rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. NIRP announcements and complementary policies**

|**Central Bank (policy rate)**|**Date**<br>|**Rate**|**Complementary Policies Announced**|
|---|---|---|---|
|**European Central Bank**||||
|**_(Overnight deposit facility rate)_**|June-2014<br>September-2014|-0.10<br>-0.20|Targeted Longer-Term Refinancing Operations program with 2-<br>year maturity. Announced preparations for an Asset Backed<br>Securities Purchase Program<br>Asset Backed Securities Purchase Program initiated|
||December-2015|-0.30|6-month extension of asset purchase programs. Expanded pool<br>of available assets for purchase, no increase in monthly amount|
||March-2016|-0.40|Expanded size of Quantitative Easing program from €60 billion to<br>€80 billion per month until March 2017, added non-financial<br>corporate bonds to list of assets elibible for purchase, new<br>Targeted Longer-term Refinance Operations program with four<br>year maturity|
|**Riksbank**||||
|**_(One week repo rate)_**|February-2015|-0.10|Forward guidance, SEK 10 billion of government bond purchases|
||March-2015|-0.25|Expanded bond purchases to SEK 30 billion|
||July-2015|-0.35|Expanded bond purchases to SEK 75 billion|
||February-2016|-0.50|Reinvested maturing bonds and coupons from QE program|
|**Danmarks Nationalbank**||||
|**_(One week certificate of deposit rate)_**|July-2012|-0.20||
||September-2014|-0.05||
||January-2015|-0.20||
||January-2015|-0.35||
||January-2015|-0.50|Suspension of new bond issuance|
||February-2015|-0.75||
|**Swiss National Bank**||||
|**_(Overnight sight deposit rate)_**|December-2014|-0.25|Minimum exchange rate reaffirmed|
||January-2015|-0.75|Franc floor abandoned as overall "overvaluation has decreased,"<br>if necessary promises currency intervention in the future|
|**Bank of Japan**||||
|**_(Current account deposit rate)_**|January-2016|-0.10|No changes to existing Qualitative and Quantitative Easing<br>program|
|**Central Bank of Hungary**<br>**_(Overnight deposit rate)_**|March-2016|-0.05||

Sources: European Central Bank, Riksbank, Danmarks Nationalbank, Swiss National Bank, Bank of Japan, and Central Bank of Hungary. Notes: “Rate” refers to the main deposit policy rate in most cases, and the main refinancing rate for the Riksbank. Euro area, Denmark, and Switzerland have exemption thresholds and Japan has a three-tier system for negative policy rates. Danmarks Nationalbank also employed currency interventions to maintain the Danish krone’s peg against the euro.

10
<!-- Start of picture text --> 80<br>19 79<br>i)<br>60 66 63<br>% 40<br>34 37<br>27<br>20<br>21 21<br>)<br>1995 2000 2005 2010 2015<br>°<br>80 84 84 83<br>68<br>60 62 60<br>58<br>% %<br>40 42 40<br>38<br>32<br>20 25<br>23 20 yi 24<br>16 16 17<br>0 0<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> 80 84 84 83<br>60<br>%<br>40<br>20 yi 24<br>16 16 17<br>0<br>1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> °<br>68<br>60 62<br>58<br>%<br>40 42<br>38<br>32<br>20 25<br>23<br>0<br>1995 2000 2005 2010 2015<br><!-- End of picture text -->
# _3.2.2_ **_EV and components_**

**The EU CO2 emission standards, China EV policies, and the US IRA are expected to boost demand for green tech components from developing countries, particularly those used in EVs.** The countries whose EV components sectors stand to benefit most are those exporting mainly to China, the European Union, and the United States. These include Moldova, Albania, North Macedonia, Tunisia, Ukraine, Mexico, Morocco, and Honduras, all of which send more than 80 percent of their EV components to those three markets. In the case of North Macedonia and Tunisia, EV components bound for those markets make up more than 5 percent of GDP – making for a potentially significant economic impact (figure 10).

<!-- Start of picture text --> Figure 10 Top 10 developing countries according to economic exposure to impacts of the EU, US, and<br>China policies on EV components exports<br>Economic exposure (LHS) Sector exposure (RHS)<br> 7 100<br> 6<br> 80<br> 5<br> 4 60<br> 3 40<br> 2<br> 20<br> 1<br> - -<br>North Tunisia Nicaragua Moldova Mexico Morocco Honduras Serbia Ukraine Albania<br>Macedonia<br>Percent Percent<br><!-- End of picture text --> _Source_ : 2022 data from the World Bank’s WITS.

_Note:_ Data includes components of EVs, excluding batteries. Sector and economic exposure are export of EV components to the three economies divided by exports of EV components to the world and GDP, respectively.

**Regarding EV batteries, the sector is most exposed in the Democratic Republic of Congo, Papua New Guinea, Armenia, North Macedonia, and Bosnia and Herzegovina, with more than 80 percent of their battery exports going to the three regions** (Figure 11). Apart from North Macedonia, where exports of batteries to the EU, US and China represent 1.4 percent of GDP, the impact of battery-related policies on the overall economy of these countries is expected to be minor since battery components form a small portion of their economies.

**The impact of the US IRA will change over time in the case of EV batteries.** By 2029, the US demand for imported EV batteries is expected to drop significantly due to tax credits requiring that battery components be made or assembled in North America.<sup>25</sup> This shift will likely boost battery and battery component exports from Mexico to the US but hurt battery exports from countries like Viet Nam and Malaysia.

> 25 See section 2.2.3.

21
<!-- Start of picture text -->
> US$ 178bn > US$ 113bn<br>US$ bn<br>“<br>so g<br>o = Bri MP iil<br>1960 1955 1960 1965 1970 1976 1980 1986 1990 1995 2000<br>a Economic losses (2004 values) -_- Trend of economic losses:<br>a of which insured losses (2004 values) —_ Trend of insured losses<br>Average economic losses per decade<br><!-- End of picture text -->
**Table 5: Types of Employment as a Percentage of the Sample Population GHS 1999 - 2006 (in percent, weighted)**

||**1999**|**2004**|**2006**|
|---|---|---|---|
|Family agriculture|30.8|36.6|37.8|
|Non-agriculture self-employed|24.1|25.8|22.9|
|Non-agriculture unpaid family work|0|0.08|0.06|
|Wage employment|15.0|10.4|10.0|
|Apprenticeship|2.1|1.1|1.9|
|Unemployed|1.7|2.4|1.9|
|Not in the labor force|26.4|23.7|25.5|

_Source: Francis Teal / Luke Haywood NLSS 2003-2004 and GHS 1999-2004_ 17. From 1999 to 2006, the most important structural changes that have occurred in Nigeria’s labor force have been a shift _into_ agriculture employment and _out of_ wage employment: the proportion of the sample population aged 15 to 65 (excluding those in full-time education) with wage jobs _declined_ over this period (from 15 percent in 1999 to 10 percent in 2006). The same is also true for those classified as non-agriculture self-employed (their share of the population fell from 24 to 22.9 percent). The category that saw a major increase in this share of the population was family agriculture, which rose from 30.8 to 37.8 percent.<sup>4</sup> 18. Table 6 provides further insights into the development of wage employment since 1999: Wage employment in parastatals, ministries and public companies has declined, while employment in the private sector and others (including NGOs, international organizations and associations) has risen.

> 4 It is important to note that this finding does not necessarily imply that people in wage employment moved into family agriculture. It could also mean that those who previously reported no activity (i.e. outside the labor force) but were at least temporarily involved in agriculture, became engaged in agriculture to an extent that they now reported employment in family agriculture. That means they moved from under-employment to employment. Rodrik (2010) finds evidence that labor moved from the wholesale/retail sectors (which have a reasonably high productivity) to agriculture.

10
# **The impact on poverty**

The combination of taxes and social spending also led to an important decline in poverty in 2014. The overall decline amounts to 16 percentage points from market to consumable income for the legal poverty line and 18 percentage points for the extreme poverty line.<sup>11</sup> Most of this decline is on account of contributory pensions, which could be thought of as deferred incomes. Starting from market income, the extreme poverty headcount rate measured using the National extreme poverty line<sup>12</sup> declined from 27.2 to 6 percent mostly on account of contributory old-age pensions (Table 4). Similarly, the legal poverty rate declined from 29.2 to 7.5 percent.

Table 4. Main Scenario: Changes in Poverty on account of taxes and transfers

|Market<br>Income<br>|Market income<br>+ contributory<br>pensions|<br>Disposable<br>Income|Consumable<br>Income|
|---|---|---|---|
|(1)|(2) = (1) +<br>contributory<br>pensions|(3) = (2) - direct<br>taxes -<br>contributions +<br>direct transfers|<br>(4) = (3) -<br>indirect<br>taxes|
|**Poverty headcount**||||
|National legal<br>29.2%|7.5%|8.6%|12.9%|
|National extreme<br>27.2%|6.0%|5.5%|8.9%|
|US $5PPP a day<br>27.3%|6.1%|6.0%|9.2%|
|US $2.5PPP a day<br>19.0%|2.6%|1.9%|2.9%|
|**Poverty gap**||||
|National legal<br>20.5%|3.5%|3.0%|4.7%|
|National extreme<br>19.6%|2.9%|2.3%|3.6%|
|US $5PPP a day<br>19.1%|2.9%|2.4%|3.7%|
|US $2.5PPP a day<br>14.3%|1.6%|1.3%|1.9%|
|**Poverty severity**||||
|National legal<br>17.1%|2.3%|1.9%|2.9%|
|National extreme<br>16.5%|2.0%|1.6%|2.4%|
|US $5PPP a day<br>16.0%|2.0%|1.7%|2.5%|
|US $2.5PPP a day<br>12.4%|1.3%|1.2%|1.7%|
|Source: Own estimates based on HBS 2014.||||

11 The extreme poverty rate reported by GUS in 2014 was 7.4 percent, while the legal poverty rate was 12.2 percent. The discrepancy is largely on account of the fact that these are measured using expenditures instead of income (see GUS: <u>http://swaid.stat.gov.pl/en/WarunkiZyciaLudnosci_dashboards/Raporty_predefiniowane/RAP_DBD_WZL_11.aspx)</u> However disposable income is typically very close to consumption for the population at the bottom of the distribution who typically would not be able to save.

12 We use the definitions used by the Polish Central Statistics Office (GUS) to define the national extreme and legal poverty lines. The starting point adopted for constituting the extreme poverty threshold is subsistence minimum of PLN 544.09 in 2014 estimated by the Institute of Labor and Social Studies for a 1-person household. This value is multiplied by the number of persons in the household according to the original OECD equivalence scale.

17
Table 4 shows the correlations between the logarithm of per capita GDP, poverty headcounts, and income inequality (Gini) by region worldwide. Globally, all indices are positively correlated with per capita GDP and negatively with monetary poverty and income inequality. Correlations are usually stronger for per capita GDP, followed by poverty and then inequality. By dimension, the correlation is strongest between each of the income variables and process legitimacy, followed by inclusion. When correlations are estimated by region, their statistical significance frequently wanes as a result of much-reduced sample sizes (results available upon request to the authors).

# **Table 4. Correlations between per capita GDP PPP international $ 2017 (log) and poverty headcounts at national poverty lines**

||Per capita<br>GDP (log)|Monetary<br>Poverty|Income<br>Inequality<br>Gini|
|---|---|---|---|
|P1|0.77*|-0.46*|-0.05|
|P2|0.46*|-0.38*|-0.11|
|P3|0.17|-0.40*|-0.17|
|P4|0.80*|-0.47*|-0.38*|

* Significant at 95% confidence interval Source: Social Sustainability Global Database 2022

# **4.3 Robustness checks**

We first check for the best-fitting association between each social sustainability component and per capita GDP, poverty and inequality. To do that we compare our results in figures 2 to 4 (fitting a linear association between social components and incomes) with an alternative nonlinear, quadratic, specification. For monetary poverty and income inequality, the linear specification is preferred across all components (as a quadratic fit no longer estimates a statistically significant correlation). In the case of per capita GDP, the quadratic form (first decreasing, then increasing) is preferred for resilience and empowerment (but not for inclusion and all three aggregated components). Table 5 below summarizes the results (see online Annex C for the actual results).

**Table 5: Preferred specification of the association between social sustainability and incomes**

||**Log per capita GDP**|**Monetary**<br>**poverty**|**Income**<br>**inequality**|
|---|---|---|---|
|P1: Social inclusion|Linear|Linear|Linear|
|P2:Resilience|Quadratic (U)|Linear|Linear|
|P3: Social cohesion|Quadratic (U)|Linear|Linear|
|P4:Processlegitimacy|Linear|Linear|Linear|

Source: Social Sustainability Global Database 2022.

19
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis analysis focuses on resource dependence rather than resource abundance, as the former is more immediately relevant to economic growth and income inequality.

Table 2: Average Nonrenewable Resource Dependence and Abundance by Income Group (1980-2012)

|**D**|**ependence**|**Log_Abundance**|
|---|---|---|
|High income|2%|7.8|
|Upper middle income|15%|8.6|
|Lower middle income|8%|7.3|
|Low income|11%|6.6|

The data on income inequality is sourced from the World Bank’s “All-the-Ginis Database,” which was last updated in 2014.<sup>8</sup> The database collects Gini indexes from multiple sources into long time series. The data has been standardized for this analysis via the so-called “choice-by-precedence approach,” which reflects each dataset’s reliability, degree of variable standardization, and consistency of geographical coverage. GDP and population figures have been collected from the United Nation’s UNCTAD-STAT database. Table 2 presents the descriptive statistics for per capita GDP and the Gini index by country income group. The Gini index peaks among the upper-middle-income group, falls among the lower-middle-income and low-income groups and is lowest among the high-income group. These data are consistent with the relationship between inequality and GDP described by Kuznets (1955).

Table 3: GDP per capita and Gini index for Countries in Dataset, classified by Income Group (1980-2012)

|**GDP per capita**<br>**(US$ thous)**|**Gini index**|
|---|---|
|High income<br>34.5|33.9|
|Upper middle income<br>8.4|49.1|
|Lower middle income<br>2.8|40.5|
|Lowincome<br>1.4|42.7|

The data for control variables was collected from different sources. Data on the structural and cyclical characteristics of national economies comes from the World Bank’s World Development Indicator Database<sup>9</sup> and UNCTAD-STAT;<sup>10</sup> the figures on education are sourced from Barro & Lee (2013);<sup>11</sup> those on the real effective exchange rate (REER) are taken from Darvas (2012);<sup>12</sup> and metal- and oil-price figures are sourced from the IMF’s Primary Commodity Prices database.<sup>13</sup> Referring to the original datasets will provide further details on the methodology and sources used. Table 4 shows income-level-related heterogeneity across countries for the specified variables.

> 8 Available at <u>http://econ.worldbank.org/projects/inequality</u>

> 9 Available at <u>http://data.worldbank.org</u>

> 10 Available at <u>http://unctadstat.unctad.org</u>

> 11 Available at <u>http://www.barrolee.com</u>

> 12 Available at <u>http://bruegel.org</u>

> 13 Available at <u>http://www.imf.org</u>

8
_-_ _<u>Figure 1: Vulnerable Employment as Percentage of Total (1993 2017)</u>_ <!-- Start of picture text --> Vulnerable Employment (%)<br>90<br>Female<br>75<br>Total<br>60<br>Male<br>45 Murambatsvina<br>Economic<br>& Political Dollarization<br>Crisis<br>30<br>1993 1999 2005 2011 2017<br>Year<br>Percentage of Employment (by total and gender)<br><!-- End of picture text --> Source: ILO, via WDI. Each share is of the relevant total (Female to Female, e.g.) _-_ _<u>Figure 2: Gross Domestic Product Per Capita (constant USD) (1993 2017)</u>_ <!-- Start of picture text --> GDP per capita (constant)<br>1500<br>1000<br>Economic<br>500 & Political Dollarization<br>Crisis<br>Murambatsvina<br>0<br>1993 1999 2005 2011 2017<br>Year<br>Source: WDI<br><!-- End of picture text --> What characterizes Zimbabwe’s informal sector, given this context? Generally, informal businesses have been found to be smaller and less productive than their formalized counterparts (La (9)
**Table 10:** Heterogeneity for HHs in districts where women perform relatively few oxen tasks

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Impacts in Di**<br>**Women Perfor**<br>**More Ox Tasks**|**stricts where**<br>**m Relatively**<br>**Few Ox Tasks**|**p-value**|
|---|---|---|---|---|---|
||||**[1]**|**[2]**|**[1]=[2]**|
|Total land cultivated (ha)<sup>_⋄_</sup>|1718|12.64|0.76<sup>_∗_</sup><br>(0.45)|0.40<br>(0.51)|0.60|
|Producer total active time|1718|69.95|-0.85<br>(2.28)|-2.96<br>(1.95)|0.48|
|Producer household plot labor supply|1718|43.36|0.61<br>(1.27)|-1.66<br>(1.22)|0.20|
|Spouse total active time|1718|76.61|-1.90<br>(1.99)|-1.77<br>(1.99)|0.96|
|Spouse household plot labor supply|1717|37.73|-1.64<br>(1.29)|-2.77<sup>_∗∗_</sup><br>(1.24)|0.53|
|Spouse other agric work|1718|1.02|0.16<br>(0.28)|-0.06<br>(0.26)|0.55|
|Spouse non-farm labor|1716|5.55|-0.53<br>(0.76)|1.78<sup>_∗∗_</sup><br>(0.81)|0.04|
|Spouse household chores|1714|28.12|-0.36<br>(1.11)|-0.28<br>(1.24)|0.96|
|Spouse hobbies|1713|4.29|0.19<br>(0.50)|-0.35<br>(0.58)|0.47|
|Boys’ total active time|1264|61.99|0.01<br>(2.45)|-3.46<br>(2.56)|0.33|
|Boys’ household plot labor supply|1263|36.61|1.02<br>(1.67)|-1.72<br>(1.98)|0.29|
|Girls’ total active time|1124|59.76|-2.46<br>(2.44)|-3.62<br>(2.69)|0.75|
|Girls’ household plot labor supply|1124|29.61|-1.80|-3.04<sup>_∗_</sup>|0.60|
||||(1.63)|(1.71)||

Note: Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. † indicates hours in last week. Spouse outcomes are averaged across wives in polygamous households. All regressions include controls for household composition, household-head education, 26
NORTH AFRICA

# 1. **Ustizates** and Projectioz,s

|Tow|PeGPAtimn|ow.<br>ratlo|Period|c,dw<br>**rate**|Cm|cm|TFI|00<br>tm"|Femle|_INN_|15<br> a100|
|---|---|---|---|---|---|---|---|---|---|---|---|
|19M<br>1990|123430<br>140834|86.0<br>83.6|1985-89<br>1990-94|2.64<br>2.58|36.7<br>34.8|10.1<br>8.9|5.14<br>4.66|58.7<br>60.9|61.7<br>63.9|84<br>72|121<br>102|
|**1995**|160196|79.7|1995-9|2.3t|31.7|7.8|4.16|63.0|66.2|62|**86**|
|**2000**|180466|73.9|2000-04|2.19|28.9|6.9|3.67|65.0|68.4|52|71|
|2005<br>2010|201342<br>222369|68.8<br>62.8|2005-09<br>2010-14|1.99<br>1.78|26.4<br>23.9|6.5<br>6.2|3.26<br>2.90|66.2<br>67.5|69.8<br>71.3|46<br>40|62<br>54|
|2015|243023|58.0|2014-19|1.56|21.5|5.9|2.58|68.8|72.8|33|45|
|2020|262737|54.3|2020-24|1.42|20.0|5.8|2.41|70.1|74.4|27|35|
|2025|282067|51.7|2025-49|1.04|16.5|6.3|2.18|73.4|7?.2|15|19|
|2050|365637|51.3|2050-74|0.51|13.5|8.5|2.08|77.2|82.6|7|9|
|2075|41S287|62.5|2075-99|0.19|12.5|10.6|2.07|79.4|85.4|4|6|
|2100|43SO9|68.5|2100-24|0.09|12.0|11.1|2.06|81.2|87.6|2|4|
|2125<br>2150|465253<br>450298|7).5<br>73.1|2125-49|0.05|11.8|11.4|2.06|81.9|88.5|2|3|

## 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75<br>70-74<br>65-69<br>60-64<br>55-59<br>50-54<br><§ :2 ~~~~45-49 g§ E<br>40-44<br>35-39<br>30*34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>2468 Ol 8 6 4 2 0 2 4 6 8 10<br>8 ~~~~~~~~~EAST C41 ES) %ETZ 3t76X) IArC4<br>WEST86<br><!-- End of picture text --> lo 80 6 2468 Ol

## 3. Distribution of population (percent): 1990 and 2025

83
# _<u>Impact on CHW behavior, satisfaction and motivation</u>_

The results presented above do not point to an impact of the CHW cooperative performance payments on the targeted outcome indicators. We performed further analysis to estimate whether having the cooperative payments conditional on the collective performance affects individual behavior by the community health workers. Theoretically, it could be that CHWs exerted greater efforts in response to the performance payments even if these efforts did not lead to increased utilization of targeted health services by the communities they served. We also test whether the different reward schemes impacted measures of motivation and satisfaction by the CHWs.

Panel A of Table 7 reports the impact of the intervention on four self-reported measures of CHW behavior. According to the CHWs, they spent an average of 10 hours providing health services in a typical week and visited an average of 33 households in the month preceding the survey.<sup>8</sup> There is no statistically significant difference between the treatment arms with respect to these measures. The CHWs were also asked whether they seek advice from other CHWs. Overall, about 75 percent of CHWs declared frequently seeking advice from other members of their village community health team and 60 percent reported seeking advice from CHWs outside the village. There is a small and statistically insignificant difference in these reports when CHWs belong to cooperatives paid for performance.

In Panel B of Table 7, results are presented on the impact of the interventions on reports of recently pregnant women regarding interactions with CHWs during their most recent pregnancies. A high percentage of women, 78, reported receiving information on antenatal care from a CHW. On the other hand, only 26 percent of women reported receiving information on postnatal care. When comparing the two arms that received the performance payments to the two that have not, there is no statistically significant difference in these rates. There is also no statistically significant impact on whether CHWs referred or accompanied the women for antenatal consultations. Surprisingly, the rate of women who were referred or accompanied to deliveries is lower when the performance payments are provided, although this is one of the paid indicators. Among the sectors that did not receive the performance payments, 54 percent of women reported being referred or accompanied by CHWs to delivery. The rate is lower by 4 percentage points among the sectors that did and significant at the 10 percent level. Given

> 8 CHWs were asked differently in the baseline and follow-up surveys regarding the time they spent on health activities. In the baseline, they were asked how many hours per day and how many days per week they work as a CHW. In the follow-up survey, they were directly asked how many hours they spend providing health services in a typical week.

13
26 As for the investment variables, the projecdon records of PFPs for Kenya is quite good. The projected investmen **t** o GDP ratio for Kenya for the years 1989 and 1990 proved to be more or less the same as the acual rate obtained during that period. Some divergence occufred in 1991. For Uganda, the projected values of investment to GDP ratio proved to be somewhat optimistic for the years 1988 and 1989, but were somewhat closer to actual values for 1990 and 1991.

The projected estimates of domestic savings to GDP ratio in Kenya were close to the actual values in 1990 and 1991. In the case of Uganda, the prcjectei savings ratio proved to be highly optimistic and the acual savings ratio seems to be declining steadily since 1986, whereas the projections indicated a rise of the ratio until 1989, after which - a decline was expected. As for other response variables growth rates of exports and GDP - strong export growth allowed Kenya to exceed its targets in 1989 and 1990. Uganda's actual values of exports show much less than projected growth for the years 1988 to 1991. The actual values of GDP growth rates for both Kenya and Uganda have also been close to projected values for most years. The results on GDP growth rate, for Uganda indicate that target rates for 1988 and 1989 were in fact exceeded.

# Counai. of D =parent 3

- Four countries of CD3-Burundi, Comoros, M gascar, Rwanda, and Zaire have had PFPs. Of these five countries, Rwanda has had a PFP only recently (April