rafmacalaba/data-use-mentions-extended huggingface dataset:
# Figure 4. Long-term growth prospects of EMDEs

# A. Long-term consensus forecasts: output growth

<!-- Start of picture text --> Percent Percent<br>4 2000 2007 2019 8<br>3 6<br>2 4<br>1 2<br>0 0<br>World Advanced EMDEs (RHS)<br>Economies<br>C. Potential output growth<br>Percent<br>7 Potential growth<br>6<br>5 1998-2018 potential growth<br>4<br>3<br>2<br>1<br>0<br>World AEs EMDEs<br>2003-07 2013-18 2019-27 2003-07 2013-18 2019-27 2003-07 2013-18 2019-27<br><!-- End of picture text -->

# C. Potential output growth

# B. Long-term consensus forecasts: Investment growth

<!-- Start of picture text --> Percent<br>EMDEs EMDEs excl. China World<br>12<br>10<br>8<br>6<br>4<br>2<br>D. Potential per capita output growth<br>Percent<br>6<br>Potential growth<br>1998-2018 potential growth<br>4<br>2<br>0<br>World AEs EMDEs<br>2014<br>2019-27 2013-18<br>2010 2011 2012 2013 2015 2016 2017 2018 2019<br>2003-07 2013-18 2003-07 2013-18 2019-27 2003-07 2019-27<br><!-- End of picture text -->

# D. Potential per capita output growth

Source: Consensus Economics, Haver Analytics, Penn World Tables, UN Population Prospects, World Bank. A. Bars show long-term (10 years ahead) average annual growth forecasts surveyed in respective years. Sample comprises 38 countries—20 advanced economies and 18 EMDEs—for which consensus forecasts are consistently available during 1998-2019. Aggregate growth rates calculated using constant 2010 U.S.
In the choice experiment, respondents were told that they would be presented with two hypothetical employment opportunities, each with different levels of the following attributes: a written contract, termination notice, paid leave, working hours, a retirement fund (Provident Fund), and monthly income.<sup>xii</sup> The alternatives were unlabeled; that is, we did not identify jobs as “formal” or “informal”, as doing so may have caused respondents to make assumptions about other aspects associated with the jobs, rather than focusing on the attributes listed above. They were asked to assume that all other attributes not presented in the scenario were identical between the two jobs.<sup>xiii</sup> The respondents were then asked to indicate which of the two jobs they would select if given a choice.

Table 2 shows an example of a choice set. This could reflect a real-world situation in which a worker has to choose between two different job offers. Job A offers a 6-month written contract, 15 days of termination notice, working hours in the range of 30-45 hours/ week, and 10 days of paid leave, but no Provident Fund. The respondent is told that Job A would pay a monthly salary 20% above his or her current monthly income. Job B offers the same attributes in terms of termination notice, paid leave, and Provident Fund. However, this job offers a longer contract (1 year), longer working hours (45-60/week), and lower increase in income (10% increase over current income).

The full set of attributes and levels is shown in Table 3. The specific attributes and their levels were initially selected based on a review of the Bangladesh Labour Law, and were refined through a series of discussions with policymakers and other stakeholders, as well as focus groups with workers, and a pilot survey. Since the survey included respondents who were self-employed or working for family businesses, and also included 12
Kingdom, respectively. Similarly, Friedberg (2001) suggests that native wages may rise after immigrants enter high-skilled occupations in the Israeli labor market. Our empirical contribution is to provide international evidence for the theoretical possibility of positive wage effects in destination countries relative to source countries. We exploit data on bilateral migration between country pairs, thereby complementing single-country studies on labor market effects of immigration.

Another strand of literature has emphasized the positive effects of brain drain on market income in the source economy (e.g., Mountford, 1997; Stark, Helmenstein and Prskawetz, 1997; Beine, Docquier and Rapoport, 2001, 2008). This possibility arises from the idea that an increase in immigration quotas in advanced countries improves immigration prospects for skilled workers in developing countries and thereby raises incentives to acquire education. However, empirically, the net effect on the size of the skilled labor force appears to be positive, except for very poor countries and/or countries with low levels of human capital (Beine et al., 2001, 2008). In our theoretical framework, brain drain reduces the skill intensity in the source country, even when educational decisions are adjusted. Because our empirical framework investigates the effect of skilled migration on relative outcomes between destination and source, we do not test the alternative hypothesis advanced in the “brain gain” literature. We can conclude, however, that the destination country tends to gain more from skilled migration than the source country.

The remainder of this paper is organized as follows. Section I presents a simple theoretical model. The model provides the basis for the empirical analysis in section II on the effects of higher emigration on relative GDP per capita, relative TFP, and the relative 4
from the rural areas. Bangladesh has the second highest number of international extreme poor in South Asia (28 million) and Pakistan the third highest number (14 million).

But India’s high rate of poverty may partly reflect methodological differences in the way in which countries construct their national consumption aggregates. The higher poverty rate in India compared to Bangladesh, for instance, is inconsistent with the GNI per capita metric, which is almost $2,000 higher for India than Bangladesh. This suggests that differences in the way in which consumption aggregates are constructed in each country might contribute to the total error. To study how the national consumption aggregates are constructed, we look at the latest rounds of household surveys available for these countries.

> 8 The other countries for which international poverty rates are reported separately for urban and rural areas are China and Indonesia.

> 9 This proportion is calculated using PovcalNet data extracted on September 15, 2017.

5
# **1. Introduction**

The Millennium Development Goals (MDGs) consider income poverty and malnutrition indicators of poverty that should, relative to their 1990 levels, be halved by 2015. This raises the question whether nutrition and income poverty measure different dimensions of the same development strategy. In particular, would a growth strategy that might achieve a significant increase in GDP as well as a reduction in income poverty, also be sufficient to attain the nutrition MDGs? Or does malnutrition respond differently to income growth than does income poverty?

Increases in income are clearly important for reducing malnutrition. Greater incomes at the household level allow families to spend more on food, clean water, hygiene and preventive and curative health care. It allows them to have a more diversified diet and to obtain more effective childcare arrangements. At the community level, greater income will eventually lead to better access to and higher quality health care, improved water and sanitation systems and greater access to information.

Empirical evidence supports the importance of income growth for the reduction of malnutrition. A recent study using household survey data from 12 countries (including three African countries: Kenya, Mozambique, and South Africa) estimates the magnitude of the response of weight-for-age to income growth (Haddad et al. 2003). Under a scenario of sustained per capita income growth of 2.5% per annum, the average reduction in the fraction of underweight children would be between 27% and 34%. This average hides substantial inter country variation, even within Africa. The reduction could be as high as 42% (Kenya) or as low as 14% (South Africa).

Thus, Haddad et al. (2003) suggest that complementary instruments would be needed. However, while there is a body of clinical and programmatic evidence on which interventions are most promising (Allen and Gillespie, 2001), there is comparatively little data that indicates how nutrition interventions could augment income growth, especially in Africa, the one region in which malnutrition rates have not yet begun
<mark>comparative data, the definition of “employed” is the same as used in the online survey. That is, someone is defined as employed during the reference week if they performed some work for at least one hour or were temporarily not at work but had a job to go back to. Figure 5.2 presents a scatter plot of the employment to population ratio in online surveys and the comparative data.</mark><sup>24</sup> <mark>The 45-degree line is presented for reference. Evidently, the online survey estimates are mostly above the 45-degree line, indicating that the online survey perhaps overrepresents the employment to population ratio.</mark> We calculate the relative bias as the absolute value of the difference between the online estimates and the comparative data-based estimates divided by comparative data estimates. For the propensity score adjusted online estimates, the bias ranges from 9 percent to 55 percent, with an average of 30.0 percent. Without weight adjustments, the deviations ranged from 3 percent to 62 percent, with around the same average of 29.8 percent. Notably, the weight adjustment sometimes improved the accuracy of Internet sample surveys and sometimes reduced their accuracy. We also calculate bias reduction due to propensity score adjustment as (bias of unadjusted online survey estimates - bias of adjusted online survey estimates)/bias of unadjusted online survey estimates. We find the average of the bias reductions across countries/waves to be -18 percent, indicating that the weight adjustments overall worsened the accuracy of Internet sample surveys.<sup>25</sup>

**<mark>Figure 5.2: Employment to population ratio: Internet survey and comparative data</mark>**

<!-- Start of picture text --> Employment to Population Ratio comparisons<br>Avereage relative deviation of RIWI: 30.03%<br>90<br>45 deg line<br>80 Brazil<br>Indonesia<br>70
- Srinivasan, P. (1989): “Redistributive Impact of ‘Optimal’ Commodity Taxes: Evidence from Indian Data,” _Economics Letters_ , 30, 385–388.

- Tanzi, V. (1998): “Fundamental Determinants of Inequality and the Role of Government,” _International Monetary Fund_ .

- Ulyssea, G. (2018): “Firms, Informality, and Development: Theory and Evidence from Brazil,” _American Economic Review_ , 108, 2015–47.

- ——— (2020): “Informality: Causes and Consequences for Development,” _Annual Review of Economics_ , 12, null.

- Waseem, M. (2020): “Overclaimed Refunds, Undeclared Sales, and Invoice Mills: Nature and Extent of Noncompliance in a Value-Added Tax,” _CESifo Working Paper_ , No. 8231.

- Weigel, J. L. (2019): “The Participation Dividend Of Taxation: How Citizens In Congo Engage More with the State When it Tries to Tax Them,” _forthcoming Quarterly Journal_ .

- _of Economics_

- Working, H. (1943): “Statistical Laws of Family Expenditure,” _Journal of the American Statistical Association_ , 38, 43–56.

37
2000 cohort of oldest siblings.<sup>30</sup>

# 4.3 Transfers of baseline children

In Busia and Teso districts, the potential for transfers between schools is high because of the high density of schools. A primary school has on average 7.9 other schools within 4 km, while the number for preschools is slightly higher. This makes it quite easy for children to switch between schools, even though most children go to school on foot. Other projects in the same region<sup>31</sup> ...nd a rate of transfers of 6 percent per year for with primary schools. In this section, we investigate the magnitude and implications of transfers of baseline children. Section 6 elaborates on the implications of transfers of children who do not belong to any baseline.

There are three classes of transfers of baseline children that might bias the results in this

> get dropped as children grow up. Children also change names for religious reasons, if they or their parents become saved Christians. It is also quite common for children to transfer between households. Due to cultural practice and to the very high incidence of deaths due to HIV/AIDS among young adults in Western Kenya, a high proportion of children live with a guardian who is not their biological parent. The 1998 DHS survey found that approximately 10 percent of Kenyan children did not live with either of their biological parents.

> 30 We will investigate the e¤ect of new cohorts moving into the schools when the Kenya census data are available.

> 31 See for example Miguel and Kremer (2001) or Glewwe, Kremer, Moulin and Zitzewitz (2000)

16
governance indicators (Kau¤man, Kraay and Mastruzzi, 2005) (henceforth KKM) and the Docquier and Marfouk (2006) migration data set in their cross-sectional analysis. Focusing on skilled migration, they examine the impact of the brain drain on sending country’s institutional development and ...nd a positive e¤ect on “political” institutions (i.e., on “political stability”and “voice and accountability”) but a negative e¤ect on “economic”institutions at home (i.e., on “government e¤ectiveness”, “regulatory quality”, “rule of law”, and “control of corruption”). However, their results su¤er from the limits of a cross-sectional analysis<sup>9</sup> and, as they themselves acknowledge, from the weakness of their instrumentation strategy (they instrument skilled emigration rates using countries’ geographical characteristics). In this paper we look instead at migration in general, focus on democratic institutions, and consider dynamic-panel regressions. We ...nd that the emigration rate and the level of human capital both positively a¤ect democracy and economic freedom at home. This implies that unskilled migration has a positive impact while skilled migration has an ambiguous impact on institutional quality. Using the point estimates from our regressions, we simulate the marginal e¤ect of skilled emigration on institutional quality. In general the simulations con...rm the ambiguous e¤ect of high-skill emigration. It is only when the incentive e¤ects of emigration on human capital formation are taken into account that a signi...cant institutional gain obtains for some countries in the short run, and for many countries in the long-run.

# 2 Empirical analysis

## 2.1 General Considerations

Empirical investigation of the e¤ect of emigration on institutions in a cross-section or a panel setting raises a di¢ cult trade-o¤. In a cross-sectional dimension, it is possible to use better data both
At the subarea level, the satellite indicators explain an impressive amount of the variation in average non-monetary welfare. In sub-area level regressions, predictor variables explain 73 percent of the variation in average non-monetary welfare in Tanzania and 59 percent in Sri Lanka. The subarea model R<sup>2</sup> of 0.73 percent for Tanzania is substantially higher than the estimates presented in Jean et al (2016), which find that features derived from satellite data explain 58 percent of the DHS asset index in Tanzania. The stronger predictive power of the model for Tanzania likely results from the use of a richer welfare index as the dependent variable, as well as the use of interpretable features derived from imagery such as building footprints, rather than features optimized to predict night-time lights.<sup>24</sup> Meanwhile, the R<sup>2</sup> of 59 percent for Sri Lanka is remarkably similar to estimates of the explanatory power of monetary poverty reported in Engstrom et al (2017).<sup>25</sup> _Table 6: Post-lasso model of normalized household per capita consumption_

|**Sri Lanka**||**Tanzania**||
|---|---|---|---|
|Variable|Coefficie<br>nt|Variable|Coefficient|
|**Sub-area variables**||**Sub-area variables**||
|1990 built-uparea|-0.30|Sum of nighttime lights|0.03*|
|2014 built-uparea|1.82***|Meanpopulation(GHSL)|0.01|
|Standard deviation of rain|0.00|Minimum agglomeration index|-0.01*|
|Rain Z score, Q2|0.05*|Mean std. dev. of size of 5 nearest<br>buildings|-0.01|
|Rain Z-score squared, Q4|-0.45|Sum of mean size
# **4.5 Correlation between Indicators of Regulatory Governance**

As discussed above, our regulatory index includes 4 indicators. These are classified positively for: (i) the enactment of an electricity regulatory law; (ii) the existence of an independent/autonomous regulator; (iii) funding from licence fees (or equivalent) and (v) staff salaries not necessarily confined to civil service pay scales.

Although the majority of our results are based on the index, we also try to estimate their separate effects. However, the degree to which we are able to do so depends on the levels to which they are correlated with one another. Not surprisingly, they are highly inter-correlated as shown in Table 4 below.

# **Table 4:  Correlation Matrix between Regulatory Governance Variables**

||_ElAct_|_Funding_|_Orgtype_|_Cserv_|
|---|---|---|---|---|
|ElAct|1||||
|Funding|0.848968|1|||
|Orgtype|0.783066|0.703489|1||
|Cserv|0.783066|0.551221|0.442631|1|

The correlations are highest within the law/funding/autonomy grouping.

For a visual depiction of the generation capacity and GDP data, see the Data Appendix in Cubbin and Stern (2004) 24
information provided by the MoAIWD in response to the annual FAOSTAT questionnaire. If true, the increase in the national cassava yield from 14,300 kg/ha in 2005 to 22,504 kg/ha in 2014, as well as the parallel surge in the harvested area from 153,687 hectares to 222,750 hectares during the same period would be remarkable.P8F<sup>9</sup> However, given the lack of immediately-accessible documentation on the generation of the APES and FAOSTAT estimates (and disregarding for now the discrepancies between them), it is not clear whether these yields capture potential, attainable, economic or actual yields, as defined later in section 3.4.

# **3. Data**

To investigate the relative accuracy and cost-effectiveness of a range of survey methods vis-à-vis their gold standard counterparts in the areas of cassava production and productivity measurement, as well as cassava variety identification, the Malawi National Statistical Office (NSO), in collaboration with the World Bank Living Standards Measurement Study (LSMS) and the CGIAR Standing Panel on Impact Assessment (SPIA), implemented a randomized household survey

> 7 A third source is the Integrated Household Survey (IHS), which is conducted by the National Statistical Office (NSO). Previous rounds of the IHS that could be used for computing national estimates of cassava production and yield are 2004/05 (IHS2), 2010/11 (IHS3), and 2016/17 (IHS4).

> 8 The time series information on cassava production and yield was not available on the MoAIWD website at the time this paper first appeared online.

> 9 The available statistics in fact date back to 1964, supportive of the upward trend observed for the 2005-2014 period.

6
including all events and fatalities (Table A6), and when using categories of violence as in the previous section. Results using UCDP data for one-sided and non-state violence are presented in Table A7. Results using ACLED data for violence against civilians and battles are presented in Table A8.

Overall, we find a different impact of the presence of mines depending on the types of mines. In most specifications, higher revenues from gold produced by small artisanal mines is positively associated with more violence. This may be because gold is the only mineral that eludes certification and supply chain initiatives and/or because artisanal gold mines have a consistently higher presence of armed groups than other minerals (Leysen et al. 2023). Copper and cobalt, by contrast, are mined outside the most conflict-affected provinces under consideration here (North and South Kivu) and instead are subject to much more government control because of their importance for government revenues. Higher revenue from cobalt mines is associated with more total violent events, and in particular more violence against civilians and more battles using ACLED data (Table A8). Higher revenue from copper mines is associated with less violence, in particular for violence against civilians and battles.

Mining revenue is generally not interacting with roads. A crucial exception is the ACLED data which features two types of interactions. First, roads increase violence in moments/locations of high gold revenues. This is consistent with an increase of rapacity effects with roads in which high revenue areas become more accessible with roads and, hence, become more contested. On the other hand, roads have the opposite effect with cobalt revenue in the ACLED data (Table A8). An interpretation would be that roads here signal central engagement which reduces violence. However, nothing definitive should be taken away from this, as these results are not robust across violence datasets.

The completion of road projects when mining revenues increase has a very different impact if the revenues come from small artisanal gold mines or from large cobalt mines (Table A8). The completion of
for 2010, which were derived by CONEVAL from the 2010 survey and census. Six main results emerge:

1. Combining satellite indicators with household survey data substantially improves the accuracy of municipal poverty estimates relative to estimates using the survey data alone. In the preferred specification, correlation with the benchmark official estimates rises from 0.8 to 0.86 and root mean squared deviation falls 25 percent.

2. Incorporating geospatial predictors greatly improves the precision of the estimates compared with the direct survey estimates. The median coefficient of variation declines from 38.5 to 19.8 in the household level model, which rises to 25 if the mean squared error estimates are adjusted to maintain equal coverage rates across methods. Even after this adjustment, the additional precision achieved by the small area estimation procedure is equivalent to increasing the size of the sample by a factor of roughly 2.4.

3. The household-level model moderately underestimates uncertainty and yields a coverage rate of 77 percent for in-sample municipalities and 83 percent of out-of-sample municipalities. For in-sample municipalities, this is moderately lower than the 86 percent obtained using the direct survey estimates when using the Horvitz-Thompson variance estimator, but much larger than the 39 percent coverage rate when using the standard variance estimator clustered on enumeration areas, which greatly underestimates uncertainty.

4. For all satellite-based estimates, in-sample predictions are substantially more accurate and much more precise than out-of-sample predictions. The correlation between the satellitebased estimates and the official benchmark estimates declines from 0.86 for in-sample municipalities to 0.70 for out-of-sample municipalities, and the median coefficient of variation rises from 19.8 to 33.9.

5. Estimates that combine survey and geospatial data using a household model are more accurate and much more precise than those produced by a sub-area model for in-sample municipalities (correlation of 0.86 vs 0.83, median CV of 19.8 vs. 35.6). The sub-area model estimates, in turn, are more accurate but somewhat less precise than the area-level model estimates for in-sample areas