rafmacalaba/datause-encoder-data huggingface dataset:
**Assumption 2:** The predictors for poverty imputations should be key determinants of the intertemporal variations in household expenditure and be stable over time. Large shocks between surveys may compromise the model’s stability by changing what variables are meaningful predictors of household expenditure, and/or their respective coefficients. Violations to this assumption can go in two directions: first, poverty correlates might not change immediately after a big shock, such as dwelling conditions or household asset ownership; and second, some variables present a natural evolution over time and, if included, can mask the true evolution of well-being.<sup>11</sup> The large economic and social disruptions in Afghanistan since the IE-LFS survey was collected raise concerns about the validity of the survey-to-survey imputation model. However, the empirical assessment shows that models using the SWIFT-Plus approach can project poverty rates accurately even after a large economic or climate shock.

The estimation approach mitigates these risks by including dummies for consumption of certain food and non-food items as they respond to shocks quickly **.** The inclusion of consumption dummies in the model allows for a more direct measure of the current expenditure levels of households in the presence of structural changes in the economy. The SWIFT-plus models used to impute poverty in the AWMS R3 include several of these variables as they are to be more “reactive”, although the size of the shock— especially in urban areas—could also imply sufficient changes in terms of assets. Lastly, to limit concerns related to the change in the relation between expenditure and poverty correlates, we excluded labor market variables as changes in economic structure are likely to have had a significant effect on returns.

Out-of-sample tests using the IE-LFS 2021 data confirm the relative precision of model-based estimates and their superior predictive capacity compared to asset-only models. As an additional test, a model was calculated excluding consumption dummies with data from the IE-LFS 2021 survey to assess the predictive performance of the models. The urban and rural models were further tested for out-of-sample performance using the spring quarter of IE-LFS 2021-22 survey.<sup>12</sup> While more demanding, this test further confirms the relative precision of the estimates, showing only a 3.3 and 4 percent difference compared to the poverty levels directly measured in Spring 2021 (last three columns Table 4). This improved performance holds when benchmarking the model against one that excludes consumption dummies (Annex C).

> 11 A typical example is mobile phone ownership, which has increased rapidly as a consequence of the prices of cell phones and service fees going down, and not, necessarily, cellphone users becoming richer.

> 12 Data collection of the IE-LFS 2021-21 started in March 2021 and continued until the end of June, before being suspended due to the collapsing security situation in the country.

13
the valuations of extra schooling have risen for most countries and they seem high—some four times higher than the valuations typically placed by the labor market on extra schooling.

There are some contentious value judgments buried in the maths of the HDI. It can be granted that a rich person will be able to afford to spend more to live longer than a poor person, and will typically do so. But that does not justify building such inequalities into our assessment of progress in “human development.” Given what we know about the marginal costs of extending life expectancy, if one accepted the tradeoffs embodied in the new HDI, one would be drawn to conclude that the most promising way to promote human development in the world would be by investing in higher life expectancy in rich countries—surely an unacceptable implication of the HDI’s tradeoffs. And it is unclear why we would want to put so much higher a value on schooling than implied by its economic returns.

Setting the tradeoffs in a composite index is never going to be easy and it is ultimately up to users to judge for themselves if they accept the HDI’s valuations. However, the troubling tradeoffs in the new HDI are not in fact essential to relaxing the perfect substitutability property of the old HDI. The less appealing properties of the new index could have been avoided to a large extent, while allowing imperfect substitutability, by using an alternative aggregation function already found in the literature—in fact a straightforward generalization of the old HDI.

An important lesson for future composite indices is the need for transparency about the implicit tradeoffs, especially in more complicated indices. Those tradeoffs are the key to understanding the properties and implications of the index. I would hazard to guess that if the authors of the 2010 Human Development Report had calculated the tradeoffs implicit in their index they would have had second thoughts about it, and looked for alternatives.

18
where _y_ represents output per worker, _k_ denotes the capital-labor ratio, _h_ denotes the level of human capital per capita, and _A_ captures the residual, usually attributed to Total Factor Productivity (TFP). Taking the log on both sides decomposes cross-country income differences into three proximate sources: capital-output ratios, total factor productivity, and average human capital. Since we are only interested in the share of income differences that can be explained by variation in human capital, we provide decompositions of our baseline accounting results for the human capital share. In Table II, we provide decompositions as direct analogies to Schoellman (2011) who used inferred measures of quality as well as measures based on education quantity (Hall and Jones 1999; Hendricks 2002). In Table III we include an additional decomposition: #$(&$%)(#$ (&"%) which provides direct comparisons with a literature using various quality measures. #$("$%)(#$ (""%) To measure human capital, we extend the standard Mincer specification that weights education by its micro labor market returns to consider learning as well as schooling: where _S_ is the quantity of schooling and _L_ is a measure of learning, and _r_ and _w_ are their respective returns. For years of schooling, we use Barro-Lee (2010) data. For learning measures, we use the data presented in this paper. We assume rates of return based on the microeconomic literature. We take the value _r_ = .10 for the rate of return per school year, and _w =_ .20 per standard deviation increase in learning.<sup>8</sup> The .20 value is based on U.S. data. However, we might expect that returns to skills will be higher in developing countries, where the supply of skills is lower, as is the case in the returns to schooling literature. Significant work has been done to identify this parameter value. For the purpose of this paper, our intention is not to provide a final result, but rather to motivate the use of the data for future use in the development accounting literature. To this end, we take parameter values as given and conduct sensitivity analyses with values _w_ = .15 and _w_ = .25. We include 131 countries in this development accounting exercise.<sup>9</sup> Table II shows our results in comparison to Schoellman (2011), Hall and C. Jones (1999) and Hendricks (2002). We find that when our human capital measure only captures quantity ( _w_ = 0), the share of human capital accounts for roughly 9-26 percent of output per worker differences.

> 8 These values are based on Psacharopoulos and Patrinos (2004) and Hanushek and Zhang (2009) respectively. 9 This includes all countries which have both Barro-Lee data as well as learning data.

12
events served as a reminder that while the probability of a nuclear plant accident is quite low, its consequences can be extremely severe. Like the disasters at TMI and Chernobyl, Fukushima could cause political fallout, at the global level, which might in turn substantially derail a nuclear renaissance.

A public opinion survey sponsored by the industry and carried out across 24 countries in May 2011 found that 62 percent of the respondents opposed nuclear power, and one quarter (26 percent) of those opposed indicated that they had changed their previously held views because of the events in Japan.<sup>3</sup> However, the results varied significantly across countries. Opposition in much of Europe and some developing countries seems to be high: 81 percent of those surveyed in Italy are against nuclear power, 79 percent in Germany, 67 percent in France, 60 percent in Belgium, 81 percent in Mexico, 72 percent in Argentina, 69 percent in Brazil, 67 percent in Indonesia, 61 percent in South Korea, 60 percent in South Africa, and 58 percent in China and Saudi Arabia. Majorities in India, Poland, and the United States continue to support nuclear power (Ipsos, 2011), though support seems to be weakened. According to a Gallup poll conducted in summer 2011, 53 percent of Americans supported continued operation of the nuclear energy facilities that are closest to their homes despite the heightened concerns about safety (NEI, 2011a).

Critics argue that the nuclear renaissance met economic reality and began faltering even before Fukushima. They claim that unlike other industries (e.g. aircraft manufacturing) whose unit costs decline with cumulative technology deployment and output, the nuclear industry actually has been characterized by a negative learning curve and a substantial escalation of construction costs (figure 3).<sup>4</sup> It is pointed out that the history of the nuclear industry is replete with construction delays and cost overruns. The flagship EPR project at Olkiluoto, Finland—the first nuclear plant ordered in Western Europe since the 1986 Chernobyl accident—is being cited as the penultimate example of the industry‘s dismal record in meeting its cost projections and on time delivery. The project, once touted as the showpiece of the nuclear renaissance, is four years

> accident.  In the days immediately after the crisis began at the Fukushima No. 1 nuclear power plant, the government received a report saying 30 million residents in the Tokyo metropolitan area would have to be evacuated in a worst-case scenario (http://www.washingtonsblog.com/2011/09/tokyo-evacuation-fukushima-radiation.html). 3 The countries included in the survey were Argentina, Australia, Belgium, Brazil, Canada, China, France, Great Britain, Germany, Hungary, India, Indonesia, Italy, Japan, Mexico, Poland, Russia, Saudi Arabia, South Africa, South Korea, Spain, Sweden, Turkey, and the United States of America.

> 4 It should be pointed out that the econometric evidence on the learning curve of the nuclear industry is mixed.  For an insightful overview, see Jamasb and Kohler (2007).

4
to having been infected at times of giving birth, she assigns probabilities to the survival of each of her children to future periods.

Women's beliefs are also updated by receiving an HIV test result. MDICP respondents did not anticipate being oered HIV testing, and almost all of the respondents agreed to get tested. Because of these features of the data, I abstract from modeling the decision to get tested. Instead, I treat the HIV testing as an unanticipated revelation of HIV status. I rst present the updating of expectations about life horizon without testing and then proceed to discuss the updating with testing.

# 2.5.1 Updating Survival Expectations without HIV Testing

# Updating probabilities assigned to infection in dierent periods:

Let _I_ ( _τ, t_ ) be the probability a woman assigns to getting infected at period _τ,_ conditional on being alive at _t._ The probability assigned to having gotten infected at a past or present period _τ_ is given by where _S_ ( _τ, t_ ) is the probability a woman who gets infected at period _τ_ survives to period _t_ . The probability assigned at period _t_ to being HIV-positive is given by the summation of the probabilities assigned to infection happening in all periods up to _t_ : 11
inequality at the national level rises and social cohesion is weakened. These hypotheses have yet to be tested for China, but if they were correct, they would strengthen the case for the central government to use policies to address and reduce income inequality.

The failure of subjective well-being to rise across two decades should be a matter of concern for the Chinese government. China does not rank high in various recent international rankings reported in the _World Happiness Report_ , being in the 27<sup>th</sup> percentile for quality of life, the 28<sup>th</sup> percentile for life satisfaction, and the 30<sup>th</sup> percentile for happiness (Helliwell, Layard, and Sachs 2012: figures 2.3, 2.5, 2.9). Inequality is very likely to explain, in part, this relatively lower subjective wellbeing.<sup>6</sup> The Chinese government’s overriding objective of rapid economic growth has two implications for its policy on income inequality. On the one hand, there is some evidence that spatial income inequality has been bad for economic growth. For instance, Ravallion (1998) showed for rural China that asset inequality in the locality retarded the growth of individual household consumption, and Ravallion and Chen (2007) found that provinces with higher income inequality experienced slower economic growth. On the other hand, economic reforms, marketization, and institutional arrangements that promoted economic growth contributed to increases in inequality in various dimensions.

17
instrument might fail because past misallocation may be correlated with contemporaneous determinants of output per worker, this bias should remain small when we control for permanent characteristics of districts through district fixed effects.

The main challenge is that regression (7) contains three potentially endogenous explanatory variables. They are fairly highly correlated among themselves and so are their lagged values. As a result, iv estimates with three instrumented measures of misallocation are unstable. Instead, we use measures of misallocation only one at a time, focusing mainly on the misallocation of output.

The results are reported in table 18. In ols, the estimated coefficient on the misallocation of output is -0.74. Instrumenting it with its past value raises its magnitude slightly to -0.94 in column 1. Adding district fixed effects lowers it to -0.46. Weighting by initial employment raises it again to -1.14 while computing misallocation directly across all industries without going through an intermediate stage of industry aggregation leads to a coefficient of -0.55. Using instead the misallocation of employment or that of land and buildings leads to very similar point estimates. Because of larger standard errors, the coefficients when including district fixed effects are insignificant when using factor misallocation instead of output misallocation as explanatory variable.

Even there is some variation in the estimated effects of misallocation across though, specifications, they remain in the -0.5 to -1 range which is the same as the one we obtain when estimating these regressions using ols.

We experiment with another instrument that we construct using the tendencies of different industries to be misallocated. More specifically, recall that before computing misallocation at the district level, we first compute it for each district and industry. From that intermediate output, we can estimate the tendency of each industry to be misallocated using industry fixed effects. For each district and industry, we can then multiply the share of each the for this to be misallocated and sum across industry by tendency industry all industries in the district. For each district, that gives us an expected misallocation index based on the local composition of economic activity and the national tendency of 38
local communities, this literature might be informative of the overall impact of fiscal decentralization.<sup>28</sup> Early studies using cross-country regressions find a negative, or insignificant, relation between spending decentralization and growth (Davoodi and Zou 1998). Similar results have been found using data from Chinese provinces and U.S. states (Xie, Zou, and Davoodi 1999; Zhang and Zou 1998). Woller and Phillips (1998) also fail to find a statistically significant relationship between fiscal decentralization and economic growth for separate panels of developing countries.

This result seems to be sensitive to the measure of decentralization used. In particular, when using measures of revenue decentralization, such as share of revenue collected by subnational governments, the relation becomes positive. For instance, using measures of revenue decentralization, Lin and Liu (2000) and Akai and Sakata (2002) find a positive relation between decentralization and growth in the Chinese and U.S. case, respectively. A concern is that spending decentralization may fail to reflect revenue autonomy of subnational governments. To illustrate this issue, Ebel and Yilmaz (2002) replicate the cross-sectional study of Davoodi and Zou (1998) using measures of revenue decentralization. In contrast to the original study, they find a positive relation.

It is difficult to interpret these results as evidence of a causal effect of fiscal decentralization on economic growth. The positive (or negative) relation could be driven by omitted variables or confounding factors. The available studies do not satisfactorily address these endogeneity concerns. A conservative interpretation is that the evidence linking fiscal decentralization and economic growth is, at best, still inconclusive (Martinez-Vazquez and McNab 2003).

Related work examines the link between decentralization, corruption, and local capture. Based on cross-country regressions, Fisman and Gatti (2002a) find suggestive evidence that spending decentralization may be effective in reducing corruption. However, if there is a mismatch between revenue generation and expenditure, decentralization may not be beneficial (Fisman and Gatti, 2002b). Fan, Lin and Treisman (2009) find a similar result: countries with a larger number of administrative tiers and local workers report more bribery.

> 28 See Mookherjee (2015) for a more comprehensive review of the literature on the empirical relation between decentralization, corruption and government performance. 39
Figure 1. Change in Recent Migration <!-- Start of picture text --> Recent Migrants (1 or less than 1 year in Turkey) Recent Migrants (2 or less years in Turkey)<br>60.00% 60.00%<br>50.00% 50.00%<br>40.00% 40.00%<br>30.00% 30.00%<br>20.00% 20.00%<br>10.00% 10.00%<br>0.00% 0.00%<br>2010 2011 2012 2013 2010 2011 2012 2013<br>-10.00% -10.00%<br><!-- End of picture text -->

**Recent Migrants (3 or less years in Turkey)**

**Recent Migrants (4 or less years in Turkey)**

<!-- Start of picture text --> 60.00% 60.00%<br>50.00% 50.00%<br>40.00% 40.00%<br>30.00% 30.00%<br>20.00% 20.00%<br>10.00% 10.00%<br>0.00% 0.00%<br>2010 2011 2012 2013 2010 2011 2012 2013<br>-10.00% -10.00%<br>Regions Bordering Syria Rest Regions Bordering Syria Rest<br><!-- End of picture text --> _Source_ : LFS 2009-2014. World Bank author’s calculations.

Figure 1 illustrates the percentage increase of _recent foreign migrants_ when compared to the level of _recent foreign migrants_ present in Turkey in 2009 (baseline year omitted). The dashed line shows the trends in the border regions and the solid line shows trends in the rest of the country.

While there is an uptick in recent migrants near the Syrian border regions, it is important to remember that the sample size in the LFS is small, especially among recent migrants (Table 3). Notice, that the sample of recent migrants is relatively constant from 2009 to 2012, but in 2013 and 2014 there is a clear acceleration in the growth of the number of recent migrants being included in the survey. Since the number of recent migrant households begins to increase substantially in the 2013 LFS, there is evidence pointing to the representation of SUTPs in the survey. The analysis in this paper will be as transparent and rigorous as possible under the data conditions and limitations. This will translate into high standard errors in our statistical estimates, but estimates and especially trends of poverty rates can still be informative.

8
61 Interjurisdictional equalization of personal incomes<sup>may be deemed</sup> even more important by higher level policy makers<sup>than equalization</sup> of tax bases if much of the public rhetoric concerning<sup>grant objectives can be</sup> believed. The lack of adequate local income data in<sup>many countries greatly</sup> hinders analyses of these outcomes;<sup>however, there are a few instances of</sup> such research. The 1977 allocations of<sup>grants in the Philippines revealed</sup> no statistically significant relationship<sup>between grant allocations and</sup> local per capita incomes, suggesting<sup>that the systems there were neither</sup> income equalizing nor counterequalizing. 5 In their analysis of grants in Columbia, Slack and<sup>Bird used multiple</sup> regression analysis rather than simple correlation<sup>techniques to relate tax</sup> allowance grant allocations to a variety of local<sup>government (department)</sup> characteristics, including income. 4<sup>6</sup> When total real tax allowance allocations were regressed (using pooled cross<sup>sectional data) on</sup> population shares, number of primary students,<sup>mortality rates and real</sup> incomes in 22 departments over the period<sup>1974-1977, the results indicated</sup> that there was a significant, albeit small,<sup>negative relationship between</sup> income and grant size. This suggests<sup>a slight tendency for the Columbian</sup> system to redistribute in favor of lower<sup>income areas, even after</sup> accounting for the other variables.<sup>The two more explicit measures of</sup> 4Ibid.

4<sup>6</sup> N., Enid Slack and Richard M. Bird,<sup>"Local Response to</sup> Intergovernmental Fiscal Transfers:<sup>The Case of Columbia," Public</sup> Finance/Finances Publiques, Vol. XXXVIII, No. 3 (1983), pp.<sup>429-439.</sup>