rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> FIGURE C 2. STANDARD VARIANCE DECOMPOSITION OF MEAN INCOME GROWTH<br>Based on Nonfuel Commodity Prices<br>Panel A. All countries Panel B. Resource typology Panel C. Output growth typology<br>100 100 100<br>80 80 80<br>60 60<br>60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>0<br>-20 -20<br>-20 NROC ROC HOGC LOGC MOGC<br>Based on Fuel Commodity Prices<br>Panel A. All countries Panel B. Resource typology Panel C. Output growth typology<br>100 100 100<br>80 80 80<br>60 60<br>60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>0<br>-20 -20<br>-20 NROC R OC HOGC LOGC MOGC<br>Idiosyncratic: variance share Global commodity price: variance share<br>Global mean income growth: variance share Global income growth–commodity price: covariance share<br>Variance share (percentage) Variance share (percentage)<br>Variance share (percentage)<br>Variance share (percentage) Variance share (percentage)<br>Variance share (percentage)<br><!-- End of picture text --> _Source:_ Penn <mark>World Tab</mark> le 9.0, World Development Indicators, United Nations, and the author’s estimates. _Note:_ Variability is measured by the standard deviation of mean income growth. All pa <mark>nels consi</mark> der country regression information over the period 1980–2014. Similar results are obtained using the following periods instead: 1990–2014, 2000–2014, and 2005–2014. The total number of countries used in this estimation is 150. The number of NROC and ROC economies is 104 and 46, respectively. The number of countries in the classifications LOGCs, MOGCs, and HOGCs are 8, 117, and 25, respectively.

Page **43** of **45**
**Figure 2:** Average temperature and precipitation anomalies (2004-2019)

<!-- Start of picture text --> (a) Positive temperature anomaly (heat shock) (b) Negative temperature anomaly (cold shock)<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>2005 2007 2009 2011 2013 2015 2017 2019 2005 2007 2009 2011 2013 2015 2017 2019<br>Year Year<br>(c) Positive precipitation anomaly (flood shock) (d) Negative precipitation anomaly (drought shock)<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>2005 2007 2009 2011 2013 2015 2017 2019 2005 2007 2009 2011 2013 2015 2017 2019<br>Year Year<br><!-- End of picture text --> _Note:_ Average temperature and precipitation anomalies in survey sample, using individual weights. The time period refers to a time bin of 90 days before a household’s date of interview in the EPH survey.

reasons. First, raw data from the CHIRPS and ERA5 data bases are more granular than the SPEI that is downloadable for instance from Google Earth engine. As the district is the lowest level of geographical aggregation in the EPH data, we want to ensure maximum variation of our weather shock variables at the district level, and as districts in (southern) Paraguay are rather small, we opt for the more granular weather data. Furthermore, several studies have shown high and significant correlation between the SPI and SPEI, so our estimates should be effective in capturing drought conditions (Danandeh Mehr, Sorman, Kahya, & Hesami Afshar, 2020; Salimi, Asadi, & Darbandi, 2021; Torelló-Sentelles & Franzke, 2022).

Table 2 displays descriptive statistics for relevant temperature and precipitation variables. Temperature and precipitation were, on average, positive during the reference period 2004-2019, meaning that temperatures were on average higher. Similarly, more rainfall was observed than historically (1981-2003). The number of heat days ( _>_ 30°C) and extreme heat days ( _>_ 35°C) was also higher than the historical average. Regarding the range of weather shocks (anomaly), they range from 0.31 to 0.91 standard deviations, on average. Furthermore, positive anomalies such as heat and flooding shocks seem to be larger in magnitude than negative anomalies, capturing cold and drought shocks.

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Is South Asia an outlier in its sophistication of service exports? We plot individual year scatter plots of the export sophistication index (in logs) with the log level of GDP percapita for a sample of around 134 countries for the year 1995, 2000 and 2006 (Figures 9). Figures show striking evidence of the positive correlation between growth and services sophistication, a relationship which Hausmann, Hwang, and Rodrik argue runs from export sophistication to growth rather than the other way around for goods export. Thus there is clear evidence that countries that produce services of higher quality grow faster.

**Figure 9a. Log per-capita GDP and Services Export Sophistication, 1995**

<!-- Start of picture text --> GBRCHE<br>JPN<br>USA<br>FRA<br>IND CHN LKA ISRIRL<br>NPL ISL<br>PAK<br>BGD<br>6 7 8 9 10 11<br>log real GDP per capita (1995, PPP)<br>9.5<br>9<br>log Service EXPY (1995)<br>8.5<br><!-- End of picture text --> _Source_ : Flaaen, Ghani, Mishra 2009 Most of the South Asian countries were exporting services of much higher quality than their incomes suggest in 1995. While India and Sri Lanka have remained above the norm, Nepal and Pakistan had moved below the norm by 2006<sup>4</sup> . Bangladesh has always been below the norm. India is an exceptional story. They have moved way ahead in terms of service exports sophistication over the period. The level of sophistication of India’s export is very much comparable to rich countries like UK, Ireland and USA. In essence India is producing goods which are being produced by rich countries.

> 4 They were above the norm in 2000.

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why this would differ across areas according to the pattern of reforms. We do include occupation dummies to control for this. Also, as a robustness check, we include the share of the occupation in an area, allowing it to vary over time. If certain occupations were affected relatively more, this should control for this effect. We also allowed for differences in the time trend across the regions and chartered cities; the results are robust to this too. A final potential concern is that the second survey was run a couple of months later in the year than the first survey, with the last weeks potentially overlapping with the beginning of the planting season. As robustness checks we exclude all agricultural workers from the regressions, and we also look only at the urban areas and find our results to be robust.

# **_Occupations_**

Our data set has 8 occupational categories.<sup>10</sup> Figure 1 shows the distribution of women’s shares in each of these occupations. Similar to other labor market studies of Ethiopia, women are most under-represented among the professional/technical/management group followed by skilled laborers. At the other extreme, women are highly over-represented among artisan/crafts.

> 10 Professional/technical/management, teaching, clerical, sales, unskilled laborers, skilled laborers, and artisan/craft workers.

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sampling different nationalities and sub-populations of African migrants. With these caveats in mind, Figures 3 and 5 present statistics on remitting by country within Africa for the pooled dataset, and then later in the paper we explore whether any differences observed across source countries remain significant after controlling for survey dataset fixed effects and the effects of observable characteristics of the migrants. We present statistics for each African country that has 30 or more observations for the remittance measure being studied.<sup>4</sup> Figure 3 shows the fraction of migrants who remitted money in the past year (extensive margin) by country of birth, including a comparison of African migrants compared with migrants from other developing countries (rest of world) in our database. There are large differences in remitting patterns across countries in the raw data. More than 70% of migrants from the Democratic Republic of Congo and Senegal remitted money, while fewer than 20% of migrants from Algeria, Mauritius and South Africa remitted anything.

Examining the differences across countries in Figure 3 suggests that emigrants from poorer African countries are more likely to remit than emigrants from richer African countries. However, one concern is whether these differences are all driven by differences in the destination countries these migrants are located in. To test this, we regress the proportion remitting on destination survey fixed effects to remove this source of variation, and then graph the residual variation against GDP per capita (expressed in PPP terms from the Penn World Tables) in Figure 4. We see that there is a strong and significant negative slope: for each doubling in GDP per capita, the fraction of migrants from that country who remit drops 6 percentage points. The t-statistic on GDP per capita is -3.8.

> 4 Restricting it to 100 or more observations per country would eliminate Somalia from Figures 3 and 4, and Mauritania, Mali, Togo, Cameroon, Cote d’Ivoire, Madagascar, Guinea, Comoros, Republic of Congo, Tunisia, Angola, and Mauritius from Figure 3.

9
37 organizational support needed for economic uplifl, and a broader program that works in tandem with exting cedit institutions to streamline their operations and ensure that borrwers put credi **t** o its best use.

Table 3.4. AKRSP Performance in Selected Areas

|.. .....................................................................................................................................................|1983<br>.........|1988<br>.............................|Percentage change<br>...............................|
|---|---|---|---|
|14o. of Village organizations|131|993|_658_|
|Women's organizations|10|248|2,380|
|Training courses conducted per year|4|37|825|
|Projects identified|363|1346|270|
|Projects completed|23|_514_|2,135|
|Rural households benefiting (percentage)|12|52|5,100|
|Deposits byviDlage and women's organizations (Rs millions)|0.8|51.3|6,312|
|Credit disursed annually (Rs millions)|1|34.6|3,360|
|Credit recipients (thousand households)|4.7|61.0|1,198|

.Source: World Bank 'The Agha Khan Rural Support Program in Pa.istan: _A_ Second Interim Evaluatiort" Washington, D.C., 1990.

Family Planing Association of Pakistan. The Family Planning Association of Pakistan (FPAP) has provided loans to some 2,000 women for income-generating activities since 1984 at twenty locations. Each year interest-free loans are offered to five to seven women in each location. Loan recipients are selected by local women's committees based on their personal knowledge of applicant's circumstances and attitudes. The loans from Rs. 500 to Rs. 1,000 have a six to seven month term and are repaid in monthly installments of Rs. 50 to Rs. 100. Borrowers pay a nominal monthly membership fee until the loan is repaid in full. Monthly fees are used for skill training for applicants. Loans are made principally for goat rearing, poultry farming, food preservation, tailoring, and small businesses.

The FPAP also operates a UNDP-funded credit and training program at three locations. Women receive loans for preselected activities based on their skills, market potential, and FPAP's experience in the area. Women are encouraged to form a loose cooperative with a nominal membership fee. They hold regular meetings and are given training in their respective activities. The loan was from Rs. 5,000 to Rs. 10,000 at each location. This program has been running very successfully, with negligible defaults. Beneficiaries have also expressed their satisfaction with the loan scheme, and some are now contributing Rs. 500 to Rs. 15,000 per month to their family income.
to derive the tests, the differences in the blocks are not significantly different from zero. This means that the matched households have similar characteristics even if they differ by e-commerce participation. In other words, the common support assumption is satisfied. Details of the balancing tests can be found in Appendix II Table A1.

# **_4.3 Role of e-commerce participation on income growth (PSM results)_**

# _4.3.1 Full sample_

After the radius matching and balancing procedure, 515 of 620 observations in the control group are matched with all 453 observations in the treatment group. As shown in Table 5, the estimated average treatment effect on the treated, which is an estimate of e-commerce direct effect on income in Taobao Villages, is 35,388 yuan. Compared to the mean income in the control group, which is 35,019 yuan, this result is equivalent to an income growth of 101 percent for the full sample.

# **Table 5. PSM results**

||**Control mean**|**E-commerce**<br>**effect**|**T-stat**|**Growth**<br>**rate**|
|---|---|---|---|---|
|Full sample|35018.9|35388.2|10.2|101.1%|
|Wealth quartile 1|26642.9|25360.3|2.8|95.2%|
|Wealth quartile 2|27427.0|26996.1|3.8|98.4%|
|Wealth quartile 3|34957.3|33944.4|4.9|97.1%|
|Wealthquartile 4|57801.7|32906.3|3.3|56.9%|
|Head education, primary|28259.2|28236.9|3.9|99.9%|
|Head education, middle school|33908.9|31735.8|5.0|93.6%|
|Head education,high school|39011.6|28946.6|1.9|74.2%|
|Head education,technical school|54367.1|45777.1|3.0|84.2%|
|Knew e-commerce|48461.3|35820.9|3.1|73.9%|
|No urban experience or did not hear about e-<br>commerce while in urban|33205.6|29294.6|7.1|88.2%|
|Agequartile 1,mean age 29|46054.0|32914.8|3.4|71.5%|
|Agequartile 2, mean age 40|38653.5|30434.9|3.9|78.7%|
|Age quartile 3, mean age 52|30761.9|29404.0|4.9|95.6%|
|Agequartile 4,mean age 66|25608.8|26346.8|2.6|102.9%|

Note: The wealth index is constructed from principal component analysis of the household’s ownership of automobiles, trucks, motorcycles, tractors, personal computers, laptops, air conditioners, televisions, cellphones, and dwellings.

However, one should use caution when interpreting this result as the estimated direct effect of e-commerce participation on household income growth represents mainly an upper bound while the indirect effect of e-commerce development on the local economy in Taobao Villages is likely to be positive (for the reasons discussed in the previous section). In addition, when defining e- households, we do not take into account the number of years that households have been in e- business or have experience in conducting e-commerce. In other words, we do not differentiate those that have been operating e-shops for a long time from those that just opened recently. Within the e-households, there are variations in e-commerce experience, as shown in Figure 9. The mean years of experience, defined as the mean number of years between the year of opening the first e- shop until 2018 for the e-households is 4.6 years. In other words, the estimated average direct effect 19
Table 11: Quantile regressions results from preferred baseline specification

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||FE firm|FD|Q(0.10)|Q(0.25)|Q(0.50)|Q(0.75)|Q(0.90)|
|||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐TFP Ackerberg _et a_|_l_.‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐||
|Entryt‐2|‐0.080|‐0.057|‐0.612***|‐0.318***|‐0.068**|0.207***|0.459***|
||(0.313)|(0.529)|(0.000)|(0.000)|(0.012)|(0.000)|(0.000)|
|Conductt‐2|‐0.221***|‐0.221***|‐0.486***|‐0.316***|‐0.116***|0.018|0.073|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.566)|(0.188)|
|FDI Overallt‐2|‐0.074|‐0.102|0.100**|‐0.031|‐0.133***|‐0.197***|‐0.113*|
||(0.102)|(0.126)|(0.023)|(0.179)|(0.000)|(0.000)|(0.097)|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|||
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|||
|Observations|4983292|2406496|2406496|2406496|2406496|2406496|2406496|
|PR2|0.985|0.030|0.015|0.024|0.029|0.018|0.008|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors are in parenthesis. First difference is taken over 3‐year period.

41
number of household members and share of children directly, so that the IV is not confounded with family composition effects.

Graphs 3a and 3b plot the distributions of the actual and potential current and accumulated transfers, respectively. As shown, the distributions of the potential transfers follow those of the actual transfers very closely. As expected, the potential transfers are an over-estimate of the actual transfers (given non-compliance, administrative delays in payments, etc). The simple correlation amongst them is 0.89. If we control for time effects and baseline covariates, the potential transfer explains 55.7% of the total transfer and 65.9% of the variation of the cumulative transfer. Graph 4 plots mean cumulative transfer by quintile for the distributions of actual and potential cumulative transfers.

# **3.2. Comparison of Means**

The dependent variables for agricultural assets come from the six rounds of the ENCEL that contain information on animal ownership and amount of land in use over the 12 months preceding the interview (hectares of land for agriculture, grazing and/or forestry purposes). We define production animals to be those whose meat and/or by-products (milk, cheese, eggs, etc.) are sold and consumed. These include goats and sheep, cattle (cows), poultry (chickens, hens and turkeys), pigs and rabbits. We define draft animals to be those traditionally used for farming (plowing) and/or for transportation purposes. These include donkeys, mules, horses and oxen. We transform all of these animals into “cow equivalents” using the ratio of the price of the animal divided by the price of a cow. Therefore, we create cow-equivalent indices of production and draft animals. Land is measured in hectares and includes all plots used by the household<sup>11</sup> .

The dependent variables for (non-agricultural) micro-enterprises come from five rounds of the ENCEL surveys (October 1998 to December 2000). A set of questions ask the household head whether somebody in that household had engaged in a “self-motivated” non-agricultural activity which generates income during the month before the interview. The list of activities includes

> 11 A detailed description of the sample and variables is available upon request.

10
# **Figure 11 FDI stock in manufacturing industry**

<!-- Start of picture text --> 1999 2000 2001<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Low-technology industries Medium-low technology industries Knowledge-based industries<br>US$ Million<br>Food, products products products<br>Textile&textile products Rubber&plastic Basic metals & fabricated metal fibre equipment<br>beverages&tobacco Pulp, paper&paper Other non–metallic mineral products Chemicals, chemical Electrical& optical Transport equipment<br>Wood&wood products products & man–made Machinery&equipment<br><!-- End of picture text --> _Source_ : Bank of Latvia, BOP Statistics Division, _Foreign direct investment stock in Latvia by kind of activity_ (http://www.bank.lv/izdevumi/Latvian/maksbil/2002-02/LMB7.xls)

# <u>D. The Forestry Sector: Microcosm of Latvia’s Problems and Prospects</u>

The forestry sector is a microcosm of Latvia’s problems -- and potential opportunities. The Government’s innovation policy targets the forestry sector as a potential source of high tech exports. And indeed, its share of Latvia’s total exports increased rapidly since the early transition period. As a result, the forestry sector now accounts for approximately 40% of Latvia’s total exports.

**Figure 12 Forest Sector Share of Total Latvian Exports**

<!-- Start of picture text --> 48<br>43 43<br>39 40<br>3 6 35<br>3 1 30<br>24<br>12<br><!-- End of picture text --> _Source_ : Swedish National Board of Forestry, 2003<sup>19</sup>

> 19  National Board of Forestry, Sweden: <u>http://www.svo.se/</u>

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# Table of Contents

|Executive Summary .......................................<br>iv|
|---|
|Introduction ........................................<br>v|
|Section I Demographic Data ........................................<br>1|
|A. Historical Data ........................................<br>1|
|B. Demographic Projections ........................................<br>1|
|Section II Public Pension Schemes ........................................<br>3|
|A. Expenditures and Revenues ........................................<br>3|
|B. Pension Reserves and Investment Returns .......................................<br>16|
|C. Administrative Costs .......................................<br>22|
|D. Coverage .......................................<br>26|
|E. System Dependency Ratios .......................................<br>37|
|F. Design Features .......................................<br>38|
|SectionmPrivately-Managed Pension Schemes .......................................<br>44|
|A. Coverage .......................................<br>44|
|B. Assets and Investment Returns .......................................<br>45|
|C. Administrative Costs .......................................<br>49|
|Section IV Other Data .......................................<br>50|
|A. Informal Arrangements .......................................<br>50|
|B. Labor Force Participation .......................................<br>52|
|C. Duration of Retirement .......................................<br>53|
|D. Poverty Indicators .......................................<br>54|
|E. Wage Growth .......................................<br>55|
|F. Real Rates of Return to Capital .......................................<br>56|

ii