rafmacalaba/datause-encoder-data huggingface dataset:
while Figure 8 shows that average age at migration increases over time, except for females. Both trends may have substantial implications in terms of future remittances as a consequence of the lower earning potential of these groups. A possible explanation of these trends is the increasing flow of migrants for family reunification purposes following recent regularization schemes in the two main destination countries.

**Figure 7** _Female/male ratio for first-time_ **Figure 8** _Average age at migration by gender permanent migrants_

<!-- Start of picture text --> Year moved abroad<br>indiv. first year of migration all males females<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>.6 27<br>26<br>.4<br>avg age at migration 25<br>female/male ratio<br>.2<br>24<br>0<br><!-- End of picture text --> Furthermore, larger numbers of less educated individuals are migrating in recent years. Interestingly, female migrants are on average more educated than men, particularly in the 1990s; the gap narrows somewhat after 2000 (Figure 9). The breakdown of the education trends by main destination (Figure 10) and place of origin of the migrants (Figure 11) reveals some interesting differences.

<!-- Start of picture text --> Figure 9 Trends in educational Figure 10 Educational level of Figure 11 Educational level of<br>level of permanent migrants, permanent emigrants by permanent emigrants by<br>by gender, main destination, main destination region of origin<br>and origin (1991-2004)<br>Year moved abroad Year moved abroad Year moved abroad<br>all males females Greece Italy beyond Coastal Central Mountain Tirana<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>12 12<br>12<br>11 11<br>11<br>avg education years 10 avg education years 10 avg education years 10<br>9 9 9<br><!-- End of picture text --> 8
or any refusal to answer are coded as missing values. To capture the overall appraisal of each respondent, it is also constructed a composite indicator of public services delivery by aggregating each respondent’s appraisal of all individual public services through polychoric correlation structure of the categorical responses (Lee et al., 1995; Holgado–Tello et al., 2008). The composite indicator is thus a reflection of how each respondent evaluates the range of public services provided by the state.

Table 1 shows the distribution of the respondents for each of these policy matters. It can be observed that less than one-third of the interviewees agreed with the statement that the government is handling very well or fairly well the living standards of the poor and creating jobs, although more than half appear to approve their respective government’s performance in improving essential health services (57.92%) and addressing educational needs (60.15%) across the whole sample (four rounds of the Afrobarometer survey).

Self-reported optimism is derived from the survey question: “Looking ahead, do you expect the following to be better or worse: Your living conditions in twelve months’ time?”. The responses range from _‘much worse’_ to _‘much better’._ We create a binary variable that equals 1 if the reply of the respondent is _‘better’_ or _‘much better’_ and 0 otherwise. As this question is not included in Round 6 of the Afrobarometer survey, the corresponding estimations thus incorporate data from Round 3 to Round 5. Table 1 shows that approximately 79% of the survey respondents expect an improvement in their living conditions in the twelve months following the survey data collection.

**Table 1: Distribution of respondents in the category fairly well and well**

||_Percentage_|_Observations_|
|---|---|---|
|_Creating jobs_|26.90|130,277|
|_Improving the living standards of the poor_|29.86|109,019|
|_Providing water and sanitation services_|44.44|131,280|
|_Improving basic health services_|57.92|132,161|
|_Addressing educational needs_|60.15|131,661|
|_Optimism (Expectation of living conditions in 12 months)_|78.59|91,200|

Figure 1 displays, for each mining location (both active and inactive mine), the percentage of respondents with a positive assessment of government authorities’ performance in improving the living standards of the poor and job creation in Round 6 of the Afrobarometer survey. Such percentage is very low across the whole continent. The dissatisfaction is particularly striking in local communities near active mines, except for Botswana, where the average appraisal on government pro-poor policies tends to be positive on average (Figure 2).

8
# **References**

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- Abay, K. A., G. T. Abate, C. B. Barrett, and T. Bernard (2019). Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity relationship in agriculture. _Journal of Development Economics 139_ , 171–84.

- Abay, K. A., L. E. Bevis, and C. B. Barrett (2021). Measurement error mechanisms matter: Agricultural intensification with farmer misperceptions and misreporting. _American Journal of Agricultural Economics 103_ (2), 498–522.

- Alfani, F., A. Arslan, N. McCarthy, R. Cavatassi, and N. Sitko (2021). Climate resilience in rural Zambia: Evaluating farmers’ response to El Ni ̃no-induced drought. _Environment and Development Economics 26_ , 582–604.

- Amare, M., N. D. Jensen, B. Shiferaw, and J. Denno Ciss ́e (2018). Rainfall shocks and agricultural productivity: Implication for rural household consumption. _Agricultural Systems 166_ , 79–89.

- Anderson, S., G. W. Harrison, M. I. Lau, and E. E. Rutstrom (2008). Elicitation risk and time preferences. _Econometrica 76_ (3), 583–618.

- Arag ́on, F. M., F. Oteiza, and J. Pablo Rud (2021). Climate change and agriculture: Subsistence farmers’ response to extreme heat. _American Economic Journal: Economic Policy 13_ (1), 1–35.

- Arslan, A., N. McCarthy, L. Lipper, S. Asfaw, A. Catteneo, and M. Kokwe (2015). Climate smart agriculture? Assessing the adaptation implications in Zambia. _Journal of Agricultural Economics 66_ (3), 753–80.

- Asfaw, S. and G. Maggio (2018). Gender, weather shocks and welfare: Evidence from Malawi. _Journal of Development Studies 54_ (2), 271–91.

- Asfaw, S., A. Scognamillo, G. Di Caprera, N. Sitko, and A. Ignaciuk (2019). Heterogeneous impact of livelihood diversification on household welfare: Cross-country evidence from Sub-Saharan Africa. _World Development 117_ , 278–95.

- Barrios, S., L. Bertinelli, and E. Strobl (2010). Trends in rainfall and economic growth in Africa: A neglected cause of the African growth tragedy. _Review of Economics and Statistics 92_ (2), 350–66.

- Bosilovich, M., R. Lucchesi, and M. Suarez (2016). MERRA-2: File specification. GMAO Office Note No. 9 (Version 1.1). `http://gmao.gsfc.nasa.gov/pubs/office_notes` .

- Brodeur, A., N. Cook, and A. Heyes (2020). Methods matter: p-hacking and publication bias in causal analysis in economics. _American Economic Review 110_ (11), 3634–60.

- Brodeur, A., M. L ́e, M. Sangnier, and Y. Zylberberg (2016). Star wars: The empirics strike back. _American Economic Journal: Applied Economics 8_ (1), 1–32.

50
<!-- Start of picture text --> Effect of domestic lockdown on imports<br>Import Value<br>22 60008000<br>&g 40002000 t t t t t<br>eo<br>-2000<br>A745 13-1109 7 5 3 4°43 °5 7 9 1 13 15 47<br>Months Relative to Lockdown<br>© Point Estimate —+ 95% Cl<br>Import Quantity<br>22 60008000<br>2& 4000<br>= 2000<br>-2000<br>A745 13-409 7 5 3 4 4 3°05 7 9 1 13 15 17<br>Months Relative to Lockdown<br>© Point Estimate 95% Cl<br><!-- End of picture text --> <!-- Start of picture text --> Effect of external lockdowns on imports<br>Import Value<br>g2 100<br>53 -1000<br>é<br>-200<br>2119-47 15-43-41 -9 7-5 3-4 1 3°55 7 9 11 13:15 17 19<br>Months Relative to Lockdown<br>© Point Estimate + 95% Cl<br>Import Quantity<br>22 100<br>2& 06 Fy<br>= -100<br>-200<br>2119-47 15-43-19 7 5 3-4 1 3°55 7 9 11 13:15 17 19<br>Months Relative to Lockdown<br>® Point Estimate + 95% Cl<br><!-- End of picture text -->
first two years of the CPM regime relative to the two years prior, retail electricity prices spiked, demand declined, and the emissions intensity of generation declined leading to 8% lower emissions from the electricity sector. One important limitation of both studies is that neither could trace impacts of the CPM beyond the first two years of implementation.

Relatedly, recent evidence suggests that the CPM passed-through substantially and caused increases to wholesale electricity prices in Australia’s NEM. Maryniak et al. (2019) estimate pass through to wholesale electricity futures prices between 67% and 150% while accounting for changing risk premia. Similarly, Nazifi et al. (2021) estimate pass-through of the CPM to spot electricity prices in excess of 100% across NEM regions even after attempting to account for varying plant dispatch costs. Cross (2024) uses a quasi-experimental technique that compares trends in US electricity prices to NEM prices and finds a pass-through rate of 88%, with the incidence of price increases largely borne by consumers. Thus the existing literature provides reason to believe that Australia’s policy mix raised electricity prices, disadvantaged polluting generators, and drove emissions reductions. Ex-ante, it is unclear how large and persistent these effects were, a question which motivates our analysis.

# **3. Data and Empirical Strategy**

Australia’s political environment around 2010 was favorable to a major expansion of national climate policy. This circumstance led to an aggressive expansion of renewable energy support and a short-lived carbon pricing mechanism. We seek to jointly estimate the impact of these policies on the trajectory of Australia’s emissions in the medium- to long-term (through 2018).

To estimate the impacts of Australia’s climate policies causally and trace their dynamic effects over time, we adopt a quantitative case study approach (Abadie and Gardazebal, 2003; Abadie et al., 2015). Synthetic control and related estimation techniques for quantitative case studies have the advantage of minimal data requirements while providing a disciplined and transparent approach for the analysis of policies with aggregate data. Moreover, recent developments in synthetic control and related techniques have been shown to be more adaptable than the canonical form (Abadie, 2021) and capable of recovering causal effects where the assumptions of canonical synthetic control may not be met.

The analysis uses annual national economy-level panel data over the period 1980-2018, 8
**Table 5 Global and Regional Poverty**

|Region|Number of Poor<br>(in thousands)|Share of Global<br>Poverty|Simulated<br>Number of Poor<br>(inthousands)|(Simulated –<br>Observed)|
|---|---|---|---|---|
|East Asia|261,677|27.1|258,937|-2,740|
|Eastern Europe|3,607|0.4|3,576|-31|
|Latin America|40,075|4.1|37,677|-2,397|
|Middle East|1,614|0.2|1,544|-71|
|South Asia|466,165|48.3|481,350|15,185|
|Sub-Saharan Africa|192,555|19.9|192,461|-94|
|Global|965,693|100.0|975,545|9,851|

Notes: (1) Number of poor expressed in thousands. (2) The simulations are based on the GIDD’s results.

# **4.2.2 Zooming in: Poverty and Inequality Effects Between and Within Countries**

Global agricultural liberalization has distributional and poverty effects that vary not only across regions but also between and within countries. This section summarizes the poverty effects for each of the countries included in our sample and the distributional changes taking place within them. Table 5 shows that the roughly 10 million individuals that would be pushed into poverty as a consequence of agricultural reform is the combination of a 15 million increase in poverty in South Asia and a 5 million decrease in poverty in all other regions. Figure 10 shows the countries that contribute the most to this reduction and increase in global poverty, respectively. Among the _new_ poor, 85.2 percent—almost 13 million—are Indian nationals, while 3.5 percent are located in Bangladesh, and 2.1 percent are Mexican. Although the increase in poverty is mainly an Indian phenomenon, all 5 South Asian countries contribute significantly to the global increase in poverty. On the other hand, the gross reduction in global poverty is distributed more evenly among the _winning_ countries with the great majority of them being located in Latin America and East Asia and the Pacific (EAP). In fact, no country in EAP and only Chile and Mexico in LAC experience an increase in the number of extreme poor as a result of agricultural trade reform.

The contributions to the global entry and exit of poverty depicted in Figure 10 are, to a certain extent, the outcomes of differences in population size. For instance, a very populous country such as India can have a substantial contribution to global poverty without necessarily implying a large increase in the country’s _head count ratio_ . Another way of ranking countries in terms of their poverty outcomes is to consider the postreform change in the head count ratio. Undertaking this exercise shows that, among countries where poverty falls, Peru's reduction of 3 percentage points in the head count ratio is, by far, the largest in the developing world. The incidence of poverty in Philippines and Ecuador decreases by 1.8 percentage points, just below the fall registered in Yemen and Paraguay (1.2). On the other hand, with an increase of 1.4 percentage points in the head count ratio, India is still the country with the largest increase in poverty. At the same time, as mentioned earlier, poverty in India falls by 0.3 percentage points if changes are evaluated at the 2 dollars a day poverty line. Interestingly, these 20
The paper is organized as follows. The next section illustrates the data and methods used for the analysis. The paper then provides a comparative distributional analysis of subsidies and simulates subsidy reforms comparing the outcomes across countries.

# Data and Analytical Approach

In the following sections, we describe the microdata used for the analysis and the baseline prices (subsidized products and unit subsidies) as of 2014, our baseline year. The updates were made using published IMF macroindicators for inflation and gross domestic product (GDP) per capita as well as population statistics (see Tables B.1 and B.2). The exercise that follows estimates the distribution of subsidies and provides new simulations of subsidy reforms using the primary data files for each country and transforming expenditure into U.S. dollars ($) at purchasing power parity (PPP). This allows comparing subsidies and the outcome of subsidy reforms using a common currency.

## Microdata

Table 1 shows the population statistics estimated directly from the surveys. These numbers are not identical to all country-specific population estimates, but they are very close. We can see that the sample of countries considered amounts to a total population of almost 130 million people, approximately 34 percent of the population in the MENA region in 2014. The total household expenditure for the countries considered is approximately $0.63 trillion-PPP per year, which amounts to $3,913-PPP per capita, per year, and $17,381-PPP per household, per year. This average hides differences across countries. The Islamic Republic of Iran is by far the country with the highest per capita expenditure ($7,477-PPP). Morocco, and Tunisia follow with approximately $4,000-PPP, and Libya and Djibouti come last with approximately $2,000-PPP. The sample of countries we have is representative of three groups of countries at different levels of economic development. We also have oil-producing countries and net exporters of oil, such as the Islamic Republic of Iran and Libya; non-oil-producing countries with some natural resources, such as Morocco; and non-oil-producing countries, such as Tunisia, which have little in the way of natural resources. Therefore, we have a certain diversity also in terms of natural endowments.

4
over time, in order to assess trends in global averages of governance we need to return to our underlying individual data sources.

In Table 8 we summarize trends in world averages in a number of our individual data sources. Most of the sources in this table are polls of experts, with data extending over the whole period 1996-2006. Other than expert polls, only one of them, GCS, is a survey with sufficiently standard format to enable comparisons over a reasonable period of time, in this case from 2002 to 2006. The first column reports the number of countries covered by the source in each of the periods shown, and the next three columns present the average across all countries of each of the sources in each of the indicated years. The underlying data have been rescaled to run from zero to one, and for each source and governance component, we report the score on the same question or average of questions that we use in the aggregate indicator. The next three columns report the standard deviation across countries for each source. The final column reports the change in the global average of each indicator over the longest period for which it is available, together with a t-statistic associated with a test of the null hypothesis that the world average score has not changed.

The picture that emerges from Table 8 is sobering, as there appears not to be very strong evidence of a significant trend of improvements in governance worldwide over the more than 10 years of data covered in the table. Over this period, the average change in the global averages of these indicators is only 0.01, on a scale from zero to one. While two-thirds of changes are positive, roughly only one-third of the changes in either direction are significantly different from zero at the 90 percent confidence level. Of these five register declines and eight improvements. It is not clear how much importance ought to be ascribed to these trends in world averages based on individual indicators. On the one hand, these statistics represent the only information we have on trends over time, and so they should be taken seriously. On the other hand, it is also clear that there is substantial disagreement among sources about even the direction of changes in global averages of governance. For now we cautiously conclude that we do not have as yet any convincing evidence of significant improvements in governance worldwide. We also note that this evidence is consistent with our choice of units for the aggregate governance indicators, which are scaled to have a mean of zero and standard deviation of one in each period.

22
# Table 3: Financial development and sectorial volatility

In Panel A, the dependent variable is the standard deviation of the rate of growth of real value added for each 4 digit ISIC industry in each country. In Panel B, the dependent variable is the minimum rate of growth of real value added observed in the period 1981-1997 for each 4 digit ISIC industry in each country. Regressions included a country and industry specific effect. Liquidity needs is the ratio of total inventories over annual sales for U.S. corporate firms in the same industry between 1980 and 1989. The section “Differential in volatility” reports how much volatile is an industry at the 75th percentile level of liquidity needs with respect to an industry at the 25th percentile level when it located in a country at the 75th percentile of financial development rather than in one at the 25th percentile, both in percentage points, and as a fraction of the interquartile range of volatility. In both panels, columns (1) to (4) reports OLS regressions, while columns (5) to (8) report the 2SLS results obtained when the measure of financial development is instrumented using a country’s legal origin. Column (9) reports the 2SLS results obtained when the measure of liquidity needs is instrumented using the ratio of labor costs over shipments and financial development is instrumented using a country’s legal origin

**PANEL A: OVERALL VOLATILITY:** Dep. var. is the std. dev. of real VA <u>growth</u>

|||O|LS||||2SLS|||
|---|---|---|---|---|---|---|---|---|---|
|Variables|(A1)|(A2)|(A3)|(A4)|(A5)|(A6)|(A7)|(A8)|(A9)|
|Interaction:|-0.57*|-0.44*|-0.58*|-0.56*|-1.21*|-1.14*|-1.21*|-1.20*|-1.79*|
|Liq. needs X pvt. credit|(0.13)|(0.12)|(0.13)|(0.13)|(0.23)|(0.22)|(0.23)|(0.23)|(0.55)|
|Initial share on||-0.79*||||-0.77*||||
|manuf. VA||(0.13)||||(0.13)||||
|Avg. growth rate|||-0.05<br>(0.05)||||-0.06<br>(0.05)|||
|Interaction:||||0.56||||0.14||
|Indus. vol. X Coun. vol.||||(1.27)||||(1.31)||
|Diferential in volatility||||||||||
|Percentage points|2.0|1.5|2.0|1.9|4.1|3.8|4.2|4.1|6.1|
|Fraction of IQR|28|21|28|28|59|55|59|59|88|

## **PANEL B: DEPTH OF THE CRISES:** Dependent variable is worst output drop

|||O|LS||||2SLS|||
|---|---|---|---|---|---|---|---|---|---|
|Variables|(B1)|(B2)|(B3)|(B4)|(B5)|(B6)|(B7)|(B8)|(B9)|
|Interaction (Liq. needs|0.69*|0.65*|0.90*|0.66*|1.77*|1.81*|1.91*|1.73*|2.82*|
|X private credit)|(0.25)|(0.25)|(0.25)|(0.25)|(0.47)|(0.46)|(0.46)|(0.47)|(1.09)|
|Initial share on||0.98*||||0.95*||||
|manufact. VA||(0.23)||||(0.23)||||
|Avg growth rate|||1.06*<br>(0.08)||||1.07*<br>(0.08)|||
|Industry volatility X||||-0.49||||-0.38||
|Country volatility||||(0.38)||||(0.38)||
|Diferential in volatility||||||||||
|Percentage points|2.4|2.2|3.1|2.3|6.1|6.2|6.5|5.9|9.7|
|Fraction of IQR|19|18|25|18|49|50|53|48|78|
|Obs.|2301|2078|2301|2301|2301|2078|2301|2301|2273|

Note:*=Significant at 5% confidence. Robust standard errors in parenthesis.

24
**<u>Table 5:  Rate of return on education</u>**

||M|ale|Fe|male|
|---|---|---|---|---|
||Youth|Adults|Youth|Adults|
|Age|0.28|0.06|-0.12|0.06|
||[6.44]***|[13.04]***|[0.82]|[3.95]***|
|Age Squared|-0.01|0|0|0|
||[5.28]***|[10.98]***|[1.01]|[3.07]***|
|Married|-0.01|0.12|-0.03|0.06|
||[0.57]|[7.11]***|[0.39]|[1.49]|
|Education (no formal education a|s comparison)||||
|Below Primary Education|0.02|0.1|-0.25|0.22|
||[0.60]|[3.68]***|[1.92]*|[1.54]|
|Primary Education|0.02|0.15|-0.05|0.13|
||[0.80]|[8.92]***|[0.63]|[1.50]|
|Middle|0.05|0.25|0.24|0.38|
||[2.31]**|[13.48]***|[2.13]**|[3.55]***|
|Matric|0.14|0.43|0.01|1.03|
||[5.81]***|[26.69]***|[0.10]|[16.58]***|
|Inter|0.31|0.6|0.19|1.12|
||[8.13]***|[28.95]***|[2.04]**|[15.86]***|
|Bachelors or Higher|0.64|1.05|0.63|1.67|
||[13.21]***|[63.09]***|[6.84]***|[32.75]***|
|Urban|0.02|0.17|0.21|0.16|
||[1.20]|[15.49]***|[3.81]***|[3.99]***|
|Provinces (Punjab as comparison|group)||||
|Sindh|-0.05|-0.03|0.29|0.23|
||[2.97]***|[2.21]**|[4.19]***|[4.75]***|
|NWFP|-0.13|0|0.29|0.07|
||[5.74]***|[0.05]|[3.33]***|[1.24]|
|Balochistan|0.2|0.19|0.75|0.51|
||[6.65]***|[11.01]***|[4.72]***|[4.67]***|
|Number of observations|6167|14456|962|1905|

Note:1Absolute value of t statistics in brackets 2. * significant at 10%; ** significant at 5%; *** significant at 1% 26