rafmacalaba/data-use-mentions-extended huggingface dataset:
Services, non‐merchandize|70,32|||29,68|
|**_Total_**|_17,92_|_48,94_|_28,93_|_4,31_|

_Source_ : Agbodji (2009), data from MCS, Togo.

**The ICA data seems to suggest that the main reason for firms to join the Free Zone is the fiscal and administrative advantage.** ICA data in Figure 55 shows that firms in the Free Zone tend to first of all have concerns in fewer areas than non‐FZ firms. FZ and non‐FZ firms are more or less equally concerned about issues of access to finance and political instability, but non‐FZ firms are concerned about for example corruption, tax administration, custom and trade regulation, and business licensing while these are hardly mentioned as the main constraint by FZ firms. Given the higher growth of firms within the zone, this suggests that easier administrative procedures for running a business are binding constraints for many firms in Togo. Note however that the Togo ICA (2010) looks at the number of firms identifying a category as a main as well as a severe constraint and then compares the result between FZ and non‐FZ firms and conclude that complaints about business related constraints are not significantly different between the two groups. Given a number of differences when including both the main and severe constraint, the result should be taken with caution. Moreover, in the survey it was never specified if the FZ are responding having their particular investment climate in mind, or the general investment climate in Togo.

**Figure 55: Main Constraints to Business, FZ firms vs non‐FZ firms, Togo, 2009.**

<!-- Start of picture text --> 35%<br>FZs<br>30%<br>25% Non FZs<br>20%<br>15%<br>10%<br>5%<br>0%<br>
35<sup>***</sup>|1.75|-0.90|2.94|-3.83<sup>***</sup>|1.37|
|Philippines|-60.57<sup>**</sup>|24.58|12.66|22.42|-14.66<sup>*</sup>|8.26|
|Taiwan,China|0.90|1.24|-27.17|20.84|-3.03<sup>**</sup>|1.35|

*** p<0.01; ** p<0.05; * p<0.1 Note: Based on separate SUR’s using data on non-agricultural and agricultural employment. The statistical model is the short-run model plus the first lag of the independent variables.

# **Conclusion**

Was growth in East Asia jobless? The evidence suggests that it was not. All countries included in the analysis for this paper grew under the period of study, with minor – though pronounced – exceptions during the Asian and global financial crises. Looking at Okun’s Law as the net effect of growth on employment suggests that, at least in the short run, growth has created jobs. However, the extent to which this relationship holds differs across countries. The analysis revealed that, especially where hiring and firing rules are more flexible, growth translates into job gains. In addition, the analysis suggests that Okun’s Law holds both during economic expansions and contractions. It does thus not have a discernible bias toward creating or destroying jobs in the aggregate. Finally, the analysis has documented a countercyclical relationship between growth and jobs in agriculture, in other words a ‘reverse Okun’s Law’, which is particularly pronounced during times of economic crisis. This suggests that agriculture may serve as a shock-absorber for
6 The mode of the distribution is about 50 m (about 14 floors). Since countries are likely to have more buildings below 50 m than above 50 m, and since the distribution of buildings is smooth after 50 m, this suggests that the data set mostly captures buildings above 50 m. Thus, the data set is likely less reliable for buildings below 50 m. Given that the satellite-based data that we use should capture buildings up to 50 m, we believe that we still capture most of the buildings in the world when combining the two datasets. Nonetheless, we can investigate if some regions of the world might have better data. Comparing the six continents (as defined by the United Nations), the mode is around 45-50 m for Africa, 50-55 m for Asia, 40-45 m for Europe, 15 m for North America, 45 m in Oceania and 45 m in South America. Therefore, we are confident that we can use information from the Emporis database to update WSF3Dv1 and create WSF3Dv2.

# **C Methods**

**C1. Classifying countries into 4 groups: heights vs. areas, high-rises vs. low-rises** First of all, we “residualize” the log of the outward based volume per capita (i.e., built-up area x 2.5 meters) with respect to log per capita GDP and its square (using country population as weights). As such, the residuals that we obtain capture to what extent a country uses a lot of area per capita given its income level, i.e., relative to the world’s quadratic fit. In layman’s terms, we obtain for each country the absolute distance from the country’s value to the world’s relation. A positive (negative) value implies more (less) outward volume per capita relative to the world.

Second, we similarly residualize the log of the upward volume per capita (= total volume - outward volume). The resulting residuals capture to
and Woessman (2012) however base their analysis on a cross-section of data only, while this research estimates the parameters of a fully specified dynamic value-added model.

The data we use are unique for the developing world. Subject-matter test score data of all (around 45,000) students of a near representative sample of 240 primary schools are tracked across three rounds of measurement, November 2009, May 2011 and May 2012. Figure (1) presents a map of the 20 districts that were randomly selected to take part in the study.<sup>4</sup> The student level test score data can be linked to survey and subject-matter test score data of their respective teacher(s). A feature of this data is that both students and teachers are tested on the same three subjects – math, science and Indonesian language – in each of the three rounds of measurement. The data therefore allows for estimating value-added models on variation within each student-teacher pair, across the three subjects. At the same time, the data permits to account for biases due to measurement error in test scores (under standard assumptions on the nature of the measurement error).

**Figure 1:** Map of Indonesia, with the 20 selected districts highlighted

Kingdon and Teal (2010), and Chu, Loyalka, Chu, Qu, Shi, and Li (2015).

> 4The data was collected in support of a randomized controlled experiment designed to evaluate the effects of Indonesia’s teacher certification program (De Ree, Muralidharan, Pradhan, and Rogers (2015)). The data is nearly geographically representative for the majority public primary schools in Indonesia. (The data on 120 Junior Secondary schools is not used in this paper.) Indonesia spans across 1,700 more or less inhabited islands and is the world’s fourth most populous country, and the distance from the most western school in the sample, on mainland Sumatra, to the most eastern, on one of the remote islands of the South
to the income pool of all agents participating in the risk-sharing agreement.

On the other hand, the expanded model solves a problem that plagues all conventional risk-sharing regressions that include average consumption among the explanatory variables. As noted by Deaton (1990), pooling the observations on consumption of all agents in a linear regression that includes the cross-sectional average of the dependent variable among the regressors leads, mechanically, to a regression coefficient of unity on that variable. Crucini (1999) dealt with this problem by estimating different coefficients of partial risk sharing for each agent (regions or countries in his analysis), even though the model is built on the assumption of a common coefficient for everyone. To obtain a single estimate of partial risk sharing, as implied by the model, Crucini then takes the average of the estimated risk-sharing parameters of all agents.<sup>4</sup> In contrast, our model does not suffer from this problem because our estimating equation is stated only in terms of the (quasi)innovations to the present value of income growth of all agents. Unlike conventional risk-sharing regressions, it does not include the cross-sectional average of the dependent variable among the regressors.<sup>5</sup> Another difference between our framework and that employed by earlier literature concerns the decision problem that agents solve after the cross-country transfers are made. While the traditional literature assumes that agents act as permanent-income consumers with quadratic preferences, we assume that agents maximize a utility function of the constant relative risk aversion (CRRA) form allowing for country-specific coefficients of risk aversion and interest rates, along the lines of Blundell et al. (2008). We follow Campbell and Mankiw (1989) and derive an estimating equation log-linearizing the Euler equation and intertemporal budget constraint of the representative household’s problem.

We estimate the model using a time-series cross-section dataset comprising 50 industrialized and developing countries over the period 1970-2010. We find an average coefficient of partial risk sharing of around 0.5. In terms of the model, this
_higher the reduction in aggregate TFP generated by the tax. If the inequalities in_ (12) _are not jointly verified, then the impact of the tax, λ, on aggregate TFP is ambiguous and depends on the magnitude of the tax, λ, relative to the energy prices, zf and zx, the output elasticities, βx and βf , and the correlation between the energy wedges, {τfi, τxi}, and {ai, τki, τyi} ._ Corollary 3 outlines a sufficient condition for a homogeneous carbon tax to negatively affect aggregate TFP in presence of distortions. Intuitively, when more productive firms face lower energy distortions, i.e. when _Corr_ ( _τji, ai_ ) _<_ 0, we can expect the tax to decrease aggregate TFP in equilibrium. This is because when _Corr_ ( _τji, ai_ ) _<_ 0, then firms that are more productive have cheaper access to energy markets, which implies that distortions tend to favour more productive firms over less productive ones. Introducing the tax would increase such distortions for more productive firms relative less productive firms in the economy. Vice versa, when _Corr_ ( _τji, ai_ ) _>_ 0, then we should expect that the effect of the tax on aggregate TFP is at least ambiguous, implying that the tax, _λ_ , has the potential to increase it. This is because the tax alleviates market distortions in favor of more productive firms in the economy. Lastly, the higher the output elasticities of energy, the stronger the positive/negative impact of the tax on aggregate TFP.

# **3 Construction of the Dataset**

The empirical exercises carried out in this paper combine firm-level data and an energy balance matrix to estimate emissions at the firm level in the Dominican Republic. The first source of firm-level information is the tax forms from the Direccion General de Impuestos Internos (DGII), which compiles tax declarations for the universe of formal firms required to file taxes in the Dominican Republic between 2007 and 2021. The
and Farah 2009), particularly language (Noble et al. 2007) and executive function (Farah et al. 2006). Our results show that receptive vocabulary and sustained attention exhibit the largest gradients after the inclusion of lagged outcomes, maternal endowments (education and vocabulary), and home environment. These outcomes also exhibit a steeper widening of the gradient with age, in contrast to the other outcomes.

Next, we assess how much of the wealth gap in child development outcomes could be accounted for by household inputs such as home stimulation. Home stimulation as measured at the time of school age is shown to account for 12-18% of the wealth gap in z-scores across the various domains with the largest findings for receptive vocabulary and memory of phrases, which is a verbal measure of working memory. The estimate represents a lower bound on the role of the home environment as it does not account for the role of lagged home inputs on developmental outcomes.

Finally, the mapping of different domains of child outcomes onto learning outcomes in math and early literacy at school age is documented. The magnitudes of the gap for school learning outcomes are found to map closely with those obtained using our contemporaneous developmental outcomes (sustained attention, receptive vocabulary, and cognitive composite).

The remainder of the paper documents the data and outcomes being studied (section 1), outlines the empirical methods (section 2), describes the results (section 3), and conclusions (section 4).

# II. DATA

This analysis draws from a nationally representative survey of nutrition and child development in Madagascar ( _Enquete Anthropometrique et de Developpement de l’Enfant_ ) that was first administered in 2004. A random subsample of 150 of the original 446 communities representative of all rural and urban areas, excluding the largest urban cities, was drawn for this longitudinal study. About 1,500 children who were ages zero to three years in 2004 were identified for a prospective cohort from these communities (Galasso and Umapathi 2007). Children’s development in multiple domains
# **References**

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phone, assuming two percent of household income spent on this service and five persons per household, implies an annual per capita income of $600.

27 India, Pakistan, and eighty percent of sub-Sahara African countries have GDP below $500.

28 For a summary of the argument in favor of exclusivity in rural electricity see Reiche et al. 2000. 29 Combining rural infrastructure projects of different sectors can reduce total government costs (e.g. demand surveys, road shows, supervision) and supply costs (especially operation and maintenance). Having several projects focus on the same geographical areas first may increase development impact.

30 The targets set in 1994 were 100 percent of households with electricity by 2005 and 98 percent of households with water supply by 2000. See Wellenius 2002, Jadresic 2000, and Serra 2000.

31 Since rural services are expected to benefit society at large, subsidies should preferably be funded from general tax revenue through national or regional government budget allocations over multiple years as needed to reach service targets. As second best, subsidies are sometimes financed by mandatory contributions from profitable provision of similar services in other parts of the country; this is common in telecommunications. Key considerations for designing a mechanism to collect, allocate, and disburse these funds are simplicity and transparency, stability during the implementation period, and cost-effectiveness. Typically the cost of administering subsidies does not exceed a few percent of the monies being handled. For a discussion of financing sources and mechanisms in the telecommunications sector see Wellenius 2000.

32 In Chile, one of the new rural telecommunications companies in the long run was unable to build up its business as expected and eventually negotiated with another company to take over its customers and service commitments. The experience with pilot telecenters, for which running costs are more important than investment and startup costs, has been less satisfactory.

27
for the imputation of energy poverty indicators and energy spending shares. The literature so far has mainly focused on income-based or education-based indicators.<sup>6</sup> Moreover, to the best of our knowledge, we are the first to explore statistical matching techniques in Bulgaria's case.

# **Moreover, we make an important contribution by investigating three different multiple imputation**

**approaches and comparing them to each other.** This assessment can shed light on the quality, validity, and sensitivity of data fusion methods. To facilitate statistical matching in the future, we recommend including auxiliary variables – variables that are highly correlated with energy expenditure – in both surveys. Elevating data on energy spending for a small subsample of the EU-SILC could also improve the quality of data fusion in the context of energy spending shares.

**The paper at hand is subject to some important limitations.** First, we show that significant differences in the distribution of the matching variables between surveys persist after the intent to harmonize them. Future research could address this shortcoming by analyzing how to better harmonize variables between both surveys. Second, similar to most papers in the area of statistical matching, our results rely on the assumption of conditional independence, which is untestable in the current setup. Third, future research could analyze if our results differ from an approach that combines EU-SILC 2020 (with income reference year 2019) with HBS 2020.

**The paper is organized as follows.** Section II describes the methodology and data. Section III summarizes the main results of the imputation and how energy and income poverty overlap. Section IV concludes.

6 Most of the work to date focuses on imputing income data collected as part of EU-SILC surveys into HBS. Leulescu et al. (2013) apply a similar research question to the EU-SILC and EU-LSF while Kaplan and McCarty (2013) use statistical matching techniques in the case of educational data from PISA and TALIS surveys. Similarly, Wiest et al. (2019
capital than the skill ratio because it reflects variations in the quality of employees across firms. So we use the sectoral average wage, computed as total remuneration divided by total employment (data sourced from the UNIDO ISIC database). We expect this variable to affect positively export performance.

(vi) In this first analysis we have not accounted for the effects of trade liberalization as the tariff reductions related to the GATT membership affected all the considered manufacturing sectors. We use a proxy for trade liberalization in our second analysis, which we will discuss in the next section.

39. In addition, to test the sensitivity of the model to the selection of different deflators when adjusting the nominal automotive exports series, two sources have been used, which do not alter the results: (a) each country‟s export price index (2000=100) and (b) the US wholesale price index (2000=100), both obtained from the WDI.

40. We formulate specification (2) below by expanding specification (1) and incorporating dummies for each sector, year, and the interaction between the two, together with the vector of other sector-specific and time changing control variables. The dummies aim to control for the effect of exogenous shocks that affect exports in all sectors equally ( year fixed effects), and also for those sector specific

> 19 Elbadawi, 1999; Collier, 2002; Redding and Venables, 2004; Mayer, 2003

15
Note: GDP adjusted by PPP constant 2017 international currency of most recent year survey. Monthly average per capita income adjusted by PPP constant 2017 international currency.

Source: Own elaboration and World Bank’s World Development Indicators.

# **5 Conclusions**

<mark>This study provides a comprehensive analysis of vulnerability to poverty in the Latin America and the Caribbean region. The assessment is based on household-level data from the</mark> SEDLA <mark>C database, along with various natural hazards data sources. The study simplifies the climate disaster risk concept by reducing dimensions and treating it as a type of covariate risk that can push households into poverty. The results show that t</mark> he incidence of <mark>vulnerability to poverty is notably higher in Guatemala and Honduras, where most of the households are poverty-induced, followed by Colombia and Peru. Moreover, when population adjustments are considered, we find some heterogeneities. Meanwhile, in Guatemala and Honduras, more than 750 and 690 people per 1,000 population are vulnerable, respectively (either by the expected mean – poverty-induced- or the expected variance – risk-induced); when the results focus on</mark> climate <mark>hazards risk (CRI), Argentina and Panama have a higher proportion of vulnerable household covariate risk-induced.</mark> <mark>Overall, the study estimates that approximately 184 million people are vulnerable to poverty. Of those, around 109.5 million are PI, 14.5 million are induced by CRI and 60 million are IRI. Among the sources of risk, idiosyncratic risk is consistently more prominent than climate risk across all countries. However, when the total climate</mark> ris <mark>k effects through variance are considered, 8 percent of people in the region that are not PI or IRI are vulnerable to poverty. In addition, bi-maps enable the identification of provinces with a higher incidence of vulnerability to poverty, along with the districts with high natural hazards. They provide
presence. Importantly, this restricts the imports of food to a country that imported approximately 90 percent of its food prior to the conflict (e.g., WFP 2012), and it restricts the ability of health providers to acquire vital equipment and medicines that are badly needed in a setting where communicable diseases have been raging out of control (e.g., OCHA 2018). Although it is difficult to precisely gauge the intensity of this disruption to supply chains, reports have suggested that immediately following the escalation of the conflict, imports in Al Hudaydah‐ the port that supplies nearly the entirety of the north‐ immediately reduced to 40 percent of pre‐conflict levels (e.g., WFP 2017).

Third, the government began paying public salaries infrequently, if at all, beginning in November 2016. Nearly 30 percent of households relied at least in part on government salaries, and in the capital, this figure was nearly one of every two households (e.g., World Bank 2017). Thus, many households lost income that was helping to support them at precisely the time supply chains were disrupted and prices of basic commodities and food items began to rise (e.g., WFP 2017). In addition to the decline in incomes, anecdotal evidence suggests that the lack of payment of employees has led to an exodus of public sector employees.<sup>12</sup> This in turn has significantly reduced the amount of public services provided to the Yemeni people and likely has hampered institutional capacity.

Each of these conflict‐related shocks has likely contributed to the dramatic worsening of humanitarian conditions in the country. However, little thorough analysis has investigated the relative role played by each of these major factors. This paper investigates the relationship between temporal and geographic variation in food security and variation in each of the potential drivers described above. Conflict data were obtained from the Armed Conflict Location & Event Data Project (ACLED), which provides information on the date,