rafmacalaba/datause-encoder-data huggingface dataset:
(top) of _Y_ 0 _L_ , meaning they are worse-off (better-off) than households in the corresponding reference population.

Finally, the variable _CLASS_ is an indicator variable identifying which economic class the households belong to. We defined four thresholds, using different lines calculated by the World Bank to define different social statuses: 3.2 $ PPP; 5.5, $ PPP; 10 $ PPP; and 20 $ PPP per capita per day. Using them, we create five distinct groups based on their consumption per capita per year: these are the “poor” (annual consumption per capita ≤ 1,170 $ PPP), “vulnerable” (1,170 < annual consumption per capita ≤ 2,000 $ PPP), “lower-middle class” (2,000 < annual consumption per capita ≤ 3,650 $ PPP), “middle class” (3,650 < annual consumption per capita ≤ 7,300 $ PPP) and “upper-middle class” (annual consumption per capita > 7,300 $PPP). In model (6), this variable is interacted with _M R P_ to investigate how the correlation of the latter with households’ welfare perceptions varies across classes with different economic status.

# 4. Results

As pointed out in the previous sections, one of the main goals of this paper consists in showing how absolute measures evolve in line with households’ welfare perception. Therefore, after describing the theoretical differences between the two sets of indices and the data, we proceed by reporting the results from perception indicators obtained from the three rounds of Moroccan household surveys. Before analyzing the perception outcomes that, as we will see, are strongly correlated with the class composition of Moroccan society, we outline a brief overview on Morocco’s economic performance and welfare distribution.

## 4.a Poverty, (relative) inequality and class composition in Morocco: 2000-2013

While real GDP per capita increased by about 1.5 percent per annum and showed substantial volatility throughout the 1990s, the following decade (the 2000s) began with a much more stable pattern of growth and substantially higher annual rates. GDP per capita increased at an annual rate of 3.1 percent between 2001 and 2017, almost doubling from 15,702 Moroccan dirhams in 2001 to nearly 26,217 dirhams in 2017 (Figure 1 and Pinto Moreira, 2019).

10
mental health (Urban Institute, 1999; Yamazaki et al, 2005).<sup>22</sup> In our non-migrant sample, 4.9% of individuals have scores less than 17, 15.6% have scores below 18, 26.6% have scores below 19, and 38.8% have scores below 20. However, in the migrant sample, the lowest observed mental health score is 17, and so estimation is only possible for thresholds above this. We therefore estimate the likelihood of having a mental health score less than 18, less than 19, and less than 20 using an instrumental variable maximum likelihood probit model where whether an individual has migrated to New Zealand is instrumented by whether their household was successful in the PAC lottery.

These results are presented in Table 5. The same covariates are included in the regression as when estimating average treatment effects. We find that migrants are 17 percent less likely to have a mental health score less than 18, 31 percent less likely to have a score less than 19, and 37 percent less likely to have a score less than 20. In each case, the impact of migration on mental health is strongly significant.

Second, we follow the methodology of Chernozhukov and Hansen (2005) for estimating quantile treatment effects (QTE) using instrumental variables. We estimate the impact of migration on mental health at the 25th, 50th, and 75th percentile of the mental health distribution. The key assumption of this econometric model is that individual unobservables are rank invariant to the potential treatment status, e.g. individuals who have better (worse) mental health without migrating will also have better (worse) mental health if they migrate. Thus, rank invariance implies that a common unobserved factor, for example, general mental state, determines the ranking of a given person across treatment states.

Table 6 presents the results from this model. For our application, it is necessary to bootstrap standard errors. We present both the standard error of the bootstrapped coefficients and the nonsymmetric 80 percent confidence interval derived from the bootstrapped coefficients.<sup>23</sup>

> 22 These studies scale the MHI-5 to a 0-100 scale, and use thresholds of 56 for depression and 67 for general poor mental health, which we convert back to numbers on the 5-25 scale.

> 23 The QTE model is estimated using an iterative procedure.  We trimmed our bootstrapped coefficients to remove estimates from models that did not properly converge. This lead to the removal of 256 out of 1000 estimates for the 25th percentile, 36 estimates for the 50th percentile, and 15 estimates for the 75th percentile.

17
> <sup>17</sup>

significance rise, while the _F_ and _J_ statistics reported in Table 1b support the claim, in five of six regressions, that the colonial heritage dummy variables are valid instruments.

**Table 1a:  Competitive elections, checks and balances and lending to the private sector**

||Competi<br>|tive Electio<br>|ns(1975)<br>|Checks<br>|and Balances<br>|(1975)<br>|
|---|---|---|---|---|---|---|
|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Growth<br>75 – 00<br>(_OLS_)|Level 2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|
|Political variable|.002<br>(_.35_)|.015<br>(_.46_)|.07<br>(_.00_)|.006<br>(_.02_)|.05<br>(_.09_)|.12<br>(_0.0_)|
|Land (thousands<br>of square miles)|0.0<br>(_.966_)|-.000095<br>(_.63_)|.000015<br>(._53_)|-.000002<br>(_.89_)|-.000011<br>(_.60_)|.000010<br>(_.69_)|
|Average<br>population<br>(millions)|-.000084<br>(_.48_)|.000077<br>(_.05_)|.0003<br>(_.44_)|-.00002<br>(_.17_)|.000734<br>(_.08_)|.00034<br>(_.46_)|
|Log real||.21|||.19||
|income/capita<br>(1975)||(_0.0_)|||(_0.0_)||
|Private sector<br>lending (1975)|-.03<br>(_.10_)|||-.04<br>(_.03_)||.12<br>(_0.0_)|
|_R_<sup>_2_</sup>|.03|.36|19|.07|.38|.24|
|_N_|78|93|116|78|93|116|

N.B. _p_ -values are reported in parentheses, based on robust standard errors. Constants not reported.

The magnitude of the effects is large. The two-stage least squares estimate in Table 1a suggests that financial sector growth over the period 1975 – 2000 was two percentage points per year faster in countries with competitive elections in 1975 than in countries without; the average for all countries was 2.2 percentage points per year. Results for political checks and balances are equally large. These estimates are particularly striking because most of the regressions in the two tables control for either the level of financial sector
paper. Our analysis finds that migration results in lower infant mortality rates and higher birth weights, and that failure to account for the selectivity of migration understates these gains. However, we also find that children in migrant households receive less preventative health inputs such as breastfeeding and vaccinations, which may have a detrimental impact on child health at older ages.

Having identified a positive overall effect of migration on child health, the paper then examines the principal channels through which migration works to improve health. The Grossman (1972) health production function approach is used to explain the likely channels through which migration can affect health outcomes. The first avenue is the direct effect of migration on income and wealth, through remittances and repatriated savings, which allows households to spend additional resources on food and health services. Secondly, through exposure to U.S. practices, migrants may gain health knowledge, allowing the same amount of financial resources to result in higher health attainment. Evidence is found that migration influences health outcomes through both of these mechanisms, raising both wealth and health knowledge.

Previous research on the interaction between migration and health is limited, and has tended to focus on differences between immigrants and the native-born, finding that migrants have worse health along a number of dimensions (see for example, Institute of Medicine, 1998), although that immigrants have surprisingly positive birth outcomes in the U.S. given their socioeconomic status.<sup>5</sup> Analysis of the impact of migration on health outcomes in the sending region is much more limited, although recently two studies by sociologists have provided a first assessment. Kanaiaupuni and Donato (1999) use data from five Mexican states drawn from the Mexican Migration Project and find mixed effects of migration on infant mortality, suggesting that infant mortality increases in the early stages of the migration process before later declining. Frank and Hummer (2002) use the same ENADID data as this study to look at the effect of migration on the incidence of low birth weight, finding migrant children to be less likely to be underweight.

This paper builds on the two sociological studies and the existing literature in several important respects. Despite some discussion of possible selection bias, both Kanaiaupuni and Donato

> 5 See the discussion and references in Frank and Hummer (2002).

- 3 -
**Table 4: Parameter Estimates from Ordinary Least Squares Regressions, 1960-2004**

||_Maize_|_Wheat_|_Rice_|_Soybeans_|_Palm oil_|
|---|---|---|---|---|---|
|_Constant (β0) _|7.92***|4.98***|3.98|5.49|9.77*|
||(8.37)|(3.25)|(1.23)|(1.64)|(1.98)|
|_StktU ti (S/U) _|-0.53***|-0.54***|-0.33*|-0.24***|-0.28|
|_oc-o-se raot-1 _|(6.89)|(3.36)|(1.73)|(2.86)|(1.64)|
|_Oil rice (P_<sup>_OIL_</sup>_)_|0.13***|0.22***|0.10|0.23***|0.40***|
|_pt _|(3.20)|(4.19)|(0.88)|(4.41)|(4.11)|
|_Exchane rate (XR) _|0.32|-0.79**|-2.38***|-0.90**|-1.13|
|_gt _|(1.20)|(2.09)|(3.71)|(2.58)|(1.46)|
|_Interest rate (R) _|0.05|0.14*|0.69***|0.18|0.16|
|_t _|(1.06)|(1.79)|(6.85)|(1.31)|(0.90)|
|_Income (GDP) _|-0.55***|-0.14|-0.04|-0.13|-0.35|
|_t _|(5.86)|(1.09)|(0.22)|(0.53)|(0.97)|
|_Manufacture rices (MUV) _|0.96***|0.09|-0.14|0.08|-0.16|
|_pt _|(5.24)|(0.42)|(0.28)|(0.33)|(0.41)|
|_Adjusted-R_<sup>_2_</sup>|0.91|0.90|0.83|0.84|0.55|
|_DW_|1.50|1.24|1.24|1.41|1.21|
|_ADF_|-4.13***|-4.16***|-5.05***|-4.70***|-3.72**|
|_PP_|-4.90***|-4.43***|-4.30***|-4.46***|-3.68**|

# **_Source:_** Authors’ estimates.

**_Note:_** The independent variable is the logarithm of the respective price. For other notes see tables 1 and 2. ADF = Augmented Dickey-Fuller (statistic for unit roots); DW = Durbin-Watson (statistic of serial correlation); GDP = gross domestic product; MUV = manufacture unit value; PP = Phillips-Perron (statistic for unit roots). Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

— 23 —
mix exported by the South Asian countries to the region, with the exception of India. India’s regional exports increased both vertically (in value) and horizontally (in the number of product categories); see Annex Figure A1. India displayed an increasingly diversified export basket to the region, for which it increased its range of products by 33 percent between 1990 and 1998. Bangladesh, Pakistan, and Sri Lanka all had moderate to insignificant changes in their export baskets.

Certain regional exports among SACs dropped out during the mid-1990s owing to either supply-side weaknesses or increased competition from East Asian countries. Nepal has undergone a unique change in regional trade compared with the other members in the region. Annex Figure A1 provides a graphical view of changes in product clusters in three broad sectors: agriculture, crude material, i.e. jute, rubber, and manufactured goods. Nepal’s export basket to the region shrank considerably between 1990 and 1998, with a 60 percent reduction in the range of goods exported to the region. The raw data and Figure A1 demonstrate a critical feature: the increase in exports since reforms in the early 1990s has more or less occurred within the existing range of products for most SACs. In the case of Nepal, the expansion has been in a significantly smaller range of products compared with 1990.

B1 Pitigala et al. (2001).

In contrast, manufactured goods dominate South Asia’s extra-regional exports. India’s manufacturing exports are much more dispersed than those of the other South Asian countries. Cut diamonds, jewelry, cotton yarn, textiles, and leather products and their sub-categories are prominent Indian exports. Pakistan’s exports to the rest of the world are dominated by raw cotton and cotton-based products, representing an estimated 67 percent of its exports. Where Bangladesh and Sri Lanka are concerned the overwhelming global orientation is in textiles and apparel. For example, a staggering 75 percent of Bangladesh’s global exports consist of apparel, which experienced a fivefold increase from 1990 to 1998. Meanwhile, Sri Lanka’s exports in apparel constitute a share of 50 percent of the total in 1998. However, tea continues to be Sri Lanka’s highest single export; the country recently emerged as the world’s largest exporter in tea.

23
# In the Wake of the Global Crisis: Evidence from a New Quarterly Database of Ex ort Com etitiveness <u>p p</u>

Guillaume Gaulier*, Gianluca Santoni**, Daria Taglioni*** and Soledad Zignago* _Keywords_ : export competitiveness, trade performance, quantification, trade patterns after the global crisis. _JEL Classification_ : F10, F14, F40, C43 _Sector board_ : Economic Policy

*Banque de France, Paris, guillaume.gaulier@banque-france.fr & soledad.zignago@banque-france.fr **Banque de France, Paris School of Economics, Scuola Superiore Sant’Anna, Pisa, g.santoni1@gmail.com ***World Bank, Washington DC, dtaglioni@worldbank.org

The authors would like to thank the ITC, and particularly Yvan Decreux, for providing their complete set of monthly trade data, spanning the years 2005-2013 and 228 countries worldwide. They also thank many colleagues and peer reviewers at Banque de France, at the World Bank, at the IMF and researchers from the Compnet Research Network (Eurosystem) for useful inputs, discussions, and for successfully applying the database to many specific country cases. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the Banque de France, the Eurosystem of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or any of the aforementioned individuals or the institutions they are affiliated to.
**Figure 6:** _R_<sup>2</sup> of CC regressed again the ML prediction for each crop and overall. _Notes:_ The black curve shows the overall average. The model includes both SR and GPS extracted vars. By increasing order of sample size: Purple is for cowpea, blue for rice, green for sorghum, sky blue for maize, yellow for groundnut and red for millet.

<!-- Start of picture text --> 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>Training set percent<br>0.6<br>0.5<br>0.4<br>0.3<br>R squared<br>0.2<br>0.1<br><!-- End of picture text --> 31
# _Involvement in Business Operation_

57. Regarding state intervention in the overall economy, the use of price controls has substantially decreased since the first phase of transition (World Bank 2004). (The variable measures the existence of price regulation or administrative control of prices in air travel, road freight, telecommunications, and retail distribution sectors). However, prices for some utilities remain administered, especially in the energy sector, where tariffs are below import parity, despite significant adjustments taking place in recent years. Tariffs are also below cost recovery in the case of the least efficient electricity and heating generators.

**Figure 13. State involvement in business operations a. Price controls**

## **b. Use of command-and-control regulation**

<!-- Start of picture text --> 4.0 4.0 3.4 3.5 3.8<br>3.5 3.5<br>3.0 3.0 2.6<br>2.5 2.0 2.5 2.2 2.3 2.3 2.3<br>2.0 1.6 2.0<br>1.51.0 0.8 1.0 1.0 1.1 1.3 1.4 1.51.0<br>0.5 0.4 0.5 0.0<br>0.0 0.0<br>EU15 MICs OECDRomaniaCzech Rep. Bulgaria PolandHungary OECDCzech Rep.HungaryRomania EU15 MICs PolandBulgaria<br>Slovak Republic Slovak Republic<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable. For full data set see appendix I.

58. As to the use of command and control regulation, it remains significant, albeit to a lesser degree than the EU15 average and on par with the OECD average (Figure 13). Overall, it is in line with comparator groups, although Romania’s performance is still far from that of the best practice countries (Australia, New Zealand, Slovakia to cite a few). The indicator reflects the extent to which government uses coercive (as opposed to incentive-based) regulation, in general and in specific service sectors (box 2).

59. Romania’s relatively good comparative score is driven by the fact that authorities have pursued a regulatory reform agenda driven by the highest OECD standards of regulatory quality, for instance by enshrining in legislation best OECD practices such as the considerations of alternatives to regulation as part of the process of adoption of new legislation. Nonetheless, weak implementation may defeat the purpose of the rules themselves.

21
though a person’s recall might be imperfect, our data are the first that enables to link premigration expectations to realized earnings on an individual level for temporary migrants.

Finally, the survey collects additional information on migrants’ most-recent migration episode, such as remittances sent, monthly expenses and savings, professional and personal difficulties encountered, and overall impressions. The survey includes a detailed module on demographic characteristics, enterprises and assets for the household.

# **3.2 The Household Income and Expenditure Survey (HIES)**

Because the BRMS only covers households with return migrants, we complement it with a nationally representative household survey that samples both non-migrants and current international migrants. The Household Income and Expenditure Survey (HIES, 2016-2017 wave) was designed to be representative at the national and division levels. The HIES collects detailed data on each household member, including labor market outcomes such as employment status, earnings, and sector of employment. The data are comparable to the labor market information captured by the BRMS. Information on non-migrants and current international migrants is collected through an “absentee module” answered by the household members who stayed in Bangladesh. The HIES also includes information on the debt and assets of the household, as well as detailed expenditure information that allows us to calculate household consumption and savings rates.

The Census of Population and Housing 2011 was used as the sampling frame for both the HIES and the BRMS. While the HIES covers both rural and urban areas, the BRMS sampling was designed to capture a representative sample of recently returned migrants in rural and semi-urban areas of Bangladesh. We thus restrict the HIES sample to rural and semi-urban areas in all of our comparisons. The HIES was carried out two years earlier than the BRMS.<sup>14</sup> This is an advantage in our context as current migrants in 2016 are likely to be more comparable to the return migrants captured in 2019 by the BRMS. Indeed, among the BRMS respondents, the median returned migrant had been back in Bangladesh for two years. Ahmed, Ahmed, Bossavie, Ozden,<sup> ̈</sup> and Wang (2020) show that the sample composition of temporary migrants in the HIES and the BRMS are very similar, and that the characteristics and destinations of migrants in the two surveys are also aligned with those in administrative data covering the entire population of temporary migrants from Bangladesh.<sup>15</sup>

> 14The Bangladesh HIES was carried between April 2016 and March 2017.

> 15The Bureau of Manpower, Employment and Training in Bangladesh publishes aggregate data on the number, composition and destinations of legal migrants from Bangladesh by year of departure.

11
(Mozambique, Guatemala, Uganda, and Cameroon). Last there is a group made up primarily of the North African and South Asian countries where the gender disadvantage is small among the rich but quite large among the poor (Egypt, Pakistan, India, Central African Republic, Nepal, Morocco)."7 Figure 2: The interaction of gender and wealth differences in the enrollment of 6 to 14 year olds.

<!-- Start of picture text --> Mse-Famle gap among te ndiand poor Rdh-Porgap among naes and fmles<br>30- 70 - .. ,<br>A<br>r Mi 6 40 0<br>o 20- f 50 - uM<br>0 10 0 10 a , 20 30 E -10 to 20 30 40 soto to<br>mz- 408-<br>10 I 0ba3- mri<br>0-<br>-0- E -~d 10 -<br>0 kI<br>-10 0 10 20 30 -10 0 10 20 30 40 50 60 70<br>le-Feniale gap: rih Rd-Poor gap: nmale<br>rm 2 tMs&a 5~~~~~~ iS;/ 2<br>7e-Fere raoioa- ngCtheain and poor RdvPoo r a(o amnd Paestand ftendes<br>m ~~~~~~~~~~ ~ gasi cidmorialit ~ ~ disappear as wealth increa 10S 7-ses.<br>25 -<br>tcdE<br>a~~~~~~~~~~~~~~~~~~~~~~~~~~~~pkb<br>15 o 3<br>0) -~~~~~~~~~~~~~~~~~~~~~~~~~~~ lI~~~~~~~~~~~~~~~d c;<br>.5 -<br>.5 1 1.5 2 25 3 0 1 2 3 4 5 6 71 8'<br>rMe-Feamleratio:tdch RdihPoor ralo: rreles<br><!-- End of picture text --> 1 7Bonilla-Chacin and Hanumer (1999) find that within Egypt, India, and Pakistan, the difference in gender gaps in child mortality disappear as wealth increases.

19
# Health and Development: What Can Research Contritute?*

# by Nancy Birdsall**

|||Table of Contents||
|---|---|---|---|
|Intr|oduc|tion|1|
|I.|The|Effects of Development on Health|3|
||A.|State of the Art|3|
||B.|Potential for Breaking New Ground|11|
|||Household level studies<br>The political ecoaomy of public spending on health<br>Alternative financing and control mechanisms<br>Adult health||
||C.|Eventual Difference Research Might Make|22|
|II.|The|Effects of Health on Development|24|
||A.|State of the Art|24|
||B.|Potential for Breaking New Ground|27|
|||Productivity effects at the individual level<br>Social cost-benefit analysis<br>Consequences of poor health for households and<br>communities||
||C.|Eventual Difference Research Might Make|31|
|Conc|lusio|ns|32|
|Refe|rence|s|34|

> * Forthcoming in (untitled) book of proceedings of workshop on contribution of social science research to health transition, June 1989, Harvard University. Editors Lincoln C. Chen, Arthur M. Kleinman, Joseph Potter, and Norma C. Ware.

> ** This paper is based on remarks prepared for the Health Transition Seminar, Harvard University Center for Population Studies, Cambridge, Mass., June 1989. I am grateful to Howard Barnum, Jere Behrman, John Briscoe, Charles Griffin, Emmanuel Jimenez, William McGreevey, Philip Musgrove, Mead Over, George Psacharopoulos, and Jacques van der Gaag for comments on an earlier draft of this paper.