rafmacalaba/datause-encoder-data huggingface dataset:
# 1. **Introduction**

In the 1990s, Vietnam achieved remarkably high economic growth. It also<sup>experienced</sup> steady improvement in many social indicators, which were already high despite its low income per capita. As explained in Chapter 1, the country has seen a huge reduction in poverty incidence, a reduction that occurred for all income groups and in all regions. The<sup>Governmentof</sup> Vietnam is embarking a very challenging agenda to provide higher quality basic education<sup>for</sup> all. However, much more needs to be done ensure that this ambitious, but<sup>notimpossible,goal</sup> will be achieved.

The purpose of this paper is to document and analyze changes in enrolment<sup>and</sup> financing for education in Vietnam over the five year period from 1993 to 1998,<sup>using the</sup> Vietnam Living Standard Survey (VLSS) data collected in 1992-93 and 1997-98.<sup>Thepaper</sup> begins by looking at changes in school enrolment, focusing on primary and lower<sup>secondary</sup> education because universal enrollment at these levels is an explicit goal of<sup>theVietnamese</sup> government. Enrolment rates have increased significantly in the past 5 years at all<sup>levelsof</sup> education. The paper compares changes in enrolments for all expenditure groups, all<sup>regions,all</sup> ethnic groups, and between boys and girls, to see whether recent improvement<sup>inenrolment has</sup> been evenly spread across the population, as opposed to being concentrated in certain groups or regions.

Secondly, the paper examines education finance, including the relative role of<sup>the public</sup> and private sectors in financing education in Vietnam. Private spending on<sup>educationis</sup> compared by expenditure groups, regions, ethnic groups, and by gender, to assess any<sup>changesin</sup> the financial burden of sending children to school, especially among the poor. It also<sup>investigates</sup> possible links between household spending on education, enrolment and the quality of<sup>education.</sup> The paper also checks whether the poor benefit as much as better-off households<sup>frompublic</sup> spending on education. Does government spending reduce inequalities? Can<sup>anyunequal</sup> patterns be corrected by reallocating the government's education budget?

The final issue considered is possible changes in rates of returns to education, including whether any changes varied across different groups. This is done by estimating<sup>Mincerian</sup> earning fimctions that link wage income to the underlying determinants of earnings,<sup>suchas</sup> schooling, work experience, age, economic sector, region, and gender.

The structure of the paper follows these three questions. Section II documents and analyzes changes in enrolment. Section m looks at the pattern of financing for<sup>education,and the</sup> relative role of the public and private sectors in financing education in Vietnam. Section<sup>IVlooks</sup> at the recent changes in rate of returns to education with limited focus on wage<sup>eamersinthe</sup> private sector. Finally, section V concludes the paper with some policy suggestions.

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undertaken in terms of changes in market projections for the period 1992/93-2000/013.

The initial effect of the devaluation is to cause SSA producer prices of cotton to increase by about the same amount in percentage terms (i.e., 20%)4. This causes a supply response with SSA production and exports increasing 4% on average over the projection period (see Table 9), indicating a supply elasticity of 0.2 when all crop and input prices are considered. The supply response is modest because the own-price elasticity of cotton supply is fairly small (about 0.6) and because the devaluation causes the prices of inputs used in producing cotton (e.g., fertilizers and chemicals) to increase as well. In addition, the devaluation increases the profitability of producing crops which competing with cotton for cultivatable land such as rice, soybeans and maize.

With larger cotton exports entering the world market (averaging 34,000 tons), the world price projection is reduced on average by -0.3%, less than USC1/kg (see Table 10). World production is higher on average by 11,000 tons-the net result of an additional 34,000 tons from SSA and a decline of 23,000 tons in the rest of the world in response to the lower world prices. World mill consumption increases in response to the lower world price but by less than 0.5%. With lower production in non-SSA countries, exports from other developing countries fall on average by 15,000 tons, valued at US$4 million. Despite the lower world price, the value of cotton exported by SSA increases with the 20% real devaluation, averaging US$5 million higher in the forecast period, an increase of about 3.8%. These results indicate that exchange rate devaluations in the 1980s were unlikely to have had a major adverse impact on the world cotton market and that they could have provided substantial benefits to SSA in terms of export revenues and producer incomes.

In order to test further whether there is an adding-up problem associated with the 20% real devaluation, ERVs were calculated for years between 1992/93 and 2000/01. These are reported in Table 11. To examine the sensitivity of the results to parameter assumption, the model was simulated with the price and income elasticities of demand and the GDP growth rate s. at half their base simulation levels. The ERVs for these simulations are also reported in Table 11.

3Given that purchasing power parity is assumed to hold in the projection period, in the base simulation the nominal exchange rates and consumer price indexes are assumed constant and fixed at their 1991/92 values. The 20% real devaluation is introduced by increasing the value of the 1991/92 nominal exchange rate by 20%, keeping the price indexes at their original values, and maintaining these throughout the simulation period.

4We assume that price liberalization also takes place so that domestic prices are determined by world prices and exchange rates.

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areas. This finding calls for an analysis of the drop in FLFP in terms of other factors, such as level of education, marital status, socio-economic status and spatial heterogeneity.

# **3.2 Level of Education and Female Labor Force Participation**

To better understand the effect of educational attainment on FLFP rates, we consider two aspects. First, we assess the changes in demographic profile by level of education (Figure 2), and second, we look at patterns in the relationship between the FLFP rate and levels of educational attainment (Table 9). In terms of education, the composition of the working age female population has undergone vast changes during the period between 1993-94 to 2011-12, with increasing proportions of secondary and higher secondary educated women, who in turn have a lower propensity to participate in the labor force. In general, the share of illiterates among the female population have decreased, while the share of women with higher education, especially in urban areas, has increased substantially (Figure 2).

**Figure 2: Level of Education of Working Age Females in India**

<!-- Start of picture text --> 6.9 11.62.7 4.8 8.8 11.4 15.6<br>19.9<br>28.4 21.6 22.2<br>36.8 29.0<br>38.0<br>37.2<br>38.3<br>33.6<br>63.7<br>49.0<br>37.3 32.5 28.1 21.8<br>1993‐94 2004‐05 2011‐12 1993‐94 2004‐05 2011‐12<br>rural Urban<br><!-- End of picture text --> Source: Authors’ calculation from NSSO data.

Now, turning to the relationship between the FLFP rate and levels of educational attainment, posessing secondary and higher secondary levels of education were not found to be an incentive for women to participate in the labor market in India (Figure 3). The lowest incidence of FLFP 15
policy and economic structure that have been constant over the sample period. Whilst the year dummy controls for general trends and shocks affecting tertiary enrollment across countries. _β_ is the coefficient of interest measuring the effect of EU accession on the change in tertiary GER.

# **3.2.2 Results**

||(OLS)|(OLS)|(Clustered)|(Clustered)|
|---|---|---|---|---|
|VARIABLES|LnGER|LnGER|LnGER|LnGER|
|Accession|0.207***|0.291***|0.207*|0.178|
||(0.0542)|(0.0597)|(0.0992)|(0.105)|
|FDI||-0.00612||-0.00219|
|||(0.00568)||(0.00305)|
|Exports||-0.00381***||0.000954|
|||(0.00128)||(0.00374)|
|Year Fixed Efects|yes|yes|yes|yes|
|Country Fixed Efects|yes|yes|yes|yes|
|Observations|222|174|222|174|
|Number of country|15|15|15|15|
|_R_<sup>2</sup>|0.888|.|0.888|0.885|

Standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Estimate of impact of accession talks on ln _GER_ . The data are for the 13 countries over the period 1970–2004. Czechoslovakia split into Czech Republic and Slovakia in January 1993, and, after this date, they are included as separate observations. The panel therefore consists of a total of 442 possible observations with deviations accounted for by missing data.

The results in table 5 suggest that accession to the EU does indeed have an impact on the levels of enrollment in tertiary education. Indeed, ceteris paribus, it can be expected that on average the GER will be approximately 22
11 Es - E= Ee1,N(nw - nj7) + En17 ,(e. - e,) + Z(el78<sup>-</sup> e,) (n, - nj7) Component due Component due Interaction to changing to changing effect consumption enrollments distribution where ei, stands for the enrollment rate at date t (t= 1978, 1987) for consumption group i= 1,...,N and E, denotes the mean over all consumption groups. The share of the population in each consumption group is given by n,.<sup>9</sup> The last term in the equation reflects the interaction effect due to covariance between the two other factors. Results from these calculations are given in Table 4.

It is of interest to note that changes other than those brought on by shifts in the 1978 consumption distribution, generally account for the most significant influence on overall enrollment increases. These effects are particularly pronounced for female enroliments where they are highest for the lower secondary school age group. This strongly suggests that tastes for educating female children have substantially altered and independently from the household's economic situation. Government policy is likely to have been instrumental in these changes. General government initiatives in the education sector during this period included the waiving of primary school registration fees, expeditious investments in new primary teacher training schools, increases in teachers' salaries, and the continuation of a school construction and rehabilitation drive begun in 1973 under the INPRES S.D. program (World Bank, 1991b). Although gender specific initiatives aimed at female students do not appear to have been part of its approach, the government has been a vocal advocate of equal opportunities in education during this period.

Distributional effects become more important the higher the level of education. They also tend to be of more consequence for male than for 9 The 1978 population frequency distribution is taken from Rao, 1984.
variables in our empirical specifications: education and age of the mother, marital status of the mother, household size, urban or rural residence and various assets of the household to proxy for income or socioeconomic status. DHS surveys do not contain any direct information about the income or consumption of households. To overcome this lack of data we have to include several assets to approximate a household’s permanent income level. The assets we use in our final specification are electricity, radio, TV, bicycle and fridge as well as the material used to construct the house of residence. Since the available assets differ widely across countries and time, the selected assets were chosen to reach a maximum of included assets on the one and of included DHS surveys on the other hand.

In addition, we control for various child level characteristics that might have an influence on children’s morbidity and mortality and are standard in the literature: age and gender of the child, whether the child is the first born child, the length (in month) to the preceding birth if he or she is not the first born, and whether he or she is/was breast-fed. For diarrhea we further include whether the child was vaccinated against measles to control for household preferences for improved health. We also use vaccination in a control specification for child mortality but not in our main specification. The problem with vaccination is first, that it is usually not recorded for children that have died. Second, if children died at a very young age they never had the opportunity to get vaccinated. We could use the vaccination status of living siblings to approximate the vaccination status of dead children. But for several households we only have one child in our sample, where such a procedure is not feasible.

In all of our specifications, we also control for the survey year and regional (sub-national) or country fixed effects, to avoid potential biases from time-specific local unobservables, correlated with both water and sanitation and our health outcomes of interest. The DHS surveys recode on average 10 regions per year and country. We adjust our standard errors for clustering in the DHS surveys.

For the specification of child diarrhea, we furthermore include the month of interview in addition to the survey year. Several studies have shown that diarrhea shows high seasonal fluctuations (for example Chambers et al., 1979; Molbak et al., 1994, Curriero et al., 2001). In 17/36
because the poverty impacts are just 0.5 million in an agriculture-only scenario. The second scenario, Doha-ag-sp, shows the impacts of allowing all countries to exempt 2 percent of their tariff lines as sensitive products (plus another 2 percent in developing countries for special products). This greatly dilutes the gains from a potential agricultural deal. Indeed, according to the estimates in table 17.7, there could even be a poverty _increase_ . A final scenario assesses the impacts of the core Doha scenario in a comparative static framework. This would slightly diminish the poverty reduction to 1.7 million individuals lifted out of poverty (from 2.5 million in the corresponding dynamic scenario).

These simple calculations are not a substitute for the more detailed analysis that has been generated by the individual country case studies in Hertel and Winters (2005) through use of detailed household surveys. However, it is useful to have a global assessment, and one that provides a sense of the possible range of poverty impacts. Moreover, the elasticity approach<sup>32</sup> may be somewhat conservative because these are derived elasticity estimates from the baseline scenario.<sup>33</sup>

# **7. Conclusions**

The global economy of 2015 is likely to look quite different from the world economy of 2001 or 2005—particularly if the growth assumptions underlying this chapter’s baseline scenario obtain. The changing structure of production, demand, trade, and comparative advantage will

> 32. The World Bank’s headcount index forecast is derived from an estimated Lorenz curve based on the most recently available household survey and not from an elasticity approach. A forecast of the growth of per capita consumption is plugged into the Lorenz curve–based functional form for the headcount index, assuming distribution neutrality. See Datt (1998) for a derivation of this methodology.

> 33. Elasticities at the low end of the income spectrum should be rising over time as mean incomes rise; therefore the marginal elasticity in 2015 should normally be higher than the average implied elasticity between 2001 and 2015.

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# **<u>Table 5.  BE effects on growth by quantiles</u>**

|||||**Dep**|**endent vari**|**able: Lg**|**rowth**||||
|---|---|---|---|---|---|---|---|---|---|---|
||**5 perce**|**ntile**|**25**<sup>**th**</sup>**perc**|**entile**|**Medi**|**an**|**75**<sup>**th**</sup>**perc**<br>**High-gr**|**entile**<br>**owth**|**95**<sup>**th**</sup>**perc**<br>**Very-high**|**entile**<br>**growth**|
||**coef**|**se**|**coef**|**se**|**coef**|**se**|**coef**|**se**|**coef**|**se**|
|Corruption Obstaclec|-0.034**|0.010|-0.005**|0.001|-0.009**|0.002|-0.007|0.004|-0.019|0.011|
|Security Costc|-0.414**|0.139|-0.038*|0.017|0.012|0.024|0.066|0.042|0.020|0.132|
|Outagec|0.002|0.006|0.000|0.001|0.002|0.001|-0.002|0.003|-0.012|0.009|
|Webc|0.011|0.009|0.006**|0.001|0.009**|0.002|0.019**|0.004|0.044**|0.009|
|Skill Obstaclec|-0.020|0.011|-0.002|0.001|0.002|0.002|0.004|0.004|0.016|0.015|
|Overdraft Facilityc|0.001|0.010|0.002*|0.001|0.006**|0.002|0.017**|0.004|0.024*|0.009|
|Trade Creditc|-0.016|0.009|-0.005**|0.001|-0.003|0.002|-0.006|0.005|0.004|0.011|
|L Regu Obstaclec|0.012|0.013|0.001|0.002|0.002|0.002|-0.004|0.005|-0.001|0.013|
|Tax Rate Obstaclec|0.002|0.009|0.002|0.001|-0.003|0.002|-0.008*|0.004|-0.022*|0.009|
|Land Access Obstaclec|0.013|0.010|-0.000|0.001|-0.003|0.002|-0.008*|0.004|-0.006|0.011|
|In Big City|0.000|0.003|0.001**|0.000|0.004**|0.001|0.007**|0.002|0.008*|0.004|
|Capacity Agglomerationc|0.086**|0.014|0.005*|0.002|0.001|0.003|0.008|0.008|0.030|0.025|
|Inf Competitionc|-0.001|0.006|-0.003**|0.001|-0.007**|0.002|-0.010**|0.003|-0.019*|0.009|
|Other firm controls|Yes||Yes||Ye|s|Yes||Yes||
|Country FE|Yes||Yes||Ye|s|Yes||Yes||
|||||**Dep**|**endent vari**|**able: LPg**|**rowth**||||
|Corruption Obstaclec|0.017|0.017|0.004|0.006|0.006|0.005|0.019*|0.008|0.050*|0.022|
|Security Costc|-0.525|0.318|-0.282**|0.080|-0.011|0.051|0.187**|0.070|0.008|0.235|
|Outagec|-0.008|0.010|-0.002|0.004|-0.003|0.003|-0.009|0.005|-0.019|0.015|
|Webc|0.008|0.015|0.004|0.005|0.005|0.005|0.017**|0.006|0.026|0.020|
|Skill Obstaclec|-0.010|0.019|-0.000|0.006|-0.007|0.005|-0.001|0.007|-0.009|0.022|
|Overdraft Facilityc|0.048**|0.012|0.027**|0.005|0.026**|0.004|0.022**|0.006|0.049**|0.018|
|Trade Creditc|-0.009|0.016|0.004|0.006|0.015**|0.004|0.040**|0.006|0.070**|0.022|
|L Regu Obstaclec|-0.017|0.022|0.007|0.007|0.020**|0.005|0.009|0.009|0.024|0.025|
|Tax Rate Obstaclec|0.008|0.012|0.003|0.004|0.003|0.004|0.003|0.006|-0.042**|0.016|
|Land Access Obstaclec|0.015|0.016|0.002|0.006|-0.006|0.005|-0.019*|0.008|-0.028|0.021|
|In Big City|-0.010|0.006|-0.001|0.002|-0.002|0.002|-0.005*|0.002|-0.002|0.007|
|Capacity Agglomerationc|-0.005|0.031|0.016|0.011|0.032**|0.009|0.022|0.013|0.026|0.033|
|Inf Competitionc|0.029*|0.012|0.016**|0.004|0.014**|0.004|0.005|0.005|-0.001|0.014|
|Other firm controls|Yes||Yes||Ye|s|Yes||Yes||
|Country FE|Yes||Yes||Ye|s|Yes||Yes||

*, ** represent statistical significance at the 5 and 1 percent levels. Standard errors bootstrapped (with replications of 100) in columns indicated by “se”. The number of observations for Lgrowth is 57,455; that for LPgrowth is 44,279.

39
The current paper contributes to this limited knowledge by evaluating the impact of district hospital capitation payment in Vietnam. The country promulgated a Health Insurance Law in 2008 which laid out a roadmap to UHC. According to the Law and its sub-legal guiding documents, Vietnam should be close to achieving universal insurance coverage by 2020. The social health insurance policy encourages shifting from fee-for-service (FFS) payments to health care providers towards other methods. In particular, there is a roadmap for capitation contracts to be applied for payments to first level facilities (mostly district hospitals) to reach 60% of facilities by 2013 and 100% by 2015. Fee-for-service and eventually case-based payments are applied to referral facilities.

The shift in hospital payment from FFS to capitation and case-based payments is not unique to Vietnam. Across many transitional economies and other developing countries, reform on the purchasing side goes hand-in-hand with development of the pooling functions. Roughly three-fourths of all countries in Europe and Central Asia were reported to adopt a providerpurchaser split over the last two decades (Moreno-Serra and Wagstaff 2010). A typical model there is case-based payment for tertiary care and capitation payment for primary care. This has been applied in Armenia, Estonia, Latvia, Slovak Republic, Georgia, Kyrgyz, and Turkey, just to name a few (Kutzin, Cashin, and Jakab 2012; Gotsadze 2012). On other continents, social health insurance schemes in Argentina, Brazil, Nicaragua, Thailand, and Ghana have adopted, or are currently piloting, capitation payment as a means to remunerate public and private providers. The level of interest in reforming the way providers are paid is ever mounting while empirical evidence on the results is not catching up.

The current study evaluates the impact of district hospital capitation reform in Vietnam on efficiency, quality of care, and equity by analyzing a rather unique hospital panel data set 3
# xvi INTRODUCnON

<!-- Start of picture text --> Figure 3 Assumed Medium Trend and Altemative Slow and Rapid Trends in<br>Total Fertility During the Fertility Transition<br>5.5-<br>5.0<br>4.5<br>4.0 -<br>3.5<br>3.0<br>2.0<br>q995-90 1995-2000 2005- 10 2045-20 2025- 30 203S-40 2045-50<br><!-- End of picture text --> Figure 4 Assumed Trends in Total Fertility at the End of the Fertility Transition <!-- Start of picture text --> 3.0 _<br>2.9-<br>2.8 -lO<br>2.5-<br>2.4-<br>2.3 -<br>2.2-<br>2.0-<br>'1.9<br>, t_ 'lbelow replacement<br>1980- 85 1990- 95 2000-05 2012- 15 2020- 25 2030- 35<br><!-- End of picture text --> Figure 5 Percentage of Total Fertility Assigned to Different Ages, by Level of Total Fertility <!-- Start of picture text --> 28 - TFR,5. 6<br>29<br>24-<br>22- .<br>20 -<br>14 2<br>12<br>40<br>15- 19 20- 24 25-29 30- 34 35..39 40-44 45-49<br><!-- End of picture text -->