rafmacalaba/data-use-mentions-extended huggingface dataset:
require exit from the labor force (e.g., McGeary, 2009). To gauge the potential importance of these factors in China, Indonesia and Korea we next estimate model (3) below: where _LS_  _i_ is an indicator or whether or not a spouse is employed, and **ADL**  _i_ are measures of spouse health status. As the labor supply decisions of husbands and wives are likely to be determined jointly and have a dynamic relationship with health and shocks to health and employment, we view these models as purely descriptive but informative of the extent to which joint labor supply decisions may affect the timing of retirement. As the HRS-type surveys mature into full panel studies, it will be feasible to control for unobservables and to unlock directions of causality among these variables.<sup>28</sup>

# **Determinants of “Retirement” in China, Indonesia and Korea**

**_Employment and Pension Availability_ .** Descriptive patterns of employment in productive activity across urban and rural areas of China, Indonesia and Korea (Figure 5) are suggestive of the role that eligibility for a pension may play in retirement of older workers and elderly. In these figures, the line shows the share of population in two-year age cohorts that is employed in some productive activity (salaried work, informal sector wage labor, casual work, self-employed activities or unpaid employment in family-run enterprises) and the bars represent the pension eligible share. In urban China, the employed share decreases sharply with increases in the pension-eligible share. In rural areas of China, one does not observe a similar decline in employment, and shares of workers with access to pensions are quite low. In Indonesia and Korea, where pension coverage rates at young ages are lower than for urban China, one observes

> 28In estimating (2) and (3), it should be noted that for each country there are subsamples of the population for which there is no spouse or for which spouse information is not available
on the part of politicians and the public. Pressures to demonstrate visible achievements (that can be attributed plausibly to a donor agency‟s own aid funds) to skeptical taxpayers or elected officials will increase further the tendency to free ride on other donors‟ contributions to strengthening country systems (Williamson and Agha, 2008: 34). When bilateral donors use aid to advance diplomatic or commercial objectives, incentives to rely on their own parallel systems for aid delivery will be further aggravated. For example, using their own procurement rules will likely advantage donor-country contractors.

If using country systems for managing aid does strengthen them, then donors‟ use of country systems (in the absence of coordinated action) is likely to be sub-optimal. The benefits of using country systems are mostly external (benefiting other donors) and realized only over the long term, while costs are short term and fully internalized by the donor. When donor agency _i_ undertakes any action to strengthen recipient country systems, it is in effect providing a public good for other donors. Stronger aid management systems reduce reputational and fiduciary risks and increase the developmental impact of aid funds not only for donor _i_ ‟s future aid, but also for other donors. Meanwhile, donor _i_ incurs the full costs, in exposing its current aid funds to higher risks than if it bypassed recipient country systems. The benefits are not wholly external, however: when a donor has a larger share of the aid “market” in a country, it will internalize more of the benefits from any investments in strengthening country systems. Donor _i_ ‟s use of country systems is then expected to be positively related to its share of total aid that is received by a given country, controlling for quality of recipient systems.

The next section describes how the model‟s concepts can be operationalized and its predictions tested using a panel dataset based on the Paris Declaration‟s three monitoring surveys, with donor-recipient pairs as the main unit of analysis. Empirical results reported in 6
Learning from Developing Country Power Market Experiences: The Case of Colombia <!-- Start of picture text --> Exports NCRE<br>1% 1%<br>Oil<br>Other free<br>customers 10%<br>11% Coal<br>Mining<br>7% 8%<br>Gas<br>Manufacturin 11%<br>g industries<br>14%<br>Regulated Hydro<br>67%<br>70%<br>Figure 2-2 Structure of Colombia’s electricity demand during Figure 2-3 Share of installed capacity (in MW) per<br>2015. technology in Colombia, 2015.<br><!-- End of picture text --> Figure 2-2 Structure of Colombia’s electricity demand during 2015.

Source: Own elaboration based on data collected by the World Bank.

The power generation fleet is largely hydro-based generation (70% of national installed capacity in 2015 with 11.5 GW), followed by gas, oil and coal-fired power plants with 11%, 10% and 8% of total installed capacity, respectively (see Figure 2-3). However, most of the oil-fired installed capacity adding to the 10% is dual gas power plants which have been increasingly declaring oil as their primary fuel due to the lack of gas supply contracts, as well as the regulatory measures that incentivized installation of liquids as primary fuels to guarantee security of supply (e.g. the Firm Energy Obligations, further discussed in section 4). Those “oil-fired” power plants are therefore not necessarily producing power fueled by diesel (UPME, 2016c). A very small amount of renewable generation is installed in Colombia.

The Colombian power market experiences some degree of market decoupling (or separation) due to transmission constraints, mostly driven by hydrological conditions. However, major problems concentrate in the transmission and distribution facilities of Distco Electricaribe (at or below 110 kV), which serves about 25
For the image analysis we use ResNet-50 (He et al. (2016)), a pre-trained convolutional neural network model trained on the ImageNet dataset (Deng et al. (2009)) to extract image features and then cluster them using k-means clustering. Our final analysis uses 30 clusters.

The mixed effects models include results from ‘seeded’ topic models obtained by using a subset of messages filtered for keywords about services, local governance, or displacement. The goal of these seeded models is to pull out additional insights which may be obscured in the `Location Mentions` and `Return Date` models. For seeded analysis, we worked with a Syrian researcher who provided an initial list of words focused on services, local governance, return, and displacement. We then used word2vec (Mikolov et al. (2013)) with the Continuous Bag of Words (CBOW) embeddings to find the 20 most contextually similar terms for each keyword.<sup>8</sup> The resulting list, in Appendix 9.3, was manually filtered for relevance and the expanded keyword list was used to collect all messages that included any of the words. The seeded return model includes 672 locations with 477,581 messages from areas without return and 419,090 messages from areas with return, and the seeded displacement model includes 583 locations with 55,737 messages from areas with IDPs and 125,158 messages from areas without. Both models were run with 15 topics.

# **4.4 Mixed Effects Modeling**

For the `Return Date` model as well as the seeded return model we run a mixed effects model to identify changes in topic prevalence pre- and post- return while both enabling correlation between topics and accounting for location characteristics and time shocks which occur across all messages. Similarly, for the seeded displacement and seeded return models we run mixed effects models to identify the association between population displacement or return and percentage of topic discussion. We model the outcome percent _Yij_ as a function of
f ̈ur Technische Zusammenarbeit, 2009). As shown in Figure 7, diesel prices averaged over a balanced panel of 12 countries from the main estimation sample generally rise in parallel with oil prices. Nigeria, Gabon, and Angola, the three sample countries for which oil production represents the largest fraction of GDP, show similar, though somewhat noisier time trends (Appendix Figure A.1) despite the fact that they typically had lower prices than average in most years, most likely because of subsidies. Using data from a survey of truckers in several African countries, Teravaninthorn and Raballand (2009) estimate that fuel represented roughly 35 percent of transport costs for trucks in 2005, when oil prices were roughly the mean of the minimum and maximum annual price for the period.

Rudimentary national statistics like road density and percentage of roads paved,

> 24Teravaninthorn and Raballand (2009) provide figures for several important routes from landlocked country capitals to the ports that serve them.

> 25Specifically, the oil price used is the annual average Europe Brent Spot Price FOB, in dollars per barrel, from the United States Energy Information Administration (http://tonto.eia.doe.gov/; accessed 5 Jul 2010).

11
2.2, the data for micro and small enterprises by design covers only informal enterprises. In addition to quantitative and qualitative measures of firm performance, the survey also inquires from establishment owners about problems faced in enterprise operation. To this effect, we examine how these (self-reported) problems affect enterprise performance using the following regression: The unit of observation in (3) is the enterprise _i_ , operating in industry _j_ , and located in district _d_ . Our independent variables of interest are _Problem_<sup>_j_</sup> ; _j ∈{_ 1 _,_ 2 _,_ 3 _,_ 4 _,_ 5 _,_ 6 _}_ , corresponding to each of the 6 problems queried in the survey. These are binary variables, and cover problems faced by the enterprise in terms of a) low demand; b) accessing raw materials; c) accessing credit d) electric power; e) labor; f) recovering financial dues. **X** includes establishment specific covariates, while **Y** contains district specific covariates.<sup>10</sup> We also consider specifications where replace the district covariates with district fixed effects. _μ_ denotes industry (4-digit) fixed effects. As the establishment data is sourced from a single cross-section, the industry fixed effects control for factors such as industry-level demand or industry-specific regulations/government policies which are common to all establishments operating in the industry. All specifications include establishment-specific weights to make the data nationally representative, and standard errors continue to be clustered by district.

Our primary outcomes of interest are logged revenues and logged value addition.<sup>11</sup> _β_<sup>_j_</sup> thereby estimates the effect of each problem on enterprise performance, conditional on the controls and the fixed effects. In addition to quantitative measures of enterprise performance, the survey also provides qualitative measures of enterprise performance, recording whether the establishment is expanding, stagnating, or a new entrant
characterizes skilled immigration from Northern European countries. Thus, the change in migration factors for Yugoslav nationals is consistent with the new emphasis on skilled immigration for non-EU countries. Hence, generally speaking, under similar conditions immigrants from the former Republic of Yugoslavia tend to respond in a similar fashion as workers from other countries.

The paper is organized as follows. Section 2 presents a short survey of immigration policy in Switzerland since the 1970s. In Section 3, a statistical picture of immigration from the former Yugoslavia is presented and compared with that of other major foreign communities in Switzerland. In Section 4, we present the results of the estimation of an augmented gravity equation for the inflow of migrants by source country to Switzerland and Section 5 concludes.

# **2. A brief overview of Swiss immigration policy**

The stated goals of Swiss immigration policy have always been, and still are, to control the level of foreign resident population while accommodating the demand for labor by the economy. Since 1970 and until 1990, the government implemented a policy based on quotas for work permits, which applied indifferently to citizens from all source countries. However, in the early 1990s, after the rejection of participation to the European Economic Area (EEA) by popular vote, pressures started to build for more open relations with the European Union, particularly with respect to labor mobility (see Gross, 2006, for details). This new environment encouraged the government to amend its immigration policy and to introduce differentiated treatment for immigrants from EU/EFTA countries and from other countries of the world. As a consequence, the former Republic of Yugoslavia and the countries issued from it saw the conditions for their migrants change significantly, in particular through increasingly difficult access to the Swiss labor market.

5
building types (buildings only, not contents) within countries across the world (USGS 2015). This typology has been developed to assess vulnerability to earthquakes but here we use it for all hazards.

There are 106 building types in PAGER, which we aggregate into three categories, by vulnerability level (see Appendix). Then, we apply simple depth‐damage functions for each of the three aggregated categories, inspired Hallegatte et al. (2013). The most vulnerable buildings are aggregated into one “fragile” category, and we assume that 70 percent of the building value is lost if a building in this category is affected. In addition, we have a “median” and a “robust” category, for which losses are equal to 30 and 10 percent. The PAGER data do not tell who is living in buildings of different categories. Here, we assume that the poorest live in the most vulnerable buildings; a very reasonable assumption in most cases. These loss ratios, developed for buildings, are also applied to the rest of the capital, including infrastructure and factories. The underlying assumption is that if individuals are living in fragile homes – for instance a mud wall house – they are also serviced by fragile infrastructure and work in fragile factories and buildings.

11
, allows for the presence of zero entries in the bilateral trade matrix.<sup>6</sup> Results for the outcome (trade intensity) and selection (trade propensity) equations are in columns 5-6. We use Doing Business data on the time required to start a business (in logarithms) as the over-identifying

> 6 We apply the standard Heckman estimator, but do not pursue the additional correction introduced by Helpman et al. (2008) to deal with firm heterogeneity.

14
; and Crossley, Fisher, and Low (2021) for developed countries and Bundervoet, Davalos, and Garcia (2021); Egger et al. (2021); and Khamis et al. (2021) for developing countries. While a few studies examine COVID-19 impacts on employment and incomes at the subnational levels (for example, Beyer, Jain, and Sinha 2020), a study on within-city variations of COVID-19 impacts is still rare. For instance, by combining a variety of private and public data sources, Chetty et al. (2020) analyze COVID-19 impacts on consumer spending, employment, and various other indicators at a geographically granular level (that is, zip code level) in the United States.<sup>3</sup> Our study adds to this literature on subnational and within-city analysis of COVID-19 impacts on employment and income.

Second, our work contributes to the literature on job accessibility in urban labor markets. Poor connectivity between firms and workers—as well as among themselves—is a major friction in urban labor markets, constraining productivity gains from agglomeration economies. Urban workers are more productive when job opportunities are physically more accessible, through, among others, lower job search costs and improved matching with firms (Combes and Gobillon 2015; Duranton and Puga 2004). Unfortunately, in many African countries, urban workers endure poor job accessibility in crowded, disconnected, and costly cities, where land use is fragmented and workers’ mobility is constrained by the lack of affordable and reliable transportation (Lall, Henderson, and Venables 2017). To exacerbate lowskilled workers’ situation, limited mobility and accessibility disproportionally affect them. Previous studies have found the effect of workers’ residential areas within a city or metropolitan area on their labor outcomes (Chetty, Hendren, and Katz 2016; Gobillon, Magnac, and Selod 2011). Among the mechanisms of such location effects on employment is the limited physical accessibility to jobs, which could exacerbate labor outcomes of already disadvantaged workers, such as African Americans
6|
|Labor productivity|connections per<br>employee|176|109|203|
|Total hidden costs as % of revenue|%|130.0|144.8|84.9|
|US cents per m<sup>3</sup>|Tanzania|Scarce|water resources|Other developing<br>regions|
|Residential tariff|39.8||60.26||
|Nonresidential tariff|117.0||120.74|3.0 – 60.0|

_Source:_ Banerjee and others 2009.

Data on utility performance are from 2007 and are a weighted average of performance indicators from the seven largest utilities in the country namely Arusha, Dawasco, Dodoma, Mbeya, Morogoro, Moshi, and Mwanza Derived from AICD water utilities database, http://www.infrastructureafrica.org/aicd/tools/data.

**Table 7b. Benchmarking water and sanitation indicators across Tanzanian utilities, 2007**

|Utility|Water<br>connections|Water delivered|System<br>losses|Collection<br>ratio|Average<br>total cost|Average<br>effective tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|---|
||(‘000s)|(mns m3/year)|(%)|(%)|(US$/m3)|(US$/m3)|(US$m/year)|(% revenues )|
|Arusha|23.8|16|33|95|0.68|0.28|6|193|
|Dodoma|16.1|7|34|113|0.93|0.45|3|127|
|Mbeya|19.
availability of administrative data sources is a key determinant of what is possible. Increasing access to tax data, even in summarized form, seems like a key goal if under-capturing of the top tail of the income distribution is to be solved.

# Challenges in Using Administrative Data

The discussion above has highlighted that administrative data can be used in several ways to solve challenges encountered in measuring the upper tail in household surveys. But these data also come with their own challenges, particularly in LMICs. In this section we discuss some of these challenges. Awareness of the challenges of using admin data is not new. Kuznets (1963:12), in comparing top income shares across countries produced by several different researchers, noted that “it may not be an exaggeration to say that we deal here not with data on the distribution of income by size but with estimates or judgments by courageous and ingenious scholars relating to size distribution of income in the country of their concern.” Haq (1964) lists several specific concerns in her analysis of income inequality in Pakistan using aggregated tax data. These concerns have subsequently been highlighted in recent research using admin data to better measure the top tail of the income distribution. These include the very small proportion of the population covered by tax data, which was one tenth of one percent in Pakistan at the time, changes in tax legislation that limited comparability over time (for example changes in the tax threshold), changes in efficiency of tax collection over time and the extent of the unregistered (i.e., informal) sector.

Other issues have also been highlighted in more recent research. Ravallion (2022) cautions that comparisons of surveys and national accounts data that show lower total incomes in surveys compared to national accounts could reflect the poor quality of national accounts rather than missing top incomes. Blanchet et. al (2022) note that in LMICs tax data is almost always in the form of aggregated tables rather than microdata, which limits what it can be used for and point out that
5 Our results also contribute to the vast literature documenting the strength – and rise – of comovement in asset prices and capital flows (Fratzscher, (2012), Raddatz and Schmukler (2012), Jotikasthira et al. (2012), Ghosh et al., (2014), Broner et al. (2013), Rey, (2013), Puy (2016), Claessens, Cerutti and Puy (2015)). Most of the debate has focused on the importance of global (or external) factors for (local) asset price movements, and the role of foreign investors in propagating shocks across countries. Broadly speaking, our results support the view that external events matter and are in general stronger than local factors. In particular, global news sentiment contributes significantly to global asset prices comovement, through its impact on international investors’ behavior. However, we do not find evidence that this phenomenon has increased over time or affects more EMs than AEs.

Finally, from a technical perspective, we are connected to the recent and fast-growing literature that links textual information to both economic and financial outcomes (see Gentzkow, Kelly, and Taddy (2017) for a review). Among many others, Baker, Bloom, and Davis (2016) develop an index of economic policy uncertainty from US newspaper articles, showing that it forecasts declines in investment, output, and employment.<sup>3</sup> Using daily Internet search volume from millions of households in the US, Da, Engelberg and Gao (2015) found that the volume of queries related to household concerns (e.g., “recession,” “unemployment,” and “bankruptcy”) could predict short-term return reversals, temporary increases in volatility, and mutual fund flows out of equity funds and into bond funds.

The rest of the paper is constructed as follows. Section II presents the data used in this paper, and the text analysis we use to construct sentiment measures. Section III presents the empirical framework
accounts (GDP, GNI, or Household Final Consumption Expenditure (HFCE)) differs from growth in average household consumption measured in household surveys (Ravallion, 2003; Deaton, 2005; Pinkovskiy & Sala-i-Martin, 2016, Lakner et a. 2021). To account for the difference between growth rates in per capita household consumption expenditures in national accounts and the per capita household consumption expenditures recorded in surveys, we construct different estimates for the pass-through using global data.

Specifically, we estimate different values of the pass-through rate using survey data on monthly household consumption available in PovcalNet (http://iresearch.worldbank.org/PovcalNet/home.aspx) matched to national accounts data from the WDIs to estimate the difference in growth rates between the two. To do this, we first pool all of the available data on household welfare (measured as either income or consumption) and national accounts for country-years in PovcalNet, obtaining a data set of 1,751 country-year spells when using GDP data and 1,521 spells when using GNI data (Table _10_ in the Appendix).<sup>20</sup> Then, we construct separate samples and run separate regressions using data for: all countries except high-income economies (column 2, Table _10_ ); all lower-middle-income countries (column 3, Table _10_ ); all countries with survey-estimates based on consumption as a measure of household welfare (column 4, Table _10_ ); and for countries in Sub-Saharan Africa (column 5, Table _10_ ). Each regression estimates a different value of the pass-through rate using the growth rate of either GDP per capita or GNI per capita as a predictor. The bottom panel of Table _10_ shows the results when running the regressions on the same groups of countries but using only “comparable surveys” as defined in PovcalNet’s comparability data set.<sup>21</sup> Results are reported in Figure 4 for pass-through rates between 0.42 (pass-through estimate for countries in fragile-conflict affected situations, see Corral et al