rafmacalaba/datause-encoder-data huggingface dataset:
incomes and income distribution are based on primary household survey data. Roughly half of the surveys in the POVCALNET database report income and its distribution, while the other half report consumption expenditure and its distribution. When we study within-country changes in the distribution of income or consumption, we focus only on episodes where the initial and final survey are of the same type, i.e. both refer to income or both refer to expenditure. For terminological convenience, however, we will refer only to income. All survey means are expressed in constant 2005 US dollars adjusted for differences in purchasing power parity.

For countries that are not covered in POVCALNET, we rely on the LIS database.<sup>5</sup> This expands our sample by adding 19 OECD economies. For these countries we construct mean income and decile shares directly from the micro data at the household level. The underlying surveys are nationally representative and intended to be comparable over time. We focus on the LIS measure of household total income, which is expressed in the raw data in current local currency units. We convert the survey means to constant 2005 USD and then apply the 2005 purchasing power parity for consumption from the Penn World Table, in order to be consistent with the POVCALNET data. Also for consistency with the POVCALNET data, we use LIS data on household size and equivalence scales to convert to average income and its distribution across individuals rather than households. Figure 2 gives an overview of the annual data availability from these two sources. LIS survey data starts earlier, going back to 1967, while POVCALNET observations start in the 1980s. Both databases have better country coverage in more recent years.

For our empirical analysis, we organize the data into episodes or “spells”, defined as withincountry changes in variables of interest between two survey years. Specifically, we calculate the average annual log differences of social welfare and its components, average annual growth in mean income and in the relevant inequality measures for each spell, recognizing that different spells cover periods of different length, depending on the availability of household survey data. We work with two sets of spells corresponding to different time horizons. The first is the longest available spell for each country, i.e. from the first available to the most recently-available survey for each country, while the second is the set of all possible consecutive non-overlapping spells lasting at least five years, beginning with the first available survey for each country. We can only compute these spells for countries where at least two comparable surveys separated by at least 5 years are available.

> 5 A handful of countries have surveys available both through POVCALNET and LIS.  For these countries we use only the POVCALNET data, i.e. we do not switch within countries between POVCALNET and LIS.

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for the country effects. The D.W. test statistic (2.171) is reported only for the between-time regression, where it is relevant.<sup>28</sup> It is obvious that the coefficients in the three blocks are different and therefore represent different processes. Specifically, the relative price is positive and significant in the within and the between-country regressions and negative in the between-time regressions.<sup>29</sup> The price variability has a negative coefficient in the within and in the between-country regressions but not in the between-time regression. Schooling is positive in all the three regressions, as is the development indicator. Civil liberties has the expected negative sign in the within and between-time regression but not in the between-country regression, while political rights has a negative coefficient in the between regressions, but not in the within regression. Most of these results are carried over to the production function with the state variables, referred to as the extended model.

To sum up, it is important to note that even though the within regression has a low R<sup>2</sup> , it presents a supply function with expected signs. This is achieved by the interaction terms _W(it)s_ which are expected to cause input variations and to have a smaller impact on the technology choice. This is not the case for the between regressions.

# **<u>The extended production function</u>**

The OLS estimates of the extended model appear in Table 4. The R<sup>2</sup> of this model is 0.9694 as compared with 0.9566 in Table 2. This means that 29 percent of the unexplained error of the model in Table 2 was reduced by the introduction of the state variables. An F-test indicates that this addition is significantly different from zero. Constant returns to scale is imposed on the within inputs elasticities. The sum elasticities without this constraint is 1.22, and the difference between the constrained and unconstrained elasticities is mostly in the elasticity of land.

An examination of the input elasticities shows that the big picture presented in Table 2 has not changed in a dramatic way. Specifically, for the within estimates, the sum

> 28 We have also computed a principal components version. The results are not different in a substantive way from the OLS estimates and are therefore not reported here.

> 29 A similar result is reported in Binswanger et al. (1987).

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22 The validity of the instrumental variables, the _legal origin_ dummy variables, requires that they are uncorrelated with the error term, u, i.e., they may affect growth only through the financial development indicators and the variables in the conditioning information set, X. we test the null hypothesis that the instrumental variables are uncorrelated with the error term using Hansen’s (1982) test of the overidentifying restrictions (OIR-test). If the regression specification “passes” the test, then we cannot reject the statistical and economic significance of the estimated coefficient on financial intermediary development as indicating an effect running from financial development to per capita GDP growth.

In using instrumental variables, Levine, Loayza, and Beck (2000) and Beck, Levine, and Loayza (2000) also develop a new measure of overall financial development. The new measure, Private Credit, equals the value of credits by financial intermediaries to the private sector divided by GDP. The measure (i) isolates credit issued to the private sector, (ii) excludes credit issued to governments, government agencies, and public enterprises, and (iii) excludes credits issued by central banks. Unlike the LZ Bank Credit measure, Private Credit includes credits issued by financial intermediaries that are not classified as deposit money banks by the International Monetary Fund.

As shown in Table 4 (IV-Cross-Country), Beck, Levine, and Loayza (2000) find a very strong connection between the exogenous component of financial intermediary development and long-run economic growth when using cross-country instrumental variables. They also show that the exogenous component of financial development is linked with both capital accumulation and productivity growth. Using various conditioning information sets, i.e., different X’s, the results still hold. Furthermore, the data do not reject Hansen’s (1982) test of the over-identifying restrictions. Also, Levine (2000) confirms these findings using the La Porta et al. (2001)
INTRODUctION Xi

# _Vital Staistics Repon_ and others are estimated from diverse data.

Future trends in life expectancy. LUf **e** xpectancy is projected from year 0 to year t using a logistic function over time of the form e, -o = + k / 1 + exp [logit(eo) + rt]j, with loglt(eQ) = log, [(ko + k - e) / (ee - 0 1).

The logistic function is set to rise most rapidly from a level ef 50 years or so and increaeingly slowly at higher levels. The minimum (ko) for the logistic functions for both sexes is assumed to be 20 years, and the maxima (ico + k) are assumed to be _82.5_ vears for females and 75.8 years for males. The rate of change (r) for the logistic function is allowa vary across countries, and for a given country to vary over time.

For the first quinquennium (1985-00), the rate of change (r,) is estimated from the rate of change in the previous quinquennium ('o) and from the female secondary enrolment ratio (so), using the equations r, = .00379 + .723 r0 - .000254 s for females, and r, = .01159 + .885 ro - .000318 so for males.

Percent urban is used in a few cases, with a different equation (see Bulatao and P'v, 1989), where secondary enrolment is not available, and in fewer cases still the rate of change in the * revious decade is used instead of the rate of change in the previous quinquennium when the latter appears to have been affected by exceptional circumstances. Limits are imposed on the rate of change for the first quinquennium (and for all other quinquennia), such that it cannot be greater than -.017 (which would give slow mortality decline) or less than -.053 (which would give rapid mortality decline).

For the econd quinquennium, the rate of change is estimated as a function of the rate of change for the first quinquennium: f2 = -.007 + .8 rl.

The rate of change for the third quinquennium is estimated from the rate of change for the second quinquennium in the same manner. This equation allows rateo of change to converge toward the uniform pattern Imposed f9r subsequent quinquennia.

For these subsequent quinquennia for all countries, the rate of change in life expectancy is constant at -.035 for both sexes. At this rate, the annual increments to life expectancy vary by initial levels as il -ated by the medium pattern in Table 1. Minimum and maximum increments corresponding t6 .ae slow and rapid limits imposed on rates of char-,e in thie first three quinquennia are also shown.

Jr'-Aure trends in infant mortality. Infant mortality is projected using a similar logistic function. The rate of change for each of the first three quinquennia is obtained from the equation r, = .03 + .5 r,.1, with the restriction that this rate must be in the range [.024, .1301. These limits provide schedules of minimum and maximum annual decrements to infant mortality, varying by the initial rate, shown
# **Annex 1 – Key Variables**

# **ILO-EAPEP Data**

- Female Participation Rates. Employed and unemployed females as a percentage of the female working age population (15 years of age and more).

The series used is dated December 2011 and labeled “More complete datasets (1980*-2020) with detailed metadata and projections from various models”. The Stata version can be downloaded from: <u>http://laborsta.ilo.org/applv8/data/EAPEP/eapep_E.html. From the 1980-2020 series, we only kept the</u> data for the period 1990-2012.

# **World Bank Open Data Repository (code and description)**

- NY.GDP.PCAP.PP.KD - GDP per capita, PPP (constant 2005 international $)

- NV.AGR.TOTL.ZS - Agriculture, value added (% of GDP)

- BAR.SEC.CMPT.15UP.FE.ZS - percentage of population, 15+, female, completed secondary

- SP.DYN.TFRT.IN - Fertility rate, total (births per woman)

- BAR.SEC.CMPT.15UP.FE.ZS - percentage of population, 15+, female, completed secondary

- NV.IND.MANF.KN - Manufacturing, value added (constant LCU)

- NV.SRV.TETC.KN - Services, etc., value added (constant LCU)

- SP.POP.TOTL - Population, total

- SP.POP.1564.TO - Population ages 15-64, total

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2 better project performance may cause increased beneficiary participation rather than vice versa: a mere statistical association is not evidence of a causal impact of partcipation on performance. After a brief review of the construction of the data on project performance and beneficiary participation from 121 runl water supply projects and a presentation of the basic statistical results we address, and overcome, each of these three objections. The subjective nature of the data does not preclude inter-subjectively valid, cardinal measures of participation appropriate for statistical analysis. There is no necessary connection between objectivity and quantification with cardinal numbers: cardinal measurement using subjective criteria is common. Moreover, the cardinal rankings for each project were created by two different coders and the degree of inter-coder agreement is very high. Thjs indicates that inter-subjective reliability can be achieved even for intrinsically subjective concepts like "participation." Finally, we show that assuming cardinality or imposing linearity is not necessary to establish the basic result.

The "halo effect" in coding performance and participation data from project documents is addressed in two ways. Flrst, we show the results are the same if the first coder's performance indicators are regressed on the second coder's participation scores (and vice versa), which indicates a lack of a subjective halo effect by the coders. However, the primary danger of subjective measurement, and one that we cannot address, is that the same individual who assessed project success also assessed participation. If that person has strong views about the relationship this may induce a bias in the project documents themselves, as the performance of participatory projects (and the participation in successful projects) may be exaggerated. We do however show that the strength of the performance-participation relationship does not depend on the 'objectiveness" of the success indicator.
purposes. However, it is known which households were to be included in the beneficiary lists provided to these programs, based on their PMT score rankings in the UDB.

The SUSETI also includes all the indicators used to calculate households’ PMT scores in the UDB. This allows us to simulate the PMT process used in Indonesia under the hypothetical scenario of all households having been surveyed for inclusion in the UDB, rather than only the subset of households expected to be poor. We are thus able to distinguish between targeting errors that are due to poor households not being registered in the UDB and those due to limitations of the PMT process.

# **Methodology for Assessing the Targeting Accuracy of the UDB**

Having clarified the potential sources of targeting errors, we now develop the empirical methods for investigating the expected UDB targeting outcomes. A large literature examines different measures and methodologies for estimating targeting accuracy. Commonly used measures of targeting outcomes include undercoverage and leakage (Cornia and Stewart, 1995), the distributional characteristic (Coady and Skoufias, 2004), and the Coady-Grosh-Hoddinott measure (Coady et al., 2004). Undercoverage and leakage are used as the main targeting outcomes, in line with most of the literature. Undercoverage, or exclusion error, is defined as the share of households below a given poverty threshold that are not receiving program benefits. For the parametric analysis, two thresholds are considered: (i) _undercoverage_ is the fraction of non-recipients below the 30<sup>th</sup> percentile of household per capita consumption in the SUSETI sample,<sup>13</sup> and (ii) _severe undercoverage_ is the fraction below the 10<sup>th</sup> percentile. These levels correspond closely to the “poor” and “near poor” thresholds used by the Indonesian government to determine eligibility for its main social assistance programs. BLT, and Jamkesmas cover roughly the poorest 30% of households in the country, while the eligibility threshold for PKH is close to the poorest 10%. Conversely, leakage, or inclusion error, is defined as the share of households that are above a given threshold and yet receive benefits. Similar to undercoverage, two thresholds are used, and _leakage_ is defined using the 60<sup>th</sup> percentile, and _severe_

> 13 Household per capita expenditures (PCE) are adjusted in each district by a factor equal to the ratio of that district’s household PCE at the 30<sup>th</sup> percentile to the household PCE at the 30<sup>th</sup> percentile in the country’s richest district of South Jakarta, in order to render the PCE distribution nationally comparable. The normalization factor is innocuous for our purposes.

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16 <!-- Start of picture text --> Figure 2.1<br>Flows between SEs and Budget<br>% GDP<br>10<br>8 ..... ..... .... ..... ....<br>6 .. . . .. . . .. . . .. . . ....... . . . . . . . . .. . . . . . . . . .<br>2 .. ..... ..... ....................... .............................................<br>2 ....... .................................. .............................................<br>-6 .^,........................ ......................<br>1 9 9 0 1 9 91 1 9 9 2<br>Year<br><!-- End of picture text --> Source: llhe Authors' estirnates.

2~~~~~~~~~~~~~~~~~~~~2 government. The implication is that if profits and turnover taxes were to be relied upon as a significant revenue source, the taxes wouild have to levied on profits of firms other than in the state and municipal enterprise sector.

B. Indiract taxation: The turnover tax and VAT In October 1993, the Bulgarian Parliament passed a law replacing the turnover tax with an 18% VAT. 25The turnover tax

> **25/** For a detailed discussion and assessment of the Bulgarian VAT, see Bogetic and Varga (1993).
Subscripts i, c and t represent the sector, technology and year, respectively. SH refers to share.<sup>_Gr_</sup> denotes the growth rate.

Two issues are important here to note. First, all technologies and sub-sectors use the same growth rate of the aggregated service sector output from the CGE model. In reality, energy demand in different sub-sectors in the TIMES model grows at different rates. Different energy rates of various technologies within the sub-sectors will grow differently due to assumed technological development and policy prescription. If we start the linking process by using CGE outputs to TIMES, all these sub-sectoral and technological features will be lost. Second, energy demand in the TIMES model accounts forecasts of international prices of energy commodities, whereas price indices provided by the CGE model are domestic prices. To reflect these features, at least in some aggregated way or weighted averaged form, it would be appropriate to start the linkage from the TIMES model. Meaning that energy demand in the CGE model is first adjusted using energy demand growth of the TIMES model (in the first run of the linkage).

TIMES is an engineering model. It does not project the prices of energy commodities directly. However, it determines the unit cost of energy service delivery. These unit costs (which are average costs) reflect the prices of corresponding energy commodities. One advantage of the TIMES model is that it allows various networks to deliver energy services to complete based on their service delivery cost (or network price). For example, to meet cooking demand for the restaurant sub-sector (an activity in the service sector), different networks for energy carriers (e.g., LPG produced in petroleum refinery and transported by trucks, natural gas processed in gas plant and supplied through pipeline, fuelwood harvested from natural forests and supplied through trucks, electricity supplied through distribution network) compete. It finds the optimal mix of these networks to ensure the least cost to deliver a unit of energy service (heat for cooking here). While finding the optimal solution, environmental or policy constraints can be incorporated. For example, if the policy is to adopt more and cleaner cooking, the model can incorporate it by setting a higher share for LPG, natural gas or electricity instead of wood fuel or biomass for cooking.

The base year for the CGE is 2012, whereas it is 2015 in the TIMES model. The TIMES model used historical data for the 2012-2015 period, whereas it uses CGE projection afterward.

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In order to understand and map stakeholders’ positions on the consequences of judicialization of health, we collected two sets of data: first, with the help of a team of local consultants with an established national reputation in the fields of law and public health,<sup>37</sup> we identified a representative sample of key actors with influence and stakes on the specific issue of healthcare judicialization (representatives of several agencies within executive, legislative branch, political party leaders, members of the judiciary, pharmaceutical companies, patient/consumers groups, civil society representatives, doctors’ associations, insurers groups etc.). Subsequently, we conducted around 45 indepth face-to-face interviews in Montevideo (March-April 2011). The qualitative (semi-structured) component of the study allowed us to gather more fine-tuned theoretical insights and take into account additional stakeholders indicated to us by our respondents during the semi-structured interviews.

Second, incorporating these insights, we designed, pre-tested and implemented a quantitative stakeholder survey in order to systematically capture differences in evaluation of stakes across groups of actors within the policy domain. To our knowledge, this is one of the first attempts to test statistically stakeholders’ perceptions of the _efficiency_ and _equity_ of the public benefit plan in general, as well as the implications of rising healthcare litigation on these two issue dimensions in particular.

The survey design attempts to trace the entire process chain of health judicialization from origins to perceived consequences, and contains three main parts: art I taps into stakeholders’ general perceptions of the current Benefit Plan and priority setting; Part II shifts the focus to the origins of litigation and the perceived effectiveness of the judicial process, and Part III analyzes two major types of litigation cases (malpractice and access to high technology medication and treatments – _recursos de amparo_ ) as well as perceptions of the consequences of judicialization.

The political economy of healthcare in general has a high level of complexity, and has been conventionally characterized as a “fuzzy” domain (Brugha and Varvaszowsky : 44)<sup>38</sup> . Additionally, health litigation poses even more research and policy challenges given the fact that its increase is a relatively recent phenomenon, judicialization is a highly specific niche of inquiry, and its origins and consequences are not fully understood yet. Therefore, in the absence of other empirical studies of stakeholders’ perceptions of the origins and effects of judicialization, we aimed for comprehensiveness when designing the semi-structured and structured components of the survey.

> 37 We are grateful to Dr. Santiago Pereira Campos, Dr. Rafael Garcia Martinez and other colleagues who were instrumental for the identification of stakeholders, interviews, survey design, pre-tests and implementation.

> 38 Ruairí Brugha and  Zsuzsa Varvasovszky. “Stakeholder Analysis: a Review,” Health Policy and Planning <u>(2000) 15 (3): 239-246.</u>

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