rafmacalaba/datause-encoder-data huggingface dataset:
For simplicity, we assumed that poverty lines remain constant in real terms for the whole horizon of the simulation period.

# 3.2 An Extension with Reweighting Techniques

Adding changes in the employment structure to the household survey, we combine now the micro-accounting method described above with reweighting techniques.<sup>7</sup> The employment variable accounts in our illustrative example for three dimensions of the population structure: residence (rural or urban), sector of employment (agriculture, informal, or formal), and educational attainment (skilled or unskilled). It is intuitively clear that large changes in the employment structure may have strong effects on income distribution. Whereas in the first approach we have to assume that (the sum of) withingroup inequality remains constant over time, in this approach it will change to the extent that population and income shares of each group change over time.

In our approach reweighting is done through special statistical procedures that alter the distribution of desired characteristics Xi (the linkage variables) of the population by adjusting the weight attached to each of the N households indexed with i.<sup>8</sup> In general, the problem is to find an n-vector w<sup>0</sup> of adjustment factors optimizing an objective function Z(w<sup>0</sup> , w)–a function evaluating the distance between the new adjustment factors wi<sup>0tobe</sup> computed, and the available factors wi–satisfying a certain number m of restrictions summarized in the form Xw<sup>0</sup> = r: This adjustment problem is a simultaneous one, where for even a quite large number of characteristics just one single weighting factor has to be computed for each household, which after summing up fulfills consistently all hierarchical restrictions simultaneously (see Merz (1994)). These statistical procedures preserve the joint distribution of the other characteristics. Put differently, instead of estimating econometric models to run simulations, reweighting (or “static ageing”) takes macro aggregates and then adjusts the

> 7The method that we describe can of course be extended to account for other changes in the population structure as well; see Allie and Murphy (2000).

> 8On reweighting techniques in general, see for instance Landt et al. (1994).

10
Column (3) shows the basic unconditional association, this time omitting all urban respondents. The familiar conclusion obtains also in a sample of rural respondents, which shows that our results are not driven by differences in evaluations of living conditions between urban and rural respondents. The model in column (4) again omits urban respondents and zooms in on the two days surrounding the devaluation, with little impact on the treatment effect. Column (5), finally, omits all rural respondents, focusing only on respondents from urban areas. This also leaves conclusions unchanged. For all model specifications, we find very similar results using ordered logit instead of OLS (as reported in Panel B).

To further document that the effect of the devaluation on subjective well-being cannot be reduced to the shift in the sampling of respondents from Gaborone to rural areas, we have performed a series of placebo tests, repeating some of our analyses using data from Round 4 (2008) of the _Afrobarometer_ . In these tests, we define a placebo treatment indicator as living outside Gaborone (or urban areas more generally). If our results were in fact driven by differences in evaluations of living conditions between respondents in the capital (or urban areas) and elsewhere, the coefficient on this placebo treatment indicator should be similar in size to the coefficient on the treatment indicator reported above. However, as we document in Appendix S5, across various model specifications the difference between Gaborone and the rest of Botswana is never more than 0.07 in Round 4 of the survey. And in some cases it is both statistically and substantively indistinguishable from zero.<sup>13</sup> With the Round 3 data we use here, in contrast, the coefficient of interest is consistently significant and negative, in the magnitude of −0.16. This provides additional confirmation that our results are not driven by nonrandom treatment assignment of survey 13 Identical results (both in terms of size and significance of coefficients) follow when we use the distinction between urban and rural rather than Gaborone as distinct from the rest of Botswana. We also checked whether there were significant differences between urban and rural areas by adding a rural-treatment interaction. As we found no indications of heterogeneity, we refrain from any further discussion.

23
6 composition of exports in explaining the impact of exchange rates on exports. Freund and Pierola (2012) find that depreciation stimulates manufacturing exports in developing countries, but not in developed countries. The results in Eichengreen and Gupta (2013) indicate that the effect of the real exchange rate is stronger for exports of services than for goods. We add to this literature by showing that the _nature_ of trade, that is the extent to which countries are integrated in global production chains, also matters to explain the relationship between exchange rates and trade.

While we do not attempt to review the growing literature on this topic, it is worth stressing that a number of papers have looked at the role of cross-border production linkages in the transmission of exchange rate changes focusing on specific countries and/or episodes. Our results confirm in a cross section the recent findings by Amiti _et al._ (2014) using disaggregated data for Belgian firms that the impact of a depreciation on export volumes is lower for exporters with higher import shares. Our results are also consistent with the recent work by Mattoo _et al_ . (2014) on the effect of China’s exchange rate depreciation on exports of other developing countries in third markets. They find that this effect is substantially attenuated for products that rely more on foreign inputs, and hence have a lower content of Chinese domestic value added. Finally, Eichengreen and Tong (2015) find that two episodes of revaluation of the renminbi increased profits of firms exporting final goods to China, but not necessarily of firms that export intermediate goods for processing.

The rest of the paper is organized as follows: Section II provides new evidence of the REER elasticity of total, goods and manufacturing exports, documenting its decline over time. Section III focuses on the role of integration in GVCs and contains the main finding of the paper and a number of robustness tests. Concluding remarks follow.

# **II.   THE REER ELASTICITY OF EXPORTS**

In this section, we investigate systematically the impact of real exchange rate changes on export volumes and document the decline of this elasticity over time.
# **Table 12: Determinants of Spatial Market Integration in Indonesia**

|**Dep Var:**|**Ri**|**ce**|**Soyb**|**eans**|**Ma**|**ize**|**Sugar**|**Cooking Oil**|
|---|---|---|---|---|---|---|---|---|
|**Trace Statistic**|**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(1)**|
|**Remoteness 1**|-0.075|-0.090|0.043|-0.052|-0.296|-0.397|-0.238|-0.249|
||(-3.03)***|(-3.01)***|(0.813)|(-0.27)|(-2.12)**|(-2.44)**|(-5.9)***|(-3.84)***|
|**Remoteness 2**|-0.029|-0.048|0.164|0.112|-0.594|-0.568|0.066|0.106|
||(-1.51)|(-2.3)**|(0.397)|(0.570)|(-3.53)***|(-3.2)***|(2.76)***|(2.17)**|
|**Contiguity**|-0.001|0.000|0.009|0.009|-0.004|-0.005|0.021|0.007|
||(-0.33)|(-0.14)|(0.551)|(0.620)|(-0.22)|(-0.28)|(3.11)***|(0.580)|
|**Remote*Infra**|-0.005|0.009|-0.045|0.025|0.209|0.295|0.129|0.146|
||(-0.2)|(-0.32)|(0.794)|(0.140)|(1.73)*|(2.08)**|(3.47)***|(2.33)**|
|**Remote*Infra**|-0.011|-0.009|-0.093|-0.052|0.481|0.459|0.001|-0.142|
||(-0.51)|(-0.39)|(0.552)|(-0.33)|(3.61)***|(3.21)***|(0.030)|(-3.21)***|
|**PCI 1**|-0.063|-0.082|-0.097|-0.056|0.150|0.123|0.078|0.046|
||(-3.36)***|(-2.63)***|(0.382)|(-0.49)|(1.380)|(1.070)|(1.87)*|(1.100)|
|**PCI 2**|-0.038|-0.134|0.020|0.003|0.144|0.138|-0.054|-0.091|
||(-2.02)**|(-3.21)***|(0.848)|(0.020)|(1.85)*|(1.77)*|(-2.32)**|(-3.38)***|
|**Output PC 1**|0.009|-0.059|0.109|-0.227|-0.013|-0.294|||
||(0.590)|(-0.69)|(0.080)|(-0.9)|(-0.21)|(-1.14)|||
|**Output PC 2**|-0.023|-0.306|0.010|-0.179|-0.001|0.037|||
||(-1.5)|(-2.99)***|(0.788)|(-1.24)|(-0.02)|(0.190)|||
|**Sq Output PC 1**||0.024||0.207||0.137|||
|||(-0.83)||(1.390)||(1.140)|||
|**Sq Output PC 2**||0.129<br>(2.83)***||0.089<br>(1.400)||-0.023<br>(-0.22)|||
|**Obs**|**300**|**300**|**91**|**91**|**91**|**91**|**300**|**300**|
|**Prob> F**|**0.000**|**0.000**|**0.508**|**0.242**|**0.003**|**0.002**|**0.000**|**0.000**|
|**R2**|**0.1563**|**0.1946**|**0.0840**|**0.1217**|**0.1935**|**0.2060**|**0.1271**|**0.0672**|

**Notes: (1) *** indicates significance at 1%, ** significance at 5%, * significance at 10%**

**(2) T-statistics in brackets.**

35
mining companies in Ghana was 3 percent until 2010, which was the average rate for gold production in Africa (Gajigo, Mutambatsere, and Mdiaya 2012), but increased to 5 percent in 2010 (Standing and Hilson 2013). Of this money, 80 percent goes to the general government budget, 10 percent goes to the administration of mining oversight, and 10 percent supports district administration (Garvin et al. 2009). Between 1993 and 1998, about US$17 million was distributed to local mining communities (Aryee 2001). While considered a model of best practice, there is still a worry that the beneficial effects of allocations to the districts are undermined by elite capture and corruption at the district level (Standing and Hilson 2013). For our analysis, the scheme implies that it may be necessary to conduct a district-level analysis in addition to the more local-level analyses.

12 currently active mines dominate the sector, and there are an additional five suspended mines that have been in production in recent decades. Table 1 presents a full list of the mines, the year they opened, and their status as of December 2012. Company name and country are for the main shareowner in the mine. Most of these 17 mines have foreign ownership, such as Australian, Canadian, or South African, sometimes in partnership with Ghanaian firms or the Ghanaian state. Most are open-pit mines, although a few consist of a combination of open-pit and underground operations.

**Table 1 Gold Mines in Ghana**

|**Name**|**Opening**<br>**year**|**Closing year**|**Company**|**Country**|
|---|---|---|---|---|
|Ahafo|2006|active|Newmont Mining Corp.|USA|
|Bibiani|1998|active|Noble Mineral Resources|Australia|
|Bogoso Prestea|1990|active|Golden Star Resources|USA|
|Chirano|2005|active|Kinross Gold|Canada|
|Damang|1997|active|Gold Fields Ghana Ltd.|South Africa|
|Edikan (Ayanfuri)|1994|active|Perseus Mining|Australia|
|Iduapriem|1992|active|AngloGold Ashanti|South Africa|
|Jeni (Bonte)|1998|2003|Akrokeri-Ashanti|Canada|
|Konongo|1990|active|LionGold Corp.|Singapore|
|Kwabeng|1990|1993|Akrokeri-Ashanti|Canada|
|Nzema|2011|active|Endeavour|Canada|
|Obotan|1997|2001|PMI Gold|Canada|
|Obuasi|1990|active|AngloGold Ashanti|South Africa|
|Prestea Sankofa|1990|2001|Anglogold Ashanti|South Africa|
|Tarkwa|1990|active|Gold Fields Ghana Ltd.|South Africa|
|Teberebie|1990|2005|Anglogold Ashanti|South Africa|
|Wassa|1999|active|Golden Star Resources|USA|

_Source:_ InterraRMG 2013. _Note:_ Active is production status as of December 2012, the last available data point.

6
Table 1. GDP and non-resource GDP growth of resource-rich, non-resource-rich, and selected Sub-Saharan African countries.

|**Country/**<br>**group**|**Res.**|**Pre**<br>**boom**<br>**(A)**|**GDPg**<br>**Boom**<br>**(B)**|**rowth r**<br>**Post**<br>**boom**<br>**(C)**|**ate**<br>**B-A**|**C-B**|**Non-**<br>**Pre**<br>**boom**<br>**(A)**|**resourc**<br>**Boom**<br>**(B)**|**e GDPg**<br>**Post**<br>**boom**<br>**(C)**|**rowth**<br>**B-A**|**rate**<br>**C-B**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|SSA RR||4.6|5.4|2.7|**0.8**|**-2.7**|4.5|5.8|2.5|**1.3**|**-3.3**|
|SSA non-<br>RR||2.9|4.2|3.8|**1.3**|**-0.4**|3.2|3.5|3.2|**0.3**|**-0.3**|
|Angola|Oil|5.1|8.2|-0.9|**3.1**|**-9.1**|3.8|8.8|0.1|**5**|**-8.7**|
|Botswana|Diam.|3.8|5.2|2.5|**1.4**|**-2.7**|2.7|8.2|4.4|**5.5**|**-3.8**|
|Cameroon|Oil|4.2|4.2|4.5|**0**|**0.3**|6.1|4.5|4.6|**-1.6**|**0.1**|
|Ghana|Gold,<br>Oil|4.4|7.1|5|**2.7**|**-2.1**|4.7|6.3|4.1|**1.6**|**-2.2**|
|Nigeria|Oil|6.1|6.7|0.9|**0.6**|**-5.8**|6.6|8.3|1.4|**1.7**|**-6.9**|
|Tanzania|Gold|5.5|6.5|6.3|**1**|**-0.2**|5.4|6.5|7.1|**1.1**|**0.6**|

Notes: Comparison of average growth rates pre and during the boom, SSA, RR countries only. Source: World Bank national accounts data, and OECD National Accounts data files (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG) and UN Statistics Division, National Accounts Main Aggregates Database (https://unstats.un.org/UNSD/snaama/formulas.asp)

# **Outcome 3: Rising poverty and inequality in resource-rich countries, putting Africa on a perilous trajectory**

The commodity price boom saw large inflows to African economies via resource companies exports and government revenues from production shares, royalties, and other resource sector taxes. However, despite the economic boom, the impact on poverty alleviation and inequality was disappointing.

The overall trend in resource-rich African countries was an increasing poverty headcount during the boom – indicated by the solid red line in Figure 14 – from about 280 million in 2003 to more than 310 million living in extreme poverty by 2019.<sup>11</sup> Meanwhile nonresource-rich Africa saw more modest increases – from 110 million to over 113 million over the same period. In part this was driven by a higher average (mean) poverty rate in resource-rich Sub-Saharan African countries that failed to converge with the poverty rates

> 11 Poverty projection estimates included in this paper are based on the poverty projection methodology designed and published by Lakner et al (2021) that incorporate the impact of COVID-19 on global poverty. These projections are based on the growth forecasts from the June 2021 version of Global Economic Prospects (GEP) and take the Sustainable Development Goals (SDGs) definition of extreme poverty which groups people living below the poverty line of $1.90 a day, measured in 2011 Purchasing Power Parity prices.

23
**_Figure 10: Average Annual Growth in WIIW Labor Productivity in Services Sub-Sectors_**

<!-- Start of picture text --> Electricity, Gas & Water Wholesale, Retail & Repair of Motor Vehicles<br>20% 15%<br>15%<br>10%<br>10%<br>5%<br>5%<br>0%<br>0%<br>-5%<br>-5%<br>-10%<br>1997-2000 2000-2004 1997-2000 2000-2004<br>Hotels & Restaurants Transport, Storage & Communications<br>20% 20%<br>15%<br>15%<br>10%<br>10%<br>5%<br>5%<br>0%<br>-5% 0%<br>-10%<br>-15%<br>1997-2000 2000-2004 1997-2000 2000-2004<br>Financial Intermediation Real Estate, Renting & Business Activities<br>45% 12%<br>40%<br>10%<br>35%<br>8%<br>30%<br>25% 6%<br>20%<br>4%<br>15%<br>2%<br>10%<br>5% 0%<br>0%<br>-2%<br>-5%<br>-4%<br>-10%<br>-15% -6%<br>1997-2000 2000-2004 1997-2000 2000-2004<br>Hungary Slovenia Macedonia Poland Slovak Rep. Bulgaria Croatia Czech Rep. Romania Ukraine<br>Romania Ukraine Croatia Poland Bulgaria Hungary Slovenia Macedonia Slovak Rep. Czech Rep.<br>Bulgaria Czech Rep. Croatia Hungary Slovenia Macedonia Slovak Rep. Romania Poland<br>Slovak Rep. Croatia Romania Poland Hungary Bulgaria Slovenia Czech Rep. Macedonia<br>Hungary Macedonia Ukraine Slovenia Romania Croatia Czech Rep. Poland Slovak Rep. Bulgaria<br>Macedonia Poland Slovenia Hungary Bulgaria Croatia Czech Rep. Slovak Rep. Romania Ukraine<br><!-- End of picture text --> Source: WIIW database 2005.

Notes: For each ECA country and sub-sector, geometric averages of labor productivity growth for 1997-2000 and 20002004 are shown. For EU-15 countries the growth rate of each sub-sector shown is the simple average across all EU-15 countries of their sub-sector’s geometric average annual labor productivity growth rates in 1997-2000 and 2000-2004. In this figure, financial intermediation is NACE 65 category.

23
Policy Research Working Paper 8321

# **Abstract**

Although efforts at soil and water conservation are routinely viewed as instrumental in reducing vulnerability to climate change, their impact has rarely been quantified. Combining data on the timing and intensity of soil and water conservation interventions in select Ethiopian watersheds from 2009 to 2016 with a pixel-level panel of vegetative cover and soil moisture data derived from satellite imagery makes it possible to assess the biophysical impacts of such measures using a difference-in-differences specification. The results point toward significant effects overall that vary by season, and that tree planting and other soil and water conservation activities are more effective on degraded than cultivated land. The results are consistent with before-after regressions for daily sediment load and stream flows in a subset of micro-watersheds. It thus appears that satellite imagery can improve the design and near-real-time monitoring of sustainable land management interventions for watersheds and landscape.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dali1@worldbank.org, kdeininger@worldbank.org, and dmonchuk@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **PPP Distress and Fiscal Contingent Liabilities in South Asia**<sup>**1**</sup>

_Matías Herrera Dappe Martin Melecky Burak Turkgulu World Bank World Bank World Bank_

## JEL: E62, H54, L33, O18

Keywords: Public-private partnerships, South Asia, early termination, infrastructure, fiscal risk, survival analysis, value at risk 1 This paper was peer reviewed by David Duarte and Alvaro Pedraza Morales, both from the World Bank. It was prepared as a background paper for the South Asia Vice Presidency’s Flagship _Hidden Debt: Solutions to Avert the Next Crisis in South Asia,_ and presented at the corresponding authors’ workshop, held on November 25, 2019, where it benefited from feedback provided by the workshop participants.
percent by a consortium including GTE, AT&T, and Telefonica 6e Espalia. While the privatization program is open to foreigners, their participation during 1990-92 of around 68 percent of total revenue is mainly reflected by the overwhelming weight of the CANTV deal. For 1993, another 45 companies are scheduled for privatization<sup>40</sup> .

# B **Europe and Central Asia**

# **Czechoslovakia**<sup>**41**</sup>

Until 1989, 98 percent of all Czechoslovakian assets, were in state hands, consisting of about 7,000 medium- and large-scale '!iterprises as well as 25,000-30,000 small-scale enterprises. In the process of transforming itself to a market-based economy, the country is engaged in one of the most radical privatization programs ever. During 1992, mass privatization of a vast number of small-scale enterprises through the voucher scheme yielded a revenue of about $1.6 billion. These funds were, however, used to finance the extensive voucher scheme, thus being budget neutral.

For the privatization of medium- and large-scale companies, a bidding process was used, starting in 1991, with a focus on foreign investors given the limited capital availability domestically. In 1991, the first 14 entities were sold, and another 41 in 1992 for a total revenue of about $1.9 billion. A wide variety of companies, predominantly in the manufacturing sector, were bought, usually by foreign investors. The largest transactions occurred in car manufacturing with Volkswagen buying the Skoda Auto Works plant in 1991 for $275 million plus a total investment commitment of $5.3 billion over the next ten years, and Renault's purchase of Karosa for $222 million in 1992. In general, the foreign participation in the privatization of medium- and large-scale firms was unusually strong with almost all of the revenue stemming from foreign sources.

The prospects of the privatization program in the Czech Republic, which in the last two years was substantially stronger than the one in the Slovak Republic, appear to be quite strong if foreign interest can be maintained. During 1993, the country therefore plans to tackle the problem of a low-standard infrastructure, starting with a planned sale of up to 25 percent of the national telecommunications company SPT Telecom to foreign investors. A second wave of mass privatization was started in May 1993 with the distribution of shares for 987 state en. rprises.

# **Hungary**

Of all Eastern European economies, Hungary has the longest and most extensive experience with privatizations. Prior to 1989, when the first privatizations were carried out, the state held 1,880 SOEs.<sup>42</sup> Since then, over 140 companies have been privatized

> 40 LatinFinance Supplement (March 1993), p. 107.

> 41 "Czechoslovakia" refers to the Federation between the Czech and the **Slovak** Rcpublic prior to Januarv 1, 1993.

> 42 International Financing L2w Review, Special Supplement, (September 1992), p.<sup>24</sup> .

37
28 Weingast, Barry R. and Mark J. Moran. Bureaucratic Discretion or Congressional Control? Regulatory Policymaking by the Federal Trade Commission. _Journal of Political Economy._ Weitzman, Martin L. (1992). On Diversity. _Quarterly Joural of Economics._

-________________ (1993). What to Preserve? An Application of Diversity Theory to Crane Conservation. _Quarterly Journal of Economics._
Moore, A. S., J. DeStephano, A. Terway and D. Balwanz. 2008. ‘The Expansion of Secondary Education and the Need for Teachers: How big is the gap?’, _Equip2 Working Paper,_ USAid.

OECD. 2012. _Brazil Country Note. Education at a Glance: OECD Indicators 2012_ . OECD, Paris.

Rocha, R. and R. Soares. 2010. ‘Evaluating the Impact of Municipality-Based Health Interventions: Evidence from Brazil’s Family Health Program’, _Health Economics_ , 19, 126–158.

Soares, S. 1998. ‘The Financing of Education in Brazil: With special reference to the North, Northeast and Central-West regions (English)’, LCSHD paper series, no. 17, World Bank, DC.

World Bank. 2005. _Expanding Opportunities and Building Competencies for Young People A New Agenda for Secondary Education._ Washington, D.C.

World Bank. 2012. _Teenage Pregnancy and Opportunities in Latin America and the Caribbean. On Teenage Fertility Decisions, Poverty and Economic Achievement._ Washington, D.C.

World Bank, 2020. Adolescent Fertility Rate (births per 1,000 women ages 15-19), [Data file]. Accessed at: https://data.worldbank.org/indicator/SP.ADO.TFRT?end=2000&start=1996 25
# **Graph (6): Stability (One Step and N Down Chow Test) of Unrestricted Closed Economy VECM:**

<!-- Start of picture text --> 2020 2020 2020 2020<br>1% 2000 1% 2000 1% 2000 1% 2000<br>1up DINFL 1up CHOWs Ndn DINFL Ndn CHOWs<br>1980 1980 1980 1980<br>1.0 0.5 1.0 0.5 1.0 0.5 1.0 0.5<br>2020 2020 2020 2020<br>1% 2000 1% 2000 1% 2000 1% 2000<br>1up Drgdp 1up DDISC Ndn Drgdp Ndn DDISC<br>1980 1980 1980 1980<br>1.0 0.5 1.0 0.5 1.0 0.5 1.0 0.5<br>2020 2020 2020 2020<br>1% 2000 1% 2000 1% 2000 1% 2000<br>1up Drm2 1up DInterestrt Ndn Drm2 Ndn DInterestrt<br>1980 1980 1980 1980<br>1.0 0.5 1.0 0.5 1.0 0.5 1.0 0.5<br><!-- End of picture text --> 59