rafmacalaba/data-use-mentions-extended huggingface dataset:
- Christensen, J.H.E., and G.D. Rudebusch. 2012. “The Response of Interest Rates to US and UK Quantitative Easing.” _The Economic Journal_ 122:385–414.

- Colletaz, G., and C. Hurlin. 2006. “Threshold Effects of the Public Capital Productivity : An International Panel Smooth Transition Approach.” LEO Working Papers/DR LEO 1669, Orleans Economics Laboratory, University of Orleans, Orleans.

- Cunliffe, J. 2019. “Financial stability post Brexit: risks from global debt, a speech at CFO Agenda, London, May 7, 2019.” Speech, Bank of England.

- D’Amico, S., W. English, D. López-Salido, and E. Nelson. 2012. “The Federal Reserve’s Large-scale Asset Purchase Programmes: Rationale and Effects.” _The Economic Journal_ 122:415–446.

- De Castro, F., J.J. Pérez, and M. Rodríguez-Vives. 2013. “Fiscal Data Revisions in Europe.” _Journal of Money, Credit and Banking_ 45:1187–1209.

- Dell’Erba, S., R. Hausmann, and U. Panizza. 2013. “Debt levels, debt composition, and sovereign spreads in emerging and advanced economies.” _Oxford Review of Economic Policy_ 29:518–547.

- Driscoll, J.C., and A.C. Kraay. 1998. “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data.” _The Review of Economics and Statistics_ 80:549–560.

- Ebeke, C., and Y. Lu. 2015. “Emerging market local currency bond yields and foreign holdings -A fortune or misfortune?” _Journal of International Money and Finance_ 59:203–219.

- Engen, E.M., and R.G. Hubbard. 2005. “Federal Government Debt and Interest Rates.” _NBER Macroeconomics Annual 2004_ 19:83–138.

-
# VII. Conclusions

This paper develops an analytically tractable framework linking individual income dynamics, social mobility and welfare. This analytical framework that we develop has the merit that the links between different determinants of income mobility and social welfare are drawn out in a simple and transparent manner − allowing for a clearer analytical and quantitative discussion of these interrelated concepts than has generally been possible in the past. In particular, we discuss in detail how different determinants of measured income mobility (shocks to income, and convergence forces, for instance) may have quite different implications for welfare. This implies that two societies with the same initial distribution of income and the same level of measured income mobility may be characterized by quite different levels of social welfare. Decomposing the determinants of mobility is thus shown to be crucial from the standpoint of welfare evaluation.

An important strength of the proposed framework is its empirical implementability. The quantitative evaluation of mobility and welfare in our context entails the estimation of income process parameters may be achieved using combined cross sectional and longitudinal data on individual incomes and relatively straightforward econometric techniques. The results from Mexico are striking. Most of measured mobility is estimated to be driven by transitory shocks to income and is therefore (almost) welfare neutral. Only a small part of mobility (i.e., mobility in permanent income) is driven by either social-welfare-reducing persistent income shocks or welfare-enhancing catching-up of low-income individuals with high-income individuals. Despite their small contributions to measured mobility, the implications for welfare are large. Decomposing mobility into its fundamental components is thus crucial from the standpoint of welfare evaluation.

17
(0.002)|
|Red channel dummy|0.000|-0.001|-0.001|-0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Log initial value|||0.000<br>(0.001)|-0.000<br>(0.000)|
|Log initial unit price (rel. to internal prices)|||-0.002|-0.001|
||||(0.001)|(0.001)|
|Month-year fxed efects|Yes|Yes|Yes|Yes|
|Source country fxed efects|Yes|Yes|Yes|Yes|
|HS2-product fxed efects|Yes|Yes|Yes|Yes|
|Inspector fxed efects|No|Yes|Yes|Yes|
|Broker fxed efects|No|Yes|Yes|Yes|
|Importer fxed efects|No|No|No|Yes|
|Observations|44,522|44,522|44,497|43,669|
|R-squared|0.072|0.225|0.226|0.377|

_Notes:_ Standard errors clustered two-way by inspector and by broker are presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). "Observations" refers to the number of non-singleton observations. OLS estimation is used. The sample covers the period January 1, 2015 to November 17, 2017 (
, xi2) is not known and we thus ignore whether an individual cumulates low achievements in several dimensions. One problem is that non-monetary outcomes are often collected through different surveys than monetary outcomes. At global level, monetary outcomes are collected through LSMS-type surveys while non-monetary outcomes are collected through DHS or MICS surveys.<sup>10</sup> As a result, the joint distribution between monetary outcomes (consumption or income) and non-monetary outcomes is often missing. A similar problem arises for the mortality dimension. The joint distribution between mortality risks and other well-

> 10At country level, this constraint may not be present, which allows some countries like Mexico to construct multidimensional poverty indicators capturing both monetary outcomes and non-monetary outcomes (Bank et al., 2021).

9
12 is to scale the year-by-year growth rates from the national accounts for each sector-quantile such that they cumulatively produce the growth observed in the survey for that sector-quantile. When year-by-year growth rates calculated this way by sector and quantile are applied in the simulation, starting with the initial year survey data, they should produce a distribution in the final year which closely matches the final year survey data.

In these simulations, each quantile’s mean household consumption moves proportionally with the GDP per capita in the national accounts. This procedure is the natural extension of the inequality adjustment Datt and Walker apply in one version of their method. In the approach outlined here, rather than adjust the distribution to generate a specified change in a single inequality measure, we set the growth rates quantile-by-quantile to produce a particular distribution in the final year.<sup>7</sup> This procedure is approximately (though not exactly) equivalent to setting the growth rates for individual quantiles by shifting the national growth rate pattern up or down. To illustrate this, in Figure 2 below, the true growth rates for Tanzania from the national accounts are plotted along with growth rates calculated using the procedure for two hypothetical quantiles with cumulative growth of 1% and 40%. Recall from Table 2 that cumulative GDP per capita growth 1993-2001 in Tanzania was 6.1%.

# **Figure 2**

## **Simulated Growth Rates for Hypothetical Quantiles Using Two Survey Method**

<!-- Start of picture text --> 7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0% Per Capita GDP Growth (6.1%)<br>Quantile w/ 1% Cum. Growth<br>1.0% Quantile w/ 40% Cum. Growth<br>0.0%<br>-1.0%<br>-2.
FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1010349|1010349|1010349|1010349|1010349|1010349|
|Firms|63971|63971|63971|63971|63971|63971|

Notes: Authors’ calculations based on Orbis dataset. All regressions include firm and year fixed effects. RCA is a standard measure of comparative advantages of Germany versus Poland and the world. All measures of German influence refer to the period 1994-2004. Regressions consider only firms established before 2004. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

34
and are interacted with community characteristics . The system of equations used in the original Günther and Harttgen (2009) implementation is described by equations 3, 4, and 5. ZZcc Substituting equations 4 and 5 into equation 3 yields equation 6 in which the constant and slope terms vary with community characteristics ( 11 ) and community unobserved factors ( ). However, if and 11 are assumed to be zero and no commune-level characteristics are included, the system γγ ZZccXXcch uu1ccXXcch reduces to equation 1. 1cc<sup>14</sup> uu γγ l 00 + 01 + 01 + 11 (6) A comparison of equations 1 and 6 provides useful insights. First, although the specification does not (yycch) = γγ γγ ZZcc �γγ γγ ZZcc�XXcch + uu0cc + uu1ccXXcch + εεcch A comparison of equations 1 and 6 provides useful insights. First, although the specification does not include random slopes, the estimation of region-specific models allows, in practice, coefficients to vary entirely across regions, equivalent to interacting every coefficient with a regional dummy.<sup>15</sup> Second, the estimated variance of expenditure across households and communities in Günther and Harttgen (2009) is modeled as a function of household and community characteristics using a linear model described in equation (7).<sup>16</sup> Third , when imputed to the census, the area-level effects are only available for those geographical areas included in the sampling of the household survey.<sup>17</sup> Considering these points, the robustness check section compares the variance estimates in both models showing that, despite the estimated differences, both methods provide comparable results on the share of the vulnerable population and the ranking of the country’s departments.

> 14 It is possible to include (commune-level) characteristics interacted with household characteristics in the empirical best models to recover ( ). Including commune-level characteristics ( ) is optional because it is subject to the availability of statistically representative commune-level variables, which are usually
(ranging from -1 to +1) reaches 0.86, statistically significant at the 1% level, indicating a high degree of correlation. However, this exercise only accounts for a limited number of countries in the sample. It also considers only a single dimension – output – with many structural elements in the two statistics, such as the selective sectoral scope of the WBES and year mismatch, justifying systematic bias in some countries over other ones. Rather than validating the sample of foreign multinationals in WBES as a whole, the alignment serves primarily not to reject the WBES as a valuable source of information for foreign multinationals based on the information we have available. The true numbers and weight of multinationals in these countries remains to be evaluated in the future.

The indicators reported in this study correspond to weighted averages across establishments using sampling weights w� of the Enterprise Surveys that are based on the frame provided by official sources, as well as their differences across groups in absolute and/or relative terms. Every data point x� is calculated over the total of n firms i in each country for the national average, the subsample of foreign multinationals defined using a 10 percent threshold and then the subsample using a 50 percent threshold.
# **4.2 Data**

## **4.2.1 Surveys**

_Household phone surveys._ The woman in the household with the most knowledge and decision power, or the primary woman, completed the endline phone survey. Four short rounds of surveys, approximately 30 minutes each, were conducted between 3 and 7 weeks apart to accommodate for the limited duration of a phone survey.<sup>12</sup> Some outcomes were measured in more than one phone survey round, while others were measured only once (Table A1 in the Online Appendix). The baseline and midline surveys were conducted in person with the primary woman (lasting 2 hours approximately) and primary man (lasting 45 minutes approximately).<sup>13</sup> _Market surveys._ A market-level survey was conducted in four districts encompassing the 80 study villages to collect data on food prices. This was used to calculate consumption values from quantities in the household survey. This method replicates the approach used by the Afghan government to calculate national poverty measures before 2021. Surveyors visited the largest market in each district and collected sales prices for all food items found in the household consumption survey. These data were collected by more than one surveyor, and were administered in parallel with the household surveys.

## **4.2.2 Primary Outcomes**

Here we describe the primary outcome measures at endline, collected at the household- and individuallevel from the primary woman. These are described fully in Online Appendix Text 1, where we include the main differences with midline outcome measures, which are mostly due to the need of adjusting the indicators to fit the time limit and other constraints faced during phone surveys.

1. _Consumption proxy._ A proxy for total per capita consumption is estimated using a subset of food items consumed in the last week, and non-food expenditures in the past month or twelve months, depending on the item.<sup>14</sup> We followed
but population does not increase much in the same location, the location must be a hard place to move to. By contrast, if there is only a small decline in population in a low initial utility location, the place may be difficult to leave for any of the reasons mentioned above. By proxying initial utility in all locations within a country with the average country-wide subjective well-being (SWB) score, Desmet et al. (2018) assume that population changes in the location can be attributed either to differences in initial technology or fundamental amenities. In such a model, a situation of lower real wages in a location with similar population density to other parts of the country will be explained with much better fundamental amenities, when in fact people may have lower initial welfare and may not be able to leave the location due to institutional restrictions on their mobility or other reasons, including idiosyncratic preferences.

The assumption of uniform initial utility within countries is appropriate in smaller or advanced economies, where spatial convergence of welfare might hold. However, in many developing countries this may not be the case. Regional differences in welfare may be substantial, when proxied by differences in self-reported well-being (Burger, Hendriks, and Ianchovichina, 2021) or when proxied with differences in per capita consumption (Gollin, Kirchberger, and Lagakos, 2017). These differences in welfare might reflect higher mobility frictions in these developing economies (Bryan and Morten, 2019), differences in non-monetary amenities (Gollin, Kirchberger, and Lagakos, 2017), or idiosyncratic preferences for living in a certain place (Chauvin, Glaeser, Ma, and Tobio, 2017). In line with this evidence, one of the contributions of this paper is that we relax the assumption of uniform initial utility in the case of the Latin American countries. Using geo-coded data on subjective well-being, which we obtain from multiple survey rounds of the Gallup World Poll and Latinobarómetro, we compute the average subnational (level-1 administrative region) subjective wellbeing scores in each large Latin American country and
10 low- and middle-income countries around the world: Ethiopia, Mali, Niger, Nigeria, Uganda, El Salvador, Jamaica, Panama, Bangladesh, and Vietnam. This sample is relatively heterogeneous in terms of gross domestic product per capita, connection rates, tariff structures, and cost-recovery tariffs. Our analysis is founded upon data from household surveys that capture water expenditure, utility providers’ administrative reports, and a novel estimation of country-specific costs of service.<sup>3</sup> Our results suggest that, in all the countries analyzed, the mean unit prices charged are, on average, lower than the overall cost of producing and distributing piped water (that is, lower than would be needed to cover both capital and operating expenditures), resulting in substantial subsidies. Having said this, in some countries the average prices charged are higher than the tariffs that would be needed to cover operating expenditure. In addition, subsidies are regressive, with the amount of resources allocated to water consumption subsidies increasing over the expenditure distribution, and richer households in the top five deciles usually capturing the lion’s share. The results also suggest that, in most countries, subsidies are regressive due to a combination of factors that are not related to their design: (i) poor households are located in areas not covered by piped water networks, (ii) poor households are in areas that are covered,

> 2 The United Nations’ Sustainable Development Goals (SDGs), adopted in 2015, include the aims to “achieve universal and equitable access to safe and affordable drinking water” and to “achieve access to adequate and equitable sanitation and hygiene for all” by 2030.

> 3 These estimates are calculated using an improved methodology and new data presented in Andres et al. (2019).

2
> While our stock perspective is not new, its empirical applicability has been hampered **by** the lack of comprehensive data on foreign asset and liability stocks. In section **3** of the paper we present a new data set on foreign assets and liabilities, recently put together **by** Kraay, Loayza, Serv6n, and Ventura **(1999),** that covers a large number of developing and industrial countries and spans the years from **_1965_** to **1997.** The section also describes the variables used to measure domestic investment returns and risks as well as wealth, relative to the rest of the world.

Section 4 presents the empirical results obtained estimating our panel error-correction model for various groups of countries. The model is first implemented on the **full** available sample, and robustness checks are then performed **by** estimating it on country groups that differ in per capita income level and restrictions to portfolio diversification. The estimated parameters are then used to gauge the degree of disequilibrium in external positions --judged from a stock perspective-- over time for selected sample countries.

Section **5** summarizes the main results and concludes **by** proposing extensions for further research.

# **2. Methodology**

# **2.1 A stock approach to external sustainability**

Our approach views external sustainability as driven **by** portfolio equilibrium conditions in the long run, and **by** the dynamic forces resulting from asset reallocation in the short run. Long-run external equilibrium is achieved when international and domestic investors obtain their desired portfolio allocation of assets across countries. The current account balance is just the mechanism through which investors acquire their desired asset positions.

However, imperfections in financial and factor markets may prevent the instantaneous achievement of the optimal portfolio. Short-run external equilibrium is then given **by** the currentaccount deficit consistent with the dynamics along the adjustment
rural sector. However, our final price indices also incorporate some adjustments aimed at constructing a consistent time series over the long period of analysis; these adjustments relate to the use of the Consumer Price Index for the Working Class for the period before August 1968 for which the CPIIW did not exist, a correction for the constant price of firewood for a part of the Labour Bureau’s CPIAL series, and the use of a reweighted chain price index in both rural and urban areas to better approximate the weight of food in the consumption basket of the poor.<sup>30</sup> Our final rural and urban price indices are averages of monthly indices corresponding to the exact survey period of each NSS round.

**_Poverty measures:_** We use three poverty measures: the headcount index (H), given by the percentage of the population living in households with a consumption per capita less than the poverty line; the  poverty gap index (PG), defined by the mean distance below the poverty line expressed as a proportion of that line, where the mean is formed over the entire population, counting the non-poor as having zero poverty gap; and the squared poverty gap index (SPG), defined similarly to PG except that it is the mean of the squared proportionate poverty gaps. Unlike PG, SPG is sensitive to distribution amongst the poor, in that it satisfies the transfer axiom for poverty measurement.<sup>31</sup> In particular, to economize space we report results on H, PG and SPG for the higher line, and only H for the lower line.

The overall level of poverty at date _t_ can be additively decomposed using population weights. We will be interested in the urban-rural decomposition of the aggregate measure for date _t_ : where _nit_ and<sup>_P_</sup> _it_<sup>are the population shares and poverty measures for sectors</sup> _i = u,r_ for urban and rural areas respectively. “Time”
person consumption floor below this level. This procedure results in bottom-coding 0.3% of the population covered by both income and consumption surveys in 2019.

# **3.2 The evolution of global welfare, inequality, and poverty over time**

We begin by illustrating the features of our new measures in the setting of the world interpersonal income distribution. Table 3.1 reports our new welfare index W, comparing it with mean income in Panel A with the help of our new inequality measure I (Panel B). We also compare our new censored poverty measures C and P with the headcount ratio (Panel C). While we compare our new measures to other distribution sensitive welfare indices and poverty measures later in this section, we start with the mean and the headcount index as comparators, precisely because they are simple, commonly used, and not distribution sensitive by any of the three definitions in the literature (see Table 2.1). W is calculated using z= $6.85 as the reference income level, corresponding to the World Bank’s poverty line for upper-middle income countries, while the censored measures in Panel C are presented for z= $6.85 as well as z= $2.15, the World Bank’s international poverty line capturing extreme poverty.

**Table 3.1: Global Welfare, Poverty, and Inequality Measures 1990-2019**

||**1990**|**2019**|**Avg Annual**<br>**Change, %**||**1990**|**2019**|**Avg Annual**<br>**Change, %**|
|---|---|---|---|---|---|---|---|
|**Panel A: W**|**elfare Me**|**asures