rafmacalaba/data-use-mentions-extended huggingface dataset:
_DD_ . An example of this approach can be found in Jalan and Ravallion (2002), who identified the effects of lagged infrastructure endowments in a dynamic model of consumption growth using a sixyear household panel data set. Their econometric specification is an example of the nonstationary fixed-effects model proposed by Holtz-Eakin et al. (1988), which allows for latent individual and geographic effects and can be estimated using the Generalized Method of Moments, treating lagged consumption growth and the time-varying regressors as endogenous (using sufficiently long lags as instrumental variables). The authors found significant longerterm consumption gains from improved infrastructure, such as better rural roads.

**_Concerns about DD designs:_** Two key problems have plagued DD estimators.  The first is that, in practice, one sometimes does not know at the time the baseline survey is implemented who will participate in the program.  One must make an informed guess in designing the

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|
|Adjusted R-squared|0.066|0.066|0.084|0.083|0.055|0.054|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sample|All|All|All|All|All|All|
|Number of clusters|378|378|309|309|324|324|
|Mean dependent|0.0763|0.0763|0.0817|0.0817|0.0716|0.0716|

* p<.10  ** p<.05  *** p<.01

_Notes_ . The dependent variable “out of village” is a dummy variable that indicates whether the respondent lives outside the village where we originally surveyed their household. The cognitive skills index is the Mathematics score, computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the tablet-based computer-adaptive test. The socioemotional skills index is computed using principal-component factor analysis on 15 Big Five items and 8 Grit items. The sample for this long-term follow-up is 3,294 respondents. We only have information on where the respondent currently lives for 2,706 respondents (1259 men and 1,447 women). Among them, some respondents did not complete the tablet-based computer-adaptive test or answered “Don’t know” to the socioemotional skills questions, leading to a sample of 2,688 respondents for regressions that include these skills measures (1,249 men and 1,439 women). All regressions control for village and age fixed effects. Standard errors are in parentheses (clustered at the village level).

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z-score for GDD|
|Maximum temperature|The average maximum daily temperature|

_Note_ : The table presents definitions for included weather variables and transformations from weather sources defined in Table 1. _†_ Growing season determined for each country following FAO crop calendar (see Table A1). _∗_ For variables when “long run” is referenced, long run is defined as the entire length of the weather data set. While each weather source has a different start date, to ensure blinding all data sets were shortened to 1983, which is the latest start date of the data sources.

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different kinds of protectionism including voluntary export restraints (VERs), ‘strategic’ trade interventions, and more recently to EPZs. The ECONLIT database contained 133 references to ‘voluntary export restraints’,  324 to ‘strategic trade’ and 48 to ‘export processing zones’.

> 5 Balassa et al. (1970) discussed in detail the duty drawback policies of several developing countries, while Hufbauer and Erb (1984) surveyed such policies in developed countries.

> 6 The standard framework for the analysis of effective protection assumes a fixed coefficient, two-stage production process (component production and final good assembly), upward sloping supply curve for factors (value added) and intermediate inputs (components), perfectly competitive markets, exogenous world prices of components and final goods.

> 7 World Bank data at <u>www.worldbank.org/trade</u> indicate that 48 out of 86 economies had escalating tariffs on industrial products between 1994 and 2000. One country (Bolivia) had de-escalating tariffs and the rest had a mix of increasing and decreasing tariff rates from one stage to another.

4
# **II. Big Data at Work**

Ecuador's most recent population and national housing census was conducted in 2010 by the National Statistical Institute (INEC acronym in Spanish). It reported the population and number of households in each of Ecuador's 40,681 census sectors. The census cartography is entirely digitized, and polygon shapefiles are also available for each sector. It has been partially updated over the years, and it has been the primary source of information for sampling design of all official surveys like labor, health, and nutrition, and multipurpose surveys, among others. These surveys are representative of the Ecuadoran population residing in the country at different geographical levels. However, the census cartography is not updated enough to capture the recent influx of Venezuelan migrants. Thus, it does not allow to build a representative sample of the migrant and host populations.

Defining a sampling strategy to yield representative samples of host and displaced populations demanded a key innovation: the incorporation of Big Data for the sampling design. The flow of migrants affects mainly urban areas or main cities across the corridor from the northern border with Colombia to the southern border with Peru. The geographical aspect of the phenomenon encouraged the use of Big Data from telecommunication companies because of its accuracy in urban areas. Two leading cell phone providers were contacted in Ecuador to request their collaboration. However, only one of them, Telefonica de Ecuador, had the technical capacity to implement the sampling strategy proposed. Telefonica de Ecuador has vast experience in processing and providing the information requested for this sample.<sup>9</sup> Even though its market share is around 30 percent, which represents approximately 4.6 million clients in Ecuador, it is the only private company with a significant presence in the República Bolivariana de Venezuela's mobile phone market. Thus, it could be expected to attract most Venezuelan consumers seeking to obtain cell phone services in Ecuador.

Telefonica storages around 15 million call detail records (CDR) and 180 million external
- (ii) _MIN_WAGE_ : minimum wage level (expressed as percentage of average wage in industry and services)<sup>13</sup> ;

- (iii) _GDP_PC_ : log GDP per capita in  PPP USD (annual average, lagged 1 year);

- (iv) _GROWTH:_ Real GDP growth, y-o-y, quarterly data lagged 1 quarter;

- (v) _UNION:_ Union density, % of employees (survey-based, using ESS data)<sup>14</sup> ;

- (vi) _IMMIGR:_ Share of population with immigrant background: first generation immigrants and second generation immigrants (survey-based, using ESS data).

- In addition, specification {1} includes

- (vii) _RULE_LAW_ : the _Rule-of-Law_ indicator (on a scale from -2.5 to 2.5) from the World Governance Indicators (Kaufmann et al., 2010; World Bank, 2010).

- (viii) _TAX_WEDGE:_ Tax wedge for low wage earners (lagged 1 year).

Specification {2}, instead of _RULE_LAW_ and _TAX_WEDGE_ , includes

- (ix) _GOV_EFFECT:_ The _Government Effectiveness_ indicator (on a scale from -2.5 to 2.5) from the World Governance Indicators (see Kaufmann et al. 2010; World Bank, 2010).

- (x) _INVEST_FREE:_ Investment Freedom – the Heritage Foundation (2010) index (on a 0 to 100 scale) which ―evaluates a variety of restrictions<sup>15</sup> typically imposed on investment‖; a higher score indicates less restrictions (more freedom);

- (xi) _TRADE_FREE:_ Trade Freedom – the Heritage Foundation (2010) index (on a 0 to 100 scale) which ―is a composite measure of the absence of tariff and nontariff barriers that affect imports and exports of goods and services”; a higher score indicates less barriers (more freedom).

Both specifications explain about 80% of variation in the prevalence
prone to double counting.

Another literature, even broader in scope, ties malnutrition with climate considerations (e.g., The Food and Land Use Coalition – FOLU – 2019). FOLU (2019) presents estimates of the economic costs of, both what they term, undernutrition (stunting, wasting, and underweight among children under five years of age) as 2.0% of global GDP, and of overweight and obesity as 3.1% of global GDP, based on their SYSTEMIQ 2
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data The next table reports the estimates for the MIMIC model using Maximum Likelihood estimation.<sup>29</sup> Panel A presents the results for the structural equation with dependent variable (equation 7) and panel B and panel C show the results for the two measurement equations with dependent variables log of sales reported to the tax office (equation 10) and log of sales reported in the survey (equation 11) respectively. Panel D provides the multivariate normality tests on the residuals. Column (1) shows the results without IC variables, column (2) and column (3) add _credit_ and _corruption_ respectively, and column (4) adds both. Notice that we drop the constant in equation 10) as this allows for identification of the model without relying on the non-linear constraint (see Appendix B) and, unlike the other two constants in the model, was found to be insignificant.

Panel A shows the estimates for the structural equation for the latent variable of true sales. The coefficients for the three industry dummies suggest that true sales in construction, service and tourism are respectively 32%, 26% and 171% higher than in manufacturing (column (4))<sup>30</sup> , although only the coefficient for tourism is significant. The coefficients for _medium_ and _large_ are both significant at 5% level and imply that firms of medium and large sizes produce almost 0.6 and 2.7 times more than small firms respectively, holding everything else constant. oreover, firms’ sales increase with increasing capital and higher capacity utilization. The dummy variables _credit_ and _corruption_ control for the impact of the investment climate on productivity and prices and consequently on true sales. The

> 29 The estimated coefficients of the city dummies in are small and (jointly) insignificant and were excluded from in the final specification. Similarly the estimated coefficients for Skill and Experience in and Capacity in were insignificant and small and therefore
: Bustelo (2015) finds that young adolescents (ages 13-15) increased their workload while younger children did not, and that only boys’ school participation was affected after Tropical Storm Stan hit Guatemala. Importantly, Baez and Santos (2010) find that the largest impacts are often on the poor.

# **3.2 Empirical correlations: The role of macro variables**

The paper by Bentaouet-Kattan and Székely (2015), referred to earlier, implements the conceptual framework in Behrman, de Hoyos, and Szekely (2015) by focusing on macro variables, which complements the picture presented above.<sup>22</sup> Specifically, the authors estimate a model where changes in school enrollment across each cohort for which they have data are correlated with a set of variables characterizing the macro environment in which human capital accumulation decisions are taken. The authors present a base regression estimated through differences in differences which accounts for potential problems of omitted variables bias and endogeneity, among other issues, that can be used here for further exploration. The general specification used is: Where changes in enrollment for cohort _i_ between periods _t_ and _t-1_ for country _c_ are a function of a set of independent variables observed for each cohort for each period of time, for each country. Four groups of variables are considered. The first group (D) includes the population fertility rate

> 21 The population data is taken from the National Population Census of 2007 and extrapolated to 2012.

> 22 For a larger list of references on analysis of the causes of school dropout see Bentaouet-Kattan and Székely (2015). Other related analysis focusing on specific aspects of early dropout are Cardoso and Vermer (2007), South, et-al. (2005), Ingrum, (2006), Marcotte (2013), Dearden, et.al. (2008), Mo, et.al. (2014), Bentaouet-Kattan and Székely (2015b), Chioda (2013), Ibañez, et.al. (2013), and
prices. It can be shown that, in this framework, the pass-through rate depends on the elasticity of the slope of the demand schedule and competitive conditions ( _φd_ ). In our empirical analysis in Section 4, the main trade cost shifter of interest _xod_ is distance. To simplify the exposition, in what follows we set **X** _od_ = _xod_ .

Equation (2) illustrates three sources of bias that can arise when aiming at identifying trade costs from spatial price gaps: (1) incomplete pass-through ( _ρod_ = 1), (2) variation in demand conditions across locations, and (3) variation in the extent of competition across locations. Under additional assumptions, the pass-through rate can control for this sources of bias. In particular, under the assumption that the pass-through rate is independent of quantities _qd_ , equation (2) simplifies to where _ad_ is a local demand shifter. Under this specification, _ρod_ and the local demand shifter _ad_ are sufficient to control for local competitive and demand conditions.

The following subsection explains how this theoretical framework can guide the specification of empirical models to estimate the effect of distance on trade costs exploiting origin-destination price gaps, controlling for other factors that correlate with distance and could affect price differentials through variation of markups across locations. Section 4 describes the methods used to estimate these models.

# **3.2 Estimation strategy**

The estimation strategy exploits price data on multiple products _k_ sourced from locations _o_ and selling at locations _d_ at different time periods _t_ . With variation across products, destinations, origin locations, and time, equation (3) implies the following relationship between product prices at the origin and the destination: where we have further assumed that the pass-through rate remains fixed over time.<sup>6</sup> The latter assumption holds if local demand elasticities and competitive conditions (e.g., entry of new intermediaries) are constant over time.

In equation (4), trade costs _τ_ ( _x_<sup>
and liquidity. However, they are very different from the interlinkages estimated for the volatility of deposit rate and deposit growth. With the latter, financial inclusion shows significantly positive association for accounts, payments, savings, and credit—again more so for individuals than firms, which could reflect differences in the quality of the measures of inclusion for individuals (Findex) and firms (Enterprise Surveys). For instance, our finding of a significant correlation (of about 0.4) between the volatility of deposit growth and the use of deposits by individuals is consistent with the finding of Han and Melecky (2013) that a 10 percent increase in the use of deposits can reduce deposit withdrawal rates in stress times by about 5 percentage points.

We do not detect any significant positive effect of insurance on volatility and financial stability at large. Although non-life insurance appears to be associated with decreasing volatility of credit, relatively noisy data and limited country coverage prevent us from drawing a clear inference.

Overall, the results suggest that in normal times, financial inclusion aids financial stability, especially by decreasing through-the-cycle expected losses and costs of business for banks (recall the results for NPLs), and by lowering the volatility of deposit growth and deposit rates. However, for the volatility of credit, capital, and liquidity buffers, as well as for outright crises, financial inclusion could endanger financial stability because of its potentially negative implications for unexpected losses.

> 19 Note that account ownership and credit are related, as getting credit from an institution often requires the prospective borrower to open an account with the prospective lender. Use of electronic payments with such account then follows. Indeed, account ownership, the use of debit cards, and the use of Internet or electronic payments are highly positively correlated (Annex 1).

> 20 Note that the insurance sample is much smaller than for other services, and mainly includes higher income countries reporting to the IMF’s Financial Access Survey.

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the SCI as a benchmark to validate their new statistical indexes (Sanga _et al._ , 2011), or analyzed the SCI for various purposes such as investigating the relationship between good governance and statistical capacity, or the impacts of statistical capacity on reducing procyclical fiscal policy, or identifying countries where investments in statistical capacity can be most fruitful (Beegle _et al._ , 2016; Tapsoba _et al._ , 2017; Hoogeveen and Nguyen, 2019).

Table 1 compares the SCI with the statistical capacity measurement indexes used by other organizations, including PARIS21, the Food and Agriculture Organization of the United Nations (FAO), the United Nations Economic Commission for Europe (UNECE), the United Nations Economic Commission for Africa (UNECA), and the U.S. Census Bureau. Table 1 highlights several main advantages of the SCI over other indexes. It is the only tool that provides comparable data across different countries over time. Furthermore, the SCI also covers the largest number of countries (i.e., 146 countries) over the longest period (i.e., starting from 2004 to date).<sup>4</sup> Yet, there are several areas in which the existing SCI can be improved. First, the various aspects of the capacity of an NSS that the SCI measures have fast become outdated. Since its launch in 2004, the SCI’s methodology and coverage have remained the same, while the global data landscape has changed significantly. Technological advances with computing and data storage capacity have enabled NSSs to make significant advancements with data collection methods and better dissemination practices. The international community’s adoption of the SDGs also raised the bar for NSSs regarding their capacity to produce higher-quality and more data. For example, the SCI includes no indicators of some important surveys such as the labor force surveys

> 4 There is also a key methodological difference between the SCI and other assessment tools. The latter methods collect data directly from national statistical offices’ staff or local
African Rep. Guinea<br>Republic<br>Growth Inflation Inflation Threshold Growth<br>Source: UNSD, WDI and ECOWAS database.<br><!-- End of picture text --> <!-- Start of picture text --> 8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Growth Inflation Inflation Threshold<br><!-- End of picture text --> Note: Aggregate indicators are based on weighted averages – Weights are derived from GDP, 2010 USD constant prices.

The fact that the CFA franc is pegged to the euro has broadly helped containing inflationary pressures but also cost the independence of the regional monetary policies. In comparison with other African countries, inflationary pressures have been broadly contained in the Franc zone countries, excluding the period that followed the 1994 devaluation and outliers such as Equatorial Guinea and Guinea-Bissau. However, this result comes at the expense of the autonomy of the monetary policy as, according to the Impossible Trinity of Mundell, a country loses the autonomy of its monetary policy when it adopts a fixed exchange rate regime and it allows the free circulation of capital flows. The loss of the independence of the monetary policy can be observed by comparing the dynamics of the monetary policy rates of the regional central banks and the one of the European Central Bank (ECB) – at least until before the 2014 negative oil price shock for CAMU countries. In addition, this limited margin on the monetary policy is also Page | 11
propensity to experience financial hardship, although presence of parental background controls mitigates this problem.

<!-- Start of picture text --> Comfortable<br>Coping<br>Difficult<br>Very difficult<br>Informal dependent employees Informal self-employed<br>West-1 West-2 East South North West-1 West-2 East South North<br><!-- End of picture text -->

**Figure 19 Impact of employment status on perceived household financial situation (ordered probit marginal effects vs. formal employees)** _Source:_ Calculation with ESS data (based on model reported in Table 8).

Our findings are qualitatively similar to those by Perry et al. (2007: Chapter 3) on Latin America: in Argentina, informal salaried workers have substantially higher income-poverty and self-rated poverty rates than formal salaried workers; in Dominican Republic, the same is true for income-poverty and dissatisfaction with employee benefits (but not for self-rated poverty and dissatisfaction with earnings); and in Colombia, for dissatisfaction with both earnings and employee benefits. Moreover, earnings of informal employees are, on average, by more than 40% lower than those of formal workers in Bolivia and Dominican Republic and by more than 60% in Argentina. These earnings and welfare gaps remain significant after controlling for worker characteristics in some cases but become insignificant in others. In most cases, welfare and/or earnings disadvantage of informal self-employed is either smaller or absent (or disappears after controlling for characteristics).

We conclude this section by noting that, contrary to a popular perception, the overwhelming majority of informal employees (81 to 83% in the North and in the West; 88 to 89% in the East and in the South) mention wages (rather than social assistance benefits) as the main source of their household income; this level is not much lower than 93 to 95% found among formal employees.

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). The selected macroeconomic indicators include gross domestic product per capita (GDPPC) in euros, inflation (HICP) in nominal value, the levels of industrial production (IPI) indexes, the local currency exchange rate (FX) with the United States dollar (US$) in nominal value, the money market interest rate (MMIR) in nominal value, and the balance of payments net financial account (BOPNFA) in euros. The proxy for the regulated exchange-traded stock prices in Bosnian and Herzegovinian (B&H), Serbia, North Macedonia, Croatia, and Slovenia are the respective equity indexes: BATX, BELEX15, MBI10, CROBEX, and SBITOP. This research tracks quarterly data in an approximate full economic cycle 12 | P a g e