rafmacalaba/datause-encoder-data huggingface dataset:
which are not nationally representative.<sup>8</sup> Without comprehensive global data that cover all possible destinations, it is difficult for empirical and analytical papers to explore beyond static models.

The data in this paper come from two different sources. The global bilateral skilled migration database, as described in great detail in Artuc et al (2015), provides migrant stocks by gender and education level (tertiary and non-tertiary) for 1990 and 2000 for each pair of countries in the world. Part of the data comes from original statistical sources and comprises around 80% of the migrant stock in the world. The rest are imputed using gravity based models. Artuc et al (2015) describe the empirical methods behind the imputation based on a gravity model. Since the imputed data might bias our results, we use only the raw data for 2000. The parallel data for 2010 for OECD destinations come from the DIOC database which is described in Arslan et al (2014). The DIOC 2010 database also has information on the time of arrival of migrants by skill level and country of origin for all of the OECD destinations. This new disaggregation, unavailable for earlier decades, allows us to construct the number of new arrivals by skill and origin country for 2000-2010. We discuss in the next section how we use this new data in our empirical analysis. Finally, we have collected additional bilateral migrant stock data by skill level for around 50 non-OECD destinations in 2010. We use this data to complement the DIOC data.

The second data source is the annual American Community Survey (ACS) of the United States for 2001-2011. ACS provides detailed information on a large sample of the population, including migrants who are defined as people born outside the United States, regardless of their citizenship or residency status. The survey asks the country of birth, the year of migration to the US and where people were living a year earlier. Using these questions, we take the people who declared that they had moved to the US within the previous year, record their country of birth, and identify their country of residence prior to their move. This procedure allows us to identify those who came directly from their birth countries and those who came from another country.

> 8See Carletto et al, 2015, and Dustmann and G ̈orlach, 2016, for a detailed discussion of data shortcomings.

14
(Year fixed effects) and a set of dummy variables for the firm’s industry (2-digit ISIC Rev. 4). Thus, annual global shocks to certification are accounted for, as are industry specific requirements for certification. Based on the literature on the determinants of certification decisions reviewed in Section 2.3.3, we include several other controls. At the country-level, we control for overall economic development that is typically associated with greater use of certification and lower informality. The control is the log of GDP per capita (PPP adjusted and at constant 2007 Int’l $). We also control for country-size, proxied by the log of total population in the country-year to capture possible economies of scale in certification. The data source for both country-level controls is WDI (World Bank). In the robustness section, we include several other country-level controls.

Firm-level controls (taken from the WBES) included in the baseline mode are as follows (see Table A2 in the Appendix for a detailed description): labor productivity defined as the log of a firm’s annual sales (in USD and deflated to 2009 prices) divided by the number of workers employed at the firm; firm-size proxied by the log of the total number of workers employed at the firm; log of the age of the firm; log of the number of years of experience the top manager of the firm has in the industry; competition between formal firms proxied by the Herfindahl index of sales concentration defined at the country-year-industry level (Herfindahl index), with higher values of the index implying less competition between the formal firms; annual sales made directly abroad as a proportion of total annual sales of the firm (Exports); the percentage of the firm that is owned by foreign entities (Foreign ownership); a dummy variable equal to 1 if the firm has a woman top manager and 0 otherwise; annual growth rate of the firm proxied by the percentage increase in the number of workers over the last 3 years (Annual employment growth); dummy variable equal to 1 if the firm introduced a new product or a vastly improved one over the last 3 years and 0 otherwise (New Product); dummy variable equal to 1 if the firm has a loan or a line of 21
# _<u>D2.3 Salaries</u>_

Expenditure on medical and non-medical wages and salaries accounts for a fairly large share of total health expenditure, but the share varies widely across the sub-region. The wage bill accounted for 48% of public expenditure on health in the Federation of Bosnia and Herzegovina in 2006 (World Bank estimates), a similar share in Republika Srpska in 2004 (World Bank estimates), 46% in Macedonia in 2005 (WHO HFA-DB 2007), 29% in Serbia and in Montenegro in 2000 (WHO HFA-DB 2007), and 35% Kosovo<sup>18</sup> (World Bank estimates). By comparison, the wage bill was equivalent to 35% of public expenditure on health in the EU-12 countries in 2004 (WHO HFA-DB).

Since salaries account for such a large share of total health expenditure they are an important determinant of the financial sustainability of health systems and adopting a sustainable human resource strategy needs to be an explicit question addressed by ministries of health if costs are to be contained. The size of the wage bill is determined both by the number of employees in the health sector and by their wages.

Recent data reveal that the number of physicians, and especially the number of nurses, _per capita,_ is lower in the Western Balkans than in the EU-15 and EU-12 countries, despite the high share of salaries in total public expenditure on health. In Albania and Bosnia and Herzegovina, the density of doctors is especially low, at less than half of EU levels. The problem is not the total number of physicians but the excessive proportion of specialists. This is because in the past there was a tendency to train too many specialist doctors and too few general practitioners, which shifts expenditures upwards by providing specialized care to patients who could be treated more costeffectively at lower levels of care. In Serbia, for example, two-thirds of medical doctors are specialists and only 15% are general practitioners. The cost-effectiveness of the healthcare systems of the Western Balkans could be improved by following the OECD practice of increasing the number of nurses relative to doctors and allowing nurses to undertake a wider range of clinical functions combined with selective referral to specialists. Although an examination of the data shows that the doctor-nurse ratios are not, in fact, higher in the Western Balkans than in EU countries, nurses in the sub-region often perform a range of ancillary rather than clinical functions and their numbers are not, thus, representative of medical and clinical capacity.

> 18 The Kosovo figure is, in fact, very high if one considers that highly-specialized care is not offered in the province.

25
Ross Levine, (1996): “Stock Markets: A Spur to Economic Growth,” Finance and Development 33, no. 1 (March 1996): 8, http://www.imf.org/external/pubs/ft/fandd/1996/03/pdf/levine.pdf.

- Sachs, J. D. (1981): The Current Account and macroeconomic Adjustments in the 1970’s, Brookings papers on Economic Activity, April, 1981, (1), 201-68.

- Strassman, P. and A. Blunt. 1993. Land, Income, Mobility and Housing: The case of Metro Manila. _Journal of Philippine Development_ 20(1).

- System of National Accounts (1993): Prepared under the auspices of the Inter-Secretariat Working Group on National Accounts, Commission of the European CommunitiesEurostat, International Monetary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), United Nations (UN) and World Bank, Brussels/Luxembourg, New York, Paris, Washington, D.C., 1993.

- Topel, R. and S. Rosen (1988): Housing Investment in the United States, Journal of Political Economy, 96(4), page 718 of 718-740.

Yoon, H. J. and Zchang, S. S. (2003) _Dwellings and housing,_ Seoul: Darakbang.

- UN-HABITAT  (2011): Affordable Land and Housing in Africa, Volume 3, United Nations Human Settlements Programme (UN-HABITAT), 2011.

- UN-HABITAT  (2008) Financing for Affordable housing, 2<sup>nd</sup> African Ministerial Conference on Housing and Urban Development, 28-30 July, Abuja, Nigeria, downloaded from <u>http://www.unhabitat.org/downloads/docs/amchud/bakg5.pdf</u>

- United Nations (2012), Department of Economic and Social Affairs, Population Division, 2012. World Urbanization Prospects: The 2011 Revision, CD-ROM Edition.

- Westerlund, J. (2007). Testing for error correction in panel data.  Oxford Bulletin of Economics and Statistics 69: 709-748.

- Wooldridge, J. M.  (2002).  Econometric Analysis of Cross Section and Panel Data.  Cambridge, MA: MIT Press.

World Bank. (2013). _Planning, Connecting, and Financing Cities Now: Priorities for City Leaders_ . Washington, D.C.

32
22 Schich, S., 2008, “Financial Crisis: Deposit Insurance and Related Safety Net Aspects,” - Financial Market Trends, OECD. http://www.oecd.org/finance/financial <u>markets/41894959.pdf</u> Schich, S., 2009, “Expanded Guarantees for Banks: Benefits, Costs, and Exit Issues,” Financial - Market Trends, OECD. http://www.oecd.org/finance/financial <u>markets/42779438.pdf</u> Schich, S., and B.-H. Kim, 2011, Guarantee Arrangements for Financial Promises: How Widely Should the Safety Net Be Cast? Financial Market Trends, OECD. - <u>http://www.oecd.org/finance/financial markets/48297609.pdf</u> World Bank, 2003, Survey of Banking Supervision and Regulation, - <u>http://siteresources.worldbank.org/INTRES/Resources/469232 1107449512766/Caprio_2003_banking_regulation_database.xls</u> World Bank, 2011, Survey of Banking Supervision and Regulation, - <u>http://siteresources.worldbank.org/EXTGLOBALFINREPORT/Resources/8816096 1346865433023/8827078-1347152290218/Bank_Regulation.xlsx</u>
months the whole initial sample has been rotated and after 8 months a third different sample is being surveyed. After 12 months the initial sample is reencountered. Over a period of two years, three different panels of households are surveyed, and the process starts again with three new panels. We have concatenated panels from the January 1983 to December 2001. Regrettably, the PME was drastically modified in 2002 and it is not possible to reconcile the new and old definitions unemployment. The monthly attrition rate in the PME is between 5% and 10% of the sample.

Both datasets were purged of mismatches along the sex and age dimensions. There are, however, still some issues of comparability, especially in the definition of an informal worker. In the end, these do not appear to have significant impact on the results and the resulting cyclical patterns are quite similar between the two countries.

# _Defining Informality_

Though, generally speaking, there is broad consensus in the literature on what constitutes an informal worker, studying transitions raises some particular definitional complexities that we discuss here.

We follow the International Labour Organization (ILO) in dividing employed workers into three sectors: informal salaried (I), informal self employed (S) and formal sector workers (F).<sup>10</sup> Broadly speaking, formal workers are those working in firms licensed with the government and conforming to tax and labor laws, including minimum wage directives, pension and health insurance benefits for employees, workplace standards of safety etc. Informal workers, on the contrary, are those owners of firms that are largely de linked from state institutions and obligations and their employees who are not covered by formal labor protections.

> 10 The ILO defines informality as consisting of all own-account workers (but excluding administrative workers, professionals and technicians), unpaid family workers, and employers and employees working in establishments with less than 5.  We have also computed all the calculations presented in this paper considering the informal salaried those workers in small firms who have no social security with extremely similar results.

7
developed by Vlaicu et al. (2014) which used life expectancy at birth as the output and government health spending per capita (PPP) as the input (along with covariates population density and OECD membership, and year fixed effects). Similarly for education, we computed the efficiency scores in the education sector using the SF approach modeled by Belotti et al (2012). The outcome of interest is primary school completion rates and the input is government spending on education per capita (PPP), both measured by UNESCO Institute for Statistics.<sup>35</sup> Population density, from the World Bank Development Indicators, was used as a covariate.

It is important to note that the detailed causal chains between PFM quality and operational efficiency of service delivery cannot be mapped comprehensively. Since there are a number of other factors involved (including how health sector staff is trained and managed, the disease burden, etc.), the effects of PFM performance would have to be quite strong in order to appear as statistically significant in cross-country analysis.

# **5. Econometric Analysis**

Following the research questions formulated at the outset, this section presents the econometric evidence on: (1) which macro-level country characteristics are associated (or not) with better PFM quality– using cross-section regressions (Section 5.1); (2) how changes in PFM quality over time relate to country characteristics using first-differences regressions (Section 5.2); and (3) observable effects of PFM quality on expected outcomes using crosssection regressions (Section 5.3). Additional quantitative analyses and robustness checks are shown in Annex 2.

## **5.1 Macro-Level Country Characteristics and PFM Quality: Cross-Section**

To explore the relationship between macro-level characteristics and PFM quality, we first turn to the relationship between PFM quality and GDP per capita – which has been identified as a key variable associated with institutions in general and PFM quality in particular (see Section 2). The estimate of income on PFM quality using a simple bi-variate regression is significant at the 99 percent level with a coefficient of 0.17 and a standard error of 0.05 (Figure 3). While there is a statistically significant relationship, the magnitude of the relationship is rather small. This implies that changes in income levels have only a limited impact on PFM quality. For example, a doubling of GDP per capita is associated with an increase of 0.117 points on the (aggregate) PEFA scale of 1 to 4.<sup>36</sup> Comparing a richer country like Peru with a per capita income of nearly $8000 and a poorer country such as Bangladesh with a per capita income of around $1500, the

> 35 For additional robustness, we also formulate the education efficiency variable using adult literacy rates. 36 The expected change in Y associated with a 200% increase in X can be calculated as ln(2) x coefficient. Ln(2) =  0.693, so 0.693 x 0.169 works out as 0.117.

23
||(0.0121)|(0.0121)|(0.0121)|
|---|---|---|---|
|Improved water|0.0527*|0.0600**|0.0583**|
||(0.0270)|(0.0264)|(0.0267)|
|Water treatment|0.0305|0.0327|0.0324|
||(0.0313)|(0.0312)|(0.0312)|
|Arsenic (WHO)|-0.0581***|||
||(0.0133)|||
|Arsenic (BD)||-0.0841***<br>(0.0171)||
|Arsenic - medium (11-50 ppb)|||-0.0323*<br>(0.0178)|
|Arsenic - high (51+ ppb)|||-0.0941***<br>(0.0189)|
|Observations|7,693|7,693|7,693|
|Standard errors in parentheses||||
|*** p<0.01, ** p<0.05, * p<0.1||||

# References

Adukia, A. (2016). Sanitation and Education. _University of Chicago Policy Working Paper._ Anawar, H., Akai, J., Mostofa, K., Safiullah, S., & Tareq, T. (2002). Arsenic poisoning in groundewater: Health risk and geochemical sources in Bangladesh. _Environment International._ Asadullah, M. N., & Chaudhury, N. (2008). Poisoning the Mind: Arsenic Contamination and Cognitive Achievement of Children. _World Bank Policy Research Working Paper._ Bennear, L., Tarozzi, A., Pfaff, A., Balasubramanya, S., Ahmed, K., & van Geen, A. (2013). Impact of a randomized controlled trial in arsenic risk communication on household water-source choices in Bangladesh. _Journal of Environmental Economics and Management_ , 225-240.

Bennear, L., Tarozzi, A., Pfaff, A., Balasubramanya, S., Ahmed, K., & van Geen, A. (2013). Impact of a randomized controlled trial in arsenic risk communication on household water-source choices in Bangladesh. _Journal of Environmental Economics and Management_ , 225-240.

Burgess, et al. (2010). Vulnerability of deep groundwater in the Bengal aquifer system to contamination by arsenic. _Nature Geoscience_ , 83-87.

Calderon, J., Navarro, M., Jimenez-Capdeville, M., Santos-Diaz, A., Golden, A., Rodriguez-Leyva, I., . . . Diaz-Barriga, F. (2001). Exposure to Arsenic and Lead and Neuropsychological Development in Mexican Children. _Environmental Research Section_ , 69-76.

Chakrabotri. (2010). Status of groundwater arsenic contamination in Bangladesh: A 14-year study report. _Water Research_ , 5789–5802.

Crockett, M. J., & Haushofer, J. (2014). How poverty gets under the skull. Background paper prepared for the World Development Report 2015, _World Bank_ .

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# **Box 111: Calculating a Multiresource Budget for** Education Spending

In 1986/87, Rwanda committed about 23 percent of capital and recurrent public expenditure--about 3 percent of GDP--to public ed.;ation. Central government recurrent spending represented about 80 percent of total sector financing. Financing varied substantially by level of education. Municipalities (local government) contributed only to primary and vocational education; foreign donors supported only secondary education and university. The contribution of parents increased with the level of education, then dropped to 0 percent at the university level. Only vocational schools and the university had minor amounts of self-financing.

|||Level o|f Education|||
|---|---|---|---|---|---|
||aimarY|Vocational|Secondarv|Un versita|igW|
|Financing Source||||||
|Central governner.t|84.3%|75.8%|74.0%|92.0%|82.4%|
|Municipalities|5.5|4.0|---|---|3.9|
|Self-financing|---|2.3|---|1.9|0.4|
|<br>Parents|10.2|17.9|20.2|---|11.8|
|Foreign cooperation|---|---|5.8|6.2|1.6|
|Total|100.0%|100.0%|100.0%|100.0%|100.0%|
|Public spending/student|50|191|611|4,258 ($|US)|
|Total unit cost/student|59|253|825|4,630 ($|US)|

al985/86 Source: "Rwanda: Rapport sur le cofit et financement de l'enseignement primaire, secondaire et superieur," Report No. 7362-RW (1989).

The variation in the degree of public subsidization (government spending as a percentage of total spending) demonstrates clearly the importance of determining and including all major financing sources, although these may vary substantially from country to country, in any comprehensive sector expenditure. analysis. Since the composition of non-central-government spending on education also varies greatly bv level of education, analyses of intrasectoral resource allocation that are based only on central govermnent expenditure may seriously misrepresent the commitment of national resources to each level of education.

26
proportion of households that have a two-wheeler (scooter, motor cycle, moped, etc).<sup>22</sup> There was virtually no effect of these additional controls on our main results discussed above with the estimated coefficients of non-workers and children remaining almost unchanged from above.

# **5. Household Income-Competition Relationship**

In this section we focus on why our results fail to show any significant effect of expenditure on competition. This is important to address any concerns that the variable may be a poor proxy for household income in which case the estimated effect of nonworkers on competition discussed above could still be biased upwards.

There are three reasons for why expenditure does not show much effect on competition. First, population absorbs some of the effect of expenditure on competition.<sup>23</sup> However, this is at best a contributory factor because even without controlling for population, expenditure has at best a weak effect on competition in all the specifications discussed above. Second, higher values of expenditure are expected to have a negative effect on competition. However, these higher values are likely to pick the positive effect on competition of various covariates such as fewer children, less burdensome regulation, fewer power outages, etc.<sup>24</sup> We show that this is the main reason why expenditure shows a weak effect on competition without any controls (as in column 3 of Table 4). The natural question to ask here is that if this is indeed true then controlling for these covariates (as we did in Table 5) should have resulted in a significant effect of

> 22 Data on two-wheelers and four-wheelers was first collected in the 2001 Census and it is available at the district level. We use these data for the urban part of the district population.

> 23 The correlation between _Expenditure_ and _Population_ is .548 for the cities in our sample.

> 24 For the cities in our sample, the correlation between _Expenditure_ and _Children_ , _Outage_ and _Regulation_ equals, respectively, -.290, -.065 and -.143.

19
since some argue that banking remittance recipients will help multiply the development impact of remittance flows (see Hinojosa-Ojeda, 2003; Terry and Wilson, 2005, and World Bank, 2006).

In this paper, we use balance of payments data on remittance flows received by 99 countries over the period 1975-2003 to study the impact of workers’ remittances on financial development. We specifically examine whether remittances contribute to the development of the financial sector by increasing the aggregate level of deposits and/or the amount of credit to the private sector extended by the local banking sector.<sup>4</sup> Whether and how remittances might affect financial development is a priori unclear. The notion that remittances can lead to financial development in developing countries is based on the concept that money transferred through financial institutions paves the way for recipients to demand and gain access to other financial products and services, which they might not have otherwise (Orozco and Fedewa, 2005). At the same time, providing remittance transfer services allows banks to “get to know” and reach out to unbanked recipients or recipients with limited financial intermediation. For example, remittances might have a positive impact on credit market development if banks become more willing to extend credit to remittance recipients because the transfers they receive from abroad are perceived to be significant and stable. However, even if bank lending to remittance recipients does not materialize, overall credit in the economy might increase if banks’ loanable funds surge as a result of deposits linked to remittance flows.

Furthermore, because remittances are typically lumpy, recipients might have a need for financial products that allow for the safe storage of these funds, even if most of these funds are

> 4 A recent survey of central banks in 40 countries reveals that most countries (90 percent of the sample to be exact) collect remittance statistics from commercial banks, while less than 40 percent gather information from money transfer companies and post offices (De Luna Martinez, 2005). Therefore, balance of payment statistics tend to better reflect the portion of remittances that is transferred through banks.

2