rafmacalaba/data-use-mentions-extended huggingface dataset:
countries–38 of them in Europe–during 1990–2012 and find that trade openness increases labor force participation. Dauth et al. (2017) argue that increased trade openness with China and Eastern Europe from 1993 to 2004 allowed Germany to retain manufacturing jobs. Donoso et al. (2015) demonstrate that while Spanish provinces where Chinese imports grew faster experienced a significant decline in manufacturing employment, this decline was compensated by increasing employment in non-manufacturing sectors. Colantone (2012) shows that increased trade openness in the manufacturing sector resulted in job destruction and decreased employment growth in Belgium from 1996 to 2002. <u>Arvanitis and Loukis (2015)</u> report that in Switzerland, trade openness increased the demand for high-skill workers, destroyed employment for medium-skill workers and has no effect on low-skill workers. Balsvik et al. (2015) demonstrate that import competition from China from 1996 to 2007 explains almost 10% of the reduction in manufacturing employment in Norway. Low-skill workers are the most exposed to unemployment and non-participation in the labor market.

# **3. Data and Descriptive Statistics**

Our main data set for the analysis of job tenure is the EU-LFS constructed by Eurostat (2021), based on the results of a large household survey that provides quarterly data on labor force participation by respondents older than 15. National statistical institutes in every EU country conduct these surveys, applying harmonized concepts and definitions, and Eurostat processes the results. This structure allows for cross-country comparisons. Beginning in the early 1990s, the survey covered Iceland, Norway, Switzerland, and the United Kingdom, in addition to members of the European Union. The sample size of the EU-LFS ranges from 4.5 million to 5.2 million observations, depending on the year.<sup>6</sup> Because data on tenure are sparse before the mid-1990s, our sample is based on data for 1995– 2020. We restrict it to workers between the ages of 20 and 65, as often done in the literature
Policy Research Working Paper 9626

# **Abstract**

This paper disaggregates human development indicators across disability status to assess the situation of persons and households with disabilities. The paper uses 24 censuses and general household surveys from 21 low- and middle-income countries. Disability status is measured through self-reports of functional difficulties (for example, seeing or hearing). There are several findings of interest. First, disability is not rare in low- and middle-income countries. The median prevalence stands at 10 percent among adults ages 15 and older, and at 23 percent among households. There are consistent inequalities associated with disability and, in particular, with respect to educational attainment, work outcomes, poverty, food security, exposure to shocks, living conditions, and assets. At the same time, not all persons with functional difficulties experience deprivations. There is a gradient in inequalities associated with the degree of functional difficulty: persons with at least a lot of difficulty tend to be worse off than persons with some difficulty, who themselves tend to be worse off than persons with no difficulty. The results in this paper on the prevalence of functional difficulties and their association with socioeconomic deprivations show that disability should be central to human development policy, data, and research. More work is needed to curb the inequalities associated with disability.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mitra@fordham.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be
. Such data compilation is essential for the structuring and pricing of such insurance contracts. The key element of such contracts' success, however, is the reliability and objectivity of the weather measurement mechanism.

In the Caribbean, the Miami Hurricane (Tropical Prediction) Center provides substantial hurricane tracking data which could potentially be used in constructing the weather trigger source for such contracts. However, if such sources are not sufficiently 'fine tuned', particularly for small island areas, then a hard installation of 'on-the-ground' wind speed and rainfall measuring equipment may be necessary in the participating countries. Such hard installations would not be very costly, but would require telecommunication/radio linkages (e.g.: via satellite) to a central recording center to assure objectivity of the data reported.

From a portfolio management viewpoint, international investors would find it desirable to hold some of these instruments in their portfolio in order to diversify the usual financial market risks (ie.: achieve some non-correlation with global markets). While holding such instruments in their portfolio, they would of course be liable for potential losses or yield reduction in the event of a catastrophe. This income (as well as the risk of payout) would be factored into their overall portfolio investment return, though they would have the added assurance that a drop say, in the world stock or bond markets would have little or no effect on the performance of the catastrophe instruments during the life of the bond.

# **_Combining both Schemes in a Joint Structure and the Catalytic Role for Multilaterals_**

Due to the complementarity of both of the above schemes, the simultaneous development and feasibility testing of these would be essential, in order to best capture the range of catastrophe protection tools which might be applied in the Caribbean region.
In addition, we also constructed indicators through internet searches, for particular city attributes, such as city population, existence of city websites and availability of data on budget and business regulations in such websites. We completed this list of city attributes by including two dummies for whether the city is the country’s capital and/or a port.

At the country level, we draw from the variable “Control of Corruption” from the Worldwide Aggregate Governance Indicators discussed above, where corruption (conventionally defined as the exercise of public power for private gain) is one of its components. The indicator is the aggregate of many individual sources, covering several aspects of corruption, ranging from administrative corruption to “grand corruption” in the political arena and “state capture”. We also constructed an income per capita variable (PPP) by drawing from the Heston-Summers database and the CIA World Factbook (2001). For globalization at the country level, we draw from the A.T. Kearney/Foreign Policy Globalization Index. It tracks and assesses changes in four key components of global integration, incorporating such measures as trade and financial flows, movement of people across borders, international telephone traffic, Internet usage, and participation in international treaties and peacekeeping operations. It covers 62 countries, including industrialized and emerging economies.<sup>14</sup> In Table 1 below we present the legend of all the variables we utilize in the empirical testing of these hypotheses, and their sources and characteristics.

> 14 Specifically, in this index, economic integration combines data on trade, foreign direct investment (FDI), and portfolio capital flows, as well as investment income payments and receipts. Personal contact tracks international travel and tourism, international telephone traffic, and cross-border remittances and personal transfers (including worker remittances, compensation to employees, and other person-to-person and nongovernmental transfers). Technological connectivity counts the number of Internet users, Internet hosts, and secure servers through which encrypted transactions are carried out. Finally, political engagement tracks each
IRTS goods.‖) The structure of consumer demand for imperfectly competitive services sectors (equivalent to business services in our model) is depicted on the left side of figure 3.1 under ―Business Services‖ (value-added does not enter the demand function of consumers). The structure of production is identical to the single representative agent model.

Consumer demand, as well as firm level demand, exhibits love of variety in imperfectly competitive goods. Given that we have weak separability and homothetic functions at all levels of consumer demand, the conditions for two-stage (or multi-stage) budgeting are satisfied. Given the initial data on each of the households, and our assumptions on the structure of demand, we solve for the parameter values in each of the 55,098 household utility functions that are consistent with optimization by the households. Thus, the demand functions of all households are dependent on their initial choices and, in general, differ from one another.

# **4.3. Data**

Here we focus on the data relevant to development of the multi-household model. Unless otherwise specified, we use parameters and data from the single representative agent model.

# **Households**

Households are modeled endogenously based on the 55,098 households of the Russian Household Budget Survey (HBS). The HBS, which is representative at the regional level, has very detailed information on household consumption expenditures, and information about age, gender, education, and occupation of each member of the household. It also has information about expenditures and savings and by implication household income.

The major shortcoming of the HBS for our purposes is that it does not contain information on the sources of income of the households. For sources of household income, we must turn to the 65
Figure 3– Product-level tariffs changes: 2012-2013 and 2013-2018 Note: UN TRAINS. We excluded from the graph the motor vehicles, beverages, and tobacco products sectors that have an average tariff of 38.5 and 60.5 respectively.

FDI inflows in Pakistan have been relatively low during the period of analysis, remaining systematically below 2 percent of GDP. In absolute terms, FDI inflows surged from about US$300 million in 2000 to about US$5 billion in 2008, resulting in increases in stocks, as shown in Figure 4, which plots the accumulated stock of FDI. The increases have been largely driven by FDI in the services sector, in particular communications and financial services. FDI in both manufacturing and services has stagnated since 2010, with FDI in services experiencing a negative net inflow in 2012. As a percent of Pakistan’s GDP, overall FDI was less than 1 percent (0.06 percent) in 2015, compared to 2 percent in India and 1.5 percent in Bangladesh in the same year. Communications (27 percent of total FDI inflow in 2015) and the financial sector (19 percent) remain the dominant services sectors matched by the oil and gas sector (21 percent).

Figure 4– Accumulated FDI stock Note: Author’s calculation based on data from State Bank of Pakistan. The vertical line indicates the period of analysis. Data are in million USD (constant).

7
Change? _Ecology and Society_ , _19_ (1). <u>https://www.jstor.org/stable/26269470</u>

- Muttarak, R., & Pothisiri, W. (2013). The role of education on disaster preparedness: case study of 2012 Indian Ocean earthquakes on Thailand’s Andaman Coast _. Ecology and Society_ 18(4): 51. <u>http://dx.doi.org/10.5751/ES-06101-180451</u>

- Nguyen, M.C., Yang, J., Dang, H.A., Gonzalez, S., & Gerardo, C. (2023). On the Construction of the World Bank’s Subnational Poverty and Inequality Databases: Documentation (English). Global Poverty Monitoring Technical Note no. 31. Washington, D.C.: The World Bank.

- Pape, U. J., & Ali, R. (2023) Indonesia Poverty Assessment - Pathways towards Economic Security (English). Washington, D.C.: The World Bank.

- Pega, F., Liu, S. Y., Walter, S., Pabayo, R., Saith, R., & Lhachimi, S. K., (2017). Unconditional cash transfers for reducing poverty and vulnerabilities: Effect on use of health services and health outcomes in low- and middle-income countries. _The Cochrane Database of Systematic Reviews_ , _2017_ (11). <u>https://doi.org/10.1002/14651858.CD011135.pub2</u>

- Pesaresi, M., et al. (2017). Atlas of the Human Planet 2017: Global Exposure to Natural Hazards, EUR 28556 EN, doi: 10.2760/19837.

- Pople, A., Hill, R., Dercon, S., & Brunckhorst, B. (2021). Anticipatory Cash Transfers in Climate Disaster
<mark>structure from developing to developed countries in the European Union (EU) relate to carbon emissions. They find that carbon emissions increase initially as countries move from agriculture to manufacturing, but a rising share of the service sector and increasing per capita incomes leads</mark> to reduced emissions, thus confirming the U-shaped curve.

<mark>A more recent paper by Mrabet et al. (2021) uses panel data for 16 Middle Eastern and North African countries (period 1990–2016) to examine the relationship between Ecological Footprint and HDI. They find that improving HDI will harm the environment in the early stages of development by increasing the Ecological Footprint, but as the country develops, further increases in human development will result in lower levels of environmental degradation. These findings thus suggest that human development and political stability can have a positive effect on the environment through better education and health care systems. However, validating these</mark> mechanisms and establishing a causal relationship still requires further research.

<mark>The remainder of the paper proceeds as follows. The next section presents a synthesis of literature to establish that climate change can indeed affect human development outcomes. It also presents an overview of the adaptation and mitigation measures that can reduce the negative impact of climate change. While some interventions, such as risk assessment of buildings and disaster management, can aid adaptation and reduce climate vulnerabilities, others, such as curriculum redesign and promoting research in universities, can enable mitigation. The last section then shifts focus to the literature on the linkages between education and climate change and presents a possible framework to study these linkages. The last part of that section also presents compulsory</mark> schooling laws as one instrument that can be used to establish causal linkages.

# **2. Impact of Climate on Human Development**

<mark>The influence of our physical environment and climate on human development outcomes such as educational attainment and child mortality has been studied by researchers in different contexts and regions of the
Table D.7: _(... continued)_ Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference: Alternative Samples and Specifications - Analysis of Results from Table 4

||||Sector of|Work||||
|---|---|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|Ideal Number<br>of Children|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|(6)<br>|(7)<br>|
|||||||_Nat_|_onal_|
|||||||_(Separate)_|_(Combined)_|
||||F. U|sing 1984 Census Mat|ched Start Ages|||
|�<br>Years of Schoolingizy|0.071<br>|0.027<br>|0.088<br>|-0.061<br>|-0.345<br>|-1.116<br>|-1.198<br>|
||(0.051)|(0.014)|(0.026)|(0.036)|(0.518)|(0.933)|(0.472)|
||[0.160]|[0.057]|[0.001]|[0.089]|[0.505]|[0.232]|[0.011]|
|First Stage F-Statistic|6.22|5.79|5.79|
distribution (proposal or importance distributions) and find moments of simulated data, which are going to converge to moments of posterior distribution under specific conditions (Herbst and Schorfheide 2015).

We use Metropolis Hastings (MH) algorithm, which is part of MCMC family. MH algorithm simply evaluates each parameter vector drawn ( _θ_<sup>_i_</sup> at iteration i) from a proposal distribution at the kernel of the posterior distribution over the proposal distribution, and compares this to the ratio of the vector stored in iteration (i-1). If the ratio of the former to the latter is higher than a random number u _∈_ (0 _,_ 1), then candidate draw is accepted and stored. If not, _θ_<sup>_i−_1</sup> is stored again among accepted parameter vectors.

There are two important components of the MH algorithm. One is the proposal distribution, which is assumed to be a multivariate normal distribution with mean _θ_<sup>_i−_1</sup> (last 66
the representative reference samples.

Propensity score adjustment seems to work better than post-stratification in settings where they have been compared directly. Isaksson and Forsman (2003) study political polls in Sweden and find that propensity score adjustment gives more accurate estimates than poststratification weighting. Yeager et al (2011) compared Internet surveys using non-probability samples of American adults with aggregate benchmark surveys. They tested the results for demographic and some non-demographic variables. Results indicate that post-stratification of non-probability samples did not consistently improve accuracy.

<mark>Szolnoki and Hoffmann (2013) compared a representative face-to-face survey with 2,000 respondents and a telephone survey with 1,000 respondents were compared with two online surveys, one based on quota sampling (2,000) and the other on snowball sampling (3,000) using identical questions. The objective was to assess consumer data for research in the wine business. The study did not conduct any reweighting for their online survey based on quota sampling. For the online survey with snowball sampling, they did not undertake reweighting because a weighting factor greater than five is seen as very problematic (Bandilla et al. 2003). Face-to-face data delivered the best results, followed by the telephone interviews and finally the online quota survey.</mark> <mark>Some studies do not directly study web surveys, but use data collected during regular survey interviews from households with and without Internet access. This helps avoid the possibility that collecting data via the Internet as opposed to face to face or over the phone may directly affect the answer to survey questions. Dever et al (2008) compare estimates collected during telephone interviews from households with and without Internet access using data from the 2003 Michigan Behavioral Risk Factor Surveillance System in the United States. Their analysis results</mark> 9
the 4-5 and 6-7 year-old groups are as large as the 0-1 and 2-3 year-old groups (in absolute terms and relative to men). This indicates that tremendous churn at the entry margin is unlikely to be occurring, providing evidence against pre-trends that are influencing the timing of elections. Based upon Figure 4’s deviations for never-adopting states, we will specifically test below that our results are robust to this group’s inclusion or exclusion.

As a second approach, we look for pre-trends in women’s involvement in manufacturing on dimensions other than entrepreneurship. We can do this with respect to women’s raw employment in manufacturing and their share of manufacturing employment in three ways: the unorganized sector through the 1989 and 1994 NSSO surveys, the organized sector through the 1989 and 1994 ASI surveys, and the household surveys from 1987 and 1993. Appendix Table 2 reports an analysis similar to that in Figures 2 and 3.

We examine the degree to which the log growth in women’s employment by state or state-industry in the years preceding our sample period correlates with future election timing. These estimations find no evidence that changes in women’s employments in manufacturing in the pre-period predict the ordering of implementations across states. These tests are also admittedly imperfect, as we do not later observe a strong response on this margin, and we unfortunately cannot measure the entry margin similarly. Nonetheless, the combination of these tests with those of the 1994 establishment age distributions do provide substantial support to our identification assumption that the election timings are exogenous. After we introduce our estimation strategy, we further examine dynamic estimations that also show the responses in women’s entrepreneurship follow the reforms in a timing that is consistent with the reservations causing the increased entry rates.

# **5.  Empirical Analysis**

This section provides our core empirical results. We begin with a broad analysis of the impact for women in manufacturing that includes wage employment and establishment ownership. After observing the concentrated impact on entrepreneurship, we conduct a focused analysis on the
statistically different from one, and as Figure 2 indicates, the estimated size of negative real GDP growth shocks is even larger for antidumping alone than all TTBs jointly. One notable difference from the baseline results is that GDP growth in a foreign trading partner has no statistically significant impact on the number of products subject to antidumping. Finally, the general time trend across all of the countries in this sample is that antidumping alone is on average declining over 1995-2010.<sup>19</sup> Column (6) presents a still different approach to construction of the dependent variable whereby we broaden it (relative to the baseline) to include the count of all products subject to TTB _investigations_ , including those that may not have resulted in the imposition of new import restrictions. The results are qualitatively unchanged according to the estimated IRRs and the magnitudes of the effects illustrated in Figure 2. If anything, TTB investigations alone (relative to imposed barriers of the baseline definition) appear slightly more responsive to domestic real GDP shocks and slightly less responsive to real exchange rate appreciations. Furthermore, the overall time trend of products subject to new investigations during this period is strongly increasing.

Finally, column (7) of Table 3 presents the results from the empirical model of temporary trade barriers for an important subsample of emerging economies G20 members; i.e., Argentina, Brazil, China, India, Indonesia, Mexico, South Africa, and Turkey.<sup>20</sup> The results for this set of countries are broadly similar to those for the larger sample of emerging economies.

# **5 Investigating the Impact of the WTO on New Import Protection**

Thus far our estimates for the emerging economies’ use of TTBs have been undertaken on samples of data beginning in 1995. Our argument is that this is the period during which emerging economies faced a relatively common set of rules under the WTO regarding how to implement import protection through TTB policies. In this section we investigate empirically whether this new environment has