rafmacalaba/datause-encoder-data huggingface dataset:
18

# <u>Table 1. Concepts and indicators used in studies of out-of-pocket health expenditures</u>

|<br>**Concept**|<br>**Indicator**|<br>**Interpretation**|<br>**Study introducing the**<br>**indicator**|**Comments**|
|---|---|---|---|---|
|1<br>Expenditure in<br>absolute terms|Per capita annual out-of-pocket<br>expenditures in monetary terms|Absolute amount of expenditure<br>per capita, adjusted for inflation<br>and in international studies<br>converted to a common currency|Multiple studies in the United<br>States report trends in per capita<br>out-of-pocket expenditures in<br>dollar terms (see e.g. Banthin et<br>al. 2008).||
|2<br>Dispersion<br>(risk)|Coefficient of variation (CV)|Both interpreted as measures of<br>out-of-pocket expenditure risk|<br>Gruber and Levy (2009)|Gruber and Levy actually use the<br>variance, but this is not invariant<br>with respect to the mean,<br>quadrupling when everyone’s out-<br>of-pocket expenditures double (cf.<br>e.g. Cowell 2011)|
||Q90/Q50. The ratio of the<br>expenditures incurred by<br>households at the 90<sup>th</sup>and 50<sup>th</sup><br>percentiles of the out-of-pocket<br>expenditure distribution||Gruber and Levy (2009)|Q90/Q50 is undefined if Q50=0|
|3<br>Budget share|Budget share|Share of income or consumption<br>spent on out-of-pocket health<br>expenses|||
|4<br>Progressivity|Kakwani’s (1977) index of<br>progressivity equal to the<br>concentration index for out-of-<br>pocket expenditures minus the<br>Gini coefficient for income or<br>consumption.<sup>11</sup>|<br>A negative value of Kakwani’s<br>index indicates out-of-pocket<br>expenditures are regressive, or<br>equivalently that the budget share<br>declines with income or<br>consumption|Wagstaff et al. (1992)|Wagstaff et al. also compute<br>progressivity indices for other<br>financing sources, e.g. taxes, social<br>health insurance contributions,<br>etc. They also use Suits’ (1977)<br>progressivityindex.|
|5<br>Catastrophic<br>expenditures|CATA|Fraction of households whose out-<br>of-pocket health expenditures<br>exceed some prespecified threshold<br>(e.g. 10% or 25%) of their total<br>income of consumption|Berki (1986) and Wyszewianski<br>(1986)|CATA (10%) is SDG indicator<br>3.8.2. Wagstaff and van Doorslaer<br>(2003) add a variation that<br>captures the ‘overshoot’ of<br>expenditures above the threshold,<br>not just whether the threshold is<br>exceeded|
|6<br>Inequality in<br>incidence of<br>catastrophic<br>expenditures|Concentration index (CI) of<br>catastrophic expenditures –<br>CI(CATA)|A negative value indicates<br>catastrophic expenditures are<br>more common among those at the<br>bottom of the income or<br>consumption distribution|Wagstaff and van Doorslaer (2003)||
|7<br>Impoverishment|<br>The increase in the poverty<br>headcount and mean poverty gap<br>when out-of-pocket expenditures<br>are subtracted from income or<br>consumption – IMPOV|Out-of-pocket expenditures are<br>said to be ‘impoverishing’ if they<br>are sufficiently large to leave a<br>household below the poverty line<br>based on consumption or income<br>net ofout-of-pocket expenditures|Wagstaff and van Doorslaer (2003)||
By 1994, HDB's fee system had become more sophisticated and closer to the system in the private sector. For example, the price of an appendectomy, with a five-day stay, can be calculated from Table 5.1 in the following way. An appendectomy is valued at 50 Ks. While the private sector charged USD3.30 per third-class K in 1994, HDB charged USD1.21, amounting to USD60.50 for a 50K operation. Added to this charge were hospitalization and hotelling fees in the medical ward, amounting to USD15.80 per day. Taking five days as an average length of stay, the total cost of an appendectomy at HDB in 1994 was USD139.50 (or [1.21*50] + [15.80*5]). For comparable third-class private sector treatment, the patient would have paid USD570.00 in 1994.33

# _Explaining HDB 's decision rights complementarities_

This section will explain how HDB adopted decision rights that were complementary in some areas and explain why it failed to do so in other areas. It will contrast HDB's decision rights complementarities with those of other informally corporatized hospitals and with the new legal structure under Law #544. The data in this, and similar sections below was collected using the Decision Rights Analysis Framework (Appendix A).

Some information on other informal experiences is in order first. HDB was not unique in attempting to become autonomous before the law was passed. Other public hospitals faced the same needs, and tried to acquire some autonomy under the auspices of "support committees". Support committees (SCs) were even less formal boards than the ALSM in the sense that they were not legally incorporated. They were of three types:

- Some SCs were formed by local politicians who saw control of local health provision as an opportunity to gather political support. Not being subject to the same legal and reputational liabilities of the ALSM, these SCs were not accountable, neither to the MOH nor to the community. They did not face the pressures of having to create transparent and accountable systems, and interview data suggests that some of them were associated with graft.

- Other SCs were dysfunctional from the outset because they were much closer to the "alternative career" model of governance (Appendix A), and the members of the ALSM did not have much to add to the

32 MedNet Liban is a third-party administrator that assists insurance companies in providing quality care at affordable costs. Access to MedNet Liban data was generously provided by Mounir Kharma and Hugette Daccache.

33 A final, important source of capital HDB received was in kind, and was made possible through the creation of the decision right to solicit outside contributions. Most notable among these is an ophthalmology ward that is the most advanced in the public sector, donated by Lions International. The cost of this ward was USD400,000.00. Another such contribution was the hospital library, financed by USAID at a cost of USD27,000.00.

21
<!-- Start of picture text --> 3.9<br>3.7<br>3.5<br>3.3<br>3.1<br>2.9<br>2.7<br>2.5<br>Baseline E + P HG I HG II<br>Capital to Output Ratio<br>2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text -->

# **3.2 Required Investment given Growth Targets**

The focus of the previous section was to obtain growth rate consistent with a set of assumptions on growth drivers. In this section, I ask what assumptions on growth drivers are required to deliver a desired time path of GDP growth. As there are multiple drivers of growth, I use the model to solve for the implied investment share of GDP assuming time paths of other drivers. The goal of the exercise is to determine whether the time path of implied investment share of GDP is feasible for the country or not.

The first step here is to choose a desired path of GDP growth and it is selected based on the 7<sup>th</sup> Five Year Plan targets. In line with the plan targets, the desired GDP growth rate increases steady from 7 percent in 2016 to reach 8 percent by 2020. The desired GDP growth path remains at 8 percent from then onward. Like in the previous exercise, four different growth scenarios are considered:

- **Baseline Scenario:** All variables, for example growth rate of human capital per worker, follow the same path as in the Baseline Scenario of the previous exercise. Note that the investment share of GDP which was an input in the previous exercise is the output of this exercise.

- **Participation Scenario:** Except female labor force participation rate, all other variables follow the same path as in Baseline Scenario. The female labor force participation rate grows linearly from 34 percent in 2015 to 45 percent in 2020.

- **High Growth Scenario I:** Except TFP growth, all other variables follow the same path as in Participation Scenario. TFP grows by 1.0 percent each year.

- **High Growth Scenario II:** Except TFP growth, all other variables follow the same path as in Participation Scenario. TFP grows by 1.5 percent each year.

18
|**Table A2: Population (in mil**<br>|**lions) recla**<br>|**ssified moving fro**<br>|**m per capita to roo**<br>|**t N rule**<br>|**-$3.20**<br>|
|---|---|---|---|---|---|
|Region|Not poor<br>under<br>both<br>rules|Poor by pc rule,<br>RECLASSIFIED<br>as not poor|Not poor by pc<br>rule,<br>RECLASSIFIED<br>as poor|Poor<br>under<br>both<br>rules|Population|
|(1)|(2)|(3)|(4)|(5)|(6)|
|Middle East & North Africa|254|14|8|53|329|
|Sub-Saharan Africa|294|65|47|639|1045|
|South Asia|877|120|117|643|1757|
|**World**|**3902**|**225**|**225**|**1513**|**5865**|
|Europe and Central Asia|463|4|5|18|491|
|Other High Income|1008|0|3|9|1020|
|Latin America & Caribbean|516|5|13|49|583|
|East Asia & Pacific|489|16|34|102|640|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table is an extension of Table A1 above, so these estimates are also for the 2017 reference year. These</u> results are based on the full sample of 162 countries covered in the paper.

45
described at the beginning of Section 3. Table 1 contains results from OLS regressions with and without state level dummies and controls. They show that the districts in the sample do not display absolute convergence, in the sense that when controls and state dummies are excluded, the parameter on initial income is not significantly different from zero. They also show that conditional convergence without state dummies is weak at best. In the conditional convergence regression without state level dummies, the coefficient on initial income of -0.12 implies a convergence rate of 1.3% per year while the cross-country data usually show a rate of 2.0%.<sup>26</sup> Once state level dummies and controls are included, however, the parameter jumps to -0.28, implying a convergence rate of 3.3% per year, a number well above the consensus cross-country result. This indicates that relative convergence within states is happening at a faster rate than convergence within the sample as a whole. Policy decisions at state level likely account for these patterns.

From Table 1, we can see that the coefficient on literacy is significant and measures 0.003. Given this value, a district which moved from the 25th percentile (38.1%) to the 75th percentile (61.9%) would increase growth by 7% over the course of a decade. Population/km<sup>2</sup> is not significant in any specification and its squared term (designed to capture the special situations inherent in very high density areas) is negative but significant in only one regression. In the standard specification with state dummies included as controls, the coefficient on credit is 0.42 implying that a move from the 25th percentile of credit/NDP (7.2%) to the 75th percentile (14.3%) implies an average gain of 4% growth over the decade. This is a significant growth bonus to improving the functioning of the banking sector. Alternatively this is significant penalty to pay for not doing so. This analysis of the patterns of district growth in India shows many similarities and some differences from the results of cross country growth regressions. As with most cross country studies, conditional convergence is evident in the data but a deeper look shows patterns of cross state divergence and as well as stagnation and rapid growth. As with many cross country studies, financial development and

> 26In his survey of the convergence literature, Sala-i-Martin (1996) concludes that “the estimated speeds of convergence are so surprisingly similar across [cross-sectional] data sets, that we can use a mnemonic rule: economies converge at a speed of two percent per year.”

19
<!-- Start of picture text --> Figure 1. Distribution of mangroves by region,<br>2000<br>Sub‐Saharan Africa<br>South Asia<br>Latin America & Caribbean<br>East Asia & Pacific<br>0% 10% 20% 30% 40% 50% 60%<br><!-- End of picture text --> Note: Mangrove distribution is limited to the countries selected for this study as described in the text.

Source: Authors estimates described in the text.

Table 1. Countries with the largest mangrove areas, 2000 (in square kilometers)

|**Countries**|**Area**|**Per cent of**<br>**total area**|
|---|---|---|
|Indonesia|26,705|33%|
|Mexico|6,358|8%|
|Myanmar|4,935|6%|
|Papua New Guinea|4,705|6%|
|Bangladesh|4,290|5%|
|Cuba|4,241|5%|
|India|3,821|5%|
|Venezuela,RB|3,309|4%|
|Mozambique|2,891|4%|
|Philippines|2,482|3%|
|Remaining36 countries|16,019|20%|
|**Total**|**79,756**|**100%**|

Note: these 10 countries account for 80% of mangroves in the study area.

Source: Authors estimates described in the text.

7
> <sup>31</sup>

**Table 5b:  Two stage least squares estimates of Table 5a First stage regressions**

||Co<br>|mpetitive Electio<br>|ns(1975)<br>|C<br>|hecks and Balanc<br>|es(1975)<br>|
|---|---|---|---|---|---|---|
|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_IV_)|Level 2000<br>(_IV_) , with<br>GDP/capita|Level 2000<br>(_IV_), no<br>GDP/capita|Growth<br>75 – 00<br>(_IV_)|Level 2000<br>(_IV_) , with<br>GDP/capita|Level 2000<br>(_IV_), no<br>GDP/capita|
|British legal origin|.14<br>(_.86_)|.67<br>(_.44_)|.96<br>(_.20_)|-.03<br>(_.96_)|.34<br>(_.56_)|.28<br>(_.50_)|
|Scandinavian legal|2.5|2.0|3.30|.19|-.34|-.96|
|origin|(_.002_)|(_.003_)|(_0.00_)|(_.82_)|(._64_)|(_.11_)|
|German legal|1.35|1.93|3.14|-.17|.08|1.42|
|<br>origin|(_.22_)|(_.007_)|(_0.00_)|(_.85_)|(_.91_)|(_.02_)|
|British colonial|.72|1.02|.38|-.31|-.10|-.17|
|heritage|(_.42_)|(_.28_)|(_.63_)|(_.63_)|(_.88_)|(_.74_)|
|French colonial|-1.54|-.30|-1.39|-1.6|-.66|-1.28|
|heritage|(_.046_)|(_.52_)|(_.008_)|(_.02_)|(_.13_)|(_.008_)|
|Spanish colonial|.82|1.07|.95|-1.07|-.90|-.80|
|heritage|(_.40_)|(_.11_)|(_.19_)|(_.14_)|(_.08_)|(_.12_)|
|_F_-statistic on|6.32|2.18|8.25|3.91|1.35|4.63|
|instruments|||||||
|**Second stage regre**|**ssions**||||||
||Co<br>|mpetitive Electio<br>|ns(1975)<br>|C<br>|hecks and Balanc<br>|es(1975)<br>|
|Political variable|.014|.001|.11|.036|.22|.27|
||(_0.03_)|(_.86_)|(_0.009_)|(_.018_)|(_.13_)|(_0.005_)|
|British legal origin|.008|.15|.06|-.002|-.01|.018|
||(_.56_)|(_.22_)|(_.62_)|(_.93_)|(._93_)|(_.90_)|
|Scandinavian legal|-.02|.09|.08|-.025|.09|.019|
|origin|(_.26_)|(_.51_)|(_.70_)|(_.34_)|(_.42_)|(_.93_)|
|German legal|.009|.62|.55|.01|.53|.36|
|<br>origin|(_.67_)|(_.001_)|(_.006_)|(_.68_)|(_.01_)|(_.21_)|
|Hansen_J_-statistic<br>(Chi-squared_p_-<br>value)|.04|.04|.08|.58|.48|.71|
|_N_|78|91|103|78|91|103|

N.B. _p_ -values are reported in parentheses, based on robust standard errors. Instruments for the fourth and eighth regressions are three dummy variables indicating whether a country has British, French or Spanish colonial origins. Constants and conditioning variables not reported. Specifications, with the addition of the three legal origin variables, are as in Table 1.
The Bulgarian public enterprise sector remains important as a share of GDP.<sup>15</sup> However, it is still smaller than that of a number of EU15 countries such as Austria, France, Italy, Greece and Norway. As to its scope, it is also wide given that the Bulgarian state holds equity in the largest firm in 14 out of 23 sectors of the economy.<sup>16</sup> This however, places Bulgaria very close to the OECD average, and appears to be fairly standard (Figure 18b).

As to the instruments of control used by the Bulgarian state in the sectors where it remains involved, they seem more extensive than in most benchmark countries (Figure 18c). The similarity with Hungary, Poland, and the Slovak Republic suggests that transition countries have followed a similar approach in this domain. Direct control over business enterprises in Bulgaria has taken the form of constraints to the sales of stateowned equity stakes, special voting rights, and, to a lesser extent, control of the strategic choices of public enterprises by legislative bodies (see Annex Table A2.3 for details).

# _2.2.2 State Involvement in Business Operation_

State intervention in the overall economy is captured through price controls and “use of command and control regulations.” The price control indicator measures the existence of price regulation or administrative control of prices in air travel, road freight, telecommunications, and retail distribution sectors. The “use of command-and-control” indicator reflects the extent to which government uses prescriptive (as opposed to incentive-based) regulation both in general and in specific service sectors.

**Figure 19: State involvement in business operations a. Price controls**

**b. Use of command-and-control regulation**

<!-- Start of picture text --> 4.0 4.0 3.4 3.5 3.8<br>3.5 3.5<br>3.0 3.0 2.6<br>2.5 2.0 2.5 2.2 2.3 2.3 2.3<br>2.0 1.6 2.0<br>1.51.0 0.8 1.0 1.0 1.1 1.3 1.4 1.51.0<br>0.5 0.4 0.5 0.0<br>0.0<br>0.0<br>EU15 MICs OECDRomaniaCzech Rep. Bulgaria PolandHungary Slovak Republic OECDCzech Rep.HungaryRomania EU15 MICs Poland Bulgaria<br>Slovak Republic<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see appendix I.

> 15 This was calculated following the PMR methodology.  As its base, it uses an indicator of the size of the public enterprise sector taken from the Freedom of the World Indicators (Gwartney and Lawson 1997). This base indicator value is then adjusted each year based on the extent of privatization proceeds as a share of GDP using a perpetual inventory type approach (see Appendix table A2.2) for more details.

> 16 The indicator does not into account the number of shares or the proportion of state ownership.  In Bulgaria, for examples, state ownership in some of the sectors is 1%.

25
new Labor Law was implemented and on how it has affected the employment opportunities and welfare of China’s workers. Businesses clamored loudly that the Law would reduce employment and competitiveness. This view is supported by a majority of previous microeconomic studies on the impact of labor regulations on employment in developing countries which find that labor regulations reduce employment and increase unemployment (Besley and Burgess, 2004; Ahsan and Pages, 2009; Fledmann, 2009; Djankov and Ramalho, 2009; Kaplan, 2009; Amin, 2007; Fallon and Lucas, 1993). Other authors have found that strict enforcement of labor regulations raises the costs of formal employment, and thus leads to greater informal employment (Almeido and Carneiro, 2005).

However, labor economists also point out that stronger labor market institutions are associated with lower inequality and can carry important benefits for firm performance by increasing communication of information within firms, improving resolution of worker grievances and reducing turnover costs, and even strengthen market outcomes when markets are not functioning well (Freeman, 2007). In its most recent World Development Report (2013), the World Bank suggests that there is little consensus on what labor policies are optimal but advises that countries avoid “the two cliffs”—one of excessive regulation that slows job creation and growth, the other of excessive lack of recognition that fails to provide voice or protection to vulnerable workers. It is of considerable interest and consequence to better understand how well China has struck this balance. Only a few studies to date have provided empirical evidence on the implementation of China’s new Labor Contract Law. Analyzing data from repeated cross-sectional surveys of workers in the Pearl River Delta conducted in 2006, 2008, and 2009, Li (2012) presents evidence that the new Law increased the percentage of workers with written contracts, raised social insurance coverage, reduced violations of workers’ rights and wage arrears, and increased the probability that firms 4
parameter in column (1) to obtain the growth contribution of -1.49% in column (5). This result reflects the idea that increases in the inflation rate are usually negatively correlated with growth. Repeating this exercise for the other _x_ variables as well adds up to a growth contribution of 2.96%. Note that we are looking at 5-year intervals, so all growth rates shall be divided by 5 to approximate annualized values.

**Table 3: Calculation of growth contributions for Bangladesh in Period 8**

||(1)<br>parameter|(2)<br>period 7<br>2000-04|(3)<br>period 8<br>2005-09|(4)<br>Difference<br>(3)-(2)|(5)<br>growth<br>contribution:<br>(1) x (4)|
|---|---|---|---|---|---|
|Inflation<br>(_linflation_na_)|-0.227|0.035|0.101|0.066|-1.49%|
|Real exchange<br>rate (_lrer_)|0.003|-5.521|-5.532|-0.011|0.00%|
|Trade openness<br>(_ltraderesid_)|0.082|0.292|0.283|-0.009|-0.07%|
|Infrastructure<br>(_infra_index_)|0.081|-0.191|0.154|0.345|2.81%|
|Financial crisis<br>(_dum_fincrisis_)|-0.048|0|0|0|0.00%|
|Climate change<br>(_sd_temperature_)|-0.024|3.556|3.400|-0.156|0.37%|
|Terms of trade<br>changes (_dltot_)<br>|-0.027|0.015|0.028|0.014|-0.04%|
|Government<br>consumption(_lkg_)|-0.047|-2.657|-2.754|-0.097|0.45%|
|Growth volatility<br>(_sd_growth_)|-0.622|0.005|0.008|0.003|-0.20%|
|Change in urban<br>pop. (_durbanpop_)|0.010|2.354|3.451|1.097|1.13%|
|**SUBTOTAL**|||||**2.96%**|
|+ persistence|0.798|(growth p|eriod 6-7)|0.159|12.71%|
|+ period dummy|(=1)|-0.037|0.011|0.047|4.71%|
|**SUM (growth rate**|**over 5 year**|**period as**|**predicted b**|**y model)**|**20.39%**|
|**ANNUALIZED gr**|**owth rate**(=|sum divide|d by 5)||**4.08%**|

-21-
respective HIV status, it is unlikely that the large proportion of discordant couples is due to an effective prevention inside the couple.

It is more likely that the large fraction of discordant couples is due to the fact that once the first partner is infected, this does not automatically imply that the other partner will be infected rapidly. Quinn and others (2000) estimate, in the Rakai study in Uganda, that the HIV incidence rate among 415 initially HIV negative partners of HIV positive individuals was 11.8 per 100 person years. At that rate, it takes several years for a discordant couple to become concordant positive. Obviously, this rate per person year depends on the frequency of sexual intercourse. Using data from the same Rakai study, Gray and others (2001) estimate that the average probability of HIV transmission per coital act is 0.0011.

The fact that a large majority of infected couples are actually discordant (Lesotho, with one the highest HIV prevalence in the world, being an exception) represents an opportunity for prevention. Prevention among couples is not easy given cultural resistances, but policy makers should be imaginative and increase their prevention efforts towards the partners of individuals who have been identified as HIV positive. Encouraging, and possibly giving explicit incentives for, joint voluntary counseling and testing might be such a strategy. Couple testing programs have been piloted and have shown promising results (Kamenga and others, 1991; Allen and others, 1992; Roth and others, 2001; and Allen and others 2003). One concern is that joint testing could lead to domestic violence, but pilot studies have shown that HIV testing and counseling of couples has beneficial long term effects on HIV-related communications (Van der Straten and others, 1995 and 1998).

# **IV A substantial fraction of infected couples are “discordant female”**

According to table 1, between 30.2 percent (Burkina Faso) and 40.6 percent (Kenya) of infected couples are “discordant female”, i.e. couples where the woman is infected and 9