rafmacalaba/data-use-mentions-extended huggingface dataset:
333 distinct household across five survey waves.

In Malawi, the LSMS-ISA data includes two separate surveys that form three different data sets: the cross-sectional Integrated Household Survey (IHS), and the longitudinal Integrated Household Panel Survey (IHPS) (NSO, 2012; NSO, 2015; NSO, 2017; NSO, 2020a; NSO, 2020b). These two surveys form three data sets: three cross sections (2010/11, 2016/17, 2019/20), two waves of panel data known as the short-term panel (2010 and 2013), and five waves of panel data known as the long-panel (2010, 2013, 2016, and 2019). This analysis relies on the short-term and long-term data sets from the IHPS, which are representative at the national-, urban/rural-, and regional-level. The long-term panel tracks households that move or split off, meaning the sample size grows from round-to-round. After data cleaning to remove tracked and non-agricultural households, we are left with 8,897 observations from 3,833 distinct households across four survey years.

In Niger, we use two waves, the first from 2011 and the second from 2014 (NIS, 2014; NIS, 2016). Unlike the other countries from the LSMS-ISA used in this paper, there has been no follow-up on data collection in Niger since 2014. The sample is representative at the national and urban/rurallevel. Data cleaning and removal of non-agricultural households gives us 3,913 observations from 2,320 distinct households across two survey waves.

In Nigeria, we use the data from the 2010/11, 2012/13, 2015/16, and 2018/19 rounds of the General Household Survey - Panel, which is representative at the national and urban/rural-level (NBS, 2012; NBS, 2014; NBS, 2016; NBS, 2019). Similar to Ethiopia, there was a panel refresh in 10
that the economy discloses the first COVID-19 case to the day that the government first announces the policy to restrict the international travel. _DelayD_ is delay in implementing domestic closedown policy, equals to the number of days, divided by 100, from the day that the economy discloses the first COVID-19 case to the day that the government first announces their domestic closedown policy, which include school closing, workplace closing, public events cancelling, restrictions on gatherings, public transport closing, stay-at-home requirement and restrictions on internal movement. Smaller _DelayI_ and _DelayD_ indicate prompt government response.

We rely on the World Development Indicator (WDI) dataset from the World Bank to obtain basic country-level characteristics including GDP per capita and the total population.<sup>5</sup> For all timevarying variables, we choose the latest year that the observations are mostly available. In addition, we control basic geographic and weather conditions of a country. Since the first large outbreak happened in China, we control for the distance to China—with two dummy variables of the distance being in the middle and the top tercile of the distance distribution.<sup>6</sup> Since temperature plays a role in pandemic spread, we also control for the average temperature since the inception of the COVID-19 on a country until November 2020. In a sensitivity check, we also control for the longitude and latitude of capital of a country.

We add data on democracy and trust from several sources. A dummy variable indicating the status of democracy is from Freedom House. Indicators of out-group trust and individualistic culture are obtained from the World Value Survey (WVS). Out-group trust (“Trust”) measures the extent of trust in strangers and people of different nationalities and religions; a higher value implies stronger general trust. Individualistic culture (Individualistic Culture) is captured by the individual empowerment index (version 2) in WVS, which measures the extent to which the people in a society are mentally and habitually empowered to make their own choices and pursue them in their
refugees did not catch up with natives and the male literacy gap only narrowed from -10 to -5 percentage points between the 1910-1917 cohorts. In contrast, in provinces where the increase in school supply was higher, the literacy gap closed entirely by 1917, with a stark catching up from -15 to +1 percentage point in just seven cohorts.

To put the social integration of refugees into perspective, it is useful to examine another group of immigrants. I focus on the largest group of immigrants in Greece (about two-third of the immigrant population), the Albanians, most of whom arrived in the 1990s, after the the collapse of the Soviet Union.

# **5.2 Refugees’ integration compared to Albanian immigrants**

**Education and occupation** Table A.4 in the appendix presents regression estimates of the educational gap between Albanian immigrants and Greek natives, for different generation of migrants. In terms of secondary schooling Albanian migrants who arrived in Greece when adults are 22 percentage points less likely to have completed secondary schooling relative to natives (71% of whom hold secondary education). There is absolutely no catching up over generations: the educational gap with natives remains almost unchanged for migrants who arrived at a younger age, i.e., below 12, and even below 5. The same is true in terms of tertiary education. Furthermore, the ESS data allows to examine the occupations of second-generation refugees, either in their current or in their last job.<sup>23</sup> As Table A.5 in the appendix shows, refugees are less likely than natives to report being farmers and they have comparable household income (by decile). In contrast, first-generation Albanian immigrants are more likely to work in low-skill occupations (in the non-primary sector) and have a lower household income than natives.

**Intermarriage** Refugees are more socially assimilated than recent Albanian immigrants in terms of intermarriage. Figure 3 reports the intermarriage rate of refugee women who were displaced
remaining sample of middle income countries. In contrast, India has emerged as a big player in the delivery of complex services as measured by EXPY in post 2000. India is followed by Singapore and Korea in their level of service export sophistication.

Figure 14 plots individual year scatter plots of the export sophistication index (in logs) with the log level of GDP per-capita for a sample of around 147 countries. The two panels highlight the relative position of Malaysia in 1995-98 and 2007-09 (averages). First, the scatter plots confirm the finding that countries with higher export sophistication have higher income per capita. Second, the two panels demonstrate the stagnancy of Malaysia’s relative sophistication level over time, and its overall position below the global mean in 1995 and 2005. Malaysia has made improvements but lags behind competitive and sophisticated service exporters like Singapore and Korea but ahead of Thailand in the sophistication of service exports. As expected, India stands out as a huge outlier in export of modern services.

The modern service sector has become an important driver of growth in both developed and developing countries. In particular, Figure 15 compares what has happened in some 136 countries between 2000 and 2005 in terms of real GDP growth, shown on the vertical axis and service value added growth, shown on the horizontal axis. Each point represents one country. The positive relationship between the two variables implies that countries with high growth in services also tend to have high overall economic growth or conversely that countries with high overall economic growth have high services growth. One cannot identify causality from a

> 16 This measure is more suggestive than authoritative, however, as we are forced to use approximately ten different service categories as income weights, rather than the few thousand commodities employed in the goods calculation.

18
We analyze the tax-to-GDP ratios of 27 economies during the period surrounding their transition from a low-income to a lower-middle-income status. To conduct this study, we utilize the World Bank income group classification to identify countries that have changed their status from one income group to another based on data from 1987 to 2021. We identify 53 countries that transitioned from low to lower-middle-income status, 67 countries from lower-middle to upper-middle-income status, and 41 observations from upper-middle to high-income status (table B1).<sup>7</sup> However, due to constraints in the availability of tax-to-GDP data, we track only a subset of these transitioning countries: 27 from low-income to lower-middle income (L to LM), 45 from lower-middle to uppermiddle income (LM to UM), and 29 from upper-middle income to high income (UM to H), with additional limitations in temporal coverage.

Economies rising from low to lower-middle income status achieve a critical tax-to-GDP ratio of 15 percent, reflecting a 3-4 percentage point or GDP increase over the decade before transitioning. Our analysis indicates that the average tax-to-GDP ratio is 15 percent when countries shift from a lowincome to a lower-middle-income category (figure 9A). This average increases to 23.4 percent and 25 percent when transitioning from lower-middle-income to upper-middle-income and from uppermiddle-income to high-income groups, respectively. Notably, as countries progress from low to lower-middle income status, we observe that their median tax capacity in the ten years preceding the transition expands by about 4 percentage points (figure 9B). In contrast, when countries transition within middle income status to upper middle income there is no notable increase in tax collection in the ten years preceding that transition.

**Figure 9 Taxes during income group transitions**

A. Tax-to-GDP at transition point B. Tax-to-GDP prior to transition to middle income status, median <!-- Start of picture text --> Percent of GDP Percent of GDP<br>30 Median Mean 19<br>18<br>17<br>25<br>16
combines survey and satellite data to generate local estimates of population density, and by extension, population counts. Instead of relying on the census, which becomes outdated over time, the proposed method exploits the updated demographic information in surveyed areas from existing periodic household surveys, and the widespread coverage and granular information offered by satellite imagery. The method predicts population density in non-surveyed areas, and in between-census years by employing updated survey and satellite data. This technique is applied in the context of Sri Lanka using the Household Income and Expenditure Survey (HIES), a nationally-representative household survey. The satellite indicators include those derived from both low- and high-resolution satellite imagery for the entire country, and additionally, object and contextual features derived from very-high resolution imagery for a randomly selected portion of the country. These indicators are used to predict population density at the Gram Niladhari (GN) division, the lowest administrative level in Sri Lanka. The GN division is similar in size to a village in many developing country settings, and we henceforth, for ease of exposition, refer to GN divisions as “villages”.

To motivate this approach, we begin by documenting the inconsistency of existing “topdown” population products at the village level, both with each other and with the census. We then address three questions that shed light on the ability of indicators derived from satellite imagery to predict population density. First, how accurately do satellite data predict census population density at the local level? Second, does including indicators derived from high and very high-resolution satellite imagery substantially improve the predictive power of the model? Third, how does the size of the training data affect prediction accuracy? We then implement the “bottom-up” approach, and finally ask how accurate are out-of-sample predictions that are derived from the HIES and satellite-imagery-based model, compared to the existing “top-down” products?

The “top-down” products considered include, WorldPop for the years 2010 and 2015, the Global Human Settlement Layer (GHSL) for 2014, High Resolution Settlement Layer (HRSL)
# **5. Concluding Remarks**

The contents of PTAs continue to expand in both scope and depth, increasing the number of trade agreements that devote significant coverage to a wide assortment of regulatory environments extending beyond traditional trade policy and market access considerations. Intellectual property rights in particular have become an area of growing focus in this context, as the ever-increasing importance of the global knowledge economy has sharpened the incentives for policy makers and PTA negotiators to implement more rigorous and consistent systems of IPRs protections across countries. While a nascent literature exists exploring the effects of these developments on IPA member countries’ trade in goods, it is striking that little work to date has investigated whether such PTAs impact the dissemination of intellectual property between and beyond agreement member countries.

To explore this question, we empirically assess the role played by the proliferation of PTAs possessing substantive and legally binding language on IPRs, as defined by the World Bank’s database on the content of deep trade agreements and which we denote as IPAs, in shaping international flows of innovation as measured by cross-border patenting. To this end, we quantify the impacts of countries’ accession to IPAs in a structural gravity setting analogous to the widely used structural gravity model of trade. We employ detailed information on bilateral international patent applications at national patent offices, a data set which covers hundreds of countries over our sample period of 1995–2015. We further consider heterogeneity in the impacts of IPA accession as originating from the attributes of the agreements, delineating between “treatment” definitions that alternatively define IPAs as those possessing legally enforceable provisions on IPRs (WTO LE2 agreements), IPRs-related PTAs negotiated by the United States or EU/EFTA, and finally, IPAs possessing three or more TRIPS-Plus IPRs provisions (CORETRIPS3 agreements).

Our baseline analysis yields evidence that IPAs encourage heightened levels of total bilateral patent flows to member countries, as well as consistent increases in patent applications when broken down across a number of IPRs-intensive, high-technology industry clusters. However, the exact interpretation of this finding hinges
by _nondiscriminatory_ but WTO-inconsistent trade policies under formal DSU litigation. That investigation found some evidence consistent with the hypothesis that legal capacity and political-economy relationships negatively affect the willingness of countries to engage in the dispute resolution process. That sample of data under investigation was much different, however, in that it analyzed WTO disputes initiated against a variety of countries (not solely the U.S.) that were applied on a quasi-MFN basis, thus negatively affecting many exporting countries.

The next two specifications (7) and (8) are crude attempts to address an econometric issue of the nonindependence of some of the observations in the estimation, given the pooling of the trade remedy data over time.<sup>29</sup> The concern that we address in specification (7) is that the disputes involving an EU member country as a complainant are somehow driving the results. This could be the case given that there are a number of instances in which multiple EU-member countries are involved in related trade remedy investigations over the same product, which may make it more likely for those related cases to be jointly challenged at the WTO. For example, common elements (WTO-inconsistencies) across cases against different EU members could make it cost effective to spread the litigation burden across the member states. Nevertheless, when we include an indicator for the affected foreign country being an EU-member, the qualitative pattern of results are virtually unchanged, and the EU indicator itself is negative, though it is not statistically significant.<sup>30</sup> Next, in specification (8) we attempt another approach, which is to include an indicator for the instances in which an imposed trade remedy is one of multiple remedies that the U.S. imposed against the same set of products from any set of different countries. In this specification, there is evidence that such a remedy is more likely to be challenged at the WTO. This could be indicative of the foreign country seeking to increase market access while its primary competitors would still be
The phone survey data seem to pick up some of the changes faster and more immediately than official employment statistics. In particular, the ILO definition of employment classifies those who are not working but expect to go back to work, for example because of vacation, as employed. Under normal circumstances this is a small share of workers, but the phone survey data from LAC mentioned above suggests a large share of workers fell into this category immediately following the onset of the pandemic. This suggests that the measure of stopping work in the phone surveys may better capture the full extent of labor market disruption than the official ILO concept of employment in the immediate aftermath of the crisis, despite legitimate concerns about the representativeness of the phone survey samples. This provides additional evidence that the HFPS phone survey is useful in measuring initial impacts and captures dimensions missed by other sources of data.

Finally, we compare the WEO macroeconomic growth projections with the ILO’s reported information on employment changes in Figure 12. The ILO measure and the WEO measure of change for this comparison include 26 countries, 12 overlapping with the HFPS phone survey and 14 that are not, which are all developing countries. Here, again it seems that the WEO GDP projections show a different pattern than the ILO employment change. For example, Georgia and Chile have similar changes in GDP growth projections despite the latter having much greater employment loss in the ILO data. This is a further indication that WEO GDP projections are not necessarily picking up employment outcomes on the ground. The HFPS phone survey data in particular appear to be reflecting short-term employment changes that might take more time to appear in the official firm and labor force surveys, national statistics and therefore in macroeconomics and employment projections.

# **4.5 Robustness: Alternative Weighting**

In our main estimates, we use the HFPS survey weights. Figure 13 shows scatterplots for six key measures (share stopped working, share with partial or no payments, share changed job during the pandemic, shared with reduced consumption,
challenges that the existing approaches have only partially addressed. First, many subjects exhibit “multiple switching behavior” (MSB), meaning they make inconsistent choices that are difficult to interpret, by switching between “buying” vs. “not buying” multiple times as prices change monotonically (Yu et al., 2021). Based on these decisions, their WTP may lie in a very wide interval or not be recoverable at all.<sup>4</sup> Second, many subjects exhibit “never-switching behavior” (NSB), meaning they choose the same option throughout the MPL, which may imply a very wide interval for the WTP including potentially + _∞_ or _−∞_ . Sometimes, one of these choices is inconsistent because the selected option is strictly dominated.<sup>5</sup> Last but not least, even consistent responses in MPLs only provide interval identification of WTP.

We begin by introducing a framework aimed at understanding choice error in WTP elicitation, and its potential to add noise to WTP estimates. We argue that MPL can be an effective tool for measuring WTP because of the repeated choice data from the same subject that permits learning about error. However, due to their format of repeated parallel questions, MPL may also be vulnerable to framing and order effects. These create bias because they introduce a correlation between the error and the (change in) value difference between the two options, which is used in an MPL to identify WTP. We argue that many widely used MPL designs do not accurately measure error and are unable to prevent or even detect order effects. Worse, some designs may introduce additional bias. Moreover, most estimation approaches do not model choice error, with implications for the estimated variation and standard errors in WTP.

We therefore propose two measures to improve how MPL-based WTP elicitation incorporates choice error and bias. The first measure is to randomize how subjects view choices in a way designed to address potential framing effects. We propose to vary both
# Reducing Environmental Risks from Belt and Road Initiative Investments in Transportation Infrastructure<sup>1</sup>

_Elizabeth Losos, Alexander Pfaff, Lydia Olander, Sara Mason, and Seth Morgan Duke University_

> 1 We thank Michael Toman, Glenn Morgan, Sven‐Uwe Mueller, Graham Watkins, Daniel Taras, Bekele Debele, Michele Ruta, and seminar participants at the World Bank for their helpful suggestions. Tristan Reed and Sasha Trubetskoy were especially generous in sharing their transport infrastructure data and knowledge. All errors remain our own.
We also use a set of control variables in our estimations. An important aspect to control for when analyzing electoral cycles is the degree of political competition. The political competition is likely to affect the incentives of incumbents with regard to public spending around election time (Block <u>2001). To measure political competition, we use the</u> <mark>index produced by the Polity5 project (Center</mark> <u><mark>for Systemic Peace 2020). This index measures the degree of political competition in a country,</mark></u> <mark>independently of the political regime. It ranges from 1 (political competition is suppressed) to 10 (political competition is institutionalized and electoral).</mark> Other aspects of governance may also affect the relationship between electoral cycles and budget outcomes. We use three indicators from the World Bank’s Worldwide Governance Indicators (WGI) (World Bank 2022b). The voice and accountability index measures the responsiveness of governments to their citizens. This index is a composite of several indicators on democracy, electoral processes, accountability of public officials, rights, reliability of government budget documents, transparency in policy making, the freedoms citizens have, and citizens’ trust in their government. The second indicator provides a measure of government effectiveness across countries. This index includes measures of the quality, coverage, and citizen satisfaction with public goods and services (roads, public transport, electricity, health and education services, water and sanitation, and bureaucratic quality). The third indicator captures the extent to which citizens and firms trust and abide by the rule of law. It includes perceptions of the quality of contract enforcement, the protection of property rights, confidence in the police and the courts, and the likelihood of crime and violence. The three indexes are normalized to mean zero and a standard deviation of one in each year. Higher values correspond to more advanced democratic institutions in a country.

Other variables used in