rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.3 Robustness checks on the procyclicality of lending**

Private banks can be domestic or foreign-owned. Foreign-owned banks tend to be subsidiaries of international banks. This potentially enables them to obtain additional funding from the international parent bank in case their local profitable lending opportunities expand. Thus, with a more elastic supply of funds, foreign banks may be able to expand their lending relatively more during economic upswings. To test this, we re-estimate regressions 1 and 2 of Table 2 after including a foreign bank variable and its interaction with GDP per capita growth. The results are reported as regressions 1 and 2 of Table 5. In the two regressions, the interaction of GDP per capita growth and the foreign bank variable obtains coefficients of 0.009 and 0.008 that are significant at 1%, implying that lending by private, foreign banks is more procyclical than lending by private, domestic banks. In both regressions, the interaction of the GDP per capita variable and the state bank variable obtains a coefficient of -0.008 that are significant at 5%, indicating that lending by state banks is less procyclical than for private, domestic banks. Regressions 3 and 5 include interactions of the GDP per capita variable with alternatively the state bank, domestic bank, and foreign bank variables. In both regressions, the three interaction terms are estimated with coefficients with significance of at least 10%. Estimated coefficients confirm increasing procyclicality of lending from state banks to private, domestic banks, and to private, foreign banks. Specifically, a 1% increase in GDP per capita growth leads to a credit supply growth of 0.5-0.6% for state bank, 1.1-1.2% for private, domestic banks and 2.1-2.2% for private foreign banks.

Next, we consider whether the relative procylicality of lending by state banks depends on the level of economic development. In particular, we estimate regressions 1-4 of Table 2 separately for the samples of high-income countries and developing countries (
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household income.<sup>24</sup> We use the method suggested by Grosh and Baker and Ahmed and Bouis to predict parental household expenditure. In our questionnaire we included questions on asset ownership drawn from the Household Income Expenditure and Welfare Monitoring Survey (HICE/WMS) 2000, a nationally representative survey, conducted by the Central Statistical Authority (CSA), which collects detailed data on household consumption and expenditures. By regressing household consumption on household assets, we obtained coefficients to predict household consumption for the parental _p_ household of our subjects. We run the weighted regression _a a ˆ a EXPi_ = ∑α _Di_ + _ui_ on the HICE/WMS 2000 data to obtain estimates of α , the coefficient for each asset, which we then use to predict _a_ = 1 _EXP_<sup>�</sup> _i_ for our own sample.<sup>25</sup> The regression has an R<sup>2</sup> of 0.17. The average student comes from a household with predicted annual expenditures of 6,606 Birr (777 USD), which corresponds to monthly expenditures of 550 Birr (65 USD) or daily expenditures of 18 Birr (2 USD). The mean estimated household expenditure for our sample is slightly higher than the mean for the entire population, which is 5,403 Birr (635 USD) per year.

To get an insight whether poorer qualified health students are more likely to serve in a rural area, we conducted a medical knowledge test. The variable _TSCORE_ is the score on this test, expressed as a percentage.<sup>26</sup> _ADDIS_ and _DIST_ capture the individual’s familiarity with rural areas. Students who grew up or whose parents are still living in a rural area may be more likely to go back.

As set out in section 2, the variable willingness to help the poor is our proxy for intrinsic motivation. It is an indicator variable for ranking ‘opportunity to help the poor
> 28 According to the Polity IV Dataset, the structure of the state is determined by broadly three interdependent elements - presence of institutions and procedures through which citizens can express effective preferences about alternative policies and leaders, the existence of institutionalized constraints on the exercise of power by the executive and the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation. Other aspects of democracy such as rule of law, freedom of the press and so on are regarded as specific manifestations of these general principles. The Democracy indicator is derived from coding of the competitiveness of political participation, the openness and competitiveness of executive recruitment and constraints on the chief executive. Polity IV also has a composite 11 point scale for Autocracy since many polities exhibit mixed qualities of both of these distinct authority patterns. The Autocracy score is derived from coding of the competitiveness of political participation, the regulation of participation, the openness and competitiveness of executive recruitment, and constraints on the chief executive.

> 29 In unreported tables we also used the Legislative and Executive Indices of Electoral Competiveness (LIEC/EIEC) from DPI as alternative indices in place of Democracy and Autocracy. LIEC/EIEC are narrower measures that focus specifically on the competitiveness of elections, whereas Democracy and Autocracy are more general measures of the openness (closedness) of the political system that include electoral competitiveness as one aspect. LIEC/EIEC explain a smaller proportion of property rights variation than Democracy and Autocracy.

25
047|0.003|0.112|
|Currently dating|0.334|0.333|0.309|-0.000|-0.038|0.988|0.208|
|Married|0.065|0.061|0.050|-0.011|-0.045|0.724|0.125|
|With sisters|0.675|0.733|0.709|0.090|0.053|0.003|0.078|
|Daily freq. social media|15.558|15.917|15.845|0.023|0.018|0.486|0.578|
|Daily freq. watch videos|2.288|2.389|2.388|0.067|0.066|0.023|0.026|
|Male friend beating|0.146|0.140|0.157|-0.012|0.021|0.691|0.503|
|Female friend beated|0.146|0.155|0.158|0.019|0.025|0.542|0.415|

Notes: Table shows sample means at baseline for different categories of respondents: those who only completed baseline, those who completed the short-term survey and those who completed the medium-term survey.

26
and air pollution. Although we find significant variables in each locale, we find no variable that meets all of our criteria for the core model: universally available and relevant, statistically significant, and identically signed. Further exploration may well identify additional core variables, and this is an important domain for future research. For the purposes of this paper, however, we believe that presentation of diverse country-specific results that cannot be replicated elsewhere would distract attention from our central themes.

# **8.  Summary and Conclusions**

This paper has been motivated by the urgent need for a model of Covid-19 transmission between locales that can be applied anywhere in the developing world. To meet this challenge, we focus on developing a model that is (1) implementable anywhere spatially-disaggregated Covid-19 infection data are available; (2) scalable for entities of arbitrary size, from individual regions to countries of continental scale; (3) reliant solely on data that are free and open to public access; (4) grounded in a rigorous, proven methodology; and (5) capable of forecasting future hotspots with enough accuracy to provide useful alerts.

After a review of existing approaches and technical resources, we opt for a model that combines a gravity-type specification of spatial interaction; the use of travel time rather than geographic distance as a measure of spatial friction; and a model of growth dynamics based on the Gompertz variant of the generalized logistic model. Using free, publicly-available data for the United States, the Philippines and South Africa’s Western Cape province, we estimate a core model of infection growth in a locale that proxies local interactions with population density (D) and incorporates interactions with other locales in a theoretically-derived variable (W) that is the summation of their infections, weighted inversely by an exponential transformation of travel time. We find high significance and the appropriate signs for D and W for all three countries, and for changes over 14 and 28 days. After extensive experimentation, we find no other variable that is common to the
Kalemli-Ӧzcan, S. 2019. “U.S. Monetary Policy and International Risk Spillovers.” ProceedingsEconomic Policy Symposium-Jackson Hole, Federal Reserve Bank of Kansas City.

Kalemli-Ӧzcan, S., and L. Varela. 2019. “Exchange Rate and Interest Rate Disconnect: The Role of Capital Flows and Risk Premia.” Conference Paper 127061, National Bureau of Economic Research.

Kaminsky, G. L., S. Lizondo, and C. M. Reinhart. 1998. “Leading Indicators of Currency Crises.” _IMF Staff Papers_ 45 (1): 1-48.

Kearns, J., A. Schrimpf, and F. D. Xia. 2019. “Explaining Monetary Spillovers: The Matrix Reloaded.” Research Discussion Paper 2019–03, Reserve Bank of Australia, Sydney.

Kose, M. A., S. Kurlat, F. Ohnsorge, and N. Sugawara. 2017. “A Cross-Country Database of Fiscal Space.” Policy Research Working Paper 8157, World Bank, Washington, DC.

Kose, M. A., P. Nagle, F. Ohnsorge, and N. Sugawara. 2021. _Global Waves of Debt: Causes and Consequences._ Washington, DC: World Bank.

Kraay, A., and V. Nehru. 2006. “When Is External Debt Sustainable?” _The World Bank Economic Review_ 20 (3): 341-365.

Kuttner, K.N., 2001. “Monetary Policy Surprises and Interest Rates: Evidence From the Fed Funds Futures Market. _Journal of Monetary Economics_ : 47(3): pp.523-544.

Laeven, L., and F. Valencia. 2020. “Systemic Banking Crises Database: A Timely Update in COVID-19 Times.” CEPR Discussion Paper 14569, Center for Economic and Policy Research, Washington, DC.

Lenza, M. and Primiceri, G.E., 2022. “How to Estimate a Vector Autoregression After March 2020.” _Journal of Applied Econometrics._ Manasse, P.,
the respective poverty line.

Additionally, I use five individual-level variables reflecting salaried labor market engagement in the 12 months immediately before the data collection in each round of the survey. Two of these are binary variables indicating whether an individual has had permanent or temporary salaried employment, while the other two variables capture the number of hours and days an individual has worked in permanent and temporary salaried employment, respectively. The fifth is a binary variable indicating whether an individual has had permanent or temporary salaried employment.

I use two household-level variables to capture self-employment in non-farm businesses in the 12 months immediately before the data collection in each round of the survey: a binary variable indicating whether a household has a non-farm enterprise and a continuous variable measuring the number of non-farm enterprises a household owns. I further use a binary variable indicating

> 17Alternatively, I constructed the poverty lines using household sample weights, household size, and household welfare measures from the ESS3. Usages of these poverty lines provide similar results.

> 18The poverty lines for consumption expenditure per capita and wealth are 3683.062 (Br) and -1.507, respectively.

11
*The OECD and RICYT basically provide the same definitions for sectors that finance and

<u>perform R&D. The OECD refers to the productive sector as “Business Enterprise Sector”, which covers private and public enterprises.</u>

# **R&D Personnel**

The data on R&D personnel corresponds to scientists and engineers, comprising persons working in those capacities, i.e. as persons with scientific or technological training (usually completion of third level education) in any field of science, who are engaged in professional work on R&D activities, administrators and other high-level personnel who direct the execution of R&D activities.

*RICYT divides R&D personnel in the following categories: Researchers (professionals who create new knowledge, methods and systems, and administer them), Graduate Students, and Support Personnel (technicians and other support personnel).

# **2. Database on Innovation: Variables and Sources**

|Variable|Definition|Units|Source|
|---|---|---|---|
|R&D Variables<br>||||
|Pat|Total patents<br>granted by the<br>USPTO by<br>year for each<br>country|Unit|U.S. Patent & Trademark Office|
|Patepo|Total patents<br>granted by the<br>EPO by year<br>for each<br>country|Unit|European Patent Office|
|Royal|Royalties and<br>license fees,<br>payments|Current U.S. dollars|-World Development Indicators through<br>World Bank electronic databases|
|Rdexp|R&D<br>expenditure|Constant U.S. dollars|-UNESCO:printed and electronic<br>versions of Statistical Yearbook.|

> 7 Red Iberoamericana de Indicadores de Ciencia y Tecnología

29
# _Evolution and structure of public spending_

In the period from 1990 to 1995, the government put greater emphasis in its budget policy on maintaining public services at the cost of running persistent budget deficits. However, following the adoption of a structural adjustment program from 1995, stability was given greater importance at the cost of reducing the quality of public services.

As noted, the PRSP adopted in 2003 called for a substantial real increase in spending on social sectors, with the ratio expected to reach 12.8 percent of GDP by 2004. While spending on social sectors had followed an upward trend for the period since 1991, its 2004 GDP share fell short of the PRSP target. According to the MAMS database (see below), total spending on health and education, which represent the bulk of social spending, only reached 6.7 percent of GDP in 2004. In 2006, total social spending had increased to 8.4 percent of GDP, still substantially short of the PRSP target (Bulletin of Government Finance Statistics). The majority of this spending is on education (6.1 percent of GDP) and health (1.7 percent of GDP). A significant proportion of the budget is used to subsidize electricity and oil derivatives, mostly diesel. These subsidies rose from 3.1 percent of GDP in 2002 to 6.3 percent in 2004, and peaked at close to 10 percent of GDP in 2006, substantially exceeding the allocation to education.<sup>6</sup> In 2005, the government started to reduce these subsidies on a gradual basis, bringing the petroleum subsidy down to 6.5 percent of GDP in 2009.

The combined health and education share in total government spending increased by one percentage point between 1991 and 2009, from 18.7 to 19.8 (see Table 2.1). As a share of GDP, government spending in health and education increased only very marginally, from 5.5 to 5.7 percent, during the same period
captures government spending on pre-school education institutions. About 27% of expenditures on pre-school education in 2017 were spent in the Municipality of Chisinau, mainly due to the greater number of preschoolers residing within the boundaries of the municipality.

- “ **Education: school”** : the category captures government spending on primary schools, gymnasiums and lyceums. When combined, these categories can be used as a proxy of school education in Moldova (i.e. grades I- XII), for children from about 7 to 18 years old.

- “ **Education: after school (below university)** ”:  the variable is comprised of government spending on vocational and jobs schools, as well as colleges.

- “ **Education: university** ”: this variable includes government spending on higher education institutions.

The latter two categories cannot be regionally disaggregated. Since almost all higher education institutions are located in the capital of the country and other big cities, we allocated them to receivers ate the national level.

It should also be noted that not all sector expenditures from the BOOST datasets were allocated to the variables identified in the survey. In the case of education, for example, about 9% of overall local government spending on education has not been attributed to any of the variables. Most of the unallocated spending consists of administrative expenditures and other un-allocable items (i.e. capital investments in health care).

The information presented in Table 6 focuses on 2017, the most recent year for which relevant regionally disaggregated sectoral expenditure data were publicly available<sup>5</sup> when the study was carried out. Since the detailed regionally disaggregated health expenditure data was available only for 2016, the analysis adjusted the regional composition of spending by sub-functions to match the NHIC’s annual budget execution statements for 2017.

> 5 In other words, the more recent year for which BOOST data for Moldova were available.

12
sampling of applicants, a baseline survey was conducted on the treatment and control groups, before the results of the selection process were announced.

Table 2 shows the resultant sample of events for the three cohorts. The 2010 event sample comprised 64 events across 30 districts. The 2011 sample comprised 182 events, of which 113 events were dropped from the baseline survey, either because the survey team could not reach the event on the day of applicant selection or because the event was not “oversubscribed”.<sup>28</sup> The remaining 69 events in 34 districts were included in the 2011 baseline sample. The sampling process was much improved in 2012, with 85 out of 112 sampled events successfully included in the final evaluation sample.

This non-random (in 2010) and partially-random (in 2011 and 2012) sampling of training events and applicants introduces bias in two potentially important ways. First, the training events in our study may not be representative of all EF-sponsored trainings. In all three cohorts, training events that enter our sample

> 24 For the 2010 cohort, a second follow-up was conducted on half of the cohort. Future analysis will examine the longer-term outcomes for this group.

> 25 Because of the AGEI focus of this study, we prioritize AGEI training events (identified by the T&E as we described earlier). Because the selection into the study population was based on an individuals’ proximity to the threshold score, it was not possible to stratify on AGEI status. However, events that were likely to include more AGEI candidates were purposely oversampled in 2011 and 2012 so as to increase the number of AGEI candidates in the study population.

> 26 Eighty percent of EF training events occurred during these four months.

> 27 In 2010, because a complete event listing was not available in advance, the events were chosen by convenience, based on scheduling and accessibility.

> 28 The survey team was instructed to drop the event from the sample if there were not at least 3 rejected candidates that fell
Tourist arrivals is the main outcome variable of interest. Arrivals data measure the flows of international visitors to the country of reference: each arrival corresponds to one inbound touristic trip. If a person visits several countries during the course of a single trip, his/her arrival in each country is recorded separately. In an accounting period, arrivals are not necessarily equal to the number of persons traveling (when a person visits the same country several times a year, each trip by the same person is counted as a separate arrival). Being an island that is not close to other destinations, in STP’s case, number of arrivals is closer to the number of visitors within a year. _Arrivals_ data should correspond to the _inbound visitors_ by including both tourists and same-day nonresident visitors. All other types of travelers (such as border, seasonal and other short-term workers, long-term students and others) should be excluded, as they do not qualify as visitors. As noted by UNWTO (2018), data are obtained from different sources: administrative records (immigration, traffic counts, and other possible types of controls), border surveys or a mix of them. If data are obtained from accommodation surveys, the number of guests is used as an estimate of arrival figures; consequently, in this case, breakdowns by regions, main purpose of the trip, modes of transport used or forms of organization of the trip are based on complementary visitor surveys. The main variable of interest to this note is the log of yearly tourist arrivals (LN_ARR).

Unfortunately, the UNWTO does not report data for STP on arrivals by origin. The data only show aggregate arrivals from all origins, which is a major limitation of the analysis as it does not allow to disaggregate by type of tourist. It could be assumed that tourist origins are significantly correlated with their income, and then as done in the literature, European and American tourists are considered as the representatives of tourist tastes and behavior. As noted above, flight arrivals from
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Gu, X., S. Ying, W. Zhang, and Y. Tao (2020
than town households (0.6 more years of formal education on average – maximum years of education in the household is 8.64 years on average in the city versus 8.01 in towns). The census asks whether the household experienced any death among its members in the 12 months preceding the interview, followed by a question on the sex and age of the deceased. Somewhat fewer urban households were bereaved and in cities the age of the deceased is higher, although the size effects are not large. Very few rural households have a non-seasonally employed member, but respectively 24% and 38% of town and city households do. Finally, while only 9% of households have a member working outside of the agricultural sector in rural areas, 54% and 74% do so in towns and cities.

8