rafmacalaba/data-use-mentions-extended huggingface dataset:
# **VI. Final remarks**

This study analyzes the impacts of adjusting the LAC lower and upper-middle-class regional thresholds when new and better rounds of PPPs are estimated. First, the paper proposes a two-step methodology to update these thresholds. The first step consists in building two-year synthetic panels between 2015 and 2019 for 15 countries in the LAC region. It is based on the paper by Lucchetti et al. (2020) and introduces several improvements in the methodology: matching is done using constrained donation subsets based on gender, birth year, and educational attainment; introduces weights harmonization procedure to preserve the original distribution in terms of relevant variables for the analysis; neighbor selection is based on random selection within the subset of closest neighbors, considering the sampling design of the donor survey; it implements more bootstrap repetitions to achieve more accuracy in estimates, etc. The second step defines the _lower bound_ as the median per capita household income over households who were not poor in the initial year and became poor in the final year and the _upper bound_ as the maximum per capita income (99th percentile) over those who have changed their status. This yields a vulnerability or lower-bound line of $14 in 2017 PPP per person per day and a middle-class or upper line of $81 in 2017 PPP. These thresholds are robust to expanding the set of countries, widening the time interval, increasing the number of repetitions, and implementing previous methodologies.

This study analyzes the impact on the LAC regional poor, vulnerable, and middle-class groups estimate using the international poverty lines (IPL), the regional vulnerability and middle-class lines, and the 2017 PPPs. While the changes at the upper-middle-income line ($6.85 in 2017 PPP) are significantly larger, the 2017 PPPs have small implications for extreme poverty and poverty at the lower-middle-income line ($2.15 and $3.65 lines). Extreme poverty and poverty at $3.65 would increase marginally by 0.3 and 0.9 percentage points (pp.)
determinants of structural change; and (ii) empirical studies that compare the experiences of structural change across developing regions and countries.

# 2.1 Empirical studies on the determinants of structural change

McMillan _et al_ . (2014) are among the pioneer authors who have recently undertaken quantitative analysis on structural change, while examining its determinants across countries. These authors show that since 1990 structural change has been growth reducing with labor moving from high to low productivity sectors in both Africa and Latin America. However, things seem to be turning around in Africa: after 2000, structural change contributed positively to Africa’s overall productivity. Using data over the period 1990-2005, and covering 38 countries, including 29 developing countries and 9 in Sub-Saharan Africa (SSA), McMillan _et al_ . (2014) identify three factors that determine whether or not structural change contributes to overall productivity; these include: the share of primary products in total exports, competitive or undervalued currencies, and labor market rigidity.

Mensah _et al_ . (2018) use an updated and expanded version of the Africa Sector Database developed by the Groningen Growth and Development Centre to analyze the role of structural change and job reallocation in the economic growth performance of African countries over the past 50 years. The results show that productivity growth has been generally low with moderate contributions from structural change across the 1960-2015 period. However, a regional comparison shows that structural change is more rapid in East Africa than in the other regions of SSA. Using econometric analysis covering 18 countries, the paper shows that more rigid labor markets reduce job reallocation across sectors, impeding structural change and productivity growth in Africa.

More recently, Martins (2019) also analyzes the pace, patterns, and determinants of structural change in the world economy. Using a data set comprising 169 countries and covering the period from 1991 to 2013, the paper finds that structural change has played a critical role in enhancing economic performance since the early 2000s, even if it remains comparatively less important than within‐sector productivity improvements
<mark>2.4% lower than the counterfactual. Even after ten years, these cases on average do not reach their pre-crisis GDP per capita levels, with a national per capita output level more than 24 percent below that of their synthetic control.</mark><sup>23</sup>

# **<mark>Social costs: Poverty and its facets</mark>**

<mark>Our results make the case that default leads to severe growth declines. We now take on the task of measuring the direct impact on poverty headcount and the ways in which living standards may decline in the face of a default.</mark><sup>24</sup> <mark>We start by estimating the effects of default on poverty headcounts, which our results suggest are large. Poverty ratios peak in the fourth year after default at 30 percent more individuals living on less than USD 1.9 a day than pre-crisis levels, which amounts to 70 percent more than in the synthetic counterfactual scenario. The gap in poverty headcounts between the defaulting country and synthetic controls remains large with poverty headcounts still 70 percent above the counterfactual seven years after default. Unfortunately, much of the data on poverty headcount ratios is only available starting in 1981 and many of the observations come from imputations performed by the World Bank’s Poverty and Inequality Platform (PIP) as opposed to survey observations.</mark><sup>25</sup> <mark>Yet, the findings are significant, particularly during the first two years after the default and their magnitude suggests that they are relevant to both the question we set out to explore and policy making.</mark>

> 23 <mark>Of course, this selection suffers from endogeneity. In what has been referred to as</mark> _<mark>gambling for resurrection</mark>_ <mark>creditors that observe sovereigns with steep declines in economic activity seem to portray a tendency
to study fungibility.<sup>9</sup> The CRS database disaggregates foreign aid according to a number of dimensions, most importantly the sector or purpose of aid, but has two main disadvantages. First, the CRS data are incomplete. Only some of the total disbursements that flow from each donor to each recipient in any given year are reported. Coverage becomes weaker as one examines earlier periods in time. Second, although information pertaining to commitments is available beginning from 1973, disbursement information is available only for the period after 1990. As a result, many existing papers utilize sectoral commitments even when disbursements are the more relevant quantity.

Several studies (e.g., Mavrotas, 2002; Pettersson, 2007a,b) attempt to avoid these problems with the assistance of data from OECD DAC Table 2a, as described in OECD (2000a). DAC2a contains _complete_ aggregate aid disbursements but does not include sectoral disaggregation. These studies estimate sectoral disbursements for each recipient and each year ( _d_<sup>�</sup><sup>_s_</sup> _RY_<sup>) by calculating the share of each sector</sup><sup>_s_in total CRS commitments and</sup> then multiplying these shares by aggregate disbursements from DAC2a ( _DAC_ 2 _a_<sup>_agg_</sup> _RY_<sup>):10</sup> for _s_ = 1 _, . . . , S_ . This strategy yields sectoral aid disbursements even for those years in which only commit6
estimates with 95% confidence. The main difference in estimated results is with respect to decentralization of construction (see figure 11). The 95 percent confidence interval around the estimates from the cross-country panel shows losses of economies of scale are as possible as efficiency gains from increased decentralization of construction activities. The estimates using the Korea data set, which are completely included within the confidence band of the cross-country panel estimates, shows that there are benefits to be gained after a level of decentralization of 30 percent, with the optimal being at 100 percent. It was mentioned earlier that the country conditions are especially strong for the case of construction, as there are effects such as geography and network configuration as well the as the efficiency of contracting out procedures that affect the unit costs of construction. Once you remove country-specific effects (as in the Korea case) we see that one can see efficiency gains even with decentralized construction.

The total effect of decentralization as shown in figure 12, is non-linear as predicted by Oates for both the cross-country and Korea data sets. The Korea data set demonstrates the increasing costs of decentralization after a level of about 80 percent. Oates has suggested that decentralization may result in losses of economies of scale; these losses may indeed outweigh the gains, as empirically shown for the case of Korea. However, this particular effect may be country specific.

It is important to see the position of Korea in the global scale of decentralized road provision to understand these result. In table 1, we see that the level of decentralization of administration functions for roads in Korea is on average 29 percent, a point in figure 9, just before the efficiency gains with respect to increasing degrees of decentralization start to show up. With respect to maintenance Korea has an average level of decentralization (taken across a period of 10 years) of 56 percent, a point in figure 10 where there are high gains from decentralized provision of maintenance. On the construction side, Korea's average is 64 percent, a point in figure
are competitive. And it looks at whether union membership, manufacturing sector, and

# firm location affect labor market outcomes.

I use data from the 1994 Regional Prograrn on Enterprise Development (RPED) survey on Ghana, which covers 215 manufacturing enterprises ranging from micro enterprises to very large enterprises. The questionnaire used in the data collection process has two related parts: one for management and one for workers. In addition, about 10 randomly selected workers from different occupational categories were interviewed in each enterprise. The RPED dataset matches employee and employer data and contains detailed information on both. The two types of information are merged from individual data on skills, education, and age, and from firm-level information about sector, production, sales, and employment.

Empirical studies of the determinants of wages and earnings inequality have focused primarily on factors affecting labor suppl,y. Long-run labor supply factors include variables such as education, age, gender, and experience.<sup>2</sup> Few studies have used variables controlled by the employers, the so-called demand factors. One reason is that in most industrial and developing countries more infbrmation has been collected on workers than on their employers.

The lack of data on worker productivity constrains empirical research related to issues of wage determination. For example, without direct measures of productivity, 2 Groshen (1991la) mentions that education, age, occupation, ethnicity, gender, and union variables account for only 51 percent of the variation in the log of wages, analyzing (US) data from the Current Population Survey One Quarter Earnings Sample, 1986. Introducing demand-related factors is likely to explain part of the 49 percent of the variation in wages that workers' characteristics cannot explain.

4
The examples of Mexico and Sri Lanka are important, because authorities in both countries have chosen to play an active role in collecting, comparing and disseminating the cost information of each of the available products for sending remittances from the USA (Mexico’s main source of remittances) and Middle East Countries (Sri Lanka’ main source of remittances), respectively.

Mexican authorities collect and compare the fees charged by banks and money transfer companies every month. Fees are disclosed in the internet by the Office of the Attorney for Consumer Affairs <u>(http://www.profeco.gob.mx/html/envio/docinter.htm), as well as through</u> consular offices.<sup>15</sup> In Sri Lanka, the central bank plays also an important role in collecting and disseminating data on fees on remittance products offered by banks.

Because information is critical for competition in the marketplace, authorities, but also NGOs in other developing countries could consider playing an active role in collecting, comparing and disseminating information on a regular basis about the fees of different money transfer products. This may help migrants – in particular those that do not speak the language of their host country – to make better informed choices.

# **Accounts in US Dollars or Other International Currency**

Another proposal to foster fee reductions is to allow residents in developing countries to have bank accounts in US dollars, or in any other major international currency. This would ensure that the amount in remittances that beneficiaries receive is the same amount sent by migrants. Moreover, it is argued that the beneficiary would have the option to convert the funds into local currency by choosing the institution that offers the best exchange rate.

Although this proposal seems reasonable, the survey found that in practice most developing countries already allow their citizens to open accounts in US dollars or any other major international currency. Of the 40 countries covered in the survey, 2 countries are dollarized – Ecuador and El Salvador -- and
I use this event to disentangle the effect of Spanish RTAs on its globalization dynamics.

Columns 3 to 6 of table (2) show the results of estimating the globalization dynamics of the Spanish economy.<sup>17</sup> Figure (6) plots these results. It shows how while Spain did not sign any trade agreement, the globalization dynamics controlling and not controlling for RTAs effects are the same, as expected. These dynamics started differing as soon as the Spanish accessed the European Communities in 1986. The globalization dynamics that omit RTA effects (the RTA variable is included in the regression) have the same trend along the period. But the slope of the dynamics that include the effect of RTAs (the

> 16Note that the world average effect of globalization by 2000 is different to the one shown before. In this case, I only use the Long-WIOD data. Results in figure (2a) are a more robust world average estimating since they cover many more countries.

> 17Column 3 does not include the RTA variable, column 4 includes the average world effect of RTAs, column 5 considers the specific effect of RTAs for Spain, and column 6 allows this specific RTA effect for Spain to evolve over time.

17
**Appendix B2: Variables and Sources of Data**

|**Variable**|**Definition**|**Sources**|
|---|---|---|
|Portfolio liabilities|Transactions with nonresidents in financial securities (such as<br>corporate securities,bonds,notes,and moneymarket instruments)|IFS (line 78bgd)|
|Other liabilities|Other transactions with nonresidents, major categories are:<br>transactions in currencyand deposit loans and trade credits|IFS (line 78bid)|
|Direct foreign<br>liabilities|Equity capital, reinvested earnings|IFS (line 78bgd)|
|Capital flows|Sum ofportfolio and other liabilities|IFS|
|Public debt|Gross general government debt (in some cases central government<br>debt),% of GDP|IFS/National Sources|
|Fiscal balance|Revenue (including grants) minus expense, net acquisition of<br>nonfinancial assets. % of GDP.|WEO|
|Capital controls|Overall restrictions index of all asset categories|Klein et al.,(2015)|
|Fed funds Rate|Fed fund rate(%) (US PolicyRate)|IFS|
|World GDP|World GDP(%per annum)|WDI,World Bank|
|VIX|CBOE Volatility Index|Bloomberg|
|Net foreign currency<br>position|An index which takes values between (-1; 1):value of -1<br>corresponds to zero foreign-currency foreign assets and only<br>foreign-currency liabilities, +1 corresponds to only foreign-<br>currencyforeign assets and no domestic-currencyforeign liabilities|Lane and Shambaugh (2014),<br>updated version of Lane and<br>Milesi-Ferretti (2007) dataset|
|Political risk|Risk ratings range from a high of 100 (least risk) to a low of 0<br>(highest risk)|Political Risk Services (PRS)|
|
expressed as a function of household expenditure (or income), and by consequence as a function of the model error and idiosyncratic errors when relying on imputed household expenditure. An estimate of average welfare is obtained by evaluating the expected value of the welfare function, which involves taking expectations over the error terms. The corresponding standard errors are obtained by evaluating the variance of the welfare function, where again the model error and idiosyncratic errors denote the random variables. While this can in principle be done analytically, for general non-linear welfare functions (i.e. non-linear functions of the error terms) the evaluation of the expected value and variance can pose a challenge. A practical alternative is to estimate average welfare along with the corresponding standard error by means of simulation (see Fujii and van der Weide 2012, 2013).

Another possibility relates to the use of the _estimated poverty series as a dependent or independent variable in cross-sections models_ . This application is also explored in Elbers et al. (2005). The latter paper considers the use of small area estimates of poverty and inequality obtained by combining household income survey with population census data in regression analysis. The difference with our approach is that the household incomes are imputed into a second survey (the LFS) instead of a population census, which introduces sampling error as an additional source of error (in addition to model error). Provided that this dual error structure is accounted for, the results of Elbers et al. (2005) should directly carry over; they show how the imputation-based poverty estimates can be used as both dependent and independent variables, and how accurate standard errors of the regression coefficients can be obtained. It is important to note however that the imputed household consumption data cannot be treated as if it were observed data; for example, counting the percentage of households with imputed consumption below the poverty line would provide a biased estimate of poverty (as it ignores the error term that separates observed consumption from predicted consumption).

Exploring the time variation in estimated poverty
**Figure 18. Share of Services Exports in Total Exports by Services Type, 2011**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>Gr Di Tot Gr Di Tot Gr Di Tot Gr Di Tot Gr Di Tot Gr Di Tot<br>Russia China Brazil India Turkey Indonesia<br>Transport Finance Communications<br>Water & utilities Construction Distribution & trade<br>Insurance Other business Other commercial<br>Percentage share<br><!-- End of picture text --> _Source_ : World Bank Trade Value Added Database and Francois et al, 2013. _Notes_ : Gr: Gross share; Di: direct share; and Tot: direct + forward linkages 42
The importance of non-cognitive skills emerging from such studies has led some to argue that the role of cognitive skills in shaping socioeconomic outcomes (Jensen 1998) may have been overstated and that such over-emphasis on cognitive skill to the exclusion of other factors can bias evaluation of interventions and public or private investments to promote human capital (Heckman 2000).<sup>2</sup> Yet, for many sectors, evidence on the impact of non-cognitive skills on economic outcomes in low-income contexts is only beginning to emerge. For example, studies argue that non-cognitive skills predict the probability of business innovation for micro-entrepreneurs (De Mel _et al._ 2009) and default for micro-loan applicants (Klinger _et al._ 2013).

Exploring the impacts of personality traits on adoption of agricultural technology in Sub-Saharan Africa is germane for two reasons. First, agriculture provides the main income source for the vast majority of SubSaharan Africans and is therefore fundamentally important to poverty reduction, but the continent is far from the agricultural technology frontier and most increases in agricultural production have so far been due to expansion along the extensive rather than intensive margin. Second, many studies find traditional measures of human capital to have little impact on production efficiency or adoption of beneficial innovations (Huffman 2001), suggesting that other factors may be key determinants of these outcomes. Moreover, some factors identified in recent literature as strong predictors of adoption decisions–e.g. the size of an individual’s network or his/her risk preferences–may arguably be affected by personality traits.

# **3. Data and descriptive statistics**

This section uses survey data to characterize the study setting, which is dynamic and well integrated with peri-urban labor markets, and to highlight differences among sample farmers. We explain how key dimensions of personality traits are measured and how they differ among individuals in our sample before discussing in detail the channels through which they might affect technology adoption and technical efficiency.

# **3.1 Study setting, data sources, and key household
# **_4.4. The choice of deflators_**

The fourth issue in aligning the capital estimates from the OECD and World Bank works is related to the choice of exchange rates to ensure the comparisons being made based on a common metric (currency).

The World Bank applied the market exchange rates (MER, in constant 2005 U.S. dollar prices) for this purpose, because the World Bank estimates are primarily aimed at making comparisons across broad income groups and to analyze a country’s wealth over time – its volume and composition – but with less focus on making comparisons between individual countries (World Bank, 2011). On the contrary, the OECD project used the purchasing power parities (PPPs) for private consumption to serve this purpose, as its focus is made on comparisons across countries.

Since this paper is primarily concerned with showing the pattern of wealth distribution within a country when human capital is included, we therefore estimate human capital using the MERs instead of the PPPs, to be consistent with the estimates for other assets in the World Bank’s accounts.

# **5. The wealth decomposition with human capital included**

Estimates of the composition of national wealth with human capital included, for the thirteen selected countries in 2005, are presented in Table 3. The estimates of the total wealth and net foreign assets are directly drawn from the World Bank wealth accounts, while the estimates for human capital are the OECD ones, as included in Liu (2011) after the adjustments described above. The value of the ‘intangible residual’ is calculated as the difference between total wealth and the sum of all of the other asset values.<sup>11</sup>

**Table 3. Wealth decomposition, 2005 (level in nominal US$ per capita by MER and share in percentage), Discount rate = 4.58% for both natural and human capital**

|**US$**<br>**(%)*