rafmacalaba/data-use-mentions-extended huggingface dataset:
# <mark>4. Existing indicators measuring data accessibility in MENA</mark>

## 4.1 Existing indicators

The evidence in section 3 shows that the availability and accessibility of microdata in the MENA region is very constrained. Yet the existing indicators measuring data openness do not capture this reality. These indicators include the Open Data Inventory (ODIN)<sup>20</sup> published by Open Data Watch; Open Data Barometer (ODB)<sup>21</sup> by World Wide Web Foundation and Global Open Data Index (GODI)<sup>22</sup> by Open Knowledge Foundation<sup>23</sup> (see table 5 for a summary).<sup>24</sup> Of these indicators, the <mark>ODIN ranks highest with regards to country coverage.</mark> <mark>The ODIN covers 178 countries in its 2018/19 version including 17 MENA countries and 187 countries in its 2020/21 version including all 20 MENA countries. Additionally, ODIN also has a substantial proportion of its elements assessing data accessibility or openness - it assesses the coverage and openness of data available on National Statistics Offices (NSOs) websites based on ten elements across two dimensions - coverage and openness. Five of the ten elements measure data coverage i.e., the degree to which data is available and while the others measure access/openness i.e., the degree to which available data is accessible. Each of the five elements in the</mark> coverage dimension is assessed as follows: representative indicators are available and are disaggregated appropriately; data are available for the preceding five years; data are available for the preceding ten years; data are disaggregated at the first administrative level and data are disaggregated at the second administrative level. <mark>Each of the five elements assessed in the ODIN</mark> data accessibility/ openness dimension are assessed as follows: machine readability; nonproprietary; download options
inequality is a threat to political and economic stability. When people see some living in luxury while others are impoverished, it creates social unrest that typically culminates in the overthrow of government, disruption of the local economy and collapse of foreign investor confidence.

Negative associations between the ethnic diversity of societies and growth have been reported by authors such as Easterly and Levine (1995) and Mauro (1995). Collier and Gunning (1997) demonstrate that ethnic fractionalization negatively affects policy and reduces productivity more directly. They also show that the correlations are stronger with the delivery of public services such as schools and telephones, and weaker for macroeconomic policy. The wide use of the ethno-linguistic fragmentation indicator has created a mindset in which equates ethnic diversity with economic and political disaster, particularly in Africa. In an important study, Collier and Hoeffler (1998) establish that the relationship between civil war and ethnic fractionalization is not linear. The most unstable societies are those in which the index is around 45-50, societies with two fairly equal ethnic groups. On the other hand, highly fractionalized societies, such as are found in many countries in Africa (average score of 67.6), are as stable as the most homogenous societies.

In Latin America, by contrast, if one groups all the non-Spanish indigenous people together and all the Spanish speakers together, many Latin American countries then consist of two large groups with little interaction across groups, a situation rife with latent conflict. A recently completed poverty study in Peru found that the overall poverty levels had declined in Peru. However, the non-Spanish speaking population had not been integrated politically, socially or economically. In fact it had fallen further behind in income terms even after controlling for education, location, access to services, land and or house ownership (Hentschel et al, 1998).

To complement findings from cross-country data, and to understand the importance of cross-cutting ties in reducing exclusion even in multi-racial, multiethnic industrialized countries, it is useful to examine within-country data. Sustainable economic development requires
the imputation procedure is unlikely to create biases since producer prices are similar across space: the coefficient of variation of (non-missing) producer prices across districts averages 0.032, ranging from 0.009 to 0.058 across crops. For tea, we use producer price data (in LKR/MT) for 2019 from the Food and Agriculture Organization of the United Nations (FAO) for Sri Lanka as a whole, which we assume to hold in each individual tea-producing district.

The 2018-2021 and 2019-2022 editions of DCS’ Bulletin of Selected Retail and Producer Prices are used to compute crop-level nominal producer price increases between January 2021 and January 2022. By subtracting from each crop’s nominal price increase the aggregate inflation implied by the National Consumer Price Index during the same period, we obtain each crop’s real price increase. This information is used as an input in the calibration of the cross-origin elasticity of substitution of agricultural crops (see Section VI.A.1).

To estimate the fertilizer coefficients of our model’s Cobb-Douglas agricultural production functions (see Section VI.A.3), we use data on national crop prices in 2022 (in LKR/kg) from the Central Bank of Sri Lanka’s Economic and Social Statistics 2023 report (except for groundnuts, whose national price comes from the 2019-2022 edition of the Bulletin of Selected Retail and 101The Bulletins of Prices can be found at: http://www.statistics.gov.lk/InflationAndPrices/StaticalInformation/Bulletins#gsc.tab=0
> where _Aidijt_ and _Aidijt Sijt_ are household aid receipt and the aid receipt-shock interaction term; and all

> other variables are as defined above.  The second specification has three equations, _Aidijt_ , _Aidijt SijtCurr_

and _Aidijt SijtSeq_ , with the relevant substitutions for the three predicted terms. Note that using the predicted probabilities means that our instrumented equation is exactly identified.

# <u>Second Stage:</u>

_IV Cijt_ are our household-level outcomes of interest, as specified above, _AidijtIV_ and ( _Aidijt Sijt_ ) are instrumented aid receipt and aid receipt interacted with the shock variables and all other variables are identified above.

# **3. Data and Context**

Our analysis uses data from the 2013 and 2016 waves of the Integrated Household Panel Survey (IHPS), which is representative for all Malawi, and for urban/rural areas. The IHPS was implemented by the Malawi National Statistical Office (NSO), as part of the World Bank Living Standards Measurement Study -Integrated Surveys on Agriculture (LSMS-ISA) initiative. The data include 1,907 households that were interviewed in 2013, and 2,507 households that were interviewed in 2016 and that could be traced back to the 1,907 households in 2013. For 2015, we have data on 558 households that were interviewed as part of the Flood Impact Assessment Survey (FIAS), that were mainly located in districts in the Southern Region, plus two districts in Central Region that also experienced flooding. Of the 558 households that 11
- Guo Xu, Marianne Bertrand, and Robin Burgess. Social proximity and bureaucrat performance: Evidence from india. Working Paper 25389, National Bureau of Economic Research, 2018.

- Henrik Jacobsen Kleven, Martin B Knudsen, Claus Thustrup Kreiner, Søren Pedersen, and Emmanuel Saez. Unwilling or unable to cheat? evidence from a tax audit experiment in denmark. _Econometrica_ , 79(3):651–692, 2011.

- Dina Pomeranz and José Vila-Belda. Taking state-capacity research to the field: Insights from collaborations with tax authorities. _Annual Review of Economics_ , 11:755–781, 2019.

- Joel Slemrod. Tax compliance and enforcement. _Journal of Economic Literature_ , 57(4):904–54, 2019.

- Jagdish Bhagwati. On the underinvoicing of imports. _Bulletin of the Oxford University Institute of Economics & Statistics_ , 27(4):389–397, 1964.

- Raymond Fisman and Shang-Jin Wei. Tax rates and tax evasion: evidence from “missing imports” in china. _Journal of Political Economy_ , 112(2):471–496, 2004.

- Dean Yang. Can enforcement backfire? crime displacement in the context of customs reform in the philippines. _The Review of Economics and Statistics_ , 90(1):1–14, 2008a.

- Dean Yang. Integrity for hire: an analysis of a widespread customs reform. _The Journal of Law and Economics_ , 51 (1):25–57, 2008b.

- Pushan Dutt and Daniel Traca. Corruption and bilateral trade flows: extortion or evasion? _The Review of Economics and Statistics_ , 92(4):843–860, 2010.

- Sandra Sequeira and Simeon Djankov. Corruption and firm behavior: Evidence from african ports. _Journal of International Economics_ , 94(2):277–294, 2014.

- Sandra Sequeira. Corruption, trade costs, and gains from tariff liberalization: Evidence from southern africa. _American Economic Review_ , 106(10):3029–63, 2016
education or health system (World Bank 2003) and, it appears, also a direct cause of poorer outcomes (Miller, Murnane and Willett 2007; Duflo, Hanna and Ryan 2008). But careful study of the causes and effects of absence depends on accurate indicators of a system’s performance on this indicator—that is, the actual absence rate. Only in recent years have surveys focused on carefully gathering reliable absence data through direct verification of attendance during surprise visits to schools and clinics, rather than relying on questionable administrative records of attendance.<sup>9</sup> Academic researchers and World Bank staff developed and pioneered this methodology in a series of studies in the health and education sectors over the past decade, proving the feasibility and usefulness of this indicator. For example, a 2004 World Bank study based on visits to a representative national sample of health centers in Bangladesh showed how important direct measurement of attendance is, when it found that 74 percent of doctors posted to the most remote rural clinics were absent at any given time (Chaudhury and Hammer 2004). Another study measured absence in representative samples of primary schools and primary health care clinics in six countries, finding absence rates that averaged 19 percent among teachers and 35 percent among medical workers, with far higher rates in some Indian states (Kremer et al. 2005; Chaudhury et al. 2006; Muralidharan et al. 2011). Subsequent studies by Bank operational units applied this methodology to yield reliable provider-absence estimates and inform the policy dialogue in Ecuador (Rogers et al. 2004), Mongolia (World Bank 2006), and Lao PDR

> 9 Administrative records can be easily manipulated and therefore do not serve as a reliable source for absence data.

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variations in educational and welfare policies and regulations could help (partially) disentangle the contributions of different correlates of social mobility.

This paper analyzes the link between changes in intergenerational mobility and long-term development. The paper combines data on intergenerational educational mobility from three rounds of the Life in Transition Survey (2010, 2016, and 2022–23) (EBRD 2024), which covers more than 30 countries in Europe and Central Asia, and data from the Global Database on Intergenerational Mobility (GDIM, <u>Van der Weide and others 2023). Data on mobility is</u> complemented by data on present and past economic outcomes to form a panel of 68 countries over the period 2000-2020. This paper contributes to the literature on intergenerational mobility and development in two ways. Our primary contribution is that, unlike existing studies that focus on subnational districts or specific regions, we carry out a worldwide cross-country analysis of the relationship between intergenerational mobility and country income. This allows us to provide evidence of the nature of this relationship across different development contexts. The second contribution of our paper is to introduce a new measure of intergenerational mobility in education—the mobility gap—and compare its performance with several existing mobility measures. This paper presents the first empirical application of this new measure.

The patterns of intergenerational mobility across countries and generations vary significantly depending on the mobility measure used. In Europe and Central Asia, upward absolute mobility has been declining across birth cohorts, while relative mobility has not changed significantly, suggesting that an absolute reduction in intergenerational mobility in education may be common across the entire education distribution. South Asia has seen increases in absolute and relative mobility, while Latin America has experienced little change in absolute mobility but increases in relative mobility.

The empirical analysis of the association between intergenerational mobility and country income levels shows a significant context-specific relationship. Indicators of relative mobility in Europe and Central Asia are not correlated with country income levels over time. Only a specific dimension of absolute mobility—upward mobility in higher education (
from Ethiopia, Malawi, Niger, Nigeria, and Uganda (see also Section 4) combined with high-resolution precipitation data to study the effects of cumulative climate shocks on long-term migratory flows in SubSaharan Africa. Overall they find evidence of a persistent impact of droughts on rural households in these countries, which translates into a much larger effect on migration compared to the period in the aftermath of the shock as the impacts accumulate over time. The authors also detect the existence of a relationship between rainfall shortage and accelerating urbanization trends in four of the five countries considered in their analysis. At the country level, their findings contrast with previous studies that use similar multicountry micro-level data sets to examine the effect of climate shocks on rural out-migration, which found no significant or consistent migration-inducing effect of droughts in the short- and long-run. Conversely, the authors notice that their results are in line with those of macro-level cross-country studies that corroborate the contribution of rainfall deficits to faster urban development.

Overall, this review confirms that the relationship between migration and climate change in Sub-Saharan Africa is far from univocal, with some studies considering migration as a direct consequence of weather shocks and climatic changes, and others that do not find that these factors exert a clear or significant impact on people’s mobility. The majority of the SSA literature focuses on slow-onset rainfall and temperature events, whereas only a few studies specify the type of migration. As an aside, we notice that there is unequal country coverage in this literature, with repercussions in terms of external validity. For instance, the Sahel region is particularly underrepresented, despite being one of the areas identified among the hotspots of climate change (IPCC, 2014).

# **3. Open empirical issues and data gaps**

## _3.1 Open empirical issues_

From the conceptual discussion above, a number of key open empirical issues stem:

- How to disentangle, from an empirical point of view, short-run elasticities of weather shocks on migration from the compounding effects of slow-onset, long-run, eventually
95|63|10|26|7|
|_3_|84|100|72|9|17|5|
|_4_|90|100|77|8|16|2|
|_Richest_|89|100|77|11|13|2|
|_All_|85|96|71|9|19|5|
|**Rural**|||||||
|_Poorest_|47|95|29|3|57|39|
|_2_|49|99|29|4|51|43|
|_3_|56|100|37|4|51|39|
|_4_|59|100|42|4|51|32|
|_Richest_|65|100|50|2|46|24|
|_All_|56|99|39|3|51|35|

_Source:_ Author’s calculations based on 2008 ILCS

# **_How much do households spend on energy consumption?_**

On average, Armenian households spend about 8 percent of their budget on energy, with slightly more than half of it on gas. According to the ILCS, which provides self-reported data on energy consumption expenditures, about 60 percent of the energy consumption is for heating purposes (Table 6). The poorest quintile spend 7 and 6.5 percent of their budget on energy overall and 11
2|33.2|62.5|47.5|37.3|1.9|
|Capital|7.8|5.0|5.9|-5.4|-0.7|-4.2|4.3|3.9|-0.7|3.8|-0.2|0.0|2.1|2.8|0.6|21.0|
|Pension||||14.9|17.9|15.8|4.6|23.6|6.2|3.2|10.4|-3.0|0.6||25.1|-2.8|
|Transfers|15.7|10.0|23.0|7.3|9.1|41.8|15.6|22.4|13.6|3.4|16.4|-1.0|10.0|33.1|37.2|8.7|
|Other nonlabor income|-15.9|||9.3|4.8|-32.5|9.4|-16.1|3.8|6.0|0.5|-0.3|0.7|0.3|10.6|11.0|
|Total change|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|Source: Author’s calculations wi<br>Consumption-based measures of<br>Colombia, Costa Rica, Ecuador, H|th data from<
# **B Data Appendix**

## **B.1 The Correlates of War Diplomatic Exchange Data Set**

The Diplomatic Exchange data set captures diplomatic representation at the level of charg ́e d’affaires, minister, and ambassador between states from 1817-2005. The data set covers all members of the Correlates of War (COW) interstate system. The data is hosted on the COW website. For the post-World War II era, the data is collected from various data sources including Europa World Year Book series, ministries of foreign affairs of various countries, Statesman’s Year Book, and the _Code Diplomatique_ .

In this paper, we rely on the most recent version of the data—the 2006 version (Bayer, 2006). The 2006 version includes information for the following years: 1817, 1824, 1827, 1832, 1836, 1840, every five years between 1844 and 1914, every five years between 1920 and 1940, and every five years between 1950 and 2005. The data is therefore available every five years for the period we cover in our analysis (from 1960 onward). The dyadic data describes the level of diplomatic representation and diplomatic exchange between members in the COW system.

For each pair of countries (country 1, country 2) in the COW interstate system, the diplomatic exchange data set provides information on the following 3 variables: the diplomatic representation of country 1 in country 2, the diplomatic representation of country 2 in country 1, and if any diplomatic representation between country 1 and country 2 exists.

The dyadic data captures the level of diplomatic representation between countries. The diplomatic exchange variables that describe the level of diplomatic representation for a given pair of countries is coded as 0 if there is no evidence of diplomatic exchange, 1 if diplomatic representation is at the level of charg ́e d’affaires, 2 if diplomatic representation is at the level of minister, 3 if diplomatic representation is at the level of ambassador, and 9 for all other levels of diplomatic
6</sup> With specific

> 5 The average share of assets held by government-owned banks in the 8 Eastern European countries we focus on fell from 71 percent in 1995 to 10 percent in 2010, while among the 6 Latin American countries, average government bank ownership dropped from 41 percent to 19 percent.

> 6 For example, a study of 1,565 banks from twenty emerging markets from 1989 to 2001 finds weak evidence that foreign banks’ credit levels were less sensitive to monetary conditions in the host country, while their lending and deposit rates were less volatile than those of domestic banks during crises (Arena, Reinhart, and Vazquez, 2010).

3
is that there are no better detailed comprehensive choices available.<sup>8</sup> The Bureau of Labor Statistics (BLS) reports data on import and export price indexes for the US, but these indexes are available only at a fairly aggregate level: 129 commodities overall, and only 72 commodities at 3-digit SITC (Rev 3). Furthermore, the BLS indexes are reported using the third revision of the SITC, while the trade data described above use the second revision. Imputing BLS prices to trade data requires a concordance between these two classications. Since the concordance is not one to one (a 4-digit sector from SITC Rev 2 may belong to more than one 3-digit sector in Rev 3), the number of well mapped sectors would be even smaller. Working at this level of aggregation would miss the point of the paper that exposure to exchange rate risk varies across sectors. Nevertheless, to ease concerns and provide some further validation to the use of global unit values as proxies for prices, I computed the global unit values for 70 commodities that have BLS price data and where there was a clear map to SITC Rev 2 commodities and checked the within-commodity across-time correlation between the two price series by running a xed eect regression between the growth rates of prices from the two sources. Although the time span is short because BLS data are available only since 1993, the regression shows a positive and highly signicant relation between the two series. The results are summarized in the two plots reported in Figure 1 that show the partial regression between the two price growth series for the whole sample, and dropping the extremes. Each scatterplot also reports the estimated coecient in each subsample and shows that both relations are highly signicant. This evidence suggests that, despite their shortcomings, uctuations in global unit values are good proxies for uctuations in international prices.

The BLS data also shed some light on the validity of the assumption that the correlation between nominal exchange rate growth and the growth of the sectoral prices
. Although our partner bank covers a broad, socially and geographically diverse customer base, its clients are naturally not a fully representative sample of the population. They are on average younger, more educated, more likely to reside in urban areas, and wealthier (for more details, see Section 5). Credit card usage in this setting is high and in fact comparable to high-income economies. Using the bank’s administrative data, we estimate that monthly credit card spending in our data accounts, on average, for 32% of consumers’ estimated monthly income. In comparison, Ganong and Noel (2019) use data from the JP Morgan Chase Institute and find that average credit and debit card spending accounted for 51% of monthly income in the United States.<sup>7</sup>

> 5. The 2019 data cover the period when our experiment was conducted and are similar to other years.

> 6. _The Star_ , whose archive is available at www.thestar.com.my.

> 7. Based on results from Table 1 of Ganong and Noel (2019), who use a sample of credit and debit card customers in the three months prior to becoming unemployed.

9