of the GVC participation in shaping FDI spillovers? Does the FDI only affect the recipient sector? Can FDI spillovers occur through the suppliers or the buyers (forward and backward linkages)? Moreover, if there is FDI in a country/sector, can it benefit foreign suppliers and buyers?<sup>1</sup> To answer these questions, we construct a database with detailed country-sector coverage from 2011 to 2017. Our data is constructed by merging three panel data sets: (i) the fDi Markets data collected by the Financial Times; (ii) the Multi-Regional Input-Output tables (MRIOT) compiled by the Asian Development Bank which includes 61 countries and 35 sectors from 2011 to 2017; and (iii) the World Bank Enterprise survey (WBES) for over 100 countries consisting of detailed data on more than 8,000 firms. The fDi Markets data consist of rich information
8 China’s poorest provinces. But it also probably reflects the limitations of the single-question approach to eliciting income in household surveys. We have trimmed outliers on both the out-ofpocket payments and income distributions: the top half of one percent of cases on the out-ofpocket payments distribution and the bottom half of one percent of cases on the income distribution were removed. We define health expenses as ‘high’ if they exceed 5% of the sample mean per capita household income, and as ‘catastrophic’ if they exceed 10% of the household’s _own_ per capita income (cf. e.g. Ranson 2002; Wagstaff and van Doorslaer 2003; Xu, Evans et al. 2003). As is apparent from Table 3, the rates reporting ‘high’ and ‘catastrophic’ out-of-pocket payments vary considerably across the surveys (the CHNS having far lower rates), and to a lesser extent over time within each of the two longitudinal surveys.<sup>6</sup> The far lower rates in the CHNS reflect in part the relatively low out-of-pocket payment amounts in our data. The shares are well below those in the Chinese Rural Household Survey, for example. In part, this probably reflects the relatively large number of cases in the CHNS with missing values for the various questions on medical expenses. We have adopted a conservative strategy in such cases, by replacing missing values by zero. Since missing values are somewhat more common in the data among those with insurance, we will end up _underestimating_ the effect of insurance on the probability of ‘high’ or ‘catastrophic’ medical expenses.

Health insurance coverage varies across the surveys and across waves in the case of the longitudinal surveys. There is no clear trend in the case of the CHNS in the fraction of the sample with some form of coverage, although a continued decline of the traditional schemes

> 6  It seems likely that the short (4-week) recall period used in the CHNS would result in a larger annualized out-of-pocket spending figure than the long