rafmacalaba/data-use-mentions-extended huggingface dataset:
(2011). The framework suggests an identification strategy that relies on the following reasoning: the more a country competes with China in a third market, the more a given depreciation of the renminbi is likely to hurt its exports in that market. We develop indices of competition between China and its competing exporting countries at the exporter-importer-product level to implement this strategy. The empirical specification, with a battery of very general fixed effects that control for all observable and unobservable importer-exporter-product, importer-exporter-time, exporterproduct-time and importer-product-time varying characteristics, helps us overcome to a large extent the problems of omitted variables that plague estimation of trade-exchange rate equations using aggregated data. Moreover, our estimates are less susceptible to reverse causality concerns as exports, measured at a disaggregated level, are unlikely to affect a macroeconomic variable like the exchange rate, more so when the latter is the exchange rate of another country, China.<sup>2</sup> We find robust evidence for the existence of a statistically and economically significant spillover effect. In particular, exports to third markets of countries with a greater degree of competition with China tend to rise/fall significantly more as the renminbi appreciates/depreciates. Our estimates suggest that a 10 percent appreciation of the renminbi increases a developing country’s exports at the product-level on average by about 1.5-2 percent. For high indices of competition, the increase could be as large as 6 percent. The results imply that, going forward, an appreciation of the renminbi could provide a substantial boost to developing country exports. Our spillover estimates are robust to a variety of statistical tests, to alternative measures of exchange rates, to alternative disaggregation of the trade data, and also across exporting and importing regions. They are also robust to incorporating the effect of competition from countries (other than China) whose currencies move with the renminbi.

We also find that the magnitude of the spillover effect is consistent with the predictions from the analytical framework. For example, as implied by theory, the spillover effect is greater for
ularly the tax administrative data) only have a limited number of variables and there are likely to be other factors, such as the determination of the firm manager/owner, that may be impacting firm performance. These caveats need to be kept in mind when examining the findings of the econometric analysis. For both data sources we conduct an ordinary least squares (OLS) regression in the form of a linear probability model.<sup>4</sup> Specifically, we have four main outcomes (Y) that are in the form of a dummy variable. These are: (1) whether a firm experienced a decline in sales compared to pre-pandemic levels in June 2020; (2) whether a firm experienced a decline in employment compared to pre-pandemic levels in June 2020; (3) whether a firm experienced a complete recovery in sales compared to pre-pandemic levels in July 2021; and (4) whether a firm experienced a complete recovery in employment compared to pre-pandemic levels in July 2021. The econometric model can be expressed formally as follows: whereby, _β_ 1 captures the extent to which the size of a firm (small, medium or large) is associated with an outcome, _β_ 2 captures the extent to which the age of a firm (in years) is associated with an outcome, _β_ 3 captures the extent to which the activity of a firm (manufacturing, retail and wholesale services or other) is associated with an outcome, _β_ 4 captures the extent to which the location of a firm (Lusaka or elsewhere) is associated with an outcome, _β_ 0 is the intercept and _ε_ is the model error term.

For the survey data we conduct further analysis to fully utilize the richer information available on respondents. First, we rerun the regression above and include additional baseline variables captured in the baseline ES (top manager’s years of experience, >5 competitors entering the market in the last two years, new products introduced over the last three years, access to finance is a very
# **1. Introduction**

An increasing number of surveys in both developed and developing countries elicit the subjective expectations that individuals have about a wide range of future outcomes.<sup>1</sup> Such data are being used for understanding many economic and social behaviors, such as saving for retirement, contraceptive choice, migration decisions, and schooling.<sup>2</sup> In developed countries surveys usually collect subjective expectations by directly asking respondents questions like, “What is the percent chance your monthly income in one year will be below $1000?” (e.g. Dominitz and Manski, 1997). While this direct approach has been used in developing countries by McKenzie et al. (2007) and Attanasio and Kaufman (2009), in many settings it is felt that asking respondents with low levels of education directly for a probability is too abstract, and so visual aids are used to help individuals express probabilistic concepts. Such an approach could also be useful for surveying groups in developed countries with lower education levels, such as children or the poor.

The typical approach using visual aids is to give respondents physical objects like beans, balls or stones, and then ask them to allocate into intervals or bins in accordance with their subjective expectations of different events occurring.<sup>3</sup> In practice individuals are usually given 10 or 20 items to allocate, allowing them to express probabilities in units of 0.10 or 0.05. When eliciting a distribution, a major design issue is then how to specify a set of intervals for respondents to allocate these items to, and how to define the support over which expectations are elicited. Delavande et al. (2009)’s survey of the existing literature reveals two common methods for doing this. The first is to use a common, pre-determined support for all respondents. Pre-existing data or prior knowledge of the range of possible values of the outcome is used to define the support, and a
This leads to a very important, albeit not sufficiently appreciated, difference between Concepts 2 and 3. This is not a conceptual difference but rather the difference in what is a commonly used metric of welfare. To be sure, Concept 2 inequality can be calculated across GDIs per capita or across mean disposable incomes retrieved from countries’ household surveys (HS). It is however almost never calculated using the latter because HS means are much more difficult to obtain than National accounts (NA) data. In contrast, Concept 3 inequality _must_ be based on household surveys because the only source of distributional data are, as said before, household surveys. Because there is no world-wide household survey this means that the best one can do is to combine individual countries’ surveys, and use disposable per capita income or personal per capita consumption as welfare indicators.

Now, the problem is first, that there is a definitional difference between GDI which comes from National accounts, and disposable income which comes from surveys, and second, that there was a recent tendency for these two measures not to move in unison in several important countries (see Deaton, 2005). Thus, even if everything else were fully comparable, a commonly calculated Concept 2 measure that uses National accounts data would differ from an equivalent Concept 2 measure calculated using household surveys because welfare indicators are different and because they have recently diverged for reasons that are not yet quite clear.<sup>7</sup> This review will deal only with studies of global or Concept 3 income inequality.<sup>8</sup> The easiest point of departure for an estimate of global inequality is to calculate Concept

> 7 This area—understanding why national accounts and household surveys averages move differently— represents one of  the most important areas for further research. Bhalla (2001) must be credited through his, at times single-minded, insistence on using National accounts data for highlighting this issue.

> 8 And with global inequality conventionally defined as inequality in relative, not absolute
Similar to the SD identification strategy, the matching strategy compares beneficiaries to nonbeneficiaries with similar pre-program characteristics (Caliendo & Kopeinig, 2008; Rosenbaum & Rubin, 1983). The propensity to participate to the program ���� is estimated via a probit regression including pre-program baseline characteristics as covariates �. Then, impacts are estimated via PSM: where ��1 indicates treatment, ��1� is the outcome of interest for treated observations (beneficiary households) and ��0� for untreated observations (non-beneficiary households).<sup>26</sup> The propensity score (propensity to be treated, i.e. beneficiary of the project) is computed from the PMT score, since it is known that project eligibility is determined by this PMT score. Oneon-one matching without replacement is performed for all households, not only those in the PMT ‘common support’ sample.<sup>27</sup> A test of balance between treated and control units is performed for unmatched (before PSM is used) and matched observations (after PSM weighting) and illustrates the reduction in the standardized percentage bias obtained by the matching procedure (Figure 2). The test shows that standardized percentage bias is relatively small, except for household size and the PMT score itself (which is expected) and that the PSM weighting slightly reduces this bias.<sup>28</sup> Common support is found for all but for 89 beneficiary households, which means that most observations are used.

PSM estimates are very close to those obtained from the SD identification strategy (Table 9), with two main exceptions. First, PSM estimates also suggest a significant, negative effect in terms of housing quality, which confirms that most households have not invested in their dwelling. Second, the magnitude of the estimated effects tends to be slightly larger in PSM estimates than in DID and SD estimates. Overall, PSM results support the findings obtained from the DID and SD specifications. While the PSM specification does not address
infrastructure. In fact, there is a lot of useful institutional information on the forms of decentralization around the world and on the various ways that subnational governments organize themselves to finance and deliver local public services (see, for instance, Serageldin and others 2000).

But there is not much empirical evidence on the relative efficiency of the various delivery modes and types for infrastructure, especially in developing countries (see Shah and others 2004 for a recent survey).<sup>18</sup> In a volume of background papers to the World Bank’s _World Development Report 1994_ , on infrastructure, Estache (1995) started this line of empirical research on the levels and effects of infrastructure decentralization. In that volume, Estache and Sinha (1995) show that for a sample of 10 industrial and 10 developing countries, decentralization tends to increase total and subnational spending on infrastructure much more in developing than in industrial countries. This different response has been confirmed by other empirical studies (again, see Shah and others 2004 for a survey). Similarly, the increased demand for infrastructure services caused by decentralization has been confirmed by Faguet (2003). He shows that in Bolivia decentralization has led to rerankings of investment programs in favor of agriculture, education, and water and sanitation.

> 17 The original baseline poverty estimates used for the MDGs were based on income and consumption distribution surveys for 44 countries containing 71 percent of the population in 1990 (Ghaiha 2003). These countries accounted for about a quarter of those considered to be developing. Ghaiha also documents the many sources of data and their inconsistencies.

> 18 There is a large public administration literature documenting the impacts of various forms of public service decentralization in developing countries. There is also quite a bit of analysis of decentralized health and education services, recently reviewed in the World Bank’s _World Development Report 2003_ .
paper. Our analysis finds that migration results in lower infant mortality rates and higher birth weights, and that failure to account for the selectivity of migration understates these gains. However, we also find that children in migrant households receive less preventative health inputs such as breastfeeding and vaccinations, which may have a detrimental impact on child health at older ages.

Having identified a positive overall effect of migration on child health, the paper then examines the principal channels through which migration works to improve health. The Grossman (1972) health production function approach is used to explain the likely channels through which migration can affect health outcomes. The first avenue is the direct effect of migration on income and wealth, through remittances and repatriated savings, which allows households to spend additional resources on food and health services. Secondly, through exposure to U.S. practices, migrants may gain health knowledge, allowing the same amount of financial resources to result in higher health attainment. Evidence is found that migration influences health outcomes through both of these mechanisms, raising both wealth and health knowledge.

Previous research on the interaction between migration and health is limited, and has tended to focus on differences between immigrants and the native-born, finding that migrants have worse health along a number of dimensions (see for example, Institute of Medicine, 1998), although that immigrants have surprisingly positive birth outcomes in the U.S. given their socioeconomic status.<sup>5</sup> Analysis of the impact of migration on health outcomes in the sending region is much more limited, although recently two studies by sociologists have provided a first assessment. Kanaiaupuni and Donato (1999) use data from five Mexican states drawn from the Mexican Migration Project and find mixed effects of migration on infant mortality, suggesting that infant mortality increases in the early stages of the migration process before later declining. Frank and Hummer (2002) use the same ENADID data as this study to look at the effect of migration on the incidence of low birth weight, finding migrant children
the adults is also correlated with poverty. Except in Poland, households consisting only of elderly have the highest poverty incidence and poverty gap. We return to this later when discussing the age-dimension of poverty.

**Table 3: Poverty and Aggregate Family Composition**

|_Family Composition_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
|||**H**|**eadcount**|**(P0, in pe**|**rcent)**||
|No Children, No Elderly|18.3|13.0|13.3|23.8|37.6|31.9|
|Child(ren), No Elderly|25.0|23.9|28.1|32.8|43.1|40.5|
|No Children, Elder(ly),|39.0|26.1|18.1|39.4|43.8|43.8|
|Child(ren), Elder(ly)|28.0|22.2|32.7|35.2|42.9|_51.5_|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||**Poverty G**|**ap (in pe**|**rcent)**_V1_||||
|No Children, No Elderly|18.8|13.6|13.6|22.0|29.4|32.2|
|Child(ren), No Elderly|19.4|14.1|13.2|18.3|24.2|27.1
The primary data informing this study consists of 32 semi-structured interviews. Interview data was used to understand why each LPA was initiated; how each LPA was negotiated and by who; why specific terms were valued (and by who); how terms were interpreted in practice; how each LPA was implemented after signature; and how the key stakeholders involved in drafting, signing, and implementing each LPA perceived the LPA affected durable solutions and community relations. All interviews were conducted in Iraq in May 2021 by a female Iraqi researcher. Due to government restrictions on intra-governorate travel and meetings (in response to the COVID-19 pandemic) the interviews were all conducted by phone, in Arabic. Interviewees were drawn equally from the eight sub-districts associated with the LPAs, and were chosen based on their direct involvement in, or direct knowledge of, the process of negotiating, drafting, and implementing each LPA. As a result, most interviewees were tribal leaders (13 respondents) or representatives of the local authorities (12), with a smaller number of interviewees drawn from civil society, NGOs or academia (5) or the federal government (2). All interviewees were Iraqi and resided in the area where the LPA applied. Respondents were identified using snowball sampling, with the ‘start’ of each snowball typically a signatory of each LPA or a prominent tribal leader in the sub-district. After the data collection was completed, the authors coded the (translated) interviews utilizing a line-by-line reading of the transcripts in order to identify common themes, and then instigated a second round of focused coding that refined the themes and identified sub-themes for analysis.<sup>65</sup> The interview data exhibits several limitations. The first is that community members and IDPs affected by each LPA were not interviewed, and all interviewees occupied a relative position of power (whether as a tribal leader, government official, or as an active member of civil society). The primary reason for this narrow approach was that data collection took place during the COVID pandemic
C., Inklaar, R. and Timmer, M. (2013). The Next Generation of the Penn World Table. _NBER Working Paper 19255_

- [15] Fisher-Vanden, K., Mansur, E. and Wang, Q. (2015). “Electricity Shortages and Firm Productivity: Evidence from China’s Industrial Firms.” _Journal of Development Economics_ , 114, 172-188.

- [16] Gardner, E. S., Jr. (1985). Exponential Smoothing: The State of the Art. _Journal of Forecasting_ 4, 1–28.

- [17] Hamilton, J.D. (1994). _Time Series Analysis_ . Princeton University Press.

- [18] Harris, J. L., and Liu, L. M. (1993). Dynamic Structural Analysis and Forecasting of Residential Electricity Consumption. _International Journal of Forecasting,_ 9(4), 437-455.

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We are **now** able to match 4 and 6/8 cylinder cars separately. Table A.5 and A.6 compare the results of the single match using census data only with the differentiated matches for 4 and 6/8 cylinder cars for the Brazil/US and Mexico/US comparison respectively. It appears that the PPPs of the differentiated matches are higher than the PPPs of the single match, which implies that the original comparison using census data only showed an overvalued output **in** Brazil and Mexico.

**TABLE A.5**

Gross Value of Output and PPPs for Passenger Cars, Brazil (1975)/USA (1977)

||Bra|zil<br>(1975)|USA|**(197.71**||
|---|---|---|---|---|---|
||million<br>1975<br>cruzeiros|<br>million PPP<br>1977<br>Cr./USS<br>US**S**|million<br> 1975<br>cruzeiros|million<br>1977<br>US**S**|<br>PPP<br>Cr./USS|
|Match using<br>Census<br>only (without model<br>differentiation)<br>Passenger<br>cars|Data<br> <br>9,286.5|2,338.2<br>3.97|189,834.1|47,796.3|3.97|
|MaWch using<br>Augmente<br>Information|d|||||
|4 cylinder<br>cars<br>|5,683.8<br>|1,265.4<br>4.49<br> <br>|16
# Table 1: Correlations between Legal Disparities and Gender Indices

||**Legal disparities: Overall m**|**easure (Higher value**|**s, greater gender disparity)**|
|---|---|---|---|
||Correlation coefficient|Significance|Number of observations|
|CIRI Women's Social Rights 2004 (Higher values greater gender equality)|-0.50|***|147|
|CIRI Women's Economic Rights 2011 (Higher value greater gender equality)|-0.56|***|164|
|CIRI Women's Political Rights 2011 (Higher values greater gender equality)|-0.41|***|164|
|WEF Gender Gap Index 2015 (Higher values greater gender equality)|-0.66|***|136|
|Social Watch Gender equity index 2012 (Higher values greater gender equality)|-0.66|***|141|
|Ratio of female to male human development index 2013 (Higher values greater gender equality)|-0.57|***|136|
|UN gender inequality index 2013 (Higher values worse gender equality)|0.53|***|142|
|OECD Social Institutions and Gender Index 2014(Higher values worsegender equality)|0.70|***|98|

# Table 2: <u>Summary Statistics for Regressions Analysis</u>

||Obs|Mean|Std. Dev.|Min|Max|Maximum possible<br>disparities|
|---|---|---|---|---|---|---|
|Legal disparities: Overall measure|167|17.4|9.1|2|43.9|71|
|Legal disparities: Accessing Institutions|167|2.
Safety nets may also contribute to growth on a national scale by reducing inequality and by increasing social cohesion (World Bank 2006). If, for example, there is an inequality trap via inequality of opportunity that reduces economic growth, then to the degree that SNs address asset accumulation through programs such as CCTs, fee waivers, or school feeding they can contribute to growth. A different pathway from equity to growth is also postulated, one in which inequality of outcomes influences the path of institutional development and sets up conditions which foster rent seeking. The theoretical underpinnings for this channel were initially formulated by Sala-i-Martin (1997) and later developed further by Mehlum, Moene and Thorvick (2005) in a model in which positive growth effect is due to the deterrence of investment-reducing crime through safety nets.

Hard empirical evidence for this effect is hard to come by, but this role for SNs is supported by the observation that safety nets often cultivate social reintegration in post conflict countries (such as Liberia and Sierra Leone), where public works have been offered to disaffected or potentially socially disruptive groups including former soldiers. Similarly, the public works program in El Salvador (PATI) directly targeted potentially delinquent youth and developed a scheme (combining transfers and training) that increased opportunity cost for participation in criminal activities (Subbarao et al. 2013).

There is less evidence on these potential avenues by which safety nets can contribute to growth via social capital for a variety of reasons.<sup>14</sup> For example, it is difficult set up field trials on the impact of inequality on growth similar to those used to accumulate causal evidence of programs such as cash transfers. In addition, whether employing micro or macro level data it is a challenge to distinguish the effect of levels of assets from that of its distribution. Moreover, when looking at national data as in the claim of Boeri and Terrell (2002) that public assistance 16