rafmacalaba/data-use-mentions-extended huggingface dataset:
dependence is reminiscent of the literature on trade and business cycle co-movement: since Frankel and Rose (1998) and Kose and Yi (2001), many papers have documented the link between international trade and GDP co-movement at business cycle frequency. More recently de Soyres and Gaillard (2019) emphasized the specific role of trade in intermediate input in the rise of business cycle synchronization across countries. In this sense, GVC participation will be associated with a rising synchronization of real economic activity and inflation across countries.

The reminder of the paper is organized as follows. Section 2 presents a brief literature review. Section 3 documents the data sources. Section 4 presents our empirical strategy. Section 5 presents our main results and section 6 concludes.

# **2 Literature review**

The “Globalization of Inflation” is a phenomenon that has been discussed heavily in the literature over the past few years. The decline in the level and persistence of inflation after 1980 is seen as a common feature of the most industrialized economies in the world (Mumtaz and Surico, 2012). Ciccarelli and Mojon (2010) use data for 22 OECD countries and conclude that a common factor accounts for 70% of the variability of country inflation, on average. Changes in the conduct of monetary policy across the world have contributed on this regard (Rogoff, 2003). Interestingly, trade models also provide guidance on this regard. For instance, Melitz and Ottaviano (2008) develop a theory in which openness decreases average mark-ups and lowers inflation across economies experiencing an increase in integration through trade. In this section, rather than providing a complete review of the studies explaining the global synchronization of inflation, we cover the recent literature studying the link between the global inflation synchronization and trade integration.<sup>2</sup> While most of this empirical evidence is based on the use data for advanced economies, it allows us not only to keep the discussion limited to the scope of the paper, but also to contextualize our results based on a
**Figure 5. Concentration shares of fiscal interventions (% of total interventions per quintile)**

<!-- Start of picture text --> 100<br>90<br>94<br>80<br>70<br>60 61<br>50 56<br>40 48<br>20<br>30<br>12<br>20<br>21<br>10<br>6<br>0 1<br>1 2 3 4 5<br>Quintiles of per capita market income plus pensions<br>Direct Transfers Direct Taxes Indirect Taxes<br>Health Education MIPP<br>% of total expenditure/tax<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

# 3.4 Progressivity

Most direct transfers, education, and health benefits are pro-poor because the absolute value of the transfer is higher for lower-income households. Rural pensions, Bolsa Família, and BPC have the highest Kakwani indexes, meaning that they are the most progressive fiscal interventions because most of the benefits go to the bottom quintile and a smaller share goes to the top quintile (Figure 6). Both primary and preschool levels are more concentrated in low-income households. However, tertiary education benefits are progressive but not pro-poor. 32F<sup>33</sup> That is, the Kakwani coefficient is positive, but lower than as the Gini of MIPP (0.585). This is also the case of direct taxes and Abono Salarial. The social security contributions are proportional to the market income, with a slight tendency to be progressive, while the employer contributions to the pension system (Cota Patronal) and employer contribution to FGTS are proportional, leaning to be regressive.

> 33 In the CEQ methodology, a government transfer
agriculture or services in Bangladesh, nor agriculture in Cambodia. In India, only the states of Gujarat and Maharashtra have agriculture included in the survey. Table 2 provides the distribution of the number of firms sampled in each country, by sector and firm size group.

We exclude micro-firms with fewer than 5 employees. Micro firms, particularly in developing countries, are more likely to be informal (Ulyssea, 2018), making them less likely to be captured in the sampling frame; and this would require further adjustment in the survey instrument and sampling design.<sup>30</sup> This size threshold is aligned with other firm-level standardized surveys with comparability across countries. The World Bank Enterprise Survey (WBES) also uses a threshold of 5 employees. The World Management Survey (WMS) uses a threshold of 50 employees.

We stratify the universe of establishments by firm size, sector of activity, and geographic regions. Our sample is representative across these dimensions. In the firm size stratification, we have three strata: small firms (5-19 employees), medium firms (20-99 employees), and large firms (100 or more employees). Regarding sector, for all countries, we stratified at least for agriculture (ISIC 01), food processing (ISIC 10), Wearing apparel (ISIC 14), Retail and Wholesale (ISIC 45, 46 and 47), other manufacturing (Group C, excluding food

> 30In addition, establishments below this threshold often lack the organizational structure to respond to some of the questions.

99
, and take “the income from other sources” out of total income of the household in the econometric analysis by considering this income as exogenous.<sup>17</sup> Table 1 describes the sample characteristics of the remaining 742 households.

> 11 In 1996, the GDP per capita of these Liaoning and Hebei ranked 8th and 12th respectively among the 30 provinces in China.

> 12 The LSMS is designed to provide a comprehensive picture of household welfare and the factors that affect it. In order to minimize non-sampling error and ensure high quality data, the samples of the LSMS are relatively small. Substantial efforts and resources, including long interviewer trainings, use of direct informants, concurrent data entry with in-field corrections taking place at the households, and low supervisor-interviewer ratios, are devoted to enhance data quality. See Munoz and Scott (2005).

> 13 See “China Living Standards Survey 1995-1997: Basic Information Document”, http://www.worldbank.org/lsms/, for further information on the survey data.

> 14 See Lanjouw and Lanjouw (2001).

> 15 In rural China, households work on small plots of cultivable land.  In general, no households need to hire extra labor. Income earned from agricultural wage employment is limited.

> 16 Some households have incomes from non-productive activities, such as pensions, transfers, grants/subsidies, financial income, etc.

> 17 Among the 45 households that do not have farm income, 70% of them do not have land; among the remaining 30% households that have land, most of them have less than 2.5 _mu_ (compared to 10.5 _mu_ in average for the 742 households with farm income in the sample). The members of these households work as village teachers, village

6
: *** p < 0.01, ** p < 0.05, * p < 0.1

The survey data provides important individual and household characteristics like age, gender, marital status, location of settlement, educational attainment, access to basic amenities, and other household demographics. Table 2 presents summary statistics of these characteristics by Covid19 status. The regional distribution shows no difference between the pre- and post-Covid-19 samples for the rest of Ngazidja, the main island, which accounts for 40% of the total sample. About 43% of the sample is resident in Ndzuwani Island, the second largest in the Comoros, with observed differences between the treatment and control groups. The individual demographics are similar for pre- and post-Covid-19 except for the literacy rate, which is higher for the control group. There are some differences in household access to basic amenities 17
flows, such as foreign aid and remittance inflows, were collected from the World Bank’s World Development Indicators (WDI). The GDP level and growth data were gathered from the WDI. The set of pull factors considered in this paper also includes the Consumer Price Index (CPI) inflation (computed as log differences in the CPI) from WDI, the general government primary balance a percentage of GDP from the IMF’s World Economic Outlook, the exchange rate regime based on the Fine classification of exchange rate regimes developed by Reinhart, and Rogoff (2004) and updated by Ilzetzki, Reinhart and Rogoff (2017), and trade openness as the ratio of exports and imports to GDP from the WDI, and the index of financial openness from Chinn-Ito (2006, 2008). Other important pull factors are the quality of institutions proxied by the following ICRG components: investment profile (which accounts for contract viability, expropriation, and profits repatriation), socio-economic conditions (capturing forces at work in society that could constrain government action or fuel social dissatisfaction), government stability (which reflects the government's ability to carry out its declared policies), rule of law (which captures the strength and impartiality of the legal system, and the popular observance of the law), bureaucratic quality (reflecting the strength and expertise of the bureaucracy to govern without drastic changes in policy or interruption in government services), and corruption. Higher values of all these ICRG components imply higher quality of institutions. Push factors are foreign growth as the trade-weighted GDP growth of main trading partners, the VIX index measures volatility computed using S&P 500 index options, and US policy uncertainty is captured by the baseline overall index computed by Baker, Bloom and Davis (2015). Other external factors are commodity prices such as the international price of oil, the price index of minerals and metals, the price of agricultural commodities, and an index of commodity terms of trade which are gathered from the World Bank’s Commodity Outlook.
are summarized in the technical annex to the main report (De Vries Robbé et al. 2020).

## 5.3. Economic impacts of flooding in the tourism sector

For tourism, economic estimations are conducted for flooding and coastal erosion. The share of hotels exposed to each of the two hazards in each coastal province is sourced from the exposure analysis in section 4. For flooding, this consists of the share of exposed hotels by 25cm flooding for each flood return period. For erosion, this is the share of coastal hotels affected by strong or moderate coastal erosion. The methodology then proceeds in two steps. First, each province’s contribution to tourism GDP is calculated by dividing national tourism GDP by the share of hotels in each province. Analogously, the number of jobs in the tourism industry is distributed among provinces. In the second step, the share of hazard-affected hotels is applied to the amount of GDP or jobs affected for each province, each hazard, and each hazard return period. This results in a province-level estimate of affected GDP and jobs, once for coastal erosion and once for each flood return period. Detailed province-level results for each return period and type of flooding are summarized in the technical annex to the main report (De Vries Robbé et al., 2020).

# 6. Discussion and conclusion

In conducting a quantitative analysis, this paper relies on the quality of the underlying hazard and sectoral data. The spatial hazard data used to model coastal flooding and typhoon wind exposure are extracted from global hazard models. Such global models require global datasets as input and incur significant computational complexity. Consequently, they have a coarse spatial resolution, like a grid cell size of about 900x900m for coastal flooding in Vietnam. In reality flood depth is not uniform within each 900x900m block of land, but heterogeneous due to the elevation profile within that block. Consequently, 30
0.039)|(0.039)|(0.039)|(0.039)|(0.040)|
|Colonycj|-0.089|-0.088|-0.086|-0.089|-0.087|
||(0.084)|(0.084)|(0.084)|(0.084)|(0.084)|
|Distancecj|0.090*|0.091*|0.090*|0.091*|0.089*|
||(0.048)|(0.048)|(0.048)|(0.048)|(0.048)|
|GDPpcc x sanitary_riskk|0.00016*<br>(0.0001)|||||
|EDBc x sanitary_riskk||0.0001<br>(0.0001)||||
|LPIc x sanitary_riskk|||0.0120<br>(0.036)|||
|Infrastructurecx sanitary_riskk||||-0.052<br>(0.033)||
|TABc x sanitary_riskk||22|||-0.0007<br>(0.0005)|
|Observations|14870|14870|14870|14870|14870|
0.33|0.29|0.10|0.05|0.06|
|Agriculture|0.21|0.15|0.2|0.11|0.38|0.22|1.02|1.07|0.55|0.49|0.5|0.43|0.22|0.05|
|T&G|0|0|0|0|0|0|1.1|0|0.75|0|0.24|0|0|0|
|All sectors|0.32|0.26|0.26|0.14|0.25|0.14|0.53|0.53|0.28|0.23|0.2|0.14|0.08|0.06|

_Source:_ Calculations based on the Sri Lanka 1992–2002 2008, 2011, and 2012 Labor Force Surveys. _Note:_ T&G = textiles and garments.

# Employment fluctuates with changes in labor demand, suggesting imperfect mobility

between industries. Working conditions fluctuate less over time, suggesting that our model which focuses on wage changes in the context of imperfect labor mobility is not at odds with the labor market characteristics of Cambodia and Sri Lanka.

Table 14 shows the regression results using the hourly wages for both Cambodia and Sri Lanka. The qualitative results are similar. The higher apparel prices in the MFA period are characterized by higher industry-specific wage premiums and smaller male-female wage differences. After the MFA, when apparel prices were lower, the apparel wage premiums are lower and the male-female wage gap is larger. These results are consistent with the hypothesis
These findings are largely consistent with results recently published by the World Bank (2011). In particular, partial correlation analyses of the World Bank confirm that employment growth over a three year period is positively correlated with both the size of the firm and the location in a capital city or cities with over a million people. Furthermore, business environment constraints are perceived significantly higher in armed conflict areas when compared to low-conflict areas, which may affect firms’ growth performance. More generally, the report confirms that the entire region is struggling with instability and conflict: the business environment shows that political instability is one of the top three constraints in countries where the question was included in the underlying enterprise survey instruments of the data considered. Also, the report finds a positive relationship between employment expansion and innovation in a broad sense, which is typically linked to physical investment.

# **Summary**

South Asia Interim Enterprise Survey data of 2009/10 confirms that South Asia has performed very well – a substantial share of establishments expanded their employment, and expanding firms outweigh the share of establishments that shed employment. Despite the positive aggregate dynamics, this growth was far from uniform. There has been great variance across industries, size classes of firms, countries and regions.

At the sector and industry level, especially real Estate Renting & Business Activities, the transport industry, fabricated metal products, machinery and equipment, chemicals, rubber and plastics and construction performed well. At the country level, especially establishments in the Bhutan sample have increased employment at a significantly faster pace than other countries.

Employment generation is primarily driven by large companies with over 100 employees. This particularly holds for Sri Lanka, Bhutan, Afghanistan, Pakistan and Bangladesh. Similarly, firm age matters for employment generation. Firms older than 26 years represent 21% of the sample; however, they create approximately half of all jobs, since they are significantly larger than companies of other age classes. Young firms that are not older than five years account for 17% of
price elasticity of oil demand (measured by the ratio of the impact responses of oil production and of the real price of oil to an oil supply shock) and an upper bound of 0.025 on the impact price elasticity of oil supply (measured by the ratio of the impact responses of oil production and of the real price of oil to an oil demand shock).

**Robustness #2.** As before, we check the robustness of the benchmark model for the inclusion of countryspecific real effective exchange rates as an additional endogenous variable. The real effective exchange rate variable is ordered last in the SVAR while all other details are the same as in the benchmark model.

# **3.4. Database and descriptive statistics**

The quarterly data cover the period between 1991Q1 and 2022Q3 for 45 EMDEs for the models without the real effective exchange rates and 29 EMDEs for the models with real effective exchange rates (both due to data availability).<sup>7</sup> After considering year-on-year changes, the sample period reduces to 1992Q22022Q3, although it can change across countries due to data availability (with at least forty quarters of observations).

All energy prices are taken from the World Bank Commodities Price Data (‘ _The Pink Sheet’_ ). Global crude oil prices are measured as the average between Brent, Dubai and West Texas Intermediate crude oil prices, represented as U.S. dollars per barrel. Global natural gas prices are measured by the natural gas index. Global coal prices are measured as the average between South Africa and Australia coal prices,

> 7 The list of countries and country groups is available in the Online Appendix Table A.1.

9
goods with the aim of leveling the social status, at least in terms of appearances, among migrant households. The 2014 wave of the KMS collected domestic remittance data for the first time. These data reveal that the amount of overseas versus domestic remittances allocated to home investments and durable asset purchases was statistically indistinguishable among migrant households as a whole within the survey sample.

# **7. Conclusion**

Few papers have compared the changes in household welfare associated with international versus domestic migration, despite the fact that families thinking about sending a member away from home to work may have both pathways available to them. This study helps to fill this gap by focusing on households in Kerala, India, that chose one of these two pathways for a family member at some point between 1998 and 2003. Taking advantage of a unique panel data set available for Keralite households over this five-year period, I compare the gains in assets (represented by housing investments and purchases of durable goods) that these two groups of migrant households experienced during that timeframe. The study design is characterized by a double-difference specification with instrumental variables to help account for the non-random selection of households sending members outside Kerala to work.

An intriguing implication of the welfare analysis for the families of these migrants back home is that individual migrants are able to take advantage of international wage differentials—particularly for low or unskilled jobs that require lower levels of education—to generate sizeable gains to their origin households that are equal, if not larger, than the gains that the relatively more educated individuals who migrate domestically are able to produce. If a household can finance the upfront costs of overseas migration, less educated individuals are able to exploit the strong demand in the Arabian Gulf nations for low or unskilled labor, securing contract-based jobs that pay a substantial premium over similar positions back home. This phenomenon could be viewed as the result of 24
# **2.2 Informal employment in Eastern, Western, Northern and Southern Europe**

It appears that informality is most prevalent in the South and least prevalent in the Nordic countries, whilst the difference between the West and the East is, on aggregate, surprisingly small, especially as far as population-weighted estimates are concerned. According to population-weighted estimates based on respondents‘ status _during the survey_ (Table 2, panel A, left), the proportion of employees without a contract among all employees in 2008-2009 varies from 2.7% in the Nordic countries to 9.5% in the Southern Europe, whilst it is just above 5% in the West and in the East alike; when those who did not respond to the question regarding the contract are treated as not having a contract (which is a plausible assumption), the prevalence of work without a contract becomes higher in the East (6.7%) than in the West (5.5%), whilst it does not change much in the North and in the South. Equally-weighted estimates are substantially higher than the population-weighted ones for the South and for the West, disregarding the treatment of non-response. This is due to very high proportions of employees without contracts in a few relatively small countries: Cyprus (almost half), Greece and Israel (about one third), Ireland (close to one fifth), and Austria (one tenth); see Table 3 for details.

Total informal employment (i.e., employees without contracts, non-professional selfemployed operating solely, employers with 5 or less employees, and family workers) accounts for about 10% of extended labor force in the Northern Europe, about 14% in the West and in the East, and about 25% in the South; equally-weighted averages are again higher for the South and for the West (Table 2, panel A, right). The overall population-weighted average for the 30 countries covered is 15.7%, and equally-weighted average is 17
|**Table 18 Comparis**|**on of repo**<br>**Unconditi**|**rted constraints**<br>**onal means**|**by formal,**|**small forma**|**l and informal**<br>**Survey fixed e**|**sector firms**<br>**ffects and l**|**(manufactur**<br>**og(L) (center**|**ing & India**<br>**ed on L=5)**|**only)**|
|---|---|---|---|---|---|---|---|---|---|
||All formal|Small formal|Informal|Diff?|Small formal|Informal|Diff?|Obs (SF)|Obs (I)|
|Electricity|1.790|1.818|1.572|Smaller|1.817|1.574|Smaller|2,247|1,951|
|Telecoms|0.515|0.497|0.920|Greater|0.522|0.998|Greater|2,144|1,951|
|Transport|0.905|0.826|1.836|Greater|0.838|1.840|Greater|2,244|1,951|
|AccessLand|0.713|0.752|1.672|Greater|0.901|1.686|Greater|2,149|1,951|
|InadEducLabor|1.013|0.974|1.714|Greater|0.860|1.720|Greater|2,151|1,
# **1 Introduction**

In October 2013, the 19<sup>th</sup> International Conference of Labour Statisticians (ICLS) adopted a new resolution concerning statistics of work, employment, and labor underutilization (ILO 2013b, referred to as the “19<sup>th</sup> ICLS standards” or the “revised standards”), which supersedes previous international standards for labor statistics (dating back to 1982).<sup>1</sup> An important element of the revised standards is to narrow the definition of employment to work performed for pay or profit, that in the case of agricultural activities is based on the _self-declared main intended use of the output_ . Own-use production of goods, such as crop cultivation mainly or only for own/family consumption, is no longer considered employment and, hence, does not count towards employment or labor force participation. Instead, such work is captured as a separate form of work and used to produce the headline indicator of “own-use production work”.<sup>2</sup> The main objective of these changes was to increase visibility of all forms of work, paid and unpaid, by advocating for separate measurement and acknowledging that individuals are often engaged in different types of work. These changes have significant implications for the calculation of labor statistics in developing countries, and especially in Sub-Saharan Africa, where a large share of the population is engaged in the production of goods for family use. They also raise important issues with how to measure this definition in a consistent and robust manner.

The operationalization of the 19<sup>th</sup> ICLS standards requires household surveys that are used to measure labor market engagement – e.g. dedicated labor force surveys (LFS) and multi-topic household surveys – to differentiate between individuals engaged in own-use production work and individuals engaged in employment. While own-use production of goods can take many forms (e.g. collection of firewood or water, food preservation, etc