rafmacalaba/datause-encoder-data huggingface dataset:
11 and are paLticularly underdeveloped in the former Soviet states where public provision of all services continues to dominate. The dependent variable in the regression analysis indicates the level of service activity and is measured by the service share in value addecA and employment.

The regressions are performed on panel data sets, with OECD observations available for the 1960s, 1970s and 1980s and "world economies" observations available for the 19709 and 1980s. The panel dimension of the data does not add much to the exercise, since most of the variation is across countries rather than time. We therefore use decade averages rather than annual observations; and the decade average coefficients can be used to assess whether there were shifts in the levels of activity over time. Where the coverage of sub-sectors in the Soviet data differs from the OECD and "world economies" classifications, we typically bias the Soviet data against the hypothesis of smaller service sectors by using broader categories.

The regression results are shown in Table 1 and Figures 1-25 (see also the accompanying Notes to Figures). Table 1 shows the residuals -- or differences in actual and expected employment shares -- for Russia and other republics in a regression with "world economies" data(Individual Soviet republics were included as dummy variables, which do not affect the regression line.) The table shows two columns for each category of economic activity. The first column shows the estimated gap in service employment shares over the whole period. A positive number indicates that actual shares were above expected shares based on international norms and a negative number indicates that actual shares weve below expected shares. The second column shows the shift from the 1970s to the 1980s. Here, a positive number indicates a shift toward international norms, which are higher; and a negative number indicates a shift away from international norms.
TABL! OF CONTENTS

||Page|
|---|---|
|Introduction|1|
|Economic Change in Zambia 1970-85|1|
|Aspects of Labour Supply|5|
|Aspects of Labour Demand|9|
|Labour<br>Market<br>Rigidities|11|
|Impact<br>of the<br>1985 IMPStabilization Package on the<br>Labor<br>Market|30|
|Conclusion|32|
|FOL<br>3tes|36|
|Appendix|37|
|References|39|
|Table 1|3|
|Table 2|6|
|Table 3|7|
|Table<br>4|10|
|Table<br>5|12|
|Table<br>6|13|
|Table<br>7|15|
|Table<br>8|17|
|Table<br>9|19|
|Table<br>10|20|
|Table 11|22|
|Table<br>12|24|
|Table<br>13|27|
|Table<br>14|28|
|Table<br>15|29|
|Appendix<br>Table<br>1|38|
neighborhood N2 has a consistently negative effect on subsequent buildup predictions. A similar negative coefficient is observed in some regressions for neighborhood N1 as well. These findings are again consistent with the existence of a substitution effect in buildup: if the road is in a neighboring cell but not in my cell, my cell is likely to be built-up a decade and a half later – an outcome we would expect if local inhabitants prefer to locate immediately near these smaller roads. When we estimate regression (6) with 2014 buildup _yit_ and the 2008 road data, we find that all the road coefficients but one are positive. The only exception is for the presence of a primary road in the cell itself, which by 2014 may have too much traffic to be an attractive location. The 2008 road data is completely different from the earlier data, however, with very little correlation between the two, thereby making comparisons problematic. The 2014 road data is strongly correlated with buildup in that year, but since it is contemporary to our last data year, it cannot be regarded as a useful predictor.

We also estimate a different version of regression (6) setting _t − s_ = 1975 for lagged buildup and _t−s_ = 1976 for the road data. By regressing each of the subsequent data years 1990, 2000 and 2014 on these initial buildup and road conditions, we examine the delayed predictive effect of buildup and roads over time. Figure 7 summarizes the buildup coefficients in a way similar to Figure 4, the main difference being that now all regressions use the same 1975 buildup data as regressors. The Figure shows clearly that, over time, the predictive effect of past buildup changes. For buildup in neighborhoods N1 and N2, we see a systematic increase in buildup predictions as time passes. This is consistent with some form or preferential attachment – the presence of buildings in the past attracts new buildings later – possibly due to agglomeration externalities. Past buildup in the cell itself varies depending on the original buildup intensity: for heavily built-up cells in 1975, the own-cell predictive effect falls over time. In contrast, cells with less buildup in 1975 see their buildup predictions increase over time. The fall in predictions for heavily built-up cells may suggest some form of congestion, unless it is compensated by buildup in neighboring cells, which makes it harder to move to a nearby cell.

Figure 8 does the same thing for coefficient estimates for _γ_ 1 _, γ_ 2 and _γ_ 3. The interpretation of the coefficients is similar to that in Figure 6, except that we are now comparing the evolution of buildup predictions over time, using roads in 1976 as predictor. We immediately see that the predicted effect of class 1 roads increases massively over time both for the presence of a road in 20
INTRODUCTION

|Life***xpectancy.**1985-90|Infant mortality.<br>1985-90|Age-sex structure,<br>mid-1985|Country,<br>economy or<br>territory|
|---|---|---|---|
||||Tuvalu<br>|
|Sankest.<br>|U.N. 1989 (prelim. version)<br> <br>|Bank assessment<br>|Uganda<br>|
|U.N. 1989 (prelim. version)<br>|U.N. 1989 (prelim.<br>version)<br> <br> <br>|U.N. 1989 (prelim. version)<br>|United Arab<br>Emirates<br>|
|U.N. 1989 (prelim. version)<br>|UNPVSR<br>10/88 (official<br>est.)<br> <br> <br>|IJ.N. 1989 (prelim. veraion)<br>|United Kingdom<br>|
|U.N. 1989(prelim.version)<br>|UNPVSR<br>10/88 (official<br>est.)<br>|U.N. 1989 (prelim. version)<br>|United States<br>of America<br>|
|U.N. 1989 (prelim. version)<br> <br>|U.N.1989(prelim. version)<br>|U.N. 1989 (prelim. version)<br>|Uruguay<br>|
|Based**on BlSum**<br>**and**Pressat 1987<br>|Based on Bltus and Pressat 1987<br>|U.N. 1989 (prelim. version)<br>|U.S.S.R.<br>|
|Bank est.<br>|Bank est.<br>|Bank est.<br>|Vanuatu<br>|
|U.N. 1989 (prelim. version)<br>|U.N. 1989 (prelim. version)<br>|U.N. 1989 (prelim. version)<br>|Venezuela<br>|
|Bank est.<br>|Sank est.<br>|Bank est.<br>|Viet Nam<br>|
|Based on USBOC 1987|USBOC 1987|UNDY 1980 census|Virgin Islands<br>(U.S.)<br>|
||||Wake Island<br>|
||||WaltLis and<br>Futuna<br>|
|Bank est.|Bank est.|Bank est.|West Sank<br>|
|<br>|||Western Sahara|
|USBOC<br>1987<br>|USBOC 1987<br>|Bank est.<br>|Western Samoa<br>|
|Bank est.<br>|Bank est.<br>|Bank est.<br>|Yemen Arab Rep.<br>|
|U.N. 1989 (prelim. version)<br> <br> <br>|U.N. 1989 (prelim. version)<br>|U.N. 1989 (prelim. version)<br> <br> <br>|Yemen, People's<br>Dem. Rep. of|
|UNPVSR<br>10/88 (official<br>est.)<br>|U.N. 1989 (prelim. version)<br>|UNPVSR<br>10/88 (official<br>est.)<br> <br>|Yugostavia<br>|
|U.N. 1989 (prelim. version)<br>|U.N. 1989 (prelim. version)<br>|U.N. 1989 (prelim.<br>version)<br>|Zaire<br>|
|U.N. 1989 (prelim. version)<br>DHS 1988|U.N. 1989 (prelim. version)<br>OHS 1988|U.N. 1989 (prelim. version)<br>Bank assessment|Zambia<br>Zimbabwe|

CBR Crude birth rate C"DR Crude death rate DHS Demographic ard Health Survey IHR Infant mortality rate TFR Total fertility rate UNDY United Nations, Demooraehic Yearbook (year is given in the table) UNPVSR United Nations, Poputation and Vital Statistics Report (month and year are given in table) USBOC U.S. Bureau of the Census (1985; Jamison, Johnson, and Engets, 1987) WFS World Fertility Survey
of improved service delivery; but some forms of intermediate data points may be needed that capture steps in the causal chain more directly influenced by PFM systems than final outcomes.<sup>49</sup>

**Table 5: PFM Quality and Outcome Variables**

|_Dependent Variable:_|(1)<br>**Deficit**|(2)<br>**PI-1**|(3)<br>**PI-2**|(4)<br>**Health**|(5)<br>**Education**<sup>**50**</sup>|
|---|---|---|---|---|---|
|**PFM Quality**|-1.8614|0.6830***|0.6408***|-0.6796|0.3896|
||(1.5665)|(0.2160)|(0.2197)|(3.7362)|(4.2376)|
|**GDP per capita (log)**|0.9401|-0.0991|0.1766|9.0169***|-14.0817***|
||(0.7393)|(0.1177)|(0.1318)|(1.5627)|(2.1150)|
|**Observations**|56|102|97|60|57|
|**R-squared**|0.05|0.10|0.18|0.45|0.52|

**Notes:** Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

One possible explanation for this lack of a significant relationship could be the fact that there is considerable variation between the strength of PFM systems (as reflected in PEFA indicators) and overall government effectiveness, as shown in section 4.1. If PFM systems are relatively strong, but government effectiveness remains limited otherwise, then potential beneficial effects on service delivery would ‘dissipate’. However, at least for the sample for which data on expenditure effectiveness can be constructed, overall government effectiveness also shows no significant relationship with efficiency in service delivery.

It is important to keep in mind that the measures currently available are rather broad proxies. For example, actual spending levels reaching local levels and specifically, front-line service delivery agencies, and the quality of PFM at those levels may have more direct effects on spending efficiency, but no specific cross-country measures of these are currently available.<sup>51</sup>

> 49 There is also the challenge that health or education outcomes depend not only on public funding but also on how much citizens spent privately out of pocket. User satisfaction with public facilities (relative to public sector spending) may be useful as an intermediate indicator, but available data is typically country specific rather than comparable across cases; while there may be opportunities for comparisons across time.

50 We confirm our findings when we formulate the education efficiency variable using adult literacy rates as compared to primary school completion rates.

51 In addition to using the aggregate PEFA scores constructed as described in section 4.1, we also checked for the relationship between any of the six PEFA subdimensions and the outcome variables; but these follow similar patterns as the aggregate scores or show no clear relationship.

38
Zimbabwe (1980-2000), in particular - with more examples found among high and middle income countries.

The second alternative scenario considered – the _education improvement_ scenario – thus accounts for the downward bias of the pipeline approach and for likely improvements in educational attainment. This is done by drawing on historical data from the I2D2 database for 20-year episodes of secondary education expansion in both developing and developed countries. With these data, the actual labor supply composition changes are compared to what the pipeline assumptions applied to the historical 20-year period would suggest. This allows for a measurement of the pipeline error by age group and gender, and the estimation of a linear adjustment equal to the annualized difference realized and projected pipeline values. This adjustment can then be applied to projections made using the pipeline with the assumption that the pipeline error effectively captures improvements in educational attainment.<sup>17</sup> Accounting for improvements in educational attainment then allows the skilled labor supply to grow much more rapidly than in the skilled pipeline scenario, and almost a doubling of the skill share of labor in SSA (columns I and III, Table 6). The skill share of Africa’s labor supply would thus double from 25 percent in 2011 to about 50 percent in 2030. The 50 percent would be comparable to current levels of skilled workers’ share in several developing countries (e.g. Albania, Colombia, Jamaica, Peru and Philippines). Although high by the current standards of many African countries, these skill shares are not unattainable.

When the pipeline effect is allowed to improve the skill share of Africa’s labor supply, SubSaharan Africa’s growth experiences a slight improvement over growth in the high growth baseline (Figure 8). Africa’s real GDP is 1.9 percent greater in 2030 in the _skilled pipeline_ scenario than in the _baseline_ as a result of the greater number of skilled workers, even though the difference in growth rates between the baseline and skilled pipeline appears modest. When improvements in educational attainment are allowed to increase the region’s share of skilled workers by more than 13 percent between 2011 and 2030, the region’s 2011-30 per capita growth rate rises by more than a percentage point. The faster growth allows the region’s economy in 2030 to be 22.4 percent greater than in the baseline scenario.

> 17 Please see Appendix B for details.

23
# **3 Research Design**

We explore the e↵ects of CBI using cross-national panel data. We test both the overall prediction (CBI increasing inequality) as well as each of the mechanisms (the e↵ect of CBI on financial regulations, labor market, and social policies). None of these variables has a natural and unique proxy. This forces us to rely on several variables that capture the underlying theoretical quantities of interest. We present them briefly here. All variables are summarized in Table 1.

|**Su**|**mmary **|**statistics**||||
|---|---|---|---|---|---|
||Mean|Median<br>S.D.|Min.|Max|Obs.|
|CBI|0.50|0.20|0|1|4369|
|GDP (log)|23.68|2.38|17|31|9070|
|Population (log)|15.00|2.30|8|21|11937|
|Income share of bottom 10%|2.35|1.07|0|5|1000|
|Income share of 10-20%|3.94|1.31|1|7|1001|
|Income share of 20-30%|5.04|1.35|2|7|1001|
|Income share of 30-40%|6.06|1.32|3|8|1001|
|Income share of 40-50%|7.12|1.25|4|9|1001|
|Income share of 50-60%|8.34|1.13|5|10|1001|
|Income share of 60-70%|9.84|0.93|7|12|1001|
|Income share of 70-80%|11.91|0.64|9|14|1001|
|Income share of 80-90%|15.39|0.91|13|18|1001|
|Income share of top 10%|30.02|7.93|18|52|1001|
|Financial Reform Index|10.36|6.34|0|21|2638|
|Bank Liberalization|1.26|1.19|0|3|2638|
|Social pact|0.24|0.47|0|5|1637|
|Labor law|41.85|18.98|6|94|2335|
|Social reform|0.05|0.22|0|1|1586|
|Pension reform|0.02|0.13|0|1|1585|
|Money and quasi money growth (%)|35.26|272.63|-100|12513|6223|
|Private credit (log)|22.71|2.80|10|31|7094|
|Price level of capital stock|0.50|0.35|0|4|7436|
|Infation (%)|31.90|396.14|-18|23773|5914|
|Incidence of Part-Time Employment|14.07|6.98|2|39|908|
|SSA benefts (log)|2.47|0.48|0|3|988|

Table 1: Summary statistics.

Our central independent variable is CBI. We use a recently updated version of the CBI measure as proposed in Bodea and Hicks (2015 _b_ ). Following the coding protocol of Cukierman, Miller, and Neyapti (2002), the index is built around four dimensions of CBI (e.g., the mandate of a central bank, monetary financing prohibitions) that are normalized on a scale between 0 and 1. Higher scores of the index indicate a greater degree of CBI. The data available from Bodea and Hicks (2015 _b_ ) covers up to 144 countries between 1972 and 2014 and are one of the most comprehensive 10
|Dependent variable:<br>Variables|Need of fis<br>(1)|cal adjustm<br>(2)|ent based<br>(3)|on debt/re<br>(4)|venue<br>(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Rigid expenditure|0.023***|0.024***|0.026***|0.023***|0.051***|0.019|0.001|0.031***|
|(% of GDP)|(0.004)|(0.005)|(0.007)|(0.007)|(0.011)|(0.019)|(0.014)|(0.012)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.357***||-1.688***|-1.449***|-1.694***|-1.631***|-1.666***|
|||(0.320)||(0.334)|(0.340)|(0.335)|(0.343)|(0.332)|
|Inflation (t-1)||0.020||0.214|0.232*|0.215|0.200|0.217*|
|||(0.135)||(0.132)|(0.130)|(0.132)|(0.133)|(0.131)|
|U.S. Interest rate||0.005||0.001|-0.004|0.001|-0.003|0.001|
|||(0.011)||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|**Political factors**|||||||||
|Gini|||0.008|0.007|0.006|0.005|0.007|0.007|
||||(0.005)|(0.005)|(0.005)|(0.009)|(0.005)|(0.005)|
|Rule of law|||0.025|0.033|0.023|0.034|-0.100|0.032|
||||(0.038)|(0.046)|(0.046)|(0.046)|(0.087)|(0.046)|
|Margin of Majority|||-0.091|-0.073|-0.080|-0.072|-0.073|0.146|
||||(0.152)|(0.156)|(0.151)|(0.156)|(0.158)|(0.285)|
|Election's year|||-0.009|-0.012|-0.009|-0.012|-0.011|-0.013|
||||(0.015)|(0.015)|(0.016)|(0.015)|(0.016)|(0.016)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.043***<br>(0.014)||||
|Gini * Rigidity||||||0.000<br>(0.000)|||
|Rule of law * Rigidit|y||||||0.006*<br>(0.003)||
|Majority * Rigidity||||||||-0.013<br>(0.013)|
|Constant|-0.125|-0.106|-0.558*|-0.455|-0.508*|-0.383|0.015|-0.589*|
||(0.096)|(0.122)|(0.321)|(0.299)|(0.290)|(0.420)|(0.393)|(0.337)|
|Observations|3,046|2,911|2,066|2,007|2,007|2,007|2,007|2,007|
|R-squared|0.026|0.042|0.031|0.049|0.064|0.049|0.054|0.050|
|Number of ifscode|147|145|103|102|102|102|102|102|
|CountryFE<br>Robust standard err<br>*** p<0.01, ** p<0.|Yes<br>ors clustere<br>05, * p<0.1|Yes<br>d by countr|Yes<br>y in parent|Yes<br>heses|Yes|Yes|Yes|Yes|

# Probability of a successful fiscal adjustment

Our results for the probability of fiscal adjustment are reported in Table 5 and Table 6 using the traditional measure of fiscal rigidities, and Table 7 and Table 8 using the structural measure. In general, we find evidence suggesting that budget rigidities affect the probability of fiscal adjustment. This negative

- 21 -
**Table 5: Principles and better practices in grant design**

|**_Grant Objective_**|**_Grant Design_**|**_Better Practices_**|**_Practices to avoid_**|
|---|---|---|---|
|To bridge fiscal gap|•<br>Reassign responsibilities<br>•<br>Tax abatement<br>•<br>Tax base sharing|Tax abatement and tax base<br>sharing in Canada|Deficit grants<br>Tax by tax sharing|
|To reduce regional<br>fiscal disparities|General Non-matching Fiscal capacity<br>equalization transfers|Fiscal equalization programs of<br>Canada and Germany|General revenue sharing with<br>multiple factors|
|To compensate for<br>benefit spillovers|Open-ended matching transfers with<br>matching rate consistent with spill-out<br>of benefits|Republic of South Africa grant<br>for teaching hospitals||
|Setting national<br>minimum standards|Conditional non-matching block<br>transfers with conditions on standards of<br>service and access|Indonesia pre-2000 roads and<br>primary education grants<br>Colombia and Chile education<br>transfers, Canada Health<br>transfers , SUDS program in<br>Brazil|Conditional transfers with<br>conditions on spending alone<br>_ad hoc_grants|
|Influencing local<br>priorities in areas of<br>high national but<br>low local priority|Open-ended matching transfers (with<br>preferably matching rate to vary<br>inversely with fiscal capacity)|Matching transfers for social<br>assistance as in Canada|_ad hoc_grants|
|Stabilization|Capital grants provided maintenance<br>possible|Limit use of capital grants and<br>encourage private sector<br>participation by providing<br>political and policy risk<br>guarantee|Stabilization grants with no<br>future upkeep requirements|

_Source:_ Shah (1994, 1998, 2004, 2005).

20
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Tcble 3

**RArroseion** of Annual **Positive gavninse (Israeli lirot)*** on Two

Haior Youth Treinini Proarans. 1969-1975 1969 1970 1971 1972 1973 1974 1975 Independent Variables**:

|Training<br>Program:||||||||
|---|---|---|---|---|---|---|---|
|APP|-1055|-361|-52|-596|-1592|<br>-87|-1480|
||(2.37)|(0.88)|(0.09)|<br>(0.85)|<br>(1.56)|(0.06)|(0.40)|
|voc|--|--|--|-|--|--|--|
|Training Trades||||||||
|AUTO|-545|<br>!86|585|1147|439|-780|-905|
||(1.43)|(1.13)|(1.19)|(1.95)|<br>(0.52)|(0.66)|(0.30)|
|ELECT|71|22|87|416|396|<br>1593|<br>9460|
||(0.17)|(0.06)|(0.16)|<br>(0.64)|(0.42)|(1.20)|(2.92)|
|METAL|--|--|--|--|--|--|--|
|LEN|657|-1066|-492|-972|-159|<br>473|2860|
||(1.56)|(3.15)|(1.04)|<br>(1.66)|(0.18)|(0.40)|(0.91)|
|COMP|-971|<br>-731|-1512|-1801|-2139|-1501|-2890|
||(4.10)|(3.60)|(5.30).|(5.08)|<br>(4.22)|(2.06)|(1.62)|
|YB|-851|<br>-215|3|482|56|-798|1424|
||(1.70)|(0.72)|(0.01)|<br>(0.92)|<br>(0.07)|(0.75)|(0.54)|
|IMM|64|-291|-197|-401|-982|-2237|5047|
||(0.21)|(1.03)|(0.49)|<br>(0.82)|<br>(1.41)|(2.27)|(2.04)|
|Constant***|11057|13098|14693|19965|24021|30059|37183|
|R2|0.045|<br>0.048|0.052|0.046|0.033|0.021|0.046|
|n|884|<br>953|889|891|788|<br>835|554|

NB t statistic is shown in parentheses

- The Israeli lira has been superceded by the new shekel (1 new shekel - 10.000 lirot)

- ** For definition of independent variables, see text.

* The constant term contains VOC, those born in 1947 and Israeli born.

12
- <mark>Engineer, C. Y., Dale, E., Agarwal, A., Agarwal, A., Alonge, O., Edward, A., ... & Peters, D. H. (2016). Effectiveness of a pay-for-performance intervention to improve maternal and child health services in Afghanistan: a cluster-randomized trial.</mark> _<mark>International journal of epidemiology</mark>_ <mark>,</mark> _<mark>45</mark>_ <mark>(2), 451-459.</mark>

- Friedman, J. and R. M. Scheffler (2016). Pay for Performance in Health Systems: Theory, Evidence and Case Studies. World Scientific Handbook of Global Health Economics and Public Policy. R. M. Scheffler. Singapore, World Scientific Press

- Friedman, Jed; Qamruddin, Jumana N.; Chansa, Collins; Das, Ashis Kumar. 2016. Impact evaluation of Zambia’s health results-based financing pilot project. Washington, D.C. : World Bank Group.

- <mark>Fritsche, G. B., Soeters, R., & Meessen, B. (2014).</mark> _<mark>Performance-based financing toolkit</mark>_ <mark>. World Bank Publications.</mark>

- Gertler, P., P. Giovagnoli and S. Martinez (2014). Rewarding provider performance to enable a healthy start to life: evidence from Argentina's Plan Nacer. The World Bank, Policy Research Working Paper Series, 6884.

- <mark>Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score.</mark> _<mark>Econometrica</mark>_ <mark>,</mark> _<mark>71</mark>_ <mark>(4), 1161-1189.</mark>

- <mark>Huillery, E., & Seban, J. (2019). Financial Incentives, Efforts, and Performances in the Health Sector: Experimental Evidence from the Democratic Republic of Congo.</mark>

- Kandpal E. _Completed impact evaluations and emerging lessons from the health results innovation trust fund learning Portfolio_ : World Bank, 2017. https://www. rbfhealth. org/ sites/ rbf/ files/ IE% 20and% 20emerging% 20lessons_ Eeshani% 20Kandpal. pdf \

- <mark>Kruk, M. E., A. D. Gage, N. T. Joseph, G. Danaei, S. García-Saisó, and J. A. Salomon. 2018. “Mortality Due to Low-Quality Health Systems in the Universal Health Coverage Era: A Systematic Analysis of Amenable Deaths in 137 Countries.”</mark> _<mark>The Lancet</mark>_ <mark>.</mark>

- <mark>Miller, G., & Babiarz, K. S. (2013).</mark> _<mark>Pay-for-performance incentives in low-and middle-income country health programs</mark>_ <mark>(No. w18932). National Bureau of Economic Research.</mark>

- Peabody, J. W., R. Shimkhada, S. Quimbo, O. Solon, X. Javier and C. McCulloch (2014). "The impact of performance incentives on child health outcomes: results from a cluster randomized controlled trial in the Philippines." Health Policy and Planning 29(5): 615-621.

20