rafmacalaba/datause-encoder-data huggingface dataset:
attainment as the average number of years of education attained and its dispersion as the standard deviation of the number of years of education attained.

# **Measuring democracy**

I measure democracy as _stock_ rather than a level variable. This implies conceptualizing democracy as an asset. It implies that democracy is a fungible resource that may accumulate over time, promising increased returns in the future. Along these lines, if a democratic regime endures it is likely to yield some return, for example: social-welfare-augmenting economic policies, market-augmenting economic policies, political stability, rule of law, and efficient public bureaucracies (Gerring et al., 2005). But, why should we expect better results from a long-term democracy?

Paraphrasing Gerrin et al. (2005): In democratic regimes the policy-making process, by definition, involves a considerable number of players. Policy-making is a continual back and forth between different interest groups (e.g., legislators, citizens, economic organizations, etc.). Each of these players must learn to anticipate the goals, interests, and special sensitivities of the other players. Hence, the process of defining a “good policy” is likely to take considerable time. Not only must governing politicians learn what constitutes good policy; voters must also learn to recognize good policies. In authoritarian regimes, in contrast, this process is generally monopolized by a small number of elite actors and has few mechanisms of accountability. Naturally, the longer these elites are in power, the greater their opportunities for gaining experience in the diverse tasks of governance. But, since the political environment is highly constrained, the development of legal-bureaucratic authority is virtually impossible. Therefore, it is less likely that “good” policies institutionalize.

To measure democracy I make use of the Polity2 democracy index, which I draw from the Polity IV data set. This variable measures the extent to which “authority patterns are institutionalized” in a given country. It takes into account how the executive is selected, the degree of checks on executive power, and the form of political competition (Marshall et al., 2010). This indicator employs a twenty-one-point scale (-10 to 10) from more autocratic to more democratic, and offers good historical coverage (1800 onwards).

To create a stock measurement of democracy I first add up each person’s Polity2 score from 30 years before their year of birth to 20 years after, and between their year of birth and 20 years after. I assume that the stock of democracy depreciates at a rate of _r_ per year, with _r ∈_ [0 _,_ 1]. This captures the possibility that the democratic stock does not accumulate linearly over time. Now, given that for every individual born in any given year ( _t_ ) belonging to any given country ( _p_ ) the value of the cohort-time-varying components play little to no role.

8
<mark>Table 10 shows the budget shares for each of the categories of good and services using both the standardized and national consumption aggregates. This table shows that Maldivian households only spend 37 percent of consumption expenditure on food on average, and 53 percent on nonfood non-durables.</mark><sup>18</sup> <mark>Households in the rest of the countries spend between 43 to 59 percent of their consumption expenditure on food, and somewhere between 19 to 32 percent on non-food, non-durables. The countries spend less than 10 percent of their consumption on health, and on education and durable goods respectively. Housing, which includes rent, imputed rent (if included in the national consumption aggregate), and expenditure on utilities, shows wide variation, from a low of 6 percent for Nepal to as high as 33 percent in Maldives.</mark> <mark>Within India, rural households spend 8 percentage points more on food, reflecting lower levels of well-being in rural India. There is some difference between housing expenditures - while rural Indian households report spending 12 percent of their average budget on housing, urban India spends 16 percent (excluding imputed rent). This implies that much of the differences in poverty rates between rural India and the rest of South Asia is driven by the lack of housing expenditure (imputed rent) data in the national consumption aggregate for rural India.</mark>

**Table** **<u>9. Average daily per capita expenditure by category of goods and services (USD 2011 PPP)</u>**

|||**Banglade**|**sh**|**Bhutan**||**India**||**Maldives**|**Neal**|**Pakistan**|**Sri**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||||||||||**p**||**Lk**|
||**All**|**Rural**|**Urban**||**All**|**Rural**|**Urban**||||**ana**|
||**2010**|**2010**|**2010**|**2012**|**2011/12**|**2011/12**|**2011/12**|**2009**|**2010**|**2011/12**|**2012/13**|
|_National Consumption Agg_|_regate_|_s_||||||||||
|- Food|1.9|1.8|2.4|2.5|1.6|1.5|1.9|1.8|2.0|2.0|3.2|
|- Non-food non-durables|0.8|0.7|1.1|2.5|1.0|0.8|1.4|3.3|1.5|0.9|2.9|
|- Health|0.1|0.1|0.2|0.5|0.3|0.2|0.3|0.5||0.1|0.3|
|- Education|0.3|0.2|0.5|0.2|0.2|0.1|0.4|0.2|0.2|0.1|0.3|
|- Durable goods|0.1|0.1|0.1|0.1|0.5|0.4|0.7||0.3||0.2|
|- Housing|0.6|0.4|1.0|1.7|0.4|0.3|0.7|1.6|0.3|0.9|2.0|
|Total|3.7|3.1|5.2|7.5|4.1|3.5|5.6|7.4|4.3|4.0|8.8|
|_Standardized Consumption_|_Aggr_|_egates_||||||||||
|- Food|1.8|1.6|2.2|2.5|1.5|1.5|1.7|1.7|2.0|1.8|2.7|
|-<br>Non-food<br>non-<br>durables|0.7|0.6|1.0|2.2|1.1|0.9|1.5|2.1|0.9|1.0|1.6|
|- Health|0.1|0.1|0.1|0.3|0.2|0.2|0.2|0.5|0.4|0.1|0.3|
|- Education|0.2|0.1|0.3|0.1|0.1|0.1|0.2|0.2|0.2|0.2|0.3|
|- Durable goods|0.1|0.0|0.1|0.4|0.1|0.1|0.2|0.9|0.5|0.1|0.3|
|- Housing|0.5|0.4|0.9|1.3|0.7|0.3|1.6|1.8|0.3|0.8|1.1|
|Total|3.3|2.9|4.6|6.7|3.7|3.0|5.4|7.1|4.2|4.0|6.0|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). India’s consumption aggregates reported are based on the Uniform Recall Period (URP).

> 18 These are plutocratic rather than democratic shares.

15
use of the stoves, including fewer eye infections and less cooking time (Bensch and Peters, 2012).

Thakuri (2009) evaluates a customized improved wood burning stove and vent hood in a sample of 400 Nepali households. Though the data are observational and firewood consumption was based on 24-hour respondent recall, he estimates that improved cookstoves use 42% less wood than traditional stoves. Also based on reported data is Nepal et al. (2010), who use the nationally representative 2003/2004 Nepal Living Standards Survey to estimate the effect of improved stove use on fuelwood consumption. Using OLS, 2SLS and village level fixed effects, they conclude that user-identified ICS do not reduce fuelwood use. Evaluations of other stoves find fuelwood reductions (e.g. Johnson et al, 2009; Smith et al, 2007; Masera et al, 2007), but mass-produced brands are found to use only about 1/3 less than traditional stoves (Adkins et al., 2010).

The _Mirt_ stove has been promoted in Ethiopia since 1998 by the German aid agency GIZ. It was originally developed in the early 1990s by the cooking efficiency and new fuels marketing project, under the Ethiopian Energy Study and Research Center (EESRC) in Addis Ababa. Initial laboratory tests of the stove showed up to 35% fuel savings compared to the open fire tripod (Bess and Kenna, 1994). Further refinements yielded increases in savings of 50% compared with the open fire tripod (Gebreegziabher et al., 2012).Moreover, in 2005 GIZ introduced a less massive version in order to reduce input requirements while maintaining the stove’s efficiency.

By 2011 approximately 455,000 stoves had been commercially distributed (GIZECO, 2011) and a project impact study was conducted. The impact study focused on a variety of program aspects, including producers trained, cumulative sales and stove uptake by region (Megen Power, 2008). They also present case study vignettes of producers and users and evaluate fuelwood savings. The method to assess fuelwood savings is similar to the approach of Thakuri (2009), which is reminiscent of earlier evaluations that relied on observational data and user-reported savings with no control group. They find that _Mirt_ stoves offer fuelwood savings of 50%.

8 | P a g e
recent household surveys from six Latin American countries. We show that the two nonparametric indices differ for decomposable inequality measures other than E(0), but focus _N N_ our discussion on that index, for which θ _d_ = θ _r_ . We then examine the differences between the non-parametric and parametric estimates. Although, as expected, these differences are larger for smaller sample sizes, they are generally quite small, suggesting that our path-independent estimates of inequality of opportunity are also methodologically robust to the choice of estimation procedure. Before presenting the results for each concept in Sections 5 and 6, the next section briefly describes the data sets.

# **4. The Data and the Partition by Circumstances**

We use data from six nationally representative household surveys in Latin America, namely the Brazilian _Pesquisa Nacional por Amostra de Domicílios_ (PNAD) 1996; the Colombian _Encuesta de Calidad de Vida_ (ECV) 2003; the Ecuadorian _Encuesta Condiciones de Vida_ (ECV) 2006; the Guatemalan _Encuesta Nacional sobre Condiciones de Vida_ (ENCOVI) 2000; the Panamanian _Encuesta de Niveles de Vida_ (ENV) 2003; and the Peruvian _Encuesta Nacional de Hogares_ (ENAHO) 2001. The ENAHO and the PNAD are original national surveys, while the others are LSMS-type surveys. This particular group of surveys were selected for containing information on family background and, more specifically, on parents’ education, father’s occupation, or both.<sup>20</sup> In all countries, we restrict the sample to individuals aged 30 to 49, which are the cohorts with the highest proportion of employed persons.<sup>21</sup> Sample sizes for each survey, both before and after excluding observations with missing data, are reported in Table 1. Sample sizes with complete information range from about 6,000 (for Panama) to 72,000 observations (for Brazil), for the analysis of income and consumption, and from about

> 20 The _Encuesta Nacional sobre Niveles de Vida de los Hogares_ (MxFLS) 2002 contains a similar set of variables. Unfortunately its simple size proved to be too small relative to the partition to generate reliable results.

> 21 For Brazil, we further restrict the sample to household heads and their spouses, as the family background information was collected only for these individuals.

14
the recall period is shortened to a week. For instance, in the health inequality literature, researchers have suggested that inequality in access explains the difference in the number of doctor‘s visits between the rich and the poor (Wagstaff and Van Doorslaer, 2000). This hypothesis makes sense in our monthly recall data where the poor visit doctors less than the rich, but is not consistent with the patterns observed in the weekly data where the poor visit doctors more than the rich. It might be possible to reconcile the results with inequality in access to _high quality health care_ , but what a measure of health inequality that accounts for quality may look like (and what measurements are required to construct it) is an open question.

The relative frequency of visits to medical providers in our data by both the rich and the poor also requires us to reconsider some commonly prescribed solutions to the health care problem in poor countries. Since the Alma Ata declaration in 1978, policymakers have assumed that they should focus on providing poor people with better ―access‖ to primary medical care.<sup>15</sup> Because the main obstacles to access are the price of care and distance, the obvious solution was to open more facilities close to poor people and heavily subsidize (or, preferably, make free) primary care. Our results show that the use of health care providers by the very poor was _higher_ than that of the somewhat better-off. Furthermore, the out-of-pocket expenditures of the very poor correspond to the higher-than-previously-thought use of health services. Again, with shorter recall periods the degree to which very poor people choose to spend on health rises accordingly. Yet, the health of the poor in Delhi—as measured through outcomes data in the National Family Health Survey or the health rounds of the National Sample Survey—is much worse than that of the rich. If the use of health care providers and the extent of health spending do not decrease gaps in health outcomes between the poor and the rich, what is the missing link? Poor health outcomes may be driven by lack of knowledge, particularly about appropriate preventive measures, the inability of the poor to distinguish between bad and good quality health care, or poor diagnosis of underlying conditions.<sup>16</sup> But simple health care access is not the root of the problem, at least in this context.

> 15 This position has been reiterated in the World Health Report 2008 by the WHO on the thirtieth anniversary of Alma Ata. 16 Jalan and Somnathan (2008), Das and Hammer (2009)

19
# **1 Introduction**

Economic relations between China and the Association of Southeast Asian Nations (ASEAN) countries have expanded dramatically in the last two decades despite occasional political tensions. China’s economic reforms in 1992 and entry to the World Trade Organization (WTO) in 2001 helped these countries to come even closer economically. A year after joining the WTO, China signed a Framework Agreement on Comprehensive Economic Cooperation with the ASEAN countries. This ultimately led to the creation of China’s first free trade agreement and the largest free trade area in the world in terms of population, the ASEANChina Free Trade Area (ACFTA). To further boost the economic ties, both sides signed a protocol in 2015, which took effect in mid-2016. China has been the biggest trading partner for the ASEAN countries since 2010. Trade between them increased from less than $40 billion in 2000 to more than $500 billion in 2017<sup>1</sup> .

The economic integration between China and ASEAN countries has helped the participating countries to gain access to each other’s growing markets. Higher economic integration also means countries have increasingly become more interdependent to pursue their own stability and development. Rapid growth of the Chinese economy has made it the engine of economic growth in Asia. Despite the subdued growth in the developed West after the financial crisis, the Chinese demand has kept the export led growth prospects of the ASEAN countries alive. Under these circumstances, it has become increasingly important to understand the inter dependencies of these nations in order to take the correct policy measures.

The aim of this paper is to investigate the impact of Chinese trade and growth on the ten ASEAN economies using a global vector autoregression (GVAR) model. In doing so, it will control for other macroeconomic variables like the real effective exchange rate and short term interest which can potentially affect the transmission mechanism. Examining the potential effects of China on East Asian countries is not new in the literature. Different

> 1Source: Ministry of Commerce, China.

1
month. The resulting increase in household consumption lowers the national poverty measures. We see that removing the Samurdhi payments from households would increase the poverty headcount index by 0.79 and 1.19 percentage points, respectively, under the national and $3.20 poverty lines. However, results from the third scenario suggest that poverty would drop to 1.63 percent and 5.50 percent, under the national and $3.20 lines, respectively, if these budget‐neutral transfers were made to beneficiary households identified by the PMT formula. These results suggest that using the PMT method would significantly improve the poverty impact of Samurdhi transfers by effectively targeting benefits towards the most needy households.

# V. Robustness

This section examines the robustness of our PMT model to various issues. First, we examine performance of the PMT model when the analysis is conducted with different sample sizes. Second, we examine how much the performance of the model varies by whether individual survey quarters or the full annual sample is used to estimate the PMT coefficients. We finally examine PMT performance at sub‐national levels and for certain demographic groups.

# _PMT performance and sample size_

We often rely on household surveys to design, verify, and refine the PMT model. Cost and logistical considerations are often an important factor when implementing household surveys. This raises the following question: how much does changing the sample size of the household survey affect the performance of the PMT model built from it? Having the answer to this question would help relevant organizations optimize scarce resources. We address this question by taking random subsamples of HIES 2016, which surveyed 21,765 households. We do not change variables in the PMT model presented in Table 1, although we recognize that the variables in the PMT model would depend on the specific sample used to design it. We estimate the PMT coefficients, assign PMT scores to households, and simulate the beneficiaries based on the cutoffs also computed from samples of varying sizes. We then examine the performance of the PMT model in terms of undercoverage, leakage, eligibility, and estimated poverty rates for varying rates of sample sizes.

14
**Table 5. Policy determinants of fiscal policy volatility**

|||PrimaryEx<br>|penditures<br>||
|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|
|Fiscal policy rules|-1.171***|-0.947***|-1.040**|-0.694**|
||(0.336)|(0.348)|(0.411)|(0.308)|
|Capital account openness|-8.908***|-7.312***|-4.256**|-4.202**|
||(2.192)|(2.574)|(1.729)|(1.666)|
|Exchange rate regime|-4.245***|-4.722***|-4.541***|-3.206***|
||(0.899)|(1.093)|(1.294)|(0.857)|
|EMDE||-0.398|0.536|-1.255|
|||(1.276)|(1.151)|(0.970)|
|Commodity exporters||3.356***|0.0398|1.082|
|||(1.134)|(0.849)|(0.753)|
|Resource rents|||0.543***|0.402***|
||||(0.180)|(0.0678)|
|GDP per capita|||1.023|0.282|
||||(1.091)|(0.651)|
|SWF|||-0.324|-0.349|
||||(0.832)|(0.703)|
|Political constraints||||3.963|
|||||(4.676)|
|Control of corruption||||-0.761*<br>(0.388)|
|Constant|16.48***|14.04***|0.928|8.710|
||(2.113)|(2.836)|(10.191)|(5.579)|
|Observations|93|93|93|77|
|R-squared|0.324|0.371|0.636|0.713|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘EMDE’ is a dummy variable that takes the value “1” if a country is classified as an EMDE, and “0” otherwise. ‘Commodity exporters’ is a dummy variable representing whether a country is a commodity exporter or not. ‘SWF’ is a dummy equal to “1” if a country has a sovereign wealth fund and “0” otherwise.

35
<!-- Start of picture text --> 5.0% 40.0<br>37.1<br>4.7%<br>35.0<br>4.5% 33.8<br>4.2% 4.1% 30.0<br>4.0% 3.9%<br>4.0%<br>3.8% 25.7<br>24.1 25.5 24.4 3.6% 25.0<br>3.5%<br>21.8 21.6 3.4% 3.3%<br>20.1 3.3% 20.0<br>19.4<br>3.0%<br>15.0<br>2.5%<br>10.0<br>2.0%<br>5.0<br>1.5% 0.0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>share of imports value of imports<br>1000 million Euro<br><!-- End of picture text --> Table 1 demonstrates which export schemes were used by the different countries.<sup>12</sup> It is apparent from the table that GSP preferences were utilised only by a few countries (mainly by South Africa, Swaziland, and Namibia) and only to a limited extent. The utilization rate (share of exports requested preferences in total exports) for the country group was around 6%. Instead ACP countries tended to use Cotonou preferences rather than GSP preferences. The utilization rate of Cotonou preferences was close to 50%. However, there are important differences between countries in the utilization rate. While certain countries, such as Senegal, Seychelles, Dominica, Cameroon etc, have high utilization rates, several countries utilise the Cotonou preferences only limitedly.

**Table 1 Utilization rate by countries (exports requested preferences as a percentage of total trade) in 2000**

||Cotonou|GSP|
|---|---|---|
|Antigua.Barb|25.0%|0.0%|
|Bahamas|87.6%|0.0%|
|Barbados|33.2%|0.1%|
|Belize|76.0%|0.2%|
|Botswana|87.7%|0.1%|
|Cameroon|89.2%|0.1%|
|Congo|41.0%|0.0%|
|Dominica|96.0%|0.0%|
|Dominican R.|55.3%|0.5%|

12 The columns of the table show the share of exports which requested Cotonou or GSP preferences. The preferential access actually granted might be less than these figures, thus the table could overstate the actual utilization of the preferential schemes.

8
understand first the global and domestic economic context. As shown in Figure 1 (left panel), at the outset of the decade, world growth rates were around 5% on average with relatively small differences between developed and emerging economies. The 2001 financial crisis determined the first visible decline in world output affecting all groups of countries and reducing average growth rates by about 2-3 percentage points. We then see an exceptional period between 2001 and 2007 where all groups of countries enjoy increasing growth rates. During this period, it is also evident a diverging trend between developed and emerging economies with the emerging economies clearly outpacing developed economies. The Euro zone, which includes some of the major trade partners of Morocco, was the worst performer among the group of countries considered in terms of average growth during and after the crises.

During the same period, Morocco experienced significant annual changes in GDP growth but remained in line with world growth overall, with a growth rate hovering around 4-5% a year (Figure 1, right panel). The significant annual fluctuations of the Moroccan economy are explained by the performance of the agricultural sector, which affects the trend in GDP growth despite accounting for only about 15% of GDP. In particular, Morocco has been able to counter balance the effects of the 2001 and 2007-2009 world crises thanks to exceptional agricultural years that happened in conjunction or following the two global crises. As shown in Figure 1, the performance of the agricultural sector in 2001, 2008 and 2009 was very good.

**Figure 1 - World and Morocco GDP growth 2000-2010**

<!-- Start of picture text --> 10<br>25.0<br>8<br>6<br>15.0<br>4<br>5.0<br>2<br>0 -5.0<br>-2<br>-15.0<br>-4<br>-6 -25.0<br>Agriculture Industry<br>EA-SA-LAC EU-US<br>MENA SSA Services Total<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010*<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: World Bank Open Data and Morocco National Accounts, HCP (2000-2010). EA=East Asia ; SASouth-Asia ; LAC=Latin America and Caribbean ; EU=EUrope and Central Asia ; US=United States ; MENA=Middle East and North Africa ; SSA=Sub-Saharan Africa Macro and micro indicators concord in determining that GDP growth has trickled down to households. Household final consumption growth has closely followed the evolution of GDP growth (HCP 2000-2010). This macro picture is also supported by micro data. The HCP (2010) estimates that household consumption in real terms has increased at an average rate of 2.8 percent a year between 1999 and 2007 as compared to - 1.8 percent of the previous decade. And the poverty rate has declined at an annual rate of -7.3 percent as compared to a growth rate of +2.8 percent of the previous decade. Comparing the 2001 and 2007 household consumption surveys shows that the poverty rate has declined from 15.3% to 8.9% and this has resulted in an effective reduction in the number of the poor, from 3.4 m. in 2001 to 2.7 m. in 2007. These positive 4