rafmacalaba/datause-encoder-data huggingface dataset:
# **APPENDIX 3: SUPPLEMENTARY TABLES**

Table A3.1. Macro indicators: annual real growth 2014-2030 by simulation (% per year)

||2013|base se|r-eq+fg|ser-eq+db<br>ser-eq+tx md|g-eq+fg|mdg-eq+tx|
|---|---|---|---|---|---|---|
|Absorption|115.0|5.2|5.4|4.9<br>5.2|5.6|5.2|
|Consumption - private|87.1|5.2|5.3|5.0<br>5.1|5.4|4.8|
|Consumption - government|8.4|5.5|6.1|6.2<br>6.2|7.2|7.4|
|Investment - private|13.6|5.0|5.1|3.2<br>4.8|5.3|4.3|
|Investment - government|5.9|5.6|6.1|6.2<br>6.2|7.5|7.8|
|Exports|23.2|5.4|5.2|4.8<br>5.3|4.7|5.1|
|Imports|-38.2|5.3|5.5|5.0<br>5.3|5.7|5.2|
|GDP at factor cost|92.6|5.2|5.3|5.0<br>5.2|5.4|5.2|
|Total factor employment (index)||3.9|4.0|3.8<br>3.9|4.1|4.0|
|Total factor productivity (index)||1.3|1.3|1.2<br>1.3|1.3|1.2|
|GNI|95.0|5.2|5.3|4.9<br>5.2|5.4|5.2|
|GNDI|112.0|5.2|5.4|5.0<br>5.2|5.7|5.2|
|Real exchange rate (index)||0.0|-0.1|-0.1<br>0.0|-0.3|0.0|
|Unemployment rate(%)|18.4|14.1|14.0|14.9<br>14.3|13.8|14.9|

Note: The column 2013 shows GDP shares (%) whereas the the other columns show annual real growth 2014-2030 (%). The only exception is unemployment; in all columns its unit is the level in percent. An increase (decrease) in the real exchange rate signals depreciation (appreciation). The average annual rate of population growth is 2.0%.

Table A3.2. Macro indicators in 2013 and by simulation in 2030 (% of GDP)

||2013|base|ser-eq+fg|ser-eq+db|ser-eq+tx m|dg-eq+fg m|dg-eq+tx|
|---|---|---|---|---|---|---|---|
|Absorption|115.0|115.0|116.1|115.2|114.9|118.7|114.8|
|Consumption - private|87.1|86.0|85.7|86.6|84.5|85.2|80.8|
|Consumption - government|8.4|9.0|9.8|10.4|9.9|11.5|11.9|
|Investment - private|13.6|13.6|13.6|10.7|13.3|13.6|12.6|
|Investment - government|5.9|6.4|6.9|7.5|7.1|8.3|9.5|
|Exports|23.2|23.5|22.3|22.4|23.1|19.5|22.1|
|Imports|-38.2|-38.5|-38.4|-37.5|-38.0|-38.1|-36.8|
|Net indirect taxes|7.4|8.2|8.1|8.0|9.3|8.1|12.4|
|GDP at factor cost|92.6|91.8|91.9|92.0|90.7|91.9|87.6|
|GNI|95.0|95.0|95.0|94.4|95.0|95.2|95.2|
|GNDI|112.0|112.0|113.2|112.1|111.9|115.9|111.8|
|Foreign savings|3.0|3.0|2.9|3.1|2.9|2.8|2.9|
|Gross national savings|16.5|17.0|17.6|15.1|17.5|19.1|19.2|

33
Phase payouts are based on accumulated rainfall between the start and end dates of the phase, measured at a nearby reference weather station or rain gauge.<sup>1</sup> The start of the first phase is triggered by the monsoon rains. Namely, phase 1 (sowing) begins on the first date on which accumulated rain since June 1 exceeds 50mm, or on July 1 if accumulated rain since June 1 is below 50mm.

Insurance payouts in the first two phases are linked to low rainfall. The payout structure in these cases is illustrated in figure 1. Contract details in the figure are from the phase 1 contract linked to the Mahabubnagar weather station, which is representative of the policies studied in our empirical analysis. The policy pays zero if accumulated rainfall during the phase exceeds an upper threshold, or ‘strike’, which in this case is 70mm. Otherwise, the policy pays Rs. 10 for each mm of rainfall deficiency relative to the strike, until the lower threshold, or ‘exit’, is reached. If rainfall is below the exit value, the policy pays a fixed, higher indemnity of Rs. 1000. Phase 3 policies have the same structure, but in reverse, they pay out only when rainfall exceeds the strike, meant to correspond to unusually heavy rainfall during the harvest that causes damage to crops.

Depending on the policy, the reference weather station is one of three types: an Indian Meteorological Department (IMD) station, mandal rainfall station (a mandal is a local geographic area roughly equivalent to a U.S. county) or one of a network of automated rain gauges installed by ICICI Lombard. For this paper, we focus on IMD 1 Some adjustments are made to accumulated rainfall when constructing the rainfall index used to calculate payouts. If daily rainfall exceeds 60mm, only 60mm is counted towards the cumulative rainfall index. Also, rainfall <2mm is ignored. These adjustments reflect that heavy rain may generate water runoff, resulting in a less than proportionate increase in soil moisture, while very light rain is likely to evaporate before it soaks into the soil. We take these adjustments into account when constructing putative insurance payouts.

5
using the 2013-11 period instead of the 2013-12 one. Therefore, as mentioned in the previous section, these exceptions reinforce the idea that past information is key to produce nowcasts of good quality under the PE and QGC methods.

# _6.2 Sensitivity analysis of the Pass-through Error_

All previous poverty nowcasts under the QGC and NDG were computed using a pass-through θ= 1. However, any value selected would be arbitrary since income growth from surveys tends to differ from GDP per capita growth. Table 5 analyzes the sensitivity of 2014 nowcasts to the value of the pass-through. The objective is to study the sensitivity of the PTE to changes in the passthrough value keeping everything else unchanged. In general, the two methods perform similarly irrespective of the value of the pass-through selected and results are robust to the underlying assumption of the value of the pass-through.

# _6.3 Sensitivity analysis of Distributional Error_

Poverty nowcasts in section 4 were done using 20 quantiles under the QGC method. A relevant question is whether the QGC performs well at nowcasting regional and sub-regional poverty estimates when changing the number of quantiles. Table 6 shows 2014 poverty nowcasts using a large set of quantiles from 5 to 100 and keeping everything else unchanged. The objective is to study the sensitivity of the DE to changes in the number of quantiles. In general, the QGC method performs similarly irrespective of the number of quantiles selected.

# **7 Conclusions**

LAC has witnessed an impressive poverty reduction since 2003. Unfortunately, given that data are usually available with more than one year lag in the region, we cannot say much yet about the poverty impact of the lower economic growth that the region is currently experiencing. There are several methods to forecast and nowcast poverty estimates, however little is known about their level of accuracy and performance. To overcome this limitation, we use a comprehensive set of about 180 harmonized micro household surveys available in 15 LAC countries from 2003 to 2014 to analyze the performance of three different forecasting methods: (i) the Poverty Elasticity (PE), (ii) the Neutral Distribution Growth (NDG), and (iii) the Quantile Growth Contribution (QGC) methods.

19
On the contrary, the brain drain is strongly underestimated in countries such as the Gambia, Madagascar, Gabon, Brunei, Papua New Guinea. These countries send a relatively small number of emigrants to non-US areas. They are usually not identi...ed in the OECD migration statistics. Consequently, their emigration rates are largely underestimated.

The last ...gure (...g 3.C) represents the percentage of emigrants on the basis of which educational attainment is extrapolated (let us call this proportion a ”reliability rate”). On average, our method is based on 92.7 percent of observations. A method relying on US educational attainment is based on 44.1 percent of observations. However, the reliability rate is heterogenous bewteen countries. Figure 3.C represents the number of reliable country estimates as a function of the reliability criterion required by any user of the database. The function is obviously decreasing: the number of reliable observations decreases with the degree of reliability imposed by the user. For example, setting the reliability rate at 80 percent, our database provides 175 reliable country observations in 2000 (160 reliable observations in 2000). Transposing the US immigration structure to the total stock of immigrants, we would only provide 23 reliable observations in 2000 (22 countries in 1990) at the same reliability rate. By extending the number of observations and their degree of reliability, our method provides useful material to examine the changes in the international distribution of migration rates, to test for the (push and pull) determinants of migration per skill group or to evaluate the macroeconomic consequences of migration on source and destination countries.

# 4 Key indicators

Let us summarize the results by a few ...gures.

Immigration structure in OECD areas. The intensity of the brain drain clearly depends on the structure of immigration in OECD countries. Figure 4 describes that structure. In ...gure 4.A, it clearly appears that the US is the major destination country for skilled workers. The proportion of OECD educated migrants residing in the US increased from 48 percent to 53 percent between 1990 and 2000. The European Union is the second destination, attracting 16.3 percent of the educated in 2000 and 15.2 in 1990 (the migration between European countries is counted), just above Canada (13.9 percent in 2000 and 15.7 in 1990). Figure 4.B shows that the proportion of skilled immigrants in the total immigration stock is very high in Canada (58 percent). It is lower in Australia (46 percent) and the US (44%), and quite low in the European Union (22 percent). Such disparities are due to the historical composition of immigration ‡ows over the last decades. Focusing on the variation between 1990 and 2000 (capturing recent immigration policies), it appears that the rise in educational attainment of immigrants has been very strong in Australia and, to a lesser extent, in Canada, compared to the US and Europe. These trends are compatible with the stylized facts and policy measures presented in the introduction.

28
# **Stability and Evolution of Preferences for Improved Cookstoves - A Difference-inDifference Analysis of a Choice Experiment from Ethiopia**

Sahan T. M. Dissanayake<sup>*</sup> , George Voigt, Abbie Cooper, Abebe Damte Beyene, Randall Bluffstone, Zenebe Gebreegziabher, Daniel LaFave, Peter Martinsson, Alemu Mekonnen, Michael Toman

**Keywords:** temporal stability, preferences, improved cookstoves, choice experiment, Ethiopia

**JEL Codes:** Q51, Q56, D00

> * Corresponding author: Dissanayake, Portland State University, Portland, OR, USA <u>sdissan2@gmail.com</u> Dissanayake: Portland State University, Portland, OR, USA; Beyene: Ethiopian Development Research Institute, Addis Ababa, Ethiopia; Cooper: Colby College, Waterville, ME, USA; Gebreegziabher, Ethiopian Development Research Institute and Mekelle University, Addis Ababa and Mekelle, Ethiopia; LaFave: Colby College, Waterville, ME, USA; Martinsson, P: Gothenburg University, Gothenburg, Sweden; Mekonnen, Ethiopian Development Research Institute and Addis Ababa University, Addis Ababa, Ethiopia; Toman, The World Bank, Washington DC, USA; Voigt: Colby College, Waterville, ME, USA. Financial support for this research from the World Bank and from the Knowledge for Change Trust Fund is gratefully acknowledged.
**Figure 1 Emigration is highest for middle-income countries**

_Sources_ : Original estimation based on migration data from UN DESA 2020 and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure plots the relationship between total migrant stock (as a percentage of origin country population) in 2020 against the per capita GDP (in constant 2017 US dollars in purchasing power parity terms, 2017 PPP$) in 2020. The solid line presents a locally linear fit. GDP for 2020 is calculated by applying the GDP growth rate applied to the 2019 GDP from PWT 10.0. Emigration is capped at 30 percent for countries with higher emigration rates for the scatterplot. Data labels use International Organization for Standardization (ISO) country codes.

However, early empirical research on this relationship was limited by lack of data. Early results relied on a cross-sectional relationship or used only a subset of countries for which reliable historic data were available (such as the member countries of the Organisation for Economic Co-operation and Development, OECD). This changed with a comprehensive compilation of historical migration data from 1960 to 2000 by Özden et al. (2011), which collated information from various censuses and surveys in origin and destination countries and using parsimonious modeling to fill in the gaps in data. For recent years, the United Nations, using a similar methodology, has been providing regular updates to data on the bilateral migrant stock (UN DESA 2020). Combining the data on migrant stock from 1960 to 2018 from these sources, Clemens (2020) produced one of the first studies to thoroughly examine how emigration changes as countries develop.

This paper extends the non-parametric analysis in Clemens (2020) to provide a nuanced picture of the relationship between development and emigration. It divides countries into three groups based on their baseline levels of income (for 1960 or the first year in which data are available) and analyzes the migration response separately by country income group. The analysis shows that for low-income countries, emigration dips slightly at early levels of development and steadily increases thereafter. This is driven by a sharp reduction in emigration to neighboring or other low-income countries and a slow increase in emigration to high-income countries. For middle-income countries, emigration increases steadily with development and plateaus at sufficiently high levels of income. This is driven by migration to high-income destinations. Larger (more populous) middle-income countries experience a muted response compared to smaller (less populous) ones. Overall, the migrant destinations of low-income countries diversify as they develop, whereas the concentration of destination countries remains similar for middle-income countries through their development trajectory.

3
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specification in table 2, but since it is at the firm level, we can bring in more firm-level control variables to rule out other possible effects. The coefficients of the interaction term are statistically significant with negative signs. Other control variables are statistically significant and of the expected sign: the higher the share of ownership of the firm that is foreign; and the higher the number of skilled workers in the firm in the previous period, the greater the firm’s labor productivity. We find that services firms tend to be more productive, but this may be marred by the very low proportion of firms in the WBES sample that are in the services sectors.<sup>7</sup> We believe that the enabling characteristics of services in the productivity of other firms is better captured in the sectorial regression results (tables 2-8).

**Table 11: Sectoral FDI has a positive impact on firm productivity**

|Dependent Variable: ln labor<br>productivity **at the firm level**|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_ln (FDI)_|0.038***|0.038***|0.051***|0.061***|0.061***|0.059***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|_ln (FDI) × GVC_||||-0.052**|-0.058**|-0.059***|
|||||(0.02)|(0.02)|(0.02)|
|_GVC_|||0.880***|0.940***|0.856***|0.832***|
||||(0.10)|(0.11)|(0.11)|(0.11)|
|_Foreign ownership share_|||||0.433***|0.465***|
||||||(0.04)|(0.04)|
|_Log (# skilled workers at t-1)_||0.113***|0.079***|0.079***|0.069***|0.064***|
|||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|_ln (years of top mgmt)_||||||0.146***<br>(0.02)|
|_Service_||||||3.097***<br>(0.12)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|City-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|50,884|35,855|31,311|31,311|30,866|30,415|
|R-squared|0.137|0.145|0.173|0.173|0.182|0.185|

**Notes:** The dependent variable is the log of labor productivity at firm level. FDI is at sector level. GVC is at firm level. GVC is defined as the average of intermediates import share and the exporting intensity.

The latter defines as the ratio of exports over sales. Ln (# skilled workers at t-1) is the log of the number of skilled workers of a firm in the previous year. Ln (years of top management) is the log of years of experience of the top managers. We divide firms into two sectors: manufacturing and services. The standard errors are clustered at the country-sector level. City-year, sector-year and year fixed effects are included. *** p<0.01, ** p<0.05, * p<0.1.

> 7 We tried these specifications only looking at the services firms, but they are very few and scattered so the power of the tests is lower. Moreover, there are major conceptual problems with measuring the performance of services firms by looking at labor productivity or total factor productivity.
Nevertheless, good targeting performance is by no means guaranteed. Table 13 reports performance data on two social tariff schemes in Guatemala and Honduras that use electricity consumption levels as their sole eligibility criterion (Foster and Araujo, 2003; Wodon, Ajwad and Siaens, 2000). However, in both cases the consumption threshold is 300 kilowatt-hours, compared with average household electricity consumption of 102 kilowatt-hours per month in Guatemala and 108 kilowatt-hours per month in Honduras. Thus, 95% of Guatemalan households and 85% of Honduran households are eligible for the social tariff, so that 60-65% of subsidy beneficiaries are not poor, and 80-90% of subsidy resources are captured by the non-poor. Moreover, only around 50% of poor households in Guatemala and Honduras have an electricity connection, and consequently 55-60% of the poor fail to benefit from the social tariff. As a result, both these subsidy schemes are highly regressive and deliver much lower subsidies to the poor than could be achieved via a random allocation of money across the population.

**<u>Table 13: Comparative overview of electricity subsidy schemes</u>**

||**Argentina**|**Colombia**|**Guatemala**|**Honduras**|
|---|---|---|---|---|
|**Design**<br>Consumption limit|100-200kWh/m|200kWh/m|300kWh/m|300kWh/m|
|Eligibility criteria|Variable by<br>Jurisdiction|Neighborhood<br>characteristics|Consumption<br>level|Consumption<br>level|
|Discount<br>Administration|25-75%<br>Local<br>government,<br>andlocalutility|10-25%<br>Local<br>utility|30-50%<br>Local<br>utility|50%<br> <br>Central<br>government<br>and utility|
|**Scale**<br>Total beneficiaries|<0.4m|30m|8m||
|(as<br>percentage<br>population)<br>Total cost<br>(ofwhich fiscalcost)|(<1%)<br><US$20m<br>(0%)|(90%)<br>US$250m<br>(20%)|(70%)<br>US$50m<br>(0%)|US$17m<br>(100%)|
|**Performance**|||||
|Error of inclusion<br>Error of exclusion|39%|51%|65%|60%|
|• Connected poor<br>• All poor|93%<br>94%|2%<br>6%|0%<br>60%|9%<br>55%|
|Leakage rate||69%|90%|81%|
|Coady statistic<br>Concentration<br>coefficient|-0.37|1.03<br>+0.02|0.25<br>+0.50|0.48|

Source: Wodon, Ajwad and Siaens, 2000; Foster and Araujo, 2003; Foster, 2004; World Bank, 2004 It is interesting to compare this performance against two other social tariff schemes that employ additional eligibility criteria, over and above the level of electricity consumption. Two examples are give. The first is the Colombian cross-subsidy scheme, which is based on geographical eligibility, and is virtually identical to the water cross-subsidy scheme already described above. The other is the social tariff scheme practiced in a number of Argentine provinces, based on the socio-economic characteristics of the household, and similar to those already described above for the water sector. Both of these schemes 33
between the EU and the UK.<sup>18</sup>

# **4 Results: Bilateral Elasticity Estimation**

This section discusses bilateral trade elasticity, constructed from the estimated translog � parameter, _ann_ : We restrict the estimated elasticities to be non-positive as well as winsorize to drop the outliers. Missing elasticity estimates are replaced with the averages within the exporting partner country and product groups.<sup>19</sup> Overall, the bilateral trade elasticities for the UK appear to be well estimated. The average first stage F-statistic is 12.38, while the average t-statistic for the estimated bilateral elasticity is -2.60. Table 2 summarizes the results for the exports from the UK. The sample average import elasticity estimate for an HS 6-digit product imported from the UK to the EU is -13.9, with a sample median of -1.76 and a standard deviation of 38.8. This indicates that there is a wide range of trade responsiveness across all products, which highlights the importance of allowing trade elasticities to vary across products when calculating trade

> 18The EU MFN tariff schedule includes ad valorem tariffs and specific duties. The conversion from specific duties to ad-valorem tariffs is automated in WITS and relies on reference prices (world prices) and price elasticities, both of which may not be representative of EU-UK trade. Therefore, the analysis of this paper discards the products subject to specific duties (about five percent of HS-6 digits products).

> 19Thus, for each product _n,_ the difference in bilateral elasticities across trading partners is due to the difference in the share of these partners in the GDP of the EU, _snd_ . However, this does not imply that partner countries with a larger share will face a less elastic import demand in the EU, since _a_ � _nn_ could be positive or negative empirically. Specifically,

> If _a_ � _nn > s_<sup>2</sup> _nd_<sup>_,_then</sup><sup>_<u>∂ε</u>_</sup> _∂s_<sup>_<u>nnd</u>_</sup> _nd_<sup>_<_0</sup><sup>_,_implyingthatanexportingcountrywithasmallershareinmagnitude(given</sup> that _snd <_ 0) will face a more elastic demand in the EU (given that _εnnd <_ 0) _._ The converse is also true. If _a_ � _nn < s_<sup>2</sup> _nd_<sup>_,_then</sup><sup>_<u>∂ε</u>_</sup> _∂s_<sup>_<u>nnd</u>_</sup> _nd_<sup>_>_0</sup><sup>_,_whichimpliesthatanexportingcountrywithasmallersharein</sup> �<sup>magnitude</sup> (given that _snd <_ 0) will face a less elastic demand in the EU ( given that _εnnd <_ 0) _._ The size of _ann_ depends on the regressions and could be positive or negative empirically.

13
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