rafmacalaba/data-use-mentions-extended huggingface dataset:
# Figure 10.  Country correlations for commodity exporters and political risk

<!-- Start of picture text --> Low Political Risk High Political Risk<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Low Political Risk = 0.28***<br>-0.2<br>High Political Risk = 0.49***<br>-0.4<br>-0.6<br>-0.8<br>AzerbaijanYemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep.NicaraguaEcuador UruguayGuyanaLiberia Argentina<br>Brazil Namibia NigerBolivia KenyaZimbabwe AngolaQatar<br>Sudan SenegalMali Burkina Faso<br>Ghana Saudi Arabia MozambiqueThe Gambia Colombia Tanzania LibyaGabon Iceland EthiopiaMongolia<br>AlgeriaMyanmarSouth Africa Paraguay<br>Iran, Rep. of Madagascar<br>UgandaCameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>NigeriaPeru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>Australia Guatemala<br>Brunei Darussalam United Arab Emirates<br>government spending and real GDP 1984-2020<br>Correlation between cyclical components of real<br>Canada<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) political risk, defined as political risk above (below) the sample median. The difference between the two averages is significant at the ten percent level. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations
the LFS- report to own non-residential agricultural property.<sup>22</sup> While this could explain part of the above findings,<sup>23</sup> it raises concerns about a large number of agricultural properties having been omitted. If correct, this may compromise the ability of Prindex data to provide information on any issues involving agricultural land, including reporting of statistics on SDG indicator 5.a.1.

A second concern emerging from Zambian data is that, rather than obtain property information from the most knowledgeable household member and then possibly cross-check responses against information supplied by other members with legitimate interests, Prindex obtains information on tenure, the type of formal documents, and other land characteristics from an adult member who is randomly selected.<sup>24</sup> In the case of Zambia, more than half (53%) of information on formal land documents comes from owners’ uncles, aunts, sons, daughters, brothers, sisters, cousins, grandchildren, grandparents, nieces, nephews, fathers, mothers or in-laws. As reliable information on the type of formal documents is difficult to obtain without physical inspection of relevant documents even from owners, this may introduce measurement error.<sup>25</sup> As respondents who are not owners also provide the majority of responses on perceived tenure security, any analysis of such data would need to be preceded by a discussion of the extent to which responses to the same question by owners and non-owners are comparable or if, as tenure security perceived by non-owners will be affected by factors entirely different from those relevant for owners, they need to be analyzed separately. At the same time, co-owners are not asked to provide information on owners’ gender and in cases where a document exists no information is requested on gender of individuals listed on documents. This limits the scope for gender analysis, e.g. by checking the extent to which documented and perceived rights match.

# **5. Conclusion and policy implications
the GDP per capita data allows us to put Latin American labor market integration into context. The results suggest that Latin American labor markets are generally more integrated than other regions of the world and more integrated than East Asia and Pacific nations in particular. Labor market integration within Latin America, however, is far from complete. Using comparable data from Mexico and the United States, we find (not surprisingly) that labor markets across countries are less integrated than labor markets within countries. Overall, these results suggest that market integration within Latin America is far from complete.

19 | P a g e
1|0.9|1.5|2.1|0.9|1.0|1.6|
|- Health|0.1|0.1|0.1|0.3|0.2|0.2|0.2|0.5|0.4|0.1|0.3|
|- Education|0.2|0.1|0.3|0.1|0.1|0.1|0.2|0.2|0.2|0.2|0.3|
|- Durable goods|0.1|0.0|0.1|0.4|0.1|0.1|0.2|0.9|0.5|0.1|0.3|
|- Housing|0.5|0.4|0.9|1.3|0.7|0.3|1.6|1.8|0.3|0.8|1.1|
|Total|3.3|2.9|4.6|6.7|3.7|3.0|5.4|7.1|4.2|4.0|6.0|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). India’s consumption aggregates reported are based on the Uniform Recall Period (URP).

> 18 These are plutocratic rather than democratic shares.

15
<!-- Start of picture text --> Figure 3 Gopalpur vis-à-vis other urban settlements in West Bengal<br>4<br>Other CTs Shortlisted CTs STs<br>3<br>2<br>1<br>0<br>Gopalpur (CT)<br>-1<br>-2<br>-3<br>Population<br>-4<br>0 10,000 20,000 30,000 40,000 50,000<br>Standardised Score<br><!-- End of picture text -->

# **IV. The shift towards nonfarm employment**

As stated earlier, the literature on CTs is sparse and within that the economy of CTs is sparser still. Most news reports. such as some published in the Mint referred to later, indicate a diversified broad based service dominated economy, but at relatively low scale. In some places like Barjora investigated by Samanta (2017), the mainstay is mining and small sponge-iron plants, and in others, the basis is farm processing. However, in other studies of census towns, there is more of a variety of small scale service activities. In CTs that are close to larger towns, there is also spillover of economic activity. Punia and others (2017) find that the periphery of Yamunanagar has cement and packaging industries, numerous small-scale enterprises in timber business as well as thermal power plants and educational institutions. In Manakpur, an industrial enterprise cluster of metal and alloys has developed). In the district of Karnal, many CTs are growing due to real estate. The possible drivers of change in the north along the national highway are real estate builders, demand from agro processing industries like rice and wheat flourmills and government cooperatives for housing projects. This is what a recent research project on ordinary towns of India refers to as the importance of ordinary activities based on markets, construction, in a form of in-situ urbanization that is critical in
for larger areas of aggregation. Mexico’s labor force survey is no exception, as it can only provide estimates for urban and rural areas within states, as well as for select larger municipalities. Since Mexico has 32 states and 2,450 municipalities, reliable estimates of labor force participation – for both women and men – at the level of the municipality could greatly inform the geographic targeting of education, labor market programs, and other measures designed to improve female labor force participation. This is also true of other developing countries.

Small area estimation is a branch of statistics that combines survey data with richer auxiliary data to generate more precise and accurate estimates of statistics. It has frequently been applied to measures of poverty and labor market outcomes. An extensive literature has documented that auxiliary data, typically taken from census or other administrative sources, can improve estimates of labor force participation and unemployment (Esteban et al., 2020, Chambers et al., 2016, LópezVizcaino et al., 2015, Ugarte et al., 2009, Molina et al., 2007, Datta et al, 1999). This literature employs a variety of methods, including hierarchical Bayes, empirical Best Predictors, and multinomial logit mixed models, as well as area level models. Intuitively, small area estimation allows surveys to “borrow strength” from auxiliary data that is more geographically comprehensive, and therefore covers unsampled areas. Combining the survey data with model predictions for unsampled areas, derived from the auxiliary data, makes the estimates more accurate and precise. In addition, by modeling the average relationship between the survey and auxiliary data across the entire sample, small area estimation can help correct sampling error even in sampled areas. This method can therefore produce more precise estimates for geographic levels, such as Mexican municipalities, where the labor force survey is too small to generate reliable estimates.

2
26 In Annex 1, Jndicating that the chtange In consumption of a particular nutrient with regard to a change in Income or prices, depends<sup>mn the income or price</sup> elasticities of the foods, and the contribution of any particular food item to the total consumption of that nutrient.

Much can be said about the relative magnitudes<sup>of income elasticities</sup> from general knowledge. For instance, relatively<sup>low Income groups are</sup> likely to have high shares of expenditures on foods and high MPCs, usually leading to high income elasticities, and low price elasticities for basic foods which have no substitutes. Staples like<sup>rice and wheat are likely to</sup> be major contributors to consumption of calories and proteins in low income populations and, therefore, consumption of these nutrients is sensitive to changes in prices of these staples. At the same<sup>time, actual values of the</sup> elasticities are a matter of empirical evaluation.

Tables 3.2 and 3.3 (upper panels) exemplify estimates<sup>of income</sup> elasticities for foods and nutrients based on<sup>Indonesian data.1'9 It is</sup> noteworthy that while the estimated income elasticity<sup>for rice fall with</sup> income, it rises for dairy products for example.<sup>The elasticities</sup> fall, however, for most nutrients, but their levels of<sup>consumption</sup> rise with income (Table 3.3, panel 2).

In addition to the quantitative composition of the food basket, there is likely to be a qualitative change in food consumption as income changes.

"<sup>9</sup> For discussion and pertinent price elasticities, see Chernichovsky and Meesook (1984).
There is a rapidly growing body of literature on the economics of the mass media. This literature has led to a series of important new hypotheses and insights in an area that has been long neglected by economists.<sup>2</sup> An important strand of this literature concerns the role of mass media in political markets and its effect on public policy making. Most of this literature on the relationship between the mass media and public policy is theoretical. A few empirical studies have attempted to assess the effect of media on policy outcomes. Some key findings from this literature suggest that access to mass media empowers people politically and, as such, increases the benefits they receive from government programs (Strömberg and Snyder 2008). This influence has been found for different types of government programs and different countries, such as unemployment relief in the United States (Strömberg 2004b), public food provision and disaster relief in India (Besley and Burgess 2001, 2002), and educational spending in Uganda and Madagascar (Reinikka and Svensson 2005; Francken et al. 2009). All of these studies measure the effect within a single country, which has the benefit of holding many other factors fixed but has the potential disadvantage of limited variation in policy and media.

Our paper seeks to contribute to this empirical literature by analyzing the impact of mass media on policy making for a specific type of policy across a wide variety of countries and years. We use a new dataset from the World Bank that includes measures of agricultural subsidization and taxation for a much wider set of countries and a longer period of time than has been previously available (Anderson and Valenzuela 2008). We use these data as the dependent variable.

2 See McCluskey and Swinnen (2010) and Pratt and Strömberg (2011) for reviews.

2
Latinobarómetro Corporation conducts the annual study to determine individual perceptions on socioeconomic and political issues in Latin American countries. Covering 18 countries in Latin America and the Caribbean, the survey is the largest regional database on citizen attitudes towards democracy. It obtains representative national samples using a stratified random sampling process that is weighted to reflect each country’s population. The latest available survey data was released in October 2021 and comprises 20,204 observations that represent more than 600 million inhabitants in the region. Unfortunately, it does not include most of the insular countries in the region, such as Haiti, Trinidad and Tobago, and other small islands in the Caribbean.

The fieldwork was largely carried out face-to-face between October 26 and December 15, 2020. The only exception was Argentina, where interviews were conducted virtually between April 26 and May 16, 2021, because of local conditions related to the Covid-19 pandemic (Latinobarómetro Corporation 2021). In ten South American countries along with Mexico, samples of 1,200 representative cases were taken of citizens aged 18 and over (16 years in Brazil), and 1,000 cases in the six countries of Central America and in the Dominican Republic. The 2020 survey comprises a single questionnaire containing 81 questions on perceptions and 30 questions on socioeconomic status.

Trust in government is one of the independent variables of this study. The survey inquired about respondents’ trust in governmental institutions like the national government, the judiciary, congress, and the national electoral body. All these variables are used simultaneously as explanatory variables. We focus on institutional trust rather than generalized support for the system as a whole or confidence in specific leaders. We include also interpersonal trust in the measurement of social trust. The dependent variable in this study is citizen participation, which can take the form of signing a petition, taking part in authorized demonstrations, and working to resolve problems facing the community. Because trust may interact differently with each of these, we will investigate the relationship between trust and all three types of participation separately.
# **c. What have been historic costs of power production?**

As we have discussed, Africa faces effective tariffs that are up to twice as high as in other developing regions. This reflects the use of costly technologies as well as the small scale of most African power generation systems.

But what have been the historic costs across the various power systems? The first step toward answering this question is to attribute to each kilowatt-hour produced both the capital and operating (O&M) costs of the three power production segments: generation, transmission, and distribution.

O&M unit costs are calculated by prorating total operational costs (as reported in utilities’ income statements) by the total generated electricity over a given year. Data are derived from the AICD’s fiscal spending database,<sup>5</sup> and individual results have been verified by country power experts. Operational costs include salaries associated with system operations, fuel charges, the cost of parts needed for daily operations, and so on.

Unit capital costs are calculated using the levelized power methodology (commonly used by the International Energy Agency). This requires allocating the value of an asset over its lifetime capacity. In essence the unit capital cost is a ratio of the net present value of total lifetime investment to the total electricity produced.<sup>6</sup> For this purpose an annualization factor is applied to the value of generation, transmission, and distribution assets. The overnight investment or capital needed to replace existing assets is the proxy for asset value, to allow for cross-country comparison.

The annualization factor is a reversed version of a standard formula for the net present value of a periodic investment of equal amount. It takes into account both depreciation and interest rates: _r_ _ACC_  _OI_  1  1(  _r_ )  _T_ ; where _ACC_ is annualized capital cost, _OI_ is overnight investment, _r_ is annual discount rate, and _T_ is plant life (in years).

In most
..over time...becomes increasingly independent of the conditions that originally caused it”.

As a consequence, by virtue of living in a community with high levels of early 20<sup>th</sup> century migration, a household will have a higher likelihood of having a migrant member than an otherwise identical household living in a community with low initial migration rates. We therefore use the 1924 state migration rate as an instrument for whether a household had a migrant member in 1994 and for household migration prevalence. Table 2 shows the first-stage results for our instrumental variables estimation. Columns 1 and 2 show that the historic

> 8 See Massey, Goldring and Durand (1994), Winters, de Janvry and Sadoulet (2001) and Munshi (2003) among others.

- 8 -
2015; Hernandez et al., 2017), social media (Fatehkia et al., 2020; Sheehan et al., 2019), or some combination of these data sources (Steele et al., 2017; Pokhriyal & Jacques, 2017; Chi et al., 2022).

Most relevant to our current analysis, two prior papers investigate whether mobile phone use can approximate the wealth of individual mobile subscribers. Blumenstock et al. (2015) show that CDR data are predictive of an individual-level asset-based wealth index among a nationally representative sample of 856 Rwandan mobile phone owners ( _r_ = 0.68). Blumenstock (2018b) finds similar results with a sample of 1,234 male heads of households in the Kabul and Parwan districts of Afghanistan. While these results show that phone data can be used to _predict_ poverty levels, they do not evaluate whether those poverty estimates are of sufficient quality for real-world policy applications.

Our paper connects these two literatures by rigorously assessing the extent to which phone-based estimates of poverty can help with program targeting (Blumenstock, 2020). We believe the analysis will be especially relevant to the increasing number of interventions that rely on mobile money to distribute cash payments (Gentilini et al., 2020), and the growing 3
in North East - MICS by State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Improved Sanitation Deprivation - Improved Sanitation Deprivation -<br>Improved Sanitation Deprivation -<br>by State in South South MICS by State in South West- MICS<br>by State in South East - MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Ekiti Lagos Ogun Ondo Osun Oyo<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age. . Estimations are based in children aged 0-17.

66
In the past 10 years, the increased availability of microdata and computing power have spawned a burgeoning literature measuring these distributional impacts of trade,<sup>152</sup> yielding several novel insights:

- **The negative distributional impacts of international trade are large, localized, and long‐lived.** The emergence of China as a great economic power and its integration into the global trading system have caused regions in both developed and developing countries with a high concentration of import competing industries to fall behind other regions. Workers who are adversely affected by trade liberalization face high moving costs and are often not able to leave to find better jobs in other regions. The adverse effects of trade liberalization are consequently concentrated in particular regions, while the benefits are widespread throughout the whole economy.<sup>153</sup> These negative localized effects persist in the long run, and the affected regions take a long time to recover, if at all.<sup>154</sup> Since workers’ regions seem as important as their industries and occupations in determining the distributional impacts of globalization, it is crucial to switch from industry‐based social protection policies to more flexible and possibly region‐based policies.<sup>155</sup>

- **Trade liberalization can favor the poor because low‐income households often consume traded goods more than services and non‐traded goods** .<sup>156</sup> The consumption gains resulting from lower prices tend to be widely distributed (as most people consume many different products), but job losses in import‐competing sectors tend to be highly concentrated (as most people usually only have one job – which if lost to import competition is a major shock).<sup>157</sup>

- **Lower prices due to trade liberalization are often not fully passed on to consumers.** Evidence from Ethiopia and Nigeria suggests that intermediaries capture most of the surplus from lowering international trade barriers.<
or lack of need, or unawareness, or some other reason. The exact variables used for this purpose are described in detail in Section 4.6, along with a brief discussion of the corresponding caveats.

# **4. Data and Main Variables**

This section describes the data sources, discusses the variables we use, and provides summary statistics for both El Salvador and Georgia.

## **4.1. Data**

As a baseline pre-pandemic data source, we use the most recent Enterprise Surveys (ES) completed in El Salvador in 2016 and Georgia in 2019. The ES are firm-level surveys with a representative sample of a country’s private sector, implemented through a standard methodology with 10
If you have panel data, continue to the next questions.

# **2) What type of migration information do you have access to?**

If you have information on the motives, timing, and destination of migrant individuals, then you can reasonably distinguish between the different types of migration – short- _vs_ . long-term migrants; internal _vs_ . international migrants; voluntary (economically-motivated) _vs_ . involuntary (survival/low-return) migration, etc. In this case, you can use these different migration phenomena as dependent variables and compare the results across different categories and against existing evidence and theoretical predictions. In any case, be aware that when using nationally-representative surveys it is unlikely that you will observe a sufficiently large sample size of international migrants.

If you only have partial access to this information, stick with general definitions of migration. For example, if information regarding timing and reasons of migration is unavailable or unreliable, but you have information on destination, build migration outcomes by imposing a ‘spatial threshold’ after which moving individuals are considered as migrants rather than as simply absent (e.g., an individual could be considered a migrant if he/she has at least moved to another district or subnational administrative unit, even if you do not have data on where or when they moved).

# **3) Do you have a ‘rare event’ issue at the individual level?**

If yes (e.g., migrant individuals are less than 10 percent of the sample), collapse the individual information at the household level and use households as the main unit of analysis. If you still prefer to work at the individual level, adopt data-driven techniques to tackle the data imbalance issue (e.g., Chawla et al., 2002) and divide your original sample into disjointed training and testing sets to assess treatment effects on the original real-world (skewed) data rather than on the artificially rebalanced dataset.

If not, carry out the analysis at both the individual