rafmacalaba/datause-encoder-data huggingface dataset:
_Appendix Table 12: Labor force participation of nonbeneficiary households with 6-60-month-old children_

||Participates<br>in labor<br>force|_Males_<br>Participates in<br>labor force for<br>more than 40<br>hours per<br>week|<br>Works<br>for Pay|Works for<br>pay more<br>than 40<br>hours per<br>week|Participates in<br>labor force|_Females_<br>Participates in<br>labor force for<br>more than 40<br>hours per<br>week|Works<br>for Pay|Works for<br>pay more<br>than 40<br>hours per<br>week|
|---|---|---|---|---|---|---|---|---|
|_Panel A: Program Impact_|||||||||
|Program village|0.015<br>(0.026)|0.027<br>(0.051)|0.055<br>(0.060)|-0.023<br>(0.077)|0.003<br>(0.022)|0.055<br>(0.066)|-0.019<br>(0.075)|0.116<br>(0.092)|
|_Panel B: Interaction with satur_|_ation_||||||||
|Program village|-0.003|0.049|-0.047|0.072|0.042|0.407**|0.386*|0.367|
||(0.073)|(0.159)|(0.204)|(0.242)|(0.073)|(0.197)|(0.221)|(0.344)|
|Saturation|-0.166|-0.344|-0.763***|-0.177|-0.095|0.147|-0.311|0.105|
||(0.150)|(0.210)|(0.281)|(0.288)|(0.110)|(0.275)|(0.316)|(0.538)|
|Program village* saturation|0.036|-0.027|0.181|-0.162|-0.064|-0.629*|-0.691*|-0.451|
||(0.142)|(0.268)|(0.324)|(0.418)|(0.137)|(0.365)|(0.401)|(0.599)|
|_Panel B: Interaction with above_|_median saturat_|_ion_|||||||
|Program village|-0.006|-0.012|0.016|-0.044|-0.007|0.112|0.062|0.197*|
||(0.026)|(0.064)|(0.074)|(0.100)|(0.024)|(0.077)|(0.083)|(0.105)|
|Above median saturation|-0.037|-0.128|-0.217***|-0.039|-0.040|0.060|-0.076|-0.005|
||(0.050)|(0.082)|(0.081)|(0.110)|(0.035)|(0.107)|(0.101)|(0.145)|
|Program village* Above|0.050|0.095|0.088|0.050|0.023|-0.169|-0.193|-0.233|
|mediansaturation|(0.061)|(0.099)|(0.098)|(0.146)|(0.054)|(0.144)|(0.150)|(0.196)|
|_Panel B: Interaction with top qu_|_artile of saturat_|_ion_|||||||
|Program village|-0.009|0.007|0.065|0.007|-0.007|0.079|-0.004|0.172*|
||(0.023)|(0.057)|(0.061)|(0.085)|(0.022)|(0.072)|(0.079)|(0.094)|
|Fourth Quartile of saturation|-0.086|-0.118|-0.097|0.079|-0.067|0.063|-0.048|0.023|
||(0.070)|(0.096)|(0.090)|(0.114)|(0.052)|(0.119)|(0.123)|(0.158)|
|Program village* fourth|0.115|0.091|-0.079|-0.194|0.042|-0.130|-0.113|-0.361|
|quartile of saturation|(0.079)|(0.129)|(0.138)|(0.222)|(0.079)|(0.181)|(0.177)|(0.292)|
|Control observations|292|226|136|92|293|112|71|53|
|Treated observations|300|238|146|100|310|117|82|62|
|Control Mean|0.928|0.624|0.691|0.685|0.935|0.607|0.775|0.660|

note: *** p<0.01, ** p<0.05, * p<0.1.

All specifications include municipality fixed effects. Standard errors are clustered by village; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey.

61
**Figure 2: Restrictiveness of services trade policies by region**

<!-- Start of picture text --> South Asia (SAR) 40.7<br>Middle East (MENA) 39.5<br>East Asia (EAP) 36.5<br>World 23.9<br>Latin America (LAC) 21.2<br>Africa (AFR) 20.2<br>Eastern Europe (ECA) 18.6<br>OECD 16.9<br>0 10 20 30 40<br>Restrictiveness of services trade policy<br><!-- End of picture text --> Note: World represents the simple average STRI of 56 countries

**Figure 3: Restrictiveness of services trade policies by region and sector**

<!-- Start of picture text --> SAR MENA EAP World LAC AFR ECA OECD<br> Financial Transportation<br> Telecom Professional<br> Retailing<br>80<br>60<br>40<br>20<br>Restrictiveness of services trade policy<br>0<br><!-- End of picture text --> Note: Financial services include banking and life and non-life insurance and reinsurance, telecommunications include fixed and mobile telecom, transportation includes air passenger, maritime shipping, and auxiliary services, and professional services include accounting, auditing, and legal services.

6
# **V. Infrastructure Investment Gap**

The infrastructure investment gap is estimated as the difference between the estimated investment requirements and actual spending on infrastructure. The lack of data on infrastructure spending has been long recognized as a serious impediment to deriving reliable gaps estimates (Fay et al., 2011). There have been many efforts to calculate figures on infrastructure spending but they have had significant drawbacks (See Annex VI with the description of those attempts). The table below describes total spending in infrastructure by applying GDP deflator for inflation to the latest and most reliable regional data.

**Table 5: Total Spending in Infrastructure by region in 2011 USD billion.**

|**Region**|**% of**<br>**GDP**|**Spending**<br>**in**<br>**2011**<br>**billion**|**USD**|**Source of figure as reported in Fay and Toman (2010)**|
|---|---|---|---|---|
|EAP|6.8||356|<sup>Yepes (2008a) with Original source from Gill and</sup><br>Kharas(2007)|
|SAR|4.2||67|<br>Estimates of India’s public infrastructure spending from<br>the country’s public expenditure plans and extrapolated<br>to the rest of the region.|
|LAC|1.2||40.3|<sup>Estimates from original data collection efforts. Data was</sup><br>collected for 1990–2005 aspresented in Yepes(2008b).|
|SSA|3.2||27.4|<br>Foster and Briceño-Garmendia, 2010.|
|ECA|1.2||34.6|<sup>No data. Investments share is assumed to be similar to</sup><br>that of Latin America.|
|MENA|4.2||46|No data. Assumed to be the weighted sample average.<br>Based on Agénor et al. (2005), Public capital<br>expenditure in infrastructure (as % of GDP) is estimated<br>as 2percent.|
|**TOTAL**|**4.3**||**571.3**||

Source: Authors’ estimation Note: The developing countries that are included in our dataset are listed by regions: EAP ( Indonesia, Lao PDR, Fiji, Malaysia, Mongolia, China, Papua New Guinea, Cambodia, Vietnam, Philippines, and Thailand); SAR ( Pakistan, Sri Lanka, India, Nepal, and Bangladesh); LAC (Argentina, Uruguay, Costa Rica, Guatemala, Guyana, Trinidad and Tobago, Jamaica, El Salvador, Venezuela, RB, Colombia, Peru, Bolivia, Nicaragua, Honduras, Brazil, Suriname, Ecuador, Haiti, Panama, Paraguay, Mexico, Dominican Republic, Belize, and Chile); SSA (Chad, Niger, Angola, Mauritania, Kenya, Swaziland, Gambia, The, Tanzania, Comoros, Burundi, Ghana, Botswana, Cape Verde, Equatorial Guinea, Liberia, Nigeria, Central African Republic, South Africa, Mali, Zambia, Mozambique, Cameroon, Lesotho, Ethiopia, Congo, Rep., Zimbabwe, Malawi, Uganda, Senegal, Cote d'Ivoire, Sudan, Benin, Madagascar, Namibia, Djibouti, Guinea-Bissau, Guinea, Rwanda, Gabon, Togo, Mauritius, Sierra Leone and Burkina Faso); ECA (Lithuania, Belarus, Russian Federation, Armenia, Estonia, Turkey, Albania, Croatia, Kyrgyz Republic, Tajikistan, Bulgaria, Poland, Azerbaijan, Georgia, Kazakhstan, Slovak Republic, Czech Republic, Romania, Uzbekistan, Latvia, Turkmenistan, Hungary, and Ukraine); and MENA (Tunisia, Iran, Islamic Rep., Oman, Saudi Arabia, Egypt, Arab Rep., Jordan, Morocco, Syria, Yemen, and Algeria) 12
debt overhang induced by unhedged FX borrowing. By contrast, Ukraine which experienced the sharpest depreciation in the crisis (minus 55%) is unlikely to be affected, due to low levels of unhedged FX loans (8%). All in all, figure 4.1.c suggests that increases in the debt burden of FX borrowers due to exchange rate depreciations can hardly be viewed as a key driver of debt overhang in the region.

# **Finding 4.1: Debt Overhang in the Enterprise Sector**

- <mark>Based on a comparison of the share of highly leveraged firms and GDP contraction during the crisis, debt overhang is likely to pose a general threat to firm activity in only a limited</mark> number of the more advanced countries: Latvia, Lithuania, Estonia, and Slovenia.

- <mark>Leverage levels among export-orientated firms and the widespread contraction of export income in the region suggest that debt overhang in the tradable sector may be an issue for a broader set of countries: Latvia, Lithuania Estonia, Slovenia, Macedonia, Bosnia, Turkey, the</mark> Slovak Republic, Serbia, the Czech Republic and Croatia.

- <mark>Despite sharp depreciations in many countries during the crisis, unhedged foreign currency borrowing by firms does not seem to be a source of debt overhang. The countries with a high incidence of unhedged FX loans experienced only minor depreciations during the crisis.</mark>

# **_4.2 Debt Overhang in the Household Sector_**

In this section we assess potential for debt overhang in the household sector, i.e. the extent to which built up consumer and mortgage debt may curb future household consumption and investment. We use recent survey data to examine the incidence and distribution of household debt across the region as well as how household debt is correlated to the impact of the crisis at the household level.

# <u>Household Debt towards Banks</u>

Our analysis is based on the 2010 wave of the EBRD-World Bank Life in Transition Survey (LITS).<sup>10</sup> Within this survey at least 1,000 interviews were conducted with randomly selected households in each country. In the 21 countries covered by this report a total of more than 23,525 households were interviewed. The LITS questionnaire gathers information on household composition, housing, and expenses, as well as the current and past economic activity of the respondent. Importantly for our purpose, the 2010 wave of the survey elicits information on the incidence and type of bank debt in the household. Households which own their dwelling are asked whether they have a mortgage. If they do have a mortgage they are asked about the type of mortgage they have, in particular whether it is denominated in local or foreign currency. Further households are asked whether any member of the household has a debit or credit card. The LITS dataset includes sampling weights to account for the differences in the ratio of sample size to population size across countries, as well as for sampling biases within countries. The data

> 10 We thank the EBRD for advanced access to the LITS 2010 survey data.

29
# **1. Introduction**

Agriculture remains the main source of livelihood in Sub-Saharan Africa, employing two-thirds of the workforce. Using the international poverty line ($1.25 a day of 2008 PPP), this region is also the one where poverty is the highest with a poverty headcount of 47.5 percent in 2008 compared to an average of 25.2 percent for the whole developing world excluding China (Chen & Ravallion, 2012). With most of the poor living in rural areas, it is easy to link the high poverty rates with low income in agriculture, the main income source in the countryside.

One of the key weaknesses of Sub-Sahara African agriculture is low productivity. In the early 1960s, cereals yields in all the developing world were around 1 ton per hectare, ranging from 0.8 ton per hectare for SubSaharan Africa to 1.3 ton per hectare for East Asia & Pacific and Latin America & Caribbean. Forty years later, Sub-Saharan Africa is just at 1 ton per hectare compared to 2.5 tons per hectare for three regions of the world (Europe & Central Asia, Middle East & North Africa and South Asia), 3 tons for Latin America & Caribbean and 4.5 tons per hectare for East Asia & Pacific (World Bank 2008). Because of this low performance, smallholder agricultural productivity growth has been identified as a key driver of poverty reduction and increased food security in the entire Africa region (FAO, 2009).

While productivity in agriculture is generally low and thereby hindering poverty reduction, it is even lower among female peasants. A cross-country analysis shows that estimated yield gaps based on female-male comparisons across households range widely, but are around 20–30 percent in many countries (World Bank, 2012). Even when comparisons are made within the same households, and thus accounting for possible differences in market conditions and institutional constraints, this finding still holds. The study also shows that there are potential productivity gains through greater gender equality in access to economic opportunities and productive inputs. In this respect, increased productivity among female farmers could help achieve at least two goals. First, it might result in an increase in real income and in turn reduce poverty. Second, higher female productivity may improve development outcomes for the next generation through better nutrition. A recent World Bank study has identified a positive link between the degree of female control over household resources and child nutrition outcomes (World Bank, 2012).

In order to build policy interventions to improve agricultural productivity among women, it is important to measure the potential gap between men and women and understand the determinants which explain the gap. Many studies have been devoted to the issue of the gender gap in Africa: Alderman et al. (1995) for Burkina Faso, Udry (1996) for Burkina Faso, Holden and Bezabih (2008) on Ethiopia, and Peterman et al (2011) on 2
conditions in the village economies. Figure 2 shows how ex-combatants from our sample reported spending their grant. It summarizes the self-reported information provided by 22 demobilized ex-combatants and 9 GdP/MC/AA who were interviewed in 2010. Respondents could give a maximum of three figures.<sup>8</sup> In line with the literature, we observe that a large proportion of ex-combatants used a share of the money to buy consumption goods: 48% reported purchasing food and drinks, and 26% reported buying clothes. This consumption boom is expected to be short-lived, especially if the discount rate of ex-combatants is low and if saving opportunities are scarce. In the short run, the demand increase should induce a jump in prices. This change in prices should increase the sales of shopkeepers and farmers, who are therefore expected to benefit from the return of demobilized ex-rebels. Other households may suffer from inflation. However, the global effect on nonbeneficiaries is expected to be positive, as most households are involved in some kind of trading activity.

Figure 2: DDR Grants Spending by Ex-combatants <!-- Start of picture text --> Land Drink/eat Clothing Small shop Working Wedding Housing Health Cattle Repay Financial Education<br>equipment loan saving<br>0.5<br>0.4<br>0.3<br>% of respondents 0.2<br>0.1<br>0.0<br><!-- End of picture text --> _Source_ : Authors’ analysis based on data described in the text.

Ex-combatants also invested part of their allowances in productive assets. More than 50% of ex-combatants reported investing part of the grant in a plot of land, 23% of them invested in a small shop, 19% in working equipment, and one ex-combatant reported buying a cow. The economic situation of ex-combatants who invested in productive assets is expected to improve in the short run and in the long run following their enhanced production capacity. The resulting long-run increase in consumption is expected to be marginal compared to the size of the

> 8In most households, they did not remember exactly how much they had spent for each category of expenses so figure 2 only shows the proportion of respondents that declared having spent part of their allowance in each category.

8
Figure 1: Sample Distribution of the Welfare Results of Armenian-EU DCFTA—30,000 simulations.

<!-- Start of picture text --> 3.5<br> 3<br> 2.5<br> 2<br> 1.5<br> 1<br> 0.5<br> 0<br>0.7 1.1 1.5 1.9 2.3<br>Percentage of consumption<br><!-- End of picture text --> 65
17 absorb in order to compete In the protected market)."3 The higher the effective rate of protection afforded by tariffs and other trade restraints, the more foreign exporters must reduce returns to domestic labor and capital. Thus, the effective protection concept provides insights about the effect that escalating tariffs (and NTBs) have on processing and trade. Previous empirical studies have shown that some low nominal tariffs, that appear to be unimportant may conceal high rates of effective protection."4 As previously noted, the FSU exported mainly energy, industrial inputs and low value added products for further processing. Since both enterprises and FTOs were indifferen **t** o revenues generated by exports, there was no incentive to respona to higher tariffs by moving to less protected product groups. OECD effective protection is quite high in several sectors where the NISs (e.g., Armenia, the Baltic states, and Belarus) should be able to increase processing. For example, Table 6 indicates effective tariff rates range to more than eight times the nominal rate for such value added products as vegetable oils. In general, the effective tariff rates average over two times the corresponding nominal rate-indicating that OECD trade barriers have a far more restrictive effect on the location of processing activity than a superficial analysis of nominal rates suggests.

**VI.** Nontariff Barriers Facing FSU Exporters

While tariffs have often an adverse effect on FSU exports, in some sectors nontariff measures are even more formidable barriers. As an indication of their importance, Table 6 shows the share of individual FSU export products (individual products are identified here at the level of the importing countries' national tariff line) that encounter one or more nontariff barriers. Restrictions iciudel M.ite tabulaonrs me. qUaritltti ceilings on imports (including all Muitifibre Arrangement "<sup>3</sup> Due to the importance attached to the issue, the World Bai* identified processing chains for 49 individual commodities that are exported by developing countries in primary and processed forms. See the appendix to Yeats (1991) for details. All stages of these chains are defined in terms of the SlTC system in order to facilitate analyses of interDational trade in these items.

24 For a non-tecbnical discussion of the effective rate concept, see ambel, (1971).
20 national income accounts. Thus studies which have found no interest elasticity of saving may have used data too weak to reveal it. On the other hand, specification searches over savings rate regression equations which stop once the interest rate c.oefficient becomes positive and significant could bias the literature in favor of finding a positive saving elasticity (Leamer, 1978).

The introduction of IRAs in the U.S. has provided one natural experiment to test the effect of savings incentives. The success of IRAs has been undeniable; by June 30, 1988, more than 300 billion dollars had been invested in IRAs. In itself, this is not a test of savings incentives, since the sudden increase in IRAs could represent a shift out of other, more heavily taxed, savings instrument,. That is. IRAs could have been reshuffled saving from existing (taxable) accounts, rather than new saving from foregone consumption. Feenberg and Skinner (1989), and Venti and Wise (1987) suggest that the data are consistent with the "new" savina view of IRAs; that is, IRAs did represent new saving.<sup>7</sup> Other researchers have attempted to evaluate the impact of taxation on savings in computable general equilibrium models using parameter values estimated in econometric studies (Summers, 1981; Fullerton, Shoven, and Whalley; 1983; Auerbach, Kotlikoff and Skinner, 1983; Ballard, Fullerton, Shoven, and Whalley, 1985) In 7 While the results in Feenberg and Skinner are consistent with the new saving view, they are also consistent with a more subtle reshuffling view of IRAs. At this stage it is difficult to conclude that IRA saving is entirely new saving.
harmonized by the World Bank for other regions. These revisions of the welfare aggregate constitute the move of the SEDLAC project from version 02 to version 03.<sup>25</sup> As the revision of this project is still in process, data reported in PovcalNet are based on SEDLAC03 for most recent years and on SEDLAC-02 for previous years, with the cut-off period being generally the year 2000 (except for Argentina, for which data using SEDLAC-03 begins in 2003, and Chile, which uses SEDLAC-02 throughout the whole period). Given this problem of comparability, we recalculate headcount ratios for some countries so that the SEDLAC version used in each country is the same for the whole period under analysis.<sup>26</sup> Because of this, poverty rates for some countries and years may differ from the figures reported in PovcalNet.

Table B.1 summarizes the data used for each country.

**Table B.1**

Data Summary for Poverty Calculations

|**Country**|**Period**|**SEDLAC**<br>**version**|
|---|---|---|
|Argentina|1991-2017|02|
|Bolivia|1997-2017|02|
|Brazil|1992-2017|03|
|Chile|1990-2015|02|
|Colombia|2001-2017|03|
|Costa Rica|1990-2017|03|
|Dominican Republic|2000-2016|03|
|Ecuador|2003-2017|03|
|El Salvador|1995-2017|02|
|Honduras|1991-2017|02|
|Mexico|1992-2014|03|
|Panama|1995-2017|03|
|Paraguay|1995-2017|02|
|Peru|1997-2017|03|
|Uruguay|1995-2017|03|

> 25 For details on the main methodological changes in SEDLAC-03 that directly affect welfare measurement, see Atamanov _et al_ . (2018).

> 26 Notice that even though the SEDLAC project may remain the same, the frequent changes in survey methodologies over time introduce comparability issues with previous surveys that are difficult to deal with.  In addition, there are comparability issues across countries since national agencies often use different survey standards.  However, we can still draw general conclusions from the available data.

30
misspecified for the observations which were selectively deleted on the basis of the wealth variable and that the source of the 26 misspecification is the presence of liquidity constraints .

# 6 **Conclusions**

The purpose of this paper is to analyze fiscal policy behavior in primary commodities exporters. The paper suggests that the typical surge in fiscal expenditures which accompanies a commodity boom and the tendency to maintain that level of expenditures in spite of a subsequent decline in export commodity prices may be explained by the existence of a pressure to spend, a limited disinvestment and a limited indebtedness effects. These three effects characterize the environment in which fiscal policy must be set.

The empirical work is done with data from the Franc Zone countries in Africa. The paper uses a fiscal policy optimizing

> 26A similar computation for Senegal, for which all the observations are _a priori_ constrained, given for the years 1980 to 1985 (with 1. a A0 and 1, * A 0) **_5-6_** £ lo= 10(A.-X1) - 1.25 **J-1**

which Is inconclusive. For Burkina Faso, for which the 1977 and the 1984 observations are a priori unconstrained and all the observations in between a priori constrained, we obtain (with X,r, - **u1** - 0 and XA - Is - 0) ,12 A, a0.299 **8-2** which is again consistent with the existence of liquidlty constralnts slnce the **estLmate** of the average of the Lagrange multipliers over the 7 years period is unambiguously positive.

**3.**
<!-- Start of picture text --> Figure 7<br>China<br>CO2 Intensity of Iron and Steel Production<br>(Metric Tons of CO2 per Metric Ton of Steel)<br>7<br>6<br>5<br>4<br>3<br>2<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>Source: Rock and Jiang (2011)<br>Figure 8<br>China's Iron and Steel Industry<br>CO2 Emissions Under Business as Usual<br>and After Technological Upgrading<br>(Million Metric Tons)<br>4,000<br>3,500<br>3,000<br>2,500<br>2,000 CO2 Emissions After Technological Upgrading<br>CO2 Emissions Business as Usual<br>1,500<br>1,000<br>500<br>0<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>CO2 Intensity<br><!-- End of picture text --> Source: Rock and Jiang (2011) While lack of data availability made it impossible to account for all of this saving of CO2, we were able to identify CO2 savings from reducing the iron to steel ratio<sup>95</sup> (984 MMT), modernizing and increasing the size of blast furnaces (74 MMT) and basic oxygen furnaces (59 MMT), adopting advanced sintering machines (54 MMT) and advanced coke ovens (68 MMT), and by adoption of coke dry quenching (70 MMT) (Rock and Jiang 2013). Taken together, these interventions saved 1.3 billion metric tons of CO2 or nearly 60% of total CO2 savings in 2010 reported in figure 8.

Similar developments are visible in China’s pulp and paper industry. After the large scale technological transformation in China’s pulp and paper industry described in Rock and

> 95 By shifting to electric arc furnaces using scrap steel.

24