rafmacalaba/datause-encoder-data huggingface dataset:
## **Proposition 4 (SI method is preferable to MI method when WR is the welfare measure)**

Assume that household _h_ preferences (≽h) can be represented by a continuous, increasing, and f concave utility function and that (H, k, kf, knf, α, αf, αnf ) = (H, k, kf, knf, 1,1,1 ). Then, if Ii > (<)Iinf and wh > (<)∆i SI approach provides a better estimate of WR̃h than MI approach. Proof: See the appendix.

As for the case of MMU, proposition 4 shows that we cannot identify conditions that would lead to concluding unambiguously in favor of an MI approach.

Overall, propositions 3 and 4 show that, irrespective of choice of the welfare measure, the SI approach weakly dominates the MI approach. The result is somewhat surprising, if seen from the angle statisticians are used to: the use of multiple deflators does make sense when one seeks consistency between numerators (say, food and nonfood expenditures) and denominators (the deflators). Clearly, the use of a food deflator and a nonfood deflator does a better job in capturing the change in purchasing power of the two sub-aggregates. From the economist’s angle things look different. The main aim of welfare analyst is to approximate a well-being measure that allows to make consistent interpersonal comparisons. From this point of view a single deflator could be a preferable solution.

# **5 Sequence of temporal and spatial price adjustment**

A problem faced by practitioners when dealing with price adjustment pertains to the issue of sequencing between temporal and spatial price adjustments. This issue has been mostly overlooked by the literature which has generally focused on temporal and spatial deflation independently. Notably, for what concerns welfare measurement, the focus has been prominently on spatial adjustment, i.e. how to properly compare welfare of households living in different regions, overlooking the temporal dimension, i.e. how to compare households that are interviewed at different points in time. For instance, in Deaton and Zaidi (2002), on addressing issues related to the construction of the consumption aggregate, they observe “when we are working with a single crosssectional household survey, the price variation is less temporal than spatial; people who live in different parts of the country pay different prices for comparable goods.” Deaton and Zaidi (2002: 7) 19
The pattern of growth has also influenced the evolution of inequality in China, reflecting both good inequalities (as resource flows respond to new opportunities) and bad ones (as some poorly endowed areas are caught in geographic poverty traps).<sup>21</sup> Rural and, in particular, agricultural growth tended to bring inequality down in China, and lack of growth in these sectors in some periods has done the opposite (Ravallion and Chen, 2007). Rural economic growth reduced inequality within both urban and rural areas, as well as between them.

Was rising inequality simply the price that China had to pay for growth and (hence) poverty reduction? That is a difficult question, but it should not be presumed that such a tradeoff exists. That depends crucially on the source of inequality; when it comes in the form of higher inequality of opportunity it is likely to entail a cost to aggregate growth prospects (World Bank, 2005). China’s experience actually offers surprisingly little support for the view that there is an aggregate trade-off. There are a number of empirical findings that lead one to question that view. First, while it is true that inequality tended to rise over time, the periods of more rapid growth did not bring more rapid increases in inequality; indeed, the periods of falling inequality (1981-85 and 1995-98) had the highest growth in average household income. Second, the subperiods of highest growth in the primary sector (1983-84, 1987-88 and 1994-96) did not typically come with lower growth in other sectors. Finally, the provinces with more rapid rural income growth did not experience a steeper increase in inequality; if anything it was the opposite (Ravallion and Chen, 2007). The provincial panel-data analysis in Montalvo and Ravallion (2009) suggests that, as far as poverty is concerned, there was little or no trade off between the sectoral pattern of growth and the overall level of growth, given that Montalvo and Ravallion find no evidence that non-agricultural growth helped reduce poverty.

Looking forward, it will be harder for China to maintain its past progress against poverty without addressing the problem of rising inequality. To the extent that recent history is any guide to the future, we can expect that the historically high levels of inequality found in China today will inhibit future prospects for poverty reduction. High inequality is a double handicap; depending on its source—especially how much comes from inequality of opportunity—it means lower growth and a lesser share for the poor in the gains from that growth. Inequality is

> 21 Good inequalities are those that reflect and reinforce market-based incentives that are needed to foster innovation, entrepreneurship and growth, while bad inequalities do the opposite, by preventing individuals from connecting to markets and limiting the accumulation of human capital and physical capital. The distinction comes from Chaudhuri and Ravallion (2006).

12
# **Table 3.5 WSS Tariffs, Cost Recovery, and Access for Selected Countries**

GPP per capita World Bank, 2008 World Development Indicators.

|**Water charges**|Brazil|China|France|Philippines|Turkey|UK|Spain|India|
|---|---|---|---|---|---|---|---|---|
|Tariff (in $US/m3, 2001) without<br>sanitation|0.39|0.2|1.23|0.2|0.3-1.0|1.18|0.57|0.01-0.09|
|% Recover|||||||||
|* O&M|~100%|not recover|75-100%|Low cost recovery|100%|Not always recover|100%|Low cost recovery|
|* Capital|not recovered|not recovered|>80%|Low cost recovery|not recovered|high recoverylevel|not recovered|not recovered|
|GDP/capita(in $ PPP converted)|10,296|5,962|34,045|3,510|13,920|35,445|31,955|2,972|
|% water coverage(Broad Definition)|||||||||
|* Urban|96%|93%|100%|87%||100%|100%|90%|
|% sanitation coverage (Broad<br>Definition)|||||||||
|* Urban|83%|69%||80%|||100%|62%|
|% sanitation coverage(Sewerage)|||||||||
|* Urban|53%|50%||7%|||98%||

Sources: Please see Annex 20
with all relevant stakeholders to manage the process of data production. (IOM, 2018). These are hard challenges, but non-conventional data sources offer such great potential in complementing (not in substituting) survey data that, at some point, these challenges will have to be faced. Our assessment, therefore, is that while these challenges and drawbacks currently make their potential combination with household surveys challenging in the short-run, these sources appear promising for the future, especially because of their ability to provide a large amount of more timely and granular data.

# **6. Conclusions**

<mark>It is easy to envisage that, as climate change intensifies, the climate change-migration nexus will keep gaining prominence in the international agenda. In parallel, the empirical literature will keep growing. Further refinements to the conceptual framework, as well as developments in econometric techniques, will shed new light on the relationship between these complex, multifaceted phenomena. However, all of this cannot provide concrete benefits for policy making without closing the existing data gaps.</mark> Based on the empirical review, we have identified the main data gaps on the climate-migration nexus. Using a household survey program currently at the forefront of methodological research, the LSMS-ISA project, we then identified the limitations and opportunities for household survey data to enhance our understanding of the causal relationship. A summary of this assessment is reported in Table 1, which provides a list of the most relevant conceptual and empirical issues, with the corresponding data gaps and a set of initial proposals to boost the potential of household surveys such as the LSMS-ISA. <mark>We have stressed that household surveys currently allow limited exploration of the climate-migration nexus. At the same time, we have also documented, in light of the open issues and research gaps in the literature, the great potential that LSMS-type surveys hold to help address some of the most policy-relevant research questions in the field. Our proposals are twofold: conceptual and operational. Conceptually, we call for researchers to make use of these data to investigate some of the most important but still unclear aspects of this nexus, such as the role of immobility, the complementarity or substitutability between migration and other adaptation strategies, the migration potential of cumulative slow-onset events. Operationally, we propose to integrate multi-topic, multi-purpose, longitudinal household surveys with additional pieces of information enhancing both the quality and quantity of the information collected. In particular, with only slight modifications to the current questionnaires, such as short modules on adaptation and intention to migrate, the collection of migration histories or associate modules, and the integration of face-to-face surveys with phone surveys to increase the frequency of the longitudinal information, it would be possible to maximize the potential of these tools in a cost-effective way.</mark> 35
**Box A1.** Probit model with selection bias – Heckman two-step procedure

The paper used the Heckman procedure to correct for sample bias. The first step consists in estimating the probit model (equation (1)), i.e., calculating the probability that a firm has capital and then obtaining the linear predictors from the model. The second step consists in calculating the inverse Mills ratio and including it in equation (1). The inverse Mills ratio is the ratio of the probability density function and the cumulative distribution function divided by the standard error of the probit estimation. This ratio is calculated as follows: ∅(X<sup>′</sup> i∝1⁄σω) MR̃ = . The probability density and cumulative distribution functions are Φ(X<sup>′</sup> i∝1⁄σω)̂ such that: Pr(Capital = 1| X) = ∫−∞X∝1 ∅(t)dt = Φ(X∝1).

# **Table A.4. Step 2: Probability of being a Top Performer, Full Information Maximum Likelihood Model Estimation**

|Dependent variable: Probability<br>of being a top performer|Coefficients|
|---|---|
|Age owner|0.00595***<br>(0.0016)|
|Age squared owner|-0.000071***<br>(0.000)|
|Male(=1)|0.0606***|
||(0.0106)|
|Age firm|0.00226|
||(0.00315)|
|No diploma|Ref.|
|Primary completed|0.006|
||(0.02)|
|Some secondary|0.0281<br>(0.0180)|
|Post-secondary|0.0404*<br>(0.0220)|
|Other post primary|0.0956***<br>(0.0354)|
|No opportunity in large firms|0.0591***<br>(0.0156)|
|Family tradition|0.0161<br>(0.0171)|
|Constant|-0.0821*|
||(0.0373)|
|Log likelihood|-54212.128|
|LR test of model vs. Satured|Prob > chi2 = 1|
|Observations|4,504|
|Model|FIML|
|OIM Standard errors in parentheses ***<br>The test of model vs. Satured chi2 indic|p<0.01, ** p<0.05, * p<0.1<br>ates the model is a good fit.|

32
for public use. We hope this database is a public good that will enable tracking of human capital formation and deepen understanding of the factors driving human capital formation and economic development. A large-scale effort using this database to track and understand human capital formation is the World Bank’s new Human Capital Index.<sup>6</sup>

# II. THE NEW DATABASE

The database includes 164 countries from 2000 to 2017 and was produced through a large-scale effort by the World Bank to identify, collect and collate student assessment data worldwide. We include seven assessment regimes total, including three international tests and three regional standardized achievement tests. We also include the Early Grade Reading Assessment (EGRA), which adds 48 countries to the database with at least one data point in the past 10 years, including large developing economies such as Bangladesh, Nigeria and Pakistan. Each test covers between 10 and 72 countries. By combining these assessments and making them comparable we include countries which represent 98 percent of the global population. The supplement includes a detailed description of the methodology we use to develop harmonized learning measures and all data included in the database.

The final database includes mean scores as well as standard errors for each measure to quantify uncertainty. Scores are disaggregated by schooling level (primary and secondary), subject (reading, math and science) and gender (male and female). We include year-by-year data. The database will be made publicly available by the World Bank and will be updated regularly. We do not extend the time series prior to 2000 since data quality is low and this does not significantly affect country coverage, with an addition of just two territories.

Several statistics demonstrate the coverage and detail of the database. Table I presents coverage for country-year observations by region. The database includes 2134 observations across all countries from 2000-2017. Disaggregation by gender is available for nearly all the data, with 2,105 country-year observations. Most data come from math scores, with 768 country-year observations, followed by science scores, with 690 and lastly by science scores, with 676. A third of scores are primary school scores, and two-thirds of observations are secondary school scores. Latin America

> 6 The World Bank Human Capital Index includes additional measures of human capital, such as combining measures of school enrollment with learning, as well as including measures of survival and health (Kraay 2019).

3
# **Table 8B - Robustness Checks: Effect of Equality of Opportunity on Economic Development Dissimilarity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>|Dependent Variab|le: Logaritm of Per Capita In|come,Average 2005-2010|
|---|---|---|---|
|Instrument: Logof Wheat Sugar Sustainability<br>Index of Ethnic Fractionalization (0-Low to 1-high)|OLS<br>-2.505***|OLS<br>0.192|IV<br>-1.004|
||(0.43)|(0.51)|(0.73)|
|Dissimilarity Index*||-0.095***|-0.079*|
|||(0.01)|(0.03)|
|constant|9.420***|10.622***|10.714***|
|R-sqr|(0.22)<br>0158|(0.20)<br>0496|(0.19)|
|Number of Observations|.<br>183|.<br>60|48|
|Wald F-statistics from first stage|||11.346|
|**Controlling for Legal Origin**||||
|Regression|Dependent Variable: Lo|garitm of Per Capita Income|,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|Dissimilarity Index*||-0.092***|-0.115***|
|||(0.01)|(0.02)|
|British Legal System|-0.348|0.679**|0.600*|
||(0.36)|(0.20)|(0.29)|
|French Legal System|-0.494|0.489**|0.548**|
||(0.32)|(0.16)|(0.19)|
|Germany Legal System|2.213**|0.852**|0.682*|
||(0.72)|(0.25)|(0.31)|
|Scandinavian Legal System|2.599***<br>|1.193***<br>|1.016***<br>|
||(0.72)|(0.25)|(0.25)|
|constant|8.300***|10.070***|10.342***|
||(0.27)|(0.16)|(0.24)|
|R-sqr|0.2|0.82||
|Number of Observations|142|55|48|
|Wald F-statistics from first stage|||12.814|
|**Controlling for Share of Tropical Land**||||
|Reression|Deendent Variable: Lo|aritm of Per Caita Income|Averae 2005-2010|
|g<br>Instrument: Logof Wheat Sugar Sustainability|p <br>OLS|p <br>OLS|, g <br>IV|
|% land area in eorahical troics|-1927***|-0119|-0207|
|ggp p|.<br>(0.23)|.<br>(0.35)|.<br>(0.66)|
|Dissimilarity Index*||-0.090***|-0.088*|
|||(0.01)|(0.04)|
|constant|9.116***|10.623***|10.520***|
||(0.15)|(0.17)|(0.37)|
|R-sqr<br>|0.315|0.503||
|Number of Observations|158|59|49|
|Wald F-statistics from first stage|||2313|
|<br>**Controlling for Latitude**|||.|
|Regression|Dependent Variable: Lo|garitm of Per Capita Income|,Average 2005-2010|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|latitude of country centroid|0.030***|0.002|0.003|
||(0.01)|(0.00)|(0.00)|
|Dissimilarit Index*||-0089***|-0093***|
|y||.<br>(0.01)|.<br>(0.02)|
|constant|7.565***|10.530***|10.451***|
|R|(0.15)<br>0207|(0.26)<br>0505|(0.35)|
|-sqr<br>Number of Observations|.<br>158|.<br>59|49|
|Wald F-statistics from first stage|||11.074|
|**Controlling for Longitud**<br>Ri|Dd Vibl L|i f P Ci I|A20052010|
|egresson<br>Instrument: Logof Wheat Sugar Sustainability|epenent arae: o<br>OLS|gartm o er apta ncome<br>OLS|,verage -<br>IV|
|longitude of country centroid|-0.002<br>(0.00)|-0.002<br>(0.00)|-0.003*<br>(0.00)|
|Dissimilarit Index*||-0094***|-0099***|
|y||.<br>(0.01)|.<br>(0.02)|
|constant|8.250***|10.684***|10.650***|
||(0.14)|(0.17)|(0.22)|
|R-sqr|0.006|0.517||
|Number of Observations<br>l f f|158|59|49<br>|
|Wad F-statistics rom irst stage<br>* p<0.05, ** p<0.01, *** p<0.001|||14.576|

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

29
yield a set of input-output tables that is fully consistent with the national input-output table. In particular, our ten region model is consistent with the national input-output table.

## **_4.3.7 FDI Shares_**

The methodology is explained in detail in an appendix to Rutherford and Tarr (2006), but briefly, we first employed the NOBUS survey to obtain the shares of workers working in multinationals service sectors in each sector in each region. We used this as a proxy for the share of output in each service sector in each region. We also obtained information from (1) from the Russian service sector institutes mentioned above; (2) _Regions of Russia (2003)_ by Rosstat; and (3) the “BEEPS” survey. Only the NOBUS survey provides data that allows us to estimate shares of multinational ownership by both region and sector. We thus start with our calculations based on the NOBUS information.

When found, however, that when we aggregate the NOBUS shares across regions or sectors, the other three sources of information show considerably higher foreign ownership shares than the NOBUS survey. We believe that the NOBUS survey estimates are too small, and adjust them. We adjusted our estimates from the NOBUS to be consistent with the estimates of the service sectors institutes. The service sector institute estimates are lower than those from the BEEPS or _Regions of Russia,_ and thus involve less adjustment of the NOBUS data _._ We employed least squares adjustment of the NOBUS data so that the weighted average over all of Russia in each sector is consistent with the national estimates we received from the specialist service sector research institutes in Russia. This process will give as a _structure_ of ownership based on the NOBUS survey, with the economy-wide average by sector determined by the national data. Results are presented in table 12b.

# **5. Results of Policy scenarios**

## **_5.1. Policy Scenarios_**

One of our principal interests is the quantification of economic and environmental impacts associated with Russia’s WTO accession. In our central WTO accession scenario without environmental abatement policies, we assume that: (i) barriers to foreign direct investment are eliminated or reduced (depending on the sector); (ii) following the work of Shepotylo and Tarr (2012), applied tariffs will fall according to the commitments of the Russian Federation as part of its WTO accession agreement; and (iii) in line with Rutherford and Tarr (2008; 2010), we postulate that better access to export markets for some industries will lead to improvement in terms of trade ranging from 0.5 percent (sectors: other industrial products, food industry, other goods and services) to 1.5 percent for sectors subject to more 21
# **I. Introduction**

Over the past 15 years Ghana has been one of the most rapidly growing economies in Sub-Saharan Africa. This growth has been aided immeasurably by Ghana experiencing high levels of remittances, which while difficult to measure undoubtedly exceed development aid and foreign direct investment.<sup>1</sup> These remittances, in turn, have been driven by the country’s benign and improving policy environment—one in which the average rate of inflation fell from among the highest in Africa to about 10 percent over the last few years—and the relatively large diaspora of Ghanaians who live abroad.<sup>2</sup> While statistics on how these remittances are invested are not available, a number of articles (Quartey 2006, Yeboah 2000, and Diko and Tipple 1991), a drive around Accra, and discussions with bankers and developers there, as well as local newspaper accounts, all indicate that real estate investments in the capital city account for a significant share of the high level of gross domestic product received in remittances.<sup>3</sup> At the same time, housing in Accra has become more expensive, and appears to be increasingly pricing middle- and lower-income groups out of the housing market, resulting in substandard and congested living conditions for a large majority of the city’s

> 1 Ghana’s remittances, according to Addison (2004), in a Bank of Ghana study, increased sharply over the past 10 years. Remittances are defined as that portion of migrants’ earnings sent from the migration destination to the place of origin. As typically measured, and as is done in the aforementioned study, “remittances” refer to monetary and other cash transfers by migrant workers to their families and communities back home. Such <u>estimates are of course difficult to measure, but as Quartey (2006) indicates,</u> could well be underestimates. As the OECD (2006) shows this is the fourth largest share of remittances in the world for countries with populations in excess of 5 million.

> 2 Ghana’s average annual inflation rate for the 1980s was 47 percent, almost three times the Sub-Saharan rate of less than 16 percent. For the 1990s, it was close to 30 percent. Estimates of Ghana’s diaspora range from 10 to 20 percent of its population. Adams and Page (2003) show the importance of macro conditions and the size of the diaspora as important determinants of the amount of remittances.

> 3 See the _Statesman_ article on housing in Accra (Jan. 22, 2007). On the use of remittances in Ghana, see Adams (2006) and Quartey and Blankson (2004). They both detail the generally pro-poor disposition of remittances, and the uses to which the funds are put.  In the former case, they show that remittances account for more than 20 percent of the expenditures of the lowest income quintile. However, both estimates are from a time, the latest year being 1998-99, when remittances were a small fraction of the level achieved in recent years. In addition, they are nationwide figures. Quartey (2006) suggests that housing investments are an important use of remittances.

1
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