rafmacalaba/data-use-mentions-extended huggingface dataset:
significant under the high elasticity assumption than under the low elasticity assumption. This result held for both measures of tariff reductions.

# II. The General Equilibrium Model of the Domestic Economy

In this section we present a simple general equilibrium model to use with the aggregated tariff indices calculated in the previous section, and use this model to calculate the impact of the tariff changes on factor prices, consumption levels, and welfare in Chile. The objective in formulating this general equilibrium model is to make the model sufficiently rich that it is capable of capturing the effects of tariff changes on consumption decisions and resource allocation between production sectors, while keeping the model simple enough that it can be calculated on an EXCEL spreadsheet. The specification of the model presented here is based on the data used for the case of Chile, but the formulation can easily be adjusted to allow greater or lesser detail as dictated by the availability of data. The data for domestic production was obtained from the input/output table for Chile for 1986, which contained data on the value of output, labor input, domestically produced intermediate inputs, and imported intermediate inputs for domestic production sectors. Data on domestic consumption by sector was also available, with consumption broken down between domestic and imported goods. This data was used to parameterize a specific factors general equilibrium model of the production structure.

# A. The Model

In this section we present a general equilibrium production model for the domestic economy. We will divide the domestic production sector into two types of goods: traded and non-traded goods. Let IT denote the total number of traded goods sectors, and IN the number of non-traded goods sectors. For non-traded goods, all output is sold in the domestic market and the price of output will be endogenously determined by equating local production to domestic demand. For traded goods, 13
<mark>32. B</mark> .D. Wright. Solving measurement problems with the Rasch Model. (Mesa Psychometric Laboratory, College of Education, University of Chicago, 1977).

<mark>33. Food and Agriculture Organization of the United Nations. FIES Basics – The Food Insecurity Experience Scale: Measuring food insecurity through people’s experiences.</mark> http://www.fao.org/3/a-i7835e.pdf <mark>34. Food and Agriculture Organization of the United Nations. Using the FIES App: A simple tool for the analysis of Food Insecurity Experience Scale data.</mark> <u>http://www.fao.org/3/ca9318en/ca9318en.pdf.</u> <mark>35. J</mark> -C. Deville and Y. Tillé. Efficient balanced sampling: The cube method. _Biometrika_ **91** : (4), 893-912 (2004).

<mark>36. J.D. Angrist and J-S Pischke.</mark> _<mark>Mostly Harmless Econometrics</mark>_ <mark>. (Princeton University Press, Princeton, 2009).</mark> 22
Supporters of large investment argue that, as most of the land in question is located in the lowlands, capital intensive investment is the only way to bring it to productive use and generate spillovers for smallholders. Critics point to cases of land transfers without proper verification of current occupancy or utilization (Rahmato 2011) and argue that such transfers failed to improve local livelihoods (Rahmato 2014). Yet, with quantitative estimates diverging widely,<sup>2</sup> a proper assessment is difficult.

In fact, three studies aim to use quantitative evidence for a more representative and rigorous analysis of this issue in the Ethiopian context. A review of a sample of more than 10,600 investment licenses issued by the Agricultural Investment Agency finds that less than 20% of license holders established a farm (UNDP 2013). Moreover, most lack farming experience, a business plan, or regular record maintenance, pointing towards ample scope for improvement. Based on an effort to establish an inventory of and conduct field visits to a sample of farms with more than 1,000 ha established after 2005, Keeley _et al._ (2014) draw four main conclusions, namely (i) leases cover very large areas of which only parts have been developed; (ii) there are incidences of conflict with existing occupants; (iii) the potential for job generation has not been realized; and (iv) to be effective, government efforts to make lease agreements public, while commendable as a first step, need to be followed by further efforts to improve transparency.

Ali _et al._ (2015) use the census of large farms that is annually conducted by the central statistical agency (CSA) to quantify what had been described qualitatively earlier. Doing so suggests that since the 1990s, about 1.3 mn. ha had been transferred to a total of 6,612 commercial farms,<sup>3</sup> most of which cultivated more than 50 ha. The annual rate of new farm establishment
2005; Herrmann et al., 2008; Gachter et al., 2012) have highlighted that a significant proportion of the variation in individuals‟ willingness to cooperate occurs at the society level.<sup>9</sup> We investigate whether, both at the individual- and the community-level, willingness to cooperate with others for the common good is associated with willingness to participate in both the short- and the long-route to accountability. We conduct our study in Albania, a country that slowly transitioned from

> 4 In practice, school-based management (SBM) programs may or may not give decisional power to the parents. Some programs give parents decisional power over the school budget only; others involve parents also in the choice of the curriculum and textbooks; others give parents responsibilities in the hiring and firing of teachers; others invite parents to participate in the development of a school improvement plan, and possibly in fund-raising, and so on and so forth. School-based management programs may also exclude parents from participating in the decision process.  See Wohlstetter and Odden (1992) for a categorization of school-based management models, and Barrera-Osorio et al. (2009) for a comprehensive review of schoolbased management reforms recently undertaken all over the world.

> 5 For mixed evidence not relating to the education sector, see Olken (2007) and Bjorkman and Svensson (2008).

> 6 Investigations based on observational rather than experimental data – also called “retrospective studies” – conducted in the US (Houtenville and Conway, 2007) in Argentina (Eskeland and Filmer, 2007), in El Salvador (Jimenez and Sawada, 1999), in Mexico (Gertler et al., 2008; and Skoufias and Shapiro, 2006), among others, all show a significant impact of participatory programs and parental involvement on education outcomes.

> 7 This is an important issue that has received little attention in the existing empirical literature. As stated by Platteau (2008), in order for participation to be effective, “community members must be
of Iran is exemplary in providing data access. All recent, available microdata sets are downloadable from the website, some like the household budget surveys at an annual frequency.</mark> Suggested practice 2: Provide a microdata catalog, data tab and a search button on website landing page Given the multiplicity of information that is typically available on an NSO’s website, ensuring a good routing through the website is critical. For primary microdata users, a data tab and/or microdata catalog that present all data available on the website is a useful tool. This will make microdata on the website easy to find and download. Egypt for instance has a tab “MetaData” on its landing page that leads visitors to a central data catalog. This is very helpful for website visitors interested in the country’s data. Some countries go even further. UAE’s open data portal allows users to search for data by the organization within UAE that owns the data. More generally, a search button is important to facilitate finding relevant information on the website and ultimately ensuring a favorable user experience. To date, not all MENA NSOs include a data tab, search button and/or microdata catalog on the landing page of their website. <mark>There is a</mark> 14
>developing<br>regions|
|Price of monthly mobile basket|US dollars|15.5|11.1|11.1|9.9|
|Price of monthly fixed-line basket|US dollars|21.6|13.6|13.6|n.a.|
|Price of 20-hour Internet package|US dollars|27.7|68.0|47.28|11.0|
|Price of a call to the United States per minute|US dollars|1.0|0.86|0.48|0.66|
|Price of an inter-Africa call per minute|US dollars|0.3|1.34|0.57|n.a.|

_Source_ : Senegal 2005 data together with benchmarks are taken from the AICD database.

_Note_ : Numbers reported in the table as of 2007. GSM = global system for mobile communications; Mbps = megabits per second. n.a. = Not applicable.

Growth in telecommunications has resulted in a countrywide expansion of these services. Currently, 82.7 percent of the population has access to a GSM signal, and it is estimated that up to 97 percent of the population could gain access to it on a commercial basis without the need for any government subsidy (figure 18).

In the mobile market, existing subscriptions went from 0.9 subscribers per 100 people in 2000 to 44.1 in 2007 (table 15). This translated into an average growth of 44 percent per year (table 16), a level comparable to its neighbors. In 2008 Senegal’s mobile service penetration, excluding that of Nigeria, was the highest in West Africa; GSM coverage was almost double that of other low-income countries (table 15), though below the levels of middle-income countries.

12 SONATEL began to be privatized in 2006, when Senegal’s government sold a 51 percent stake to Maroc Telecom. As of 2009, the government was selling a
# **3.3 Estimation Methods**

It is widely known that informal firms are small, more likely present in certain services sectors with a higher level of participation of women in these sectors, among others. Therefore, we correlate the adverse effect of the pandemic (or the adjustments response of firms) with informality by itself once we control for these firm-level characteristics. We estimate the model shown in equation 2: The main controls included in **_Xit_** are indicator variables to identify microenterprises (less than nine employees), age, sectors, and a female-leadership. We also control for country and time-fixed effects, the severity of the lockdown (using the Google mobility indicator), and a dummy variable for whether the firm is a panel firm. The latter controls for any recall bias in retrospective variables and differences in new firms’ characteristics. Our main outcome variable is the change in sales since the start of the pandemic which measures the size of the shock.<sup>10</sup> We implement a Shorrocks-Shapley decomposition for the change in sales to analyze the relative impact of informality against other firm-level characteristics.<sup>11</sup> To understand the heterogeneity in the impact of the shock and the adjustment responses among informal firms, we estimate the model in equation 3: _Zit_ is each of the different groups of variables included in vector **_Xit_** taken separately.

Finally, we estimate split regressions, to allow for different coefficients for different types of firms (necessity, De Soto, parasite, and fully formal).<sup>12</sup>

> 10In addition to the change in sales, we also consider other outcome variables such as the firm’s adjustment response to the pandemic through technology adoption and innovation in products and services.

> 11Such decomposition has been widely used to understand inequality and poverty measures (See Shorrocks and Shorrocks, 1999; Shorrocks, 2013; Sastre and Trannoy, 2002).

> 12To analyze the statistical significance
reforms**

In 2010, Cote d'Ivoire ranked 149 out of 177 in the UNDP "Human Development Index" (UNDP, 2010).<sup>7</sup> This followed a period of political instability and civil war (2002 to 2003). Yet, Cote d'Ivoire remains one of the largest economies in the region and accounts for close to 40% of the

> 7 This section draws on Trebesch (2008), Reuda-Sabater and Stone (1992), and Library of Congress Country Studies (1988).

6
# **3.3.3 Sample representativeness**

Given that our estimations are data demanding, we nevertheless dropped a large number of observations. To ensure that we do not draw conclusions based on smaller samples from some countries, we re-estimate our models excluding from the sample countries having fewer than 50 observations.<sup>28</sup> Estimates remain qualitatively similar although they are statistically weaker in some cases (Tables A.12 and A.13 in the Appendix).

**3.3.4 Threshold for classifying traditional countries** Our classification of countries in more or less traditional categories is based on the median value of the WVS social perception measure. We re-estimate our models using a different threshold based on terciles of the social and cultural norms measure. We define more traditional countries as those that have a social perception measure above the 66th percentile of the cross-country social perceptions measure distribution. Our results are fairly robust to this alternative threshold (Tables A.14 and A.15 in the Appendix).<sup>29</sup>

**3.3.5 Alternative measures of gender and social norms** Given that social norms are an important driver of female economic outcomes and gender inequalities (Bussolo et al., 2024), we use the female labor force participation (FLFP) rate in 2007 to classify countries on their traditionality. As social norms constrain women’s economic choices (Jayachandran, 2021), our use of the female labor force participation measure represents an outcome-based proxy for social norms, in contrast to the value-based indicator from WVS. An advantage of using FLFP over WVS is that it is consistently available for all the countries in our sample from the World Development Indicators and encapsulates many aspects of social norms. As expected, the measure of gender norms from WVS and FLFP are highly correlated (see Appendix Figure A.2). It is therefore not surprising that repeating our estimations yields results that are broadly consistent with using the WVS to
instance, Esquivel (1999) shows that, while the pace of convergence across states was relatively fast over 1940–60, it halted and started to reverse over the next 35 years. This divergence was confirmed by subsequent studies focused on 1985–2000 (Chiquiar 2005; Garc ́ıa-Verd ́u 2005; Rodr ́ıguez-Oreggia 2007; Rodr ́ıguez-Pose and S ́anchez-Reaza 2005). In general, regional divergence during these years was linked to trade liberalization and the entry into force of the North American Free Trade Agreement, which bolstered the emergence of club convergence in the states that had benefited the most from these reforms given their initial endowment of relatively high-skilled labor and better public infrastructure.

Empirical evidence on convergence at a higher level of geographical disaggregation, namely, municipalities, has been scarce in Mexico. This is primarily because of the lack of a sample with robust income information and statistical power at that level. A couple of studies have reported dramatic disparities among municipalities in income and poverty in 2000 (L ́opez-Calva et al. 2008; Sz ́ekely et al. 2007) by applying small area estimation techniques to impute incomes from the main household income survey to the population census. Using this technique and logistic regressions, Mexico’s National Council for the Evaluation of Social Development Policy (CONEVAL) has computed rates of and changes in income poverty between 2000 and 2005 and multidimensional poverty between 2010 and 2015 across municipalities. This paper provides the first long-run assessment of regional disparities and paths in income, poverty, and inequality, based on comparable data on municipalities.<sup>2</sup>

# **3 Mapping income, poverty, and inequality in municipalities**

Capturing long-run trends in income, poverty, and inequality among municipalities requires a dataset of intertemporally comparable indicators of well-being that are statistically representative of the population in each municipality. The availability of such a dataset, however, may entail a trade-off between relatively high precision in the measurement of, say, household income and significant geographical detail
- 4 Nowcasting monetary poverty in Syria: Challenges and sensitivity analysis

# 4.1 Data challenges

As discussed in Section 2.1, national accounts – the comprehensive economic statistics that measure economic activity in a country – become an input for poverty projections for years in which no survey has been conducted. The typical (distribution neutral) approach applies per capita growth rates of GDP or private consumption (household final consumption expenditure in WDI) to the baseline mean of per capita expenditure from a household survey to extrapolate poverty beyond the survey period. Besides concerns pertaining the validity of the assumption that each household’s consumption expands/contracts at the same rate of the overall economy, the reliability of NA statistics can pose further challenges. In the case of Syria, _the base year for NA estimation is 2000_ . Hence, NA statistics published by the national statistical agency available up to 2021 assume that the structure of the economy has not changed since 2000, an assumption clearly problematic given the substantial economic changes associated to economic liberalization between 2000 and 2010 and conflict thereafter.<sup>21</sup> In what follows, we discuss possible options for the use of NA statistics in poverty projections taking into account the challenges posed by data quality and data reliability issues in the context of Syria.

## 4.1.1 Measuring “growth”

Should poverty projections be based on GDP or private consumption data from national accounts? Private consumption is generally preferred as it captures a set of goods and services that more closely mirrors consumption from household surveys. In practice however, considerations such as the availability and quality of GDP and private consumption data, as well as the strength of correlations between data from national accounts and household survey data typically influence the choice.

Prior to conflict, for the years in which both NA and survey data are available, the average ratio of average per capita consumption from household survey to average private consumption per capita from NA was 0.93, whereas the corresponding ratio using GDP per capita was 0
|0.002|0.001|0.679***|0.682***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.388***|10.316***|4.808***|4.784***|
||(0.125)|(0.128)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Authors’ calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income.
which have 5 years or more of existence (at least of legal existence)<sup>49</sup> . However, because this paper is primarily concerned with electricity outcomes, we will concentrate on three of the most recent and relevant papers – Fink, Mattoo and Rathindran (2003), Wallsten (2002) and Gutierrez (2003). In addition, we will concentrate on the parts of those papers concerned with estimating the effects of regulation on (a) mainline penetration rates (a measure of capacity growth) and (b) efficiency (eg mainlines per employee).

# **5.3.1 Fink, Mattoo and Rathindran (2003)**

This paper benefits from being able to use the ITU-World Bank Database on telecommunications policy. This has been supplemented by data from the Stanford – World Bank database (for Latin America) and data from the World Bank African Telecommunications Research Project. _This is a much more comprehensive data set than has so far been collected for electricity_ . In particular, the data allow for the <u>dating</u> of policy changes and hence the use of time-based dummies for different policy changes and the exploration of optimal sequencing.

> 49 See Wallsten (2002), Table 1

38
Table 6: Road Upgrading Costs: Road Costs Knowledge System (ROCKS) - 2018 Update

|Work Type|N|Length (km)|Weight|Cost (M$’15)|Cost/km|
|---|---|---|---|---|---|
|_Continental Africa_||||||
|Upgrading|28|103.5|2898|49.73|0.513|
|Reconstruction|27|33.0|891|20.20|0.116|
|Asphalt Mix Resurfacing|14|133.8|1874|16.93|0.143|
|Strengthening|11|110.0|1210|42.97|0.316|
|Weighted Average||104.0||35.77|0.326|
|_All Low and Lower-Middle _|_Inco_|_me Countries_||||
|Upgrading|68|61.8|4199|20.63|0.320|
|Reconstruction|53|73.0|3869|20.20|0.162|
|Asphalt Mix Resurfacing|35|143.9|5036|15.50|0.090|
|Strengthening|26|92.0|2392|20.89|0.316|
|Weighted Average||95.9||18.90|0.205|

_Notes:_ Statistics are aggregated across projects using the median. The ’Weight’ column is the number of projects (N) times the median length (Length). This is used to compute a weighted average across different work types. Costs are in millions of 2015 USD. The raw costs in current USD were deflated at their project end date using the US GDP deflator with base year 2015.

These estimates are consistent with African Development Bank (2014), who report an average cost of 180,300 USD’06
on measurement error in economic data. Most research in this stream has focused on mismeasurement in the context of agricultural production (Carletto et al., 2017; Abay et al., 2019; Kosmowski et al., 2019; Lobell et al., 2020; Abay, 2020; Abay et al., 2021; Gollin and Udry, 2021; Kilic et al., 2021) but a related literature focuses on mismeasurement in everything from time preferences (Anderson et al., 2008) to contingent valuation (Flachaire and Hollard, 2006). Unlike most of the work on mismeasurement in agricultural production, we lack data on the objective fact that may be measured. As such, we develop new methods to assess the degree of measurement error relying on the richness of the LSMS-ISA data and a combination of a number of different remote sensing data sets. By quantifying the magnitude and significance of measurement error, we can better understand the potential effects of mismeasurement in the remote sensing products commonly relied upon by economists.

Our results also have implications for the large and varied stream of literature that relies on remote sensing weather data for causal identification. This includes seminal and recent work on human capital formation (Maccini and Yang, 2009; Shah and Steinberg, 2017), labor markets (Jayachandran, 2006; Chen et al., 2017; Kaur, 2019; Morten, 2019), conflict and institutions (Br ̈uckner and Ciccone, 2011; Sarsons, 2015; K ̈onig et al., 2017), agricultural production and economic growth (Miguel et al., 2004; Deschˆene and Greenstone, 2007; Barrios et al., 2010; Dell et al., 2012), intrahousehold bargaining (Corno et al., 2020), technology adoption (Suri, 2011; Jagnani et al., 2021; Arag ́on et al., 2021). Our finding suggests that economists need to be much more careful about the remote sensing data source and the metric they use to measure