rafmacalaba/data-use-mentions-extended huggingface dataset:
39 ·· ... · appropriate to reduce the probability of credit being cut off by having high fiscal savings.

Kharas and Shishido (1986) use a model similar to the one described above to examine optimal borrowing strategies for Thailand over the period 1985-92. In addition to the intertemporal equilibrium conditions described in equation (15), their multi-sector model captures some aspects of uncertainty insofar as the expected terminal year fiscal surplus is a parameter that depends on the weight attached by the borrower to the possibility of losing creditworthiness. To ensure smooth investment and borrowing paths, the model postulates an absorptive capacity constraint in each period on investment so that there is a diminishing marginal efficiency of investment within any period. Likewise, the more the country borrows relative to outstanding loans in each period, the more costly the loans become, reflecting the assumption that the borrower must shift to new lenders and more expensive financial instruments. Finally, as in the applications discussed in Section 5, technology is putty-clay with new investment directed to sectors with highest profit rates and, once in place, capital can only be removed from a sector through depreciation. 24/ The model is solved numerically by resorting to the turnpike theorem (Burmeister and Dobell 1970) so that a balanced growth state is reached by the end of the simulation, with all real values growing at the same rate in the post-terminal period. ~ This implies that the model ~valuates the penalty for the outstanding debt and the premium for the existing capital stocks in the terminal year through calculating the impact of these on future consumption. 26/ <!-- Start of picture text --> in<br><!-- End of picture text --> <!-- Start of picture text --> l<br>so<br><!-- End of picture text --> Having calibrated on historical data for the period 1975-85, Kharas and Shishido, go on to use their model to determine optimal borrowing and
_Urban versus rural._ In comparing urban versus rural areas in the 100 Village Survey sample, one must keep in mind that the "urban" areas in this survey do not include any major cities but rather are smaller cities and urbanized areas. Also, the data represent a period of a very severe crisis, in which many urban areas and occupations were hard hit.'<sup>3</sup> Nevertheless, both data sets seem to agree on many aspects. Urban households have a much higher mean of per capita expenditures and much lower poverty rate than rural areas. The variability of expenditure changes in both areas seem to be roughly the same, but the two data sets disagree on the ordering. The resulting average vulnerability and headcount vulnerable rate are much lower in urban than rural areas. However, as with education the ratio of vulnerable to poor households is much higher in urban than rural areas, Partly because it begin from a lower base. ]3ut the urban results in the Mini-Susenas show what a difference vulnerability analysis can make - while poverty is only 8 percent and hence might be thought to be "not on issue" almost 30 percent of households are vulnerable.

_Landed versus landless rural households._ Interestingly, in this category the two data sets disagree on just about everything. According to the Mini-Susenas sample, rural households which own land have higher mean of per capita expenditures, lower poverty rate, higher variability in expenditure changes, lower average vulnerability, lower headcount vulnerable rate, and higher ratio of vulnerable to poor than the rural landless. On the other hand, the 100 Village Survey sample See Poppele _et al_ (1999) and Sumarto _et al_ (1998).

23
property of continuing to face households with a positive marginal price for water consumption, thereby providing an incentive for rational use. Moreover, since only a fraction of the overall bill would be covered, the household would not lose the habit of paying for the service. The major drawback with this subsidy is its poor performance in terms of cost-effectiveness, with 25-40% of the overall budget being absorbed in administrative costs. These percentages are so high as to question the overall case for making subsidy payments of such a small absolute value.

- The willingness to pay survey suggested that the private benefits of a sewerage connection fall well short of the associated cost of provision, implying that a substantial subsidy would be required if the expansion of the sewerage network is deemed to be justified by wider social benefits. The empirical analysis suggested that an appropriate level for this subsidy would be at US$750 per new connection. Given that this subsidy would be no more complex to administrate than the consumption subsidy, while the individual payments are very much larger, it is not surprising that such a scheme

6 It is certainly questionable whether the basic consumption level of 150 litres per person per day adopted by IDAAN can truly be regarded as a _subsistence_ consumption level. This is a parameter that should always<sup>be</sup> questioned in the light of local evidence and real needs for water use.
# Annex 1: Representativeness of the scraped data on the housing market

|Country|Population<br>(2019)|Average HH<br>size|Number of<br>Households|Number of<br>scraped<br>observations|Individuals<br>using the<br>internet (%<br>of the<br>population)|Percentage<br>of all<br>households<br>covered in<br>scraping|Slum<br>Population,<br>percent of<br>urban<br>population|Ratio of<br>mortgages to<br>GDP, percent|
|---|---|---|---|---|---|---|---|---|
|Albania|2,854,191|3.66|779,688|3,389|72.24 %|0.43|n.a|12.2|
|Costa Rica|5,047,561|3.20|1,579,421|10,376|80.53 %|0.66|4|15.93|
|Morocco|36,471,769|4.77|7,646,128|10,737|84.12 %|0.14|9|23|
|South Africa|216,565,318|6.28|34,500,045|107,652|68.20 %|0.31|26|16.15|
|Pakistan|58,558,270|3.86|15,155,778|91,904|17.00 %|0.61|40|0.23|

Note: Overview on representativeness of the scraped house prices of the formal market. Numbers on average household size are drawn from countries’ latest household survey. Data for
bank branches, development institutions, and credit unions. As of 2003, the coverage limit was 50 million won per depositor per institution. The KDIC is legally authorized to borrow from the government or central bank with the approval of the ministry of finance.

_Sources_ : Garcia (1999), IADI Survey: Korea (2003), Korea Deposit Insurance Corporation (2004), Ko (1997), Kyei (1995).

**Latvia.** ( _Law on guarantees for deposits of natural persons)_ The DIS of Latvia was established in 1998. It is administered officially. Insider deposits and accounts in bank already declared bankrupt or insolvent, or in liquidation proceedings are not covered. The initial coverage limit was 500 Lats. In accordance with the EU standards, this amount will be gradually increased up to 13,000 Lats by the year 2008 according to the following schedule: 500 Lats, until December 31, 1999; maximum 1,000 Lats until December 31 2001; maximum 3,000 Lats until December 31, 2003; maximum 6,000 Lats until Dec 31, 2005; maximum 9,000 Lats until Dec 31, 2007; maximum 13,000 Lats after Jan. 2008. The Bank of Latvia and the government made initial contributions to the fund.

_Sources_ : Bank of Latvia (1998), Garcia (1999), Institute of International Bankers (1997).

**Lebanon.** ( _National Deposit Guarantee Institution, Banque du Liban, Law no. 28/67_ ) The deposit insurance scheme of Lebanon was established in May 1967. It is administered and funded jointly. The government matches the premiums paid by the banks. The central bank contributed half of the initial capital. The fund can borrow from the central bank. Initially, the scheme extended coverage only to deposits denominated in Lebanese pounds up to LL 30,000 (approximately $10,000 at the time) per depositor per bank. The insurance coverage limit was then raised to LL 250,000
scenarios, schools are closed for the time period based on actual data for February 2020 – February 2022 from the <u>UNESCO school closures tracker,</u><sup>10</sup> assuming no additional future school closures. Differences between the optimistic, intermediate, and pessimistic scenarios are defined as follows:

- Optimistic: Mitigation measures have a medium level of effectiveness.<sup>11</sup> Partial closures are assumed to affect 75% of the student population.

- Intermediate: Mitigation measures have a low level of effectiveness.<sup>12</sup> Partial closures are assumed to affect 85% of the student population.

- Pessimistic: Mitigation measures have a low level of effectiveness. Partial closures are treated as full closures (that is, to affect 100% of the student population).

UNESCO describes partial school closures as follows: “Partially open: Schools are (a) open/closed in certain areas only, and/or (b) open/closed for some grade levels/age groups only; and/or (c) open but with reduced in-person class time, combined with distance learning (hybrid approach). It also includes the countries where national governments have deferred decisions on re-opening to other administrative units (e.g. region, municipality or individual schools), and where a variety of re-opening modalities are being used” (UNESCO <u>2021). Since the information on partial closures from the UNESCO calendar is qualitative in nature, we treat</u> partial closures differently across the simulation scenarios.

Table 1 below outlines the key differences and similarities between the current and previous simulations. It is important to notice that the income<sup>13</sup> and World Bank lending classifications<sup>14</sup> used in this paper are as of July 1, 2021, and differ from the previous papers.

> 10 In the absence of actual data on school closures, the simulations
factors. Since most studies have found heterogeneous impacts based on these factors, it might be most effective, where feasible, to tailor training programs to specific groups of entrepreneurs who would benefit from different curriculums.

**Remember that complementary interventions may be needed.** There is mixed evidence as to whether subsidies or financial education for entrepreneurs have a greater effect on business outcomes (Klinger and Schündeln, 2007; Berge et al., 2011). However, entrepreneurs can also benefit from a mix of the two interventions (Giné and Mansuri, forthcoming).

In spite of the need for ongoing research, the survey evidence and program evaluations described in this paper should provide a useful starting point for policymakers in designing financial education programs for both consumers and entrepreneurs. Surveys have indicated certain areas of universal concern, such as lack of awareness of certain basic financial products, but also that financial literacy needs and desires vary widely across the region. In light of this diversity, it becomes especially important to tailor both the content and the format of education programs to their target audience, as well as to consider complementary interventions to reach the end goal, whether it be improving financial access or enhancing enterprise productivity.

43
is set equal to the MFN tariff, and in that way the impact of the first reduction in a preferential tariff will be captured. Moreover, when a country does not offer any preference in a given sector in years _t_ and _t_ – 1, we set the change in the preferential tariff to be zero, since a change in the MFN tariff in such case would _not_ be related to preferential tariff changes.

The data required for the applied MFN tariff component of the dependent variable, ∆τi−k , is taken from UNCTAD (TRAINS), the WTO, and is made available by the World Bank online via the World Integrated Trade Solution (WITS).

In our main specifications of interest, our measures of changes in levels of import protection, ∆τi−k and ∆Prefik, include not only the change in the MFN applied tariff and in the preferential tariff, respectively, but also the TTBs (AD and SG import restrictions) imposed against the rest of the world and the MERCOSUR partner. For example, if Argentina lowers its preferential tariff against Brazil under MERCOSUR but then it imposes AD or SG against Brazil on the same product, it is offsetting some of the tariff liberalization. Similarly, we assume that if Argentina (or Brazil) imposes an AD or SG duty against a non-MERCOSUR country, it reverses the MFN tariff cut that may have taken place subsequent to preferential liberalization. More precisely, if Argentina imposed any AD or SG import restrictions against Brazil during one of our sample periods, we consider it as a reversal of the preferential (tariff) liberalization implemented during that same period. The dependent variable is defined similarly. (See again Table 2.) The data on AD and SG import restrictions come from government sources from those countries, as described in the _Temporary Trade Barriers Database_ (Bown, 2015b).

Second, we adjust the baseline Estevadeordal et al. (2008) approach by examining changes in the variables defined as long differences. Long differences allow us to take into
.104|-0.152***|-0.430|-0.171|-1.052*|-0.462|0.301|1.538***|
||(0.152)|(0.050)|(0.493)|(0.592)|(0.613)|(0.892)|(0.726)|(0.559)|
|Constant|-0.901**|0.120|-2.375***|-2.710|0.647|0.007|-0.996|-2.732***|
||(0.399)|(0.195)|(0.556)|(1.845)|(0.424)|(0.851)|(0.873)|(1.030)|
|Number of observations|630|262|551|223|641|264|630|261|
|r2_w|0.455|0.531|||||||
|r2_b|0.023|0.007|||||||
|r2_o|0.003|0.034|||||||
|rho|0.775|0.858|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Size information used as the interaction terms is taken from the total number of full-time equivalent workers in the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6
employees, and those employed in the public sector. We can estimate separate earnings functions for these three categories, but the resulting estimates might be biased because we are not allowing for the selectivity factors which govern the allocation of the workers to the t-hree types of employment.

The authors propose. a model in which parents' occupational and educational backgrounds affect the individual's relative employment opportunity in the different types of job. The selection equation is a multinomial logit estimate of sectoral choice in which the explanatory variables are education and age of the individuals, as well as the parental background factors, and regions of the country. The equation applied to the 1991-2 GLSS data set provided estimates of the expected sign for almost all the variables. Thus middle school and above education helped job-seekers enter the wage sector as opposed to self-employment, as did different kinds of diplomas and certificates from training. Using Accra as a benchmark, residence in other regions decreases the probability of entering the public sector, but increases the probability of selfemployment.

Family background had a small but significant effect on the job-seekers' sectoral choice. In particular the parents' white collar occupation-clerical, professional or managerial-increased the probability of public employment. When the earnings 33
.555|
|All||0.384|0.355|0.998|0.995|36.579|
|Poor/All||0.745|0.462|1.000|1.003|0.999|
|**Vietnam**|0.858||||||
|<br>Poor||0.804|0.627|1.000|0.861|13.863|
|All||0.840|0.631|1.000|0.858|15.430|
|Poor/All||0.958|0.993|1.000|1.004|0.898|

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail.

_Note:_ Access factors: _SA = SAP/SAH_ is the share of households located in a service area for the poor relative to the population as a whole; _C = CP|SA/CH|SA_ is the relative share of households that are connected to the service conditional on potential access (being located in a service area). Subsidy design factors: _T = TP|C/TH|C_ is the relative proportion of households with a subsidy conditional on usage; _R = RP|T/RH|T_ is the relative rate of subsidization; _Q = QP|T/QH|T_ are relative quantities consumed. A factor value below 1 indicates that the factor contributes to reduced targeting performance, whereas a value above 1 indicates that the factor contributes to increased targeting performance. All figures are calculated using sample weights. Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country.

19
# **3. Soundness of Kenya’s Banking Sector**

Since 2000 there has been a significant improvement in financial stability, as indicated by the financial soundness indicators in Table 3. The aggregate indicators, however, mask a significant variation across different ownership groups.

**Table 3.  Financial Soundness Indicators (%)**

||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|
|---|---|---|---|---|---|---|---|---|---|
|Regulatory capital to risk weighted assets|17.5|17.1|17.4|17.2|16.6|16.4|16.5|18.0|18.4|
|Regulatory Tier 1 capital to risk weighted assets|14.2|14.5|14.1|14.7|16.3|16.0|16.4|16.8|16.2|
|Non-performing loans to gross loans|37.2|39.4|39.6|34.9|29.3|25.6|21.3|10.9|8.4|
|Non-performing loans net of provisions to total<br>capital|78.7|78.8|77.8|60.7|52.7|40.1|28.6|15.1|10.8|
|Return on Assets|0.5|1.6|1|2.3|2.1|2.4|2.8|3.0|2.9|
|
check controls. The results from fixed effect and System GMM show that _sEXPY_ remains positively associated with growth. Again, controlling for initial _Goods EXPY_ and the size of domestic service sector does not change the results. The regression results confirm the dynamism in export quality of services, and provide a statistical indication of the positive relation between service quality of exports and growth.

> 16 The System GMM but not our fixed effect in our specification predicts goods EXPY to be non-significant.Tthis result has also been documented in other studies (see Weiss, 2010 ). In our case, this could also exist due to the high multicollinearity between service sophistication and goods sophistication.

> 17 We further try unconventional growth regression using annual panel data estimations to have a bigger sample and understand the evolution of service EXPY and growth. We conduct sensitivity tests by restricting the sample to period after 1998, 1999 and so on. As the period gets restricted to more and more recent years, the coefficient on Service EXPY becomes bigger (Results not shown in the paper).

> 18 Since the trade statistics in services do not capture the inputs and processes under services, at best our estimation is on the lower side. If these were included we believe that the growth elasticity of service export sophistication could be substantially higher.

20
value added, manufacturing value added, and the GDP share of exports are from WDI (2011). Data on the agricultural labor force, the share of the agricultural labor force in the total labor force, and an index of agricultural production are from FAOSTAT (2011). For some summary statistics see Table 1.

# **3. Baseline Estimation Strategy**

To examine the effects that within-country changes in distortions to relative agricultural prices (Δabs( _rrai,t_ )) have on real GDP per capita growth (Δln( _GDPi,t_ )), we estimate the following econometric model: (1) Δln( _GDPi,t_ ) = _αi_ + _βt_ + ηΔabs( _rrai,t_ ) + ΓΔX _i,t_ + _ui,t_ where _αi_ are country fixed effects that account for cross-country differences in geography, history, ethnicity and other time-invariant determinants of economic growth such as initial income per capita levels. The year fixed effects, _βt_ , capture common year shocks that affect both GDP per capita growth in Africa and changes in agricultural product price distortions (for example, common shocks to economic growth that are due to changes in the world business cycle or political events such as the end of the Cold War). The vector X _i,t_ includes additional within-country controls such as variations in a country-specific international export price index, rainfall, political institutions, the government expenditure share, and civil war incidence.

We note that our main measure of within-country changes in relative price distortions is the change in the _absolute_ rate of assistance. The motivation for using the change in the absolute RRA value is that – as argued in Section 2 – economic policy reform which reduces inter-sectoral bias is characterized by movements of the RRA towards zero: either from below if an anti-agricultural policy bias had been in place, or from above if a pro-agricultural policy bias had been in place. By using the change in the absolute value of the RRA, we impose the restriction that the effects of changes in the RRA are symmetric for
to select the enumeration sections, with a probability proportional to population size. Eight households are then selected randomly in each section. Camps are selected using the International Organization for Migration (IOM) data; the number of camps selected was proportional to the size of the communes. The sampling method yielded 1901 interviewed households, located in the disaster areas (camp and non-camp sites) as well as in some nondirectly affected areas. Geographic strata covered by the EFSA II survey are presented in Figure 3 (in Appendix).

To randomly select households, different methods were used for the urban households, the rural households and the camps. For urban households, survey investigators observe and mark the location of households on a street map that does not contain socio-economic infrastructure, and the households are randomly selected. For rural households, previously 9
Annex Table 3. Poland: Estimates of Revenue Loss through Tax Expenditures, 1998 ( 14 Tax Exemptions from Income plus 18 Tax relief programs, in PLN thousand)

|1. Scholarship for secondary school students|16,999|
|---|---|
|2. Scholarship of high school students|381,390|
|3. Scholarship for student with good results|3,189|
|4. Scholarship for foreign students and student study overseas|39,513|
|5. Social assistance in cash|1,603,801|
|6. Family benefits|4,513,625|
|7. Children allowance|541,410|
|8. Funeral benefits|696,973|
|9. Benefits for veterans, soldiers, miners, war camp workers|678,537|
|10. Alimenty|635,340|
|11. Pre-payment for buying a car|60,000|
|12. Benefit for flat|497,331|
|13. Flat expenses for professional soldiers|78,328|
|14. Expenses on solider uniforms|223,884|
|Sum of above 14 programs|9,970,320|
|Estimate of revenue loss from the above 14 programs by 19% tax rate|1,894,361|
|Sum of 18 PIT tax relief programs from Annex 2, according to Acticle 27a.|5,053,352|
|Total|6,947,713|
|Total State direct expenditures|139,494,772|
|Total State personal income tax revenue|34,644,500|
|Revenue loss from PIT (32 programs)/Direct Expenditures|5.0%|
|Revenue loss from PIT (32 programs)/Total State PIT Revenue|20.1%|
|Source: Ministry of Finance of Poland.||