rafmacalaba/data-use-mentions-extended huggingface dataset:
# FIGURE 7 Pass-through associated with domestic shocks

# A. Monetary policy shocks

# B. EMDEs: Monetary policy shocks

<!-- Start of picture text --> Ratio Ratio<br>0.5 Interquartile range Median 0.8 Interquartile range Median<br>0.6<br>0.4<br>0.4<br>0.3 0.2<br>0.0<br>0.2<br>0.1<br>0.0<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>C. Domestic demand shocks D. EMDEs: Domestic demand shocks<br>Ratio Interquartile range Median Ratio Interquartile range Median<br>0.0 0.0<br>-0.2<br>-0.4<br>-0.1 -0.6<br>-0.2<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>E. Domestic supply shocks F. EMDEs: Domestic supply shocks<br>Ratio Ratio<br>0.2 Interquartile range Median 0.4 Interquartile range Median<br>0.2<br>0.0<br>0.1<br>-0.2<br>-0.4<br>0.0 -0.6<br>-0.1<br>-0.2<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Float Peg. Non-IT IT Importer Surpluses<br>Low High Float Non-IT IT Importer Exporter Surpluses<br>Exporter Deficits<br>Peg. Deficits<br><!-- End of picture text --> Note: Pass-throughs
between the year of the data and the policy year. The household and personal characteristics, such as housing type, employment status or demographic attributes, are constant in relation to the data year. Table A-3 in the Annex contains more information on the uprating factors.

# **Limitations of the model**

As every model, RUSMOD is not without its limitations, of which users should be aware. The following three main warnings are summarized below, along with the remedies typically applied: (i) RUSMOD is a static, partial equilibrium model; (ii) RUSMOD can never fully replicate the official statistics; and (iii) not all the programs could be fully simulated.

Being a static, partial equilibrium model, RUSMOD estimates only first-order effects of policy reforms on income distribution and fiscal outcomes, and generally does _not_ take into account actions by economic agents taken in response, in order to adapt to changes. This problem, for example, can be acute if the _intention_ of policy action is to actually influence behavior (for example, reduce the labor tax wedge in order to encourage formal employment). A solution could be to link the microsimulation model with a behavioral model, or assume some elasticity between variables of interest (Creedy & Duncan, 2002). One example is presented in section IV below, where we estimate the share of formalized workers after a tax reform using a behavioral model.

Models, such as RUSMOD, cannot fully replicate the official statistics. Comparing (“validating”) model output, such as the total amounts of tax receipts and spending on social transfers and inequality and poverty indices, with external data is a standard robustness check. In that regard, for example, the total amounts of public pensions are _over-reported_ in RUSMOD by about 25 percent, expenditures on monthly cash payments are _under-reported_ by 65 percent, while simulated PIT, SIC and VAT receipts are _under-estimated_ by the model by 30-35 percent—vis-à-vis the official/external statistics. Gini index is estimated at 34.8 versus
and adapt the Exit/Voice/Loyalty theoretical framework as a game between cross-border traders and border officials to better understand the theory of change and generate testable hypotheses. Section 4 details the experimental design, describes the data, lays out the econometric approach, and presents the limitations of the study. Section 5 assesses the impact of the training on traders’ knowledge, border-crossing time and intensity, corruption, and GBV. Section 6 analyzes the degree to which the training effects are driven by the traders’ use of voice or by lower bribe demand from border officials. We conclude with a set of policy implications from these results in Section 7.

# **2 Context and intervention**

## **2.1 Context**

Small-scale, informal cross-border trade – sometimes misperceived as illegal smuggling in parts of Africa (Golub, 2015) – provides opportunities for gains from trade and reduced price dispersion across neighboring countries in the Great Lakes Region of Africa.<sup>1</sup> Yet, cross-border traders face demands for bribes which pose a threat to their small-scale trading businesses in the Democratic Republic of Congo, which is ranked 161st out of 180 countries in the Corruption Perceptions Index of 2018 according to Transparency International and well-known as a fragile and war-affected zone, particularly in its eastern provinces.<sup>2</sup> Similarly, the World Bank’s Doing Business rankings place DRC 188th out of 190 countries in terms of the time and cost to trade across borders, while Rwanda’s more favorable institutional environment is ranked 88th (World Bank, 2018).

Demands for bribes also coincide with gender-based violence (GBV) during border crossings given the gender dimensions of cross-border trade. This is particularly important in DRC, where sexual violence is prevalent and the large majority of cross-border traders are women and most border officials are men. According to the most recent Demographic and Health Survey data from DRC, 28% of women aged 15-49 in North Kivu and 35% of those in South Kivu
some statistical precision while other estimates are often very different from the enterprise and employment regressions.

The analysis suggests that even when augmenting the Landsat models flexibly with moments of all spectral bands, the prediction power of the simple OLS model in the time series is very low. While Landsat performed well at capturing the spatial variation of economic activity in the cross-section, changes over time cannot be easily modeled using simple variables from either satellite measure. There are several possibilities that could lead to this result. Measurement error over time in either the economic or satellite data could increase the noiseto-signal ratio in the regression. Another speculative reason is that while land use patterns and economic activity are strongly correlated in the long term, land use change might lag behind short-term economic growth and decline. For example, an increase in production might only manifest itself in recognizable changes on the ground through construction of factories and roads after several years. Likewise, a potential asymmetric response of land use between growth and decline could not be properly captured in a linear model.

# **5 Discussion and Conclusion**

This paper has introduced remotely sensed imagery data from the Landsat program and evaluated its usefulness for prediction of economic activity. Unlike the commonly used nightlight data, Landsat imagery comes at a much higher spatial resolution, is measured more frequently, and provides data on a multitude of spectral bands during daytime. While nighttime lights from the DMSP-OLS measures human activity in form of light consumption, Landsat imagery captures a bigger picture of Earth shaped by both nature and man. Thus, there is potential for detecting a variety of relevant features on the ground that correlate with socioeconomic data.

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important for countries with comparative advantages in tourism. That tourism promotes economic growth is confirmed in Dritsakis (2004) for Greece, Balaguer and Cantavella-Jordá (2002) for Spain, Oh (2005) for the Republic of Korea, Durbarry (2004) for Mauritius, and Tosun (1999) and Gunduz and Hatemi (2005) for Turkey. Cross-country studies by Sequeira and Campos (2006) and Brau et al. (2003) demonstrate that tourism-specialized countries grow more than others. Sinclair's (1998) survey indicates that the main channel through which tourism helps growth is the provision of foreign currency, which can finance imports of capital goods. Culiuc (2014), based on a gravity model, found that the income, real exchange rate and bilateral trade flows of source countries have a strong impact on world tourist arrivals, although the impact is much reduced for smaller countries. Wolfe and Romeu (2011) reached similar conclusions. Lederman and Maloney (2007) also found that tourism exports have less volatility than other natural resource-based export industries. The following sections review the literature on specific determinants of tourism demand.

# **IIa. Price elasticity of tourism**

An important feature of tourism markets is whether tourism services are price elastic or not. The price of tourism is a multivalued concept, as tourism services differ in quantity and quality more than other goods and services. The price of tourism services depends on whether they are denominated in local or foreign currencies, and most of the studies reviewed below use different measures of the real exchange rate. The price of the tourism-related services should be specific to the type of consumption; however, detailed price data are not common in tourism empirical analysis; prices are generally replaced by average spending per tourist and deflated by a real exchange rate index.

In most papers, the real exchange rate has the expected effect: an appreciation of the origin’s currency increases bilateral tourism while the appreciation of the destination reduces it, and an increase in income tends to increase demand
# 3.1.1 Skills Measures

Measures of skills are needed to construct the dependent variables in the regressions: wage bill shares and employment shares of skilled workers. Our surveys provide information on employment, wages and the average number of years of education for fi ve types of workers (management, professionals, skilled production workers, unskilled production workers and other nonproduction workers).<sup>23</sup> We consider total compensation i.e., wages and salaries plus bonuses and other bene fi ts as the measure of wages. Our fi rst measure of skilled workers is based on occupation: nonproduction workers de fi ned as the sum of management, professionals and nonproduction workers.<sup>24</sup> Most previous studies use datasets for which the skilled/unskilled workers distinction can be proxied only by this nonproduction/production worker distinction. However, this is imperfect since, for example, some of the nonproduction workers are in fact engaged in low skilled tasks. Our second measure of skilled workers is also based on occupation but improves upon the nonproduction/production worker distinction: workers in managerial, engineering and technical occupations, de fi ned as the sum of management and professionals.<sup>25</sup> This measure excludes the type of workers called in our surveys other nonproduction workers i.e., those that are not directly involved in production but that are neither part of management nor classi fi ed as professionals.<sup>26</sup> Our third measure of skilled workers is based on education: workers with at least some college education.<sup>27</sup> For China, we use information on average educational levels for each type of workers. Skilled worker wage bill shares (employment shares) are de fi ned

> 23A detailed de fi nition of these fi ve types of workers is available from the authors upon request. In the Chinese survey, some of the worker types have different labels (professionals are called engineering and
16. Urban versus rural access to water supply and sanitation, 2008<br>a. Water supply b. Sanitation<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Piped Stand Posts/ Wells and Surface Water Flush toilets Improved Traditional Open<br>Shared Boreholes Latrines Latrines Defecation<br>Urban Rural Urban Rural<br>Population gaining access per year (%) Population gaining access per year (%)<br>% population % population<br><!-- End of picture text --> Source: Access figures calculated by AICD using data from the 2000 Multiple Indicators Cluster Survey as published by the JMP in March 2010 and the 2008 Demographic and Health Survey.

Rapid population growth (particularly in urban areas) has been an important contributor to declining access (figure 16). Between 2000 and 2008 Sierra Leone‘s population grew at a pace of 3.4 percent per year, one of the highest among Sub-Saharan African countries. In urban areas, where the average population growth has been 4 percent per year, experienced important inflows of refugees who fled during the civil conflict in the 1990s.

Source: AICD water supply and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. Access figures calculated by AICD using data the 2008 Demographic and Health Survey.

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.0438|0.0723|0.312|0.142|
|N|2821|2821|2821|2821|2821|

_Notes_ : SL equals sanitation loan arm. Standard errors clustered at the village level are shown in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p-values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. HH stands for household, OD for open defecation. Data source: household survey.

of the sanitation loan, leaving overall borrowing unchanged. Thus, this offers an opportunity to test for the relevance of the loan label: an increase in sanitation loan take-up, and in sanitation investments, coupled with substitution out of other investments and other loans, and no change in overall borrowing would be consistent with the loan label mattering. However, if overall borrowing increases, we will not be able to identify the importance of each feature by considering average impacts only. In this case, we would need to study impacts for specific sub-groups (e.g. those who were not credit constrained ex-ante) in order to identify the importance of each channel.

# **6.3.1 Overall borrowing**

We start by looking at overall borrowing of the client households. As part of the household survey, respondents were asked to report on loans taken since the baseline survey was conducted. As is common in surveys of this type, we collected information on total household borrowing by asking households about the three largest loans (above Rs 500) taken since the start of the experiment.<sup>47</sup> In addition to information such as loan size and outstanding balance, respondents were asked to report on the lending source
in table 4. In columns 9-14, we also investigate if our findings are robust to the inclusion of additional controls in the regression with condom usage as dependent variable. Once again, miners and women with a miner as a partner are less likely to use a condom, especially during sexual intercourse with a spouse in the women sample and with other occasional partners in the men sample.

# **6.2 Non-migrant miners: The case of Zimbabwe**

In this section, we attempt to understand whether _miners_ per se are more likely to be HIV infected or whether _migrant miners_ have a greater chance of being HIV positive, consequently playing an important role in HIV transmission. Disentangling these channels is crucial for designing appropriate prevention policies. To this end, we estimate the probability of HIV infection for miners and for women having a miner as a partner in Zimbabwe. As described in section 2, Zimbabwe has a well-developed local mining industry and the miners interviewed in the Zimbabwe DHS are therefore likely to be local miners. The same argument may be applied to women with a miner as a partner. Indeed, figures from the DHS show that the fraction of partners employed in the mining industry that lives within the household of origin is 66 percent in Zimbabwe, while the same percentage decreases to 29 percent in Swaziland and to 23 percent in Lesotho (not displayed). Furthermore, figure 2 shows that the number of migrants from Zimbabwe working in the South Africa gold mines is very close to zero.

[Insert table 8] 17
make a son desirable economically. For instance, Albanian sons are considered more economically productive than daughters not only because they obtain higher wages, but also because they tend to remain part of the extended family, while daughters and their contribution to the family where they are born counts only for a few years.

Old-age support from mature sons is another influential factor in parental decisions to have sons in Albania. They are still more preferred than daughters because they provide a greater source of protection and expected support for the parents at old age, a need reinforced by the uncertainties of the economic and social environment since the exit from the communist regime. Moreover, Albanian tradition and culture dictates that sons should provide financial support and protection for parents. In particular, Albanian custom obliges the youngest son (and his wife) to take care of his parents in their old age - indeed the youngest son is referred to in Albania as “the son of old age” (King and Vullnetari, 2006; King, Mata-Codesal and Vullnetari, 2013).

On the contrary, the obligation of daughters is much weaker, in part because they are considered as transient members of their native family and also because they are thought to be

> 14Monthly labor income is in real terms with base year 2007 using the national CPI provided by the Albanian National Institute of Statistics (INSTAT).

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village meetings discussed in the previous section, with a study that I was involved in (Parthasarathy et al, 2019) that used NLP methods to analyze 100 transcripts of village meetings recorded and transcribed in the Indian state of Tamil Nadu in 2014 (and, consequently, took six months rather than ten years to analyze). The NLP paper studies the degree of gender bias in the village meetings and whether having a woman village president can correct gender bias. The transcripts are analyzed using “topic-models,” a method which 12
shortage of public capital relative to their development aspirations, but not relative to their current development level. That is, people in low‐ income countries have many unmet needs, with public infrastructure capital being in just as short supply as everything else.

> 29 Cutoffs are expressed in GNI per capita (to 2 significant figures), calculated using the World Bank Atlas method.

> 30 Replication files for the main Tables and Figures are available from the authors upon request.

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, Toader et al. (2018) find that a 1 percent increase in the use of digital infrastructure would increase GDP per capita by between 0.08 percent, if the infrastructure is measured by fixed broadband subscriptions, and 0.4 percent, if it is measured by mobile phone subscriptions. On the other hand, Stanley et al. (2018), find little evidence supporting the positive impact of the internet and fixed broadband on economic growth. These results could be explained by the findings of Andrews et al. (2016) that suggest a recent slowdown in global productivity growth explained by productivity divergence between global frontier firms (“top 5% of firms in terms of labor productivity or multi-factor productivity”) and laggard firms. Therefore, the authors suggest analyzing the effects of technology on productivity and growth at different levels (global, country) and types of firms (global frontier and laggard firms).

Given the complexities and various nuances that lie at the nexus between ICT and the developing world, there are discrepancies in the literature about the intensity of the effects of the internet on economic growth. Bertschek et al. (2015) provide a review of empirical studies from the last decade on the effects of fixed broadband internet (e.g., networks and services) and economic growth, employment, and productivity. Overall, telecommunications and fixed broadband deployment have a significant positive impact on economic growth and productivity. However, the general conclusion seems to point at a disparity between the effects found in data before and after 2000. For instance, Choi and Yi (2009), use data reported for 207 countries between 1991 to 2000 from the World Bank’s World Development Indicators (WDI) to conclude that a 1 percentage point increase in the percentage of people with access to the internet results in an increase in the growth rate between 0.049 and 0.059 percentage points. However, using the same dataset but for a longer period, 1990-2015, Maurseth (2018) finds significant
these with the RTA indicator. Figure 7 compares the overall RTA coverage for all country pairs (“Total RTA”) and the coverage of Egypt’s trading relationships as measured as the percent of Egypt’s trading relationships covered by an RTA as reported in the CEPII database. The figure shows that more than half of Egypt’s trading partners have an RTA with Egypt, and this share has remained steady from 2010 to 2019. The fraction increased in the mid-2000s when Egypt expanded its trade agreements but fell somewhat as Egypt expanded trade with countries that were not covered by a trade agreement.

**Figure 7. More than half of Egypt’s trading partners have an RTA with Egypt, 1996-2019**

Source: Authors’ elaboration using CEPPI gravity database

# • **Gravity Results**

The main results are shown in Table 1. Three estimation approaches are shown. In the first column, the Ordinary Least Squares (OLS) results include year fixed effects, importer fixed effects, and exporter fixed effects. The OLS results suggest that RTAs increase trade by over 50%, which is much larger than the 10-20% range found in most studies. Part of the reason for the large result is that the sample is restricted to country pairs with positive trade. The results in column (1) also suggest that the RTA effects for Egypt’s trade are much larger than that of the “average” trade agreement. Both imports and exports are much larger with Egypt’s RTAs. Note that the other gravity variables, including WTO, distance, and GDP have the expected signs. The magnitude of the importer and exporter GDP coefficients are smaller than expected.

The second column employs the high-dimensional fixed effects PPML estimator. This estimator includes importer and exporter time trends, year fixed effects, and country-pair fixed effects (and standard errors clustered on country pairs). The main results are similar but smaller in magnitude. The RTA estimate is now much lower than the 10-20% range found in the
Asia, but the mean estimate hides substantial heterogeneity across countries. The highest estimate is 0.77 for Madagascar which is also the highest among our 53 countries. There are three other countries with estimates of 0.70 or higher: Chad (0.74), Nigeria (0.70), and Ethiopia (0.70). The lowest estimate is 0.49, for South Africa.

The region with the highest intergenerational educational mobility is Europe and central Asia; the average sibling correlation is 0.48.<sup>33</sup> Among the 5 countries from this region in

> 31Our East Asia sample does not include Japan because DHS surveys do not cover Japan. The countries included are: Cambodia, Indonesia, Philippines, and Vietnam. The South Asia sample includes Afghanistan, Bangladesh, India, Nepal, and Pakistan.

> 32As discussed in the data section, we excluded the countries with DHS survey if the sample size is less than 1000 observations.

> 33This average estimate is slightly higher than the average of 0.44 for 6 developed countries reported by Grtz et al. (2021). The developed countries are: Finland, Norway, Germany, United States, United Kingdom, and Sweden. The countries in our sample are: Albania, Armenia, Kyrgyz Republic, Tajikistan, and Turkiye.

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