rafmacalaba/datause-encoder-data huggingface dataset:
more time while cooking (with little children at their side), than in comparison with older children and adult males, who spend more time outdoors.

**<u>Table 5.1: Health Risks of Indoor Air Pollution</u>**

||**Odds Ra**|**tios(OR)**|
|---|---|---|
||“Low”|“High”|
|Acute RespiratoryIllness(ARI)|1.9|2.7|
|Chronic obstructive pulmonary disease (COPD)|2.3|4.8|
|_Source:_Desai et al (2004).|||

The NFHS-3 reports that 90 percent of rural and 32 percent of urban households use solid fuels for cooking in India. The national weighted average is about 71 percent.

To estimate the health effects of indoor air pollution from the odds ratios in Table 5.1, baseline data for ARI and COPD need to be established. These data are presented in Table 5.2, along with unit figures for disability adjusted life years (DALYs) lost to illness and mortality. The hypothetical level against which damages are calculates is a situation in which there is no exposure to indoor air pollution and the odds ratio is one.

**Table** **<u>5.2: Baseline Data for Estimating Health Impacts</u>**

||**Baseline**||**Source:**|
|---|---|---|---|
||Urban|Rural||
|Female COPD mortality rate (% of total female<br>deaths)|9.|5%|WHO estimate for India,<br>Shibuya et al (2001)|
|Female COPD incidence rate(per 100 thousand)||79||
|ARI 2-week Prevalence in Children under 5years|22%|22%|NFHS-3,2006|
|Estimated annual cases of ARI per child under 5<br>years|1.0|1.0|Estimated from NFHS-3, 2006|
|Estimated annual cases of ARI per adult female (><br>30 years)|0.4|0.5|Estimated from a combination of<br>NFHS-3, 2006 and Krupnick et al,<br>2006|
|ARI mortality in children under 5 years (% of child<br>mortality)|2|2%|Office of Registrar General (2004)|
|DALYs per 100 thousand cases of ARI in children<br>under 5|165|165||
|DALYs per 100 thousand cases of ARI in female<br>adults(>30)|700|700|Estimated from WHO tables|
|DALYs per case of ARI mortality in children under<br>5|34|34||
|DALYs per case of COPD morbidity in adult<br>females|2.25|2.25||
|DALYs per case of COPD mortality in adult<br>females|6|6||

The results of the estimation of health losses associated with indoor air pollution are presented in Table 5.3. Estimated cases of ARI child mortality and ARI morbidity (children and female adults) from indoor air pollution represent about 38-53 percent of total ARI in India. Similarly, the estimated cases of COPD mortality and morbidity represent about 46-72 percent of total estimated female COPD from all causes.

20
While the evidence from the Lesotho experiment is suggestive that lottery incentives might perform better than standard conditional cash transfers, this experiment did not directly compare cash and lottery incentives. In an experiment evaluating incentives for employees to fill their health risk assessment, Haisley and others (2012) directly compare different types of incentives in the same setting and show that lottery-incentives (combined with group incentives) were more effective than cash incentives to incentivize employees to complete their assessments.

# **_Commitment devices_**

The principles behind commitment devices are very similar to those at play with conditional cash transfers and contingency management, with the difference that individuals pledge their own money upfront, as a deposit, and only get it back if they satisfy the condition. From a sustainability point of view, this presents the advantage that the program is, to a large extent, self-funded by the participants who pledge their own money.

A commitment device is an arrangement entered into by an agent which restricts his or her future choice set by making certain choices more expensive while also satisfying two conditions: (a) the agent would, on the margin, pay something in the present to make those choices more expensive, even if he or she received no other benefit for the payment; and (b) the arrangement does not have a strategic purpose with respect to others (Bryan, Karlan and Nelson, 2010). Commitment devices are a clear acknowledgement of bounded rationality in the behavioral economics framework. Tirole and Bénabou (2004) develop a theory of internal commitments based on self-reputation over one’s willpower, which transforms lapses into precedents that undermine future self-restraint.

25
# **Appendix S3: Model Parameters**

This paper focuses on the presentation of a general economic model rather than precise point estimates. Future research could usefully identify more refined subnational parameter values for Uganda. The parameters used for this model are presented below, and are set to a general baseline of the year 2002. Factor shares in production functions are assumed at standard values (e.g., see Eberhardt 2006). The cost of capital, depreciation rates, the FDI multiplier, share of services in consumption, and public sector service wage premiums are assumed. Capital stocks and productivity terms were estimated using the Uganda social accounting matrix for 2002 (Alarcon et al. 2006), and then scaled. Public sector import contents were set at one for simplicity.

The labor force is set to match the approximate reality of 19.6 million workers as of 2002, and the population of origin from each region matches the approximate real distribution of populations. The distribution of roads and agricultural capital stocks across the four regions provides another approximate match of the reality in Uganda. The Northern region is poorest, and therefore starts the first period with slightly higher transport losses, lower cash crop capital stock, and lower cash crop productivity.

## **Sets:**

Rural regions [east, west, central, north] _i_ Rural regions [east, west, central, north] _p_ Public service sectors [health, education, general infrastructure, public administration] _reg_ Geographic divisions [east, west, central, north, kampala] _t_ Time periods [T=10]

### **Scalars:**

|**Label**|**Description**|**Value**|
|---|---|---|
|_alphacc_|<br>labor share in cash crop production|0.4|
|_alphaf_|labor share in food production|0.5|
|_alpham_|labor share in manufacturing production|0.7|
|_alphas_|labor share in services|0.7|
|_betacc_|capital share in cash crop production|0.3|
|_betaf_|capital share in food production|0.2|
|_cmin_|minimum nonfood consumption|0.1|
|_dep_|depreciation|0.03|
|_depcc_|cash crop depreciation|0.03|
|_fdimult_|multiplier for FDI|0.1|
|_gammas_|share of services in the disposable income basket|0.5|
|_km0_|initial capital in manufacturing|10|
|_ksu0_|initial capital in urban services|10|
|_kcscale_|scaling parameter on CC capital stock|0.2|
|_khurdle_|<br>hurdle parameter on household CC constraint|0.03|
|_kmscale_|scaling parameter on M capital stock|0.2|
|_kroadscale_|scaling parameter on road stock|0.25|
|_ksscale_|scaling parameter on SR capital stock|0.2|
|_maintlabor_|road maintenance labor-capital ratio|0.005|
|_migtheta_|rural-urban income migration parameter|0.015|
|_nlossrate_|nutrient loss rate in soil|0.03|
|_phi_|food requirement per person|0.55|
|_pwageprem_|<br>public sector service wage premium|1.2|
|_rho_|logistic function parameter|1.5|
|_rworld_|world cost of capital|0.1|
|_rsavcc_|share of rural saving to cash crop|0.6|
|_savurb_|urban saving rate out of disposable income|0.3|
|_savrur_|<br>rural saving rate out of disposable income|0.25|
|_taxr_|tax rate on disposable income|0.15|
|_usavm_|share of urban saving to manufacturing|0.5|

54
that ignoring ...rm heterogeneity may lead to downward aggregation bias in the IO tablebased approach. Samples that are used to construct IO tables often consist mainly of large ...rms.<sup>3</sup> Given that large ...rms tend to have a lower DVAR due to their high import-to-sales ratios, over-sampling large ...rms in the construction of IO tables can lead to lower estimates of the aggregate DVAR.<sup>4</sup> To illustrate this point, we conduct a decomposition exercise. In particular, we show how our DVAR estimate can be lowered to a level that is not statistically di¤erent from that of KWW12, just by using a sample that includes only those large ...rms that satisfy the sample selection criteria behind the construction of the Chinese IO tables. This suggests that aggregation bias driven by ...rm heterogeneity alone is su¢ cient to explain the wedge between our estimates.

What has caused the rise in China’s aggregate DVAR? Our ...rm-level regressions reveal that it is mainly driven by individual processing exporters substituting domestic for imported materials, both in terms of volume and varieties. Other factors, such as rising production costs due to higher wages, changing composition of Chinese exports towards the high-DVAR industries, or churning of ...rms with di¤erent DVARs, cannot explain the upward trend during the sample period.

We also ...nd that the substitution of domestic for imported materials was induced by the country’s trade and FDI liberalization since the early 2000s. To guide our empirical analysis, we build a model featuring a translog cost function, which permits an estimation of the time-varying elasticity of substitution between domestic and foreign input varieties to study how various government policies may a¤ect a country’s DVAR. We ...nd that for China, increasing FDI and declining input tari¤s have led to a greater variety of domestic materials becoming available at lower prices during the sample period. For the entire processing

> 3See the United Nations Handbook of Input-Output Table Compilation and Analysis (1999).

> 4Recent research by Amiti, Itskhoki and Konings (2014) and Blaum, Lelarge and Peters (2014) shows that larger ...rms tend to have a higher import-to-sales ratio.

3
<!-- Start of picture text --> Households affected by the August, 2021 earthquake<br>Head of household: sex y<br>(Ref: male) 1<br>Female —+e—<br>croc 82 ee I<br>18 to 29 —F*—<br>30 to 49 —~—<br>Number of(Ref.1-2 children children na children) (0-17) —eo—|<br>3+ children —e—<br>Educational attainment !<br>(Ref.: Pamary or jess) i)<br>Secondary —_o— 1<br>Tertiary or more —o—_ '<br>(ret.Re Sues) iI<br>Southem I —e—<br>Northen. +—e—<br>Central —_——<br>(Ref.: Urban)Area !I<br>Rural —e—<br>-.2 0 2 4 6<br><!-- End of picture text -->

<!-- Start of picture text --> Households affected by the August, 2021 earthquake<br>Head of household: sex y<br>(Ref: male) i}<br>Female —1e—<br>croc 82 ee I<br>18 to 29 ——*_<br>30 to49—| ———#——__-<br>Number of(Ref.:1-2 children childrenno chiiden) (0-17) +—e—|<br>3+ children | ——e——<br>Educational attainment !<br>(Ref.: Pamary or jess) i)<br>Secondary ——_or——_<br>Tertiary or more —*1—<br>(ret.Re Sues) II<br>Southem I ——_7—_<br>Northen. | ———-e——_<br>Central ——<br>(Ref.: Urban)Area !1<br>Rural<br>-.2 0 2 4 6<br><!-- End of picture text -->

<!-- Start of picture text --> Households affected by the August, 2021 earthquake<br>Head of household: sex<br>(Ref: male) |<br>Female —_t+—e——_<br>I<br>Age group |<br>(Ref: 50+ years) I<br>18 to 29 ———7—_<br>30 to 49 ——*e—<br>Number of(Ref: children no chiidran, taU4) I1<br>1-2 children —-—<br>3+ children —_>——_<br>1<br>Educational attainment 1<br>(Ref: Primary or fess) 1<br>Secondary —_>_<br>Tertiary or more ————+o-—__<br>Southem ! —_o——_<br>Northem —_Le—<br>Central ce<br>Area !I<br>(Ref: Urban)Rural I!<br>2 10) 2 4 6<br><!-- End of picture text -->
FIP SFO GG GeO FG GCG Pray | |

<!-- Start of picture text --> 100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>SRSSSSSSSSSSsssss SPSSSSSSSSsssssss<br>SEZSSSSSSSSSEETTE FSSZSESSEES3<br>325 S8<br>MFCS Mnon-FCS MFCS Mnon-FCS<br><!-- End of picture text -->
**Figure 11: Rolling correlations of various consistency measures**

_(rolling correlation)_ <!-- Start of picture text --> Rolling Correlations<br>1.2<br>0.8<br>0.4<br>0.0<br>-0.4<br>-0.8 Consistency: Output Stabilization<br>Consistency: Budget Stabilization<br>-1.2<br>03 04 05 06 07 08 09 10 11 12 13 14 15 16<br><!-- End of picture text --> Source: Various Budget Reviews and authors’ calculations

# **_Policy Discussion_**

As many other EMDEs, South Africa seems to have been surprised by the length of the commodity super cycle, and its impact on extending domestic business cycles. Procyclical fiscal policy in South Africa was thus partly unintentional. However, _the cycle is the trend_ is not necessarily the only reason for this procyclicality. This section reviews potential other reasons:

1. Limited access to international credit markets and limited depth of domestic financial markets

2. Political economy and institutional factors 3. Design of countercyclical expenditure response and expenditure composition 4. Underestimated structural dynamics _1. Access to international credit markets and limited depth of domestic financial markets_ Countries experiencing adverse economic conditions find it difficult to access credit, and if available it is generally very expensive. The absence of affordable credit limits the degree to which countries can employ countercyclical fiscal policy. Adverse economic conditions may force countries to increase taxes and/or reduce government spending, which further entrenches a procyclical fiscal stance (Calderón, Chuhan-Pole, & López-Monti, 2017, Frankel, et al., 2013, and Gavin et al., 1996).

Limited access to international and domestic credit markets is highly implausible as a reason for South Africa’s observed procyclical fiscal policy stance. South Africa, after transition to democracy in 1994, was able to access domestic and international credit, despite its relatively high levels of inherited public debt. Its debt issuance increased gradually from 1994, and quite rapidly after 2006. The country’s borrowing costs declined consistently from 1994, and by 2001 credit ratings agencies Standard and Poor’s, Moody’s, and Fitch all rated the country’s foreign and local currency debt at investment grade. By 2012 the country was included in the Citibank World Government Bond Index, allowing it improved access to foreign lenders and lower rates. While the country has experienced downward revisions to its local and foreign currency credit ratings since 2014—and lost its investment grade credit rating on 18
Figure 4: Property Rights and GDP <!-- Start of picture text --> Inheritance and Property Rights Inheritance and Property Rights<br>Parental Authority Parental Authority<br>Equality of Access Index Regression Coefficient: -2.01<br>Median=0.3375 Standard Error: 0.401<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Index of Equality of Access (0=Equal Rights, 1=Unequal Rights) Index of Equality of Access (0=Equal Rights, 1=Unequal Rights)<br>.25 12<br>.2<br>10<br>.15<br>8<br>.1<br>Fraction of Countries<br>6<br>.05 Log of 2009 Per-Capita GDP<br>0 4<br><!-- End of picture text --> Index of Equality of Access: Average of values of the following subinidices from the 2009 OECD Social Institutions and Gender Inequality (SIGI) Index: Parental Authority (Rights of custody accorded to male and female prents); Inheritance Rights (to widows, daughters etc.); Women's Access to land; Women's Access to Loans;Women's Access to Nonland Property
INTRODUCTION XXXVii Figure 19 Percentage Contribution to World Population Growthtrrom World Bank Regions and Nonborrowers, 1985.2100 <!-- Start of picture text --> 50<br>40 -<br>30 AF<br>10<br>1985 2000 2015 2030 2045 2060 2075 2090<br><!-- End of picture text --> growth (Tables 17 and 18), age structure (Table 19), and vital rates.

China is the most populous country or economy, and remains so in these projections almost until the end, not quite 200 years in the future. Rankings by size among other large countries will be changing well before that point. Nigeria and Pakistan, in particular, will be moving up, from ninth-tenth today to seventh-eight by the turn of the century and to fifth-sixth by 2025. Japan will fall out of the group of ten largest countries, and by 2025 will have fewer people than Mexico, Iran, or Viet Nam.

India presently contributes more to world population growth than China or any other country, and will continue to do so throughout the projection period. After India and China, the leading contributors to world population growth are Pakistan and Nigeria. At the other extreme, the Federal Republic of C;ermany is contracting by 100,000 people a year. Even recent mass migrations from the German Democratic Republic--which occurred too recently to be incorporated in these estimates--are unlikely to produce substantial population growth. Hungary is also losing population, but only a fifth as much as the Federal Republic of Germany.

Despite contributing so much to growth, none of the large countries is growing as fast as some states in the Persian Gulf and in Sub-Saharan Africa. In the Gulf, Qatar and Oman are growing by more than 4 percent a year, and Saudi Arabia, Djibouti, and Kuwait by more than 3.7 percent. In Africa, C6te d'Ivoire and Kenya are growing by more than 4 percent, and Zambia by about 3.7 percent.

Rapid growth partly accounts for Kenya having a dependency ratio as high as 120, well above any other national ratio. Other countries with. ratios above 104 (other than small islands) are Botswana, the Yemen Arab Republic, and Zambia. By contrast, exceptionally low dependency ratios, indicating relatively large working-age populations, characterize. such vigorous
7 The 1970s displayed a more complex evolution. Income inequality rose between 1970 ad 1976, reached a peak on that year, and then fell - both for the distribution of total individual incomes in the economically active population (PEA) and for the complete distribution of household per capita incomes - from 1977 to 1981. This decline was almost monotonic, except for an upward blip in 1980. See Bonelli and Sedlacek (1989), Hoffrnan (1989) and Ramos (1993). The recession year of 1981 was a local minimum in the inequality series, whether measured by the Gini or the Theil-T index. From 1981, it rose during the recession years of 1982 and 1983. Some authors report small declines in some indices in 1984, but the increase was resumed in 1985. By that year, the Gini coefficient for the distribution of household incomes per capita had risen from 0.574 in 1981 to 0.589 (see Ferreira and Litchfield, 1996). Hoffman (1989) and Bonelli and Sedlacek (1989) report similar increases.

1986, the year of the Cruzado Plan, saw a break in the series, caused both by a sudden (if shortlived) decline in inflation, and a large increase in reported household incomes. Stability and economic growth led to a decline in measured inequality, according to all authors. Thereafter, with the failure of the Cruzado stabilization attempt and the return to stagflation, inequality resumed its upward trend, with the Gini finishing the decade at 0.606. Table A2.1 in Appendix 2 summarizes the findings of this literature, both for per capita household incomes and for the distribution of total individual incomes in the economically active population.

The general trends identified in the existing literature are mirrored in the statistics for the years with which we concern ourselves in this paper, namely 1976, 1981, 1985 and 1996. The distributions for each of these years come from the Pesquisa Nacional por Amostra de Domicilios (PNAD), run by the Brazilian Geographical and Statistical Institute (IBGE). Except where otherwise explicitly specified, we deal with distributions where the welfare concept is total household income per capita (in constant 1996 Reais, spatially deflated to adjust for regional differences in average cost-of-living), and the unit of analysis is the individual. Details of the PNAD sampling coverage and methodology, sample sizes, the definition of key income variables, spatial and temporal deflation issues, and adjustments with respect to the National Accounts baseline are discussed in Appendix 1.
<!-- Start of picture text --> Total Gini by region<br>~<br>©<br>*<br>st<br>+<br>£<br>o<br>TM<br>*<br>+<br>co<br>ce)<br>1960 1970 1980 1990 2000 2010<br>year<br>—*— NE —+— MW<br>——-s —+—w<br><!-- End of picture text -->
WPS3747 the difference between government revenue and spending; (ii) the external trade balance (in goods and non-factor services), which implicitly equates the supply and demand for foreign exchange; and (iii) savings-investment balance. We assume that savings are investment driven and adjust through flexible saving rates for firms, that foreign savings are in fixed supply with the demand for foreign exchange equated through an endogenous exchange rate, and that government income is also fixed with lost tariff revenue replaced through a variable tax rate on households. This closure is expected to be relatively “neutral” in terms of the distributive impact of the shocks implemented. An alternative tax replacement scheme will be also examined.

The original CGE model was constructed to reflect Indonesia’s economic pre-crisis situation and is based on a 1995 Social Accounting Matrix (SAM). In order to capture the postcrisis structural features of the Indonesian economy, the 1995 Indonesia SAM was updated using cross-entropy methods (see Robinson et al., 2001). The updating procedure imposes the following pieces of information from 2002: value-added by activity; the structure of imports and exports by commodity; and the data contained in a macro SAM. Table 2 summarizes the aggregate values of the resulting 2002 SAM.<sup>2</sup>

# **2.2  Specification of the Indonesia microsimulation model**

The microsimulation model is based on household and individual level data from the SKTIR survey for the year 1996 and simulates income generation mechanisms for 9,800 households corresponding to 42,400 individuals.<sup>3</sup> Four occupational choices are distinguished at the individual level: (i) inactivity, (ii) wage work, (iii) self employment, and (iv) multi-activity

> 2 The fully disaggregated SAM used has 39 activity accounts and 22 commodity accounts. Full detail is presented in Chapter Annex A.

> 3 The Special Survey on Saving and Household Investment (SKTIR) was integrated as a part of a module (submodule) of the SUSENAS survey. It was only administered to a sub-sample of the SUSENAS sample.

7