rafmacalaba/datause-encoder-data huggingface dataset:
# Taxes, Transfers, and Gender: Fiscal Policy Incidence across Fiscal and Care Categories in Jordan

Laura Rodriguez, Matthew Wai-Poi, Jeffrey Woodham<sup>1</sup> JEL: D31, H22, I38; J12

> 1 Laura Rodriguez (Economist, <u>lrodrigueztak@worldbank.org</u> ). Matthew Wai-Poi (Lead Economist, <u>mwaipoi@worldbank.org</u> ), Jeffrey Woodham (consultant).

> The authors gratefully acknowledge the financial support from the William and Flora Hewlett Foundation and the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs. This paper is an extension of the Fiscal Incidence Analysis for Jordan (Rodriguez and Wai-Poi 2021) conducted for the Jordan Fiscal Public Expenditure Review (PER). The authors thank comments from Nelly Elmallakh, Lili Mottaghi.
the Indonesian currency depreciated and the price of rise soared. These two effects buffered the effect of the financial crisis on agricultural households (33% of the workforce), whose incomes increased. Nevertheless, earnings in the urban economy decreased by almost 5%. Finally the duration of unemployment decreased. Table 1 shows the changes in labor markets caused by the crisis. We use 1997 as a benchmark pre‐crisis year, and 2000 as the post crisis equilibrium. We use data for 2000, rather 1999 or 1998, is because by 2000 employment shares and wages had stabilized. We interpret this behavior as evidence that the post crisis equilibrium was reached in 2000<sup>4</sup> .

**Table 1: Indonesian Labor Market Adjustment**

||1997|2000|% change|
|---|---|---|---|
|**Share of workers (%)**||||
|Share of Workers in Agriculture|33.16|32.9|‐0.78|
|Share of Workers in Informal|26.25|26.84|2.25|
|Share of workers in Formal|35.89|33.96|‐5.38|
|Share unemployed|4.7|6.3|34.04|
|**Annual Earnings ( Rupiah)**||||
|Earnings in agriculture|3,686|4,916|33.37|
|Earnings in Informal|7,511|8,015|6.71|
|Earnings in Formal|10,628|9,541|‐10.23|
|Average earnings in the economy|7,008|7,009|0.01|
|Duration of unemployment (weeks)|41.93|34.30|‐18.26|

It took the labor market several years to recover from the crisis. Even after GDP growth had resumed again, employment remained stagnant and workers continued to be pushed into agriculture, although at a slower peace. Salaried workers benefited from increases in wages and new legislation on severance pay, and the civil workforce witnessed massive downsizing. The two developments are blamed in part for the slow recovery of formal wage employment<sup>5</sup> . Only between 2003 and 2007 did industrial employment growth resumed again.

> 4 See Figure 1.1 in the Indonesia Jobs Report (2009)

> 5 Op. Cit.
particular areas (Rosenzweig and Wolpin 1988). On the one hand, migration selectivity in turn may be associated with preferences and capabilities described above. The individual’s region of birth, on the other hand, is independent of any program related migration, as all of the women in the sample were born before 1974, the year in which the program was launched. Note that this is unlikely to affect the explanatory power of the program effect in any way, as region of birth is highly correlated with the region in which individuals lived when they were 12 years old. Analysis of IFLS migration modules from the 1993 and 2000 waves indicated that over 86% of all women were still living in their region of birth when they were 12 years old.

A key identification assumption is that there are no omitted time varying and regionspecific factors correlated with the program. SDIP intensity was higher in less developed regions as the program targeted regions with lower enrollment rates. Mean reversion that would have taken place in poorer regions, even in the absence of SDIP could potentially confound the identification of the education impact. Furthermore, regions which started off at a lower level of development may have modernized faster than others during the 1970s, as a consequence of other targeted development programs initiated by the government following the oil boom. Demand for modern medical care may have increased in less developed regions as a consequence of these other programs, independently of SDIP. It cannot be assumed that changes in health care use patterns across cohorts would have been the same between high and low program regions in the absence of SDIP. I control for this potential source of bias by adding variables to capture time-varying factors correlated with pre-program enrollment rates: enrollment rates in 27
TFP and labor productivity than privately-owned firms which operate in the same 4-digit sector and have a comparable firm size and age, but did not obtain a public procurement contract (columns 2 and 5). Importantly, firms awarded GPP contracts with a high risk of corruption―for which at least 3 of the 6 indicators for uncompetitive procurement practices apply―are not more productive in terms of TFP than their same-sector competitors (column 3). In other words, GPP is associated with higher TFP and labor productivity only if corruption risks in the awarded contracts are low to moderate. The results imply that the impact of green procurement in reducing corruption risk in public contracts supports the allocative efficiency in the economy by channeling resources to more productive private sector firms.

_Table 5. Productivity differentials between firms awarded with public procurement contracts and their same-sector competitors._

||||Depend<br>|ent variable:<br>|<br>||
|---|---|---|---|---|---|---|
||TFP<br>ols|TFP<br>ols|TFP<br>ols|VA/L<br>ols|VA/L<br>ols|VA/L<br>ols|
||(1)|(2)|(3)|(4)|(5)|(6)|
|PP dummy|.124**|||.448**|||
||(8.00)|||(12.7)|||
|GPP dummy||.167**|.186**||.433**|.464**|
|||(4.42)|(5.47)||(10.8)|(11.5)|
|CRI mean|||.098**|||.051**|
||||(6.70)|||(11.1)|
|GPP dummy *|||-.258**|||-.655**|
|CRI>0.5|||(-2.86)|||(-5.42)|
|4-digit sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Size, Age, SOE|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.740|0.739|0.740|0.370|0.369|0.370|
|Adjustd R2|0.740|0.739|0.739|0.369|0.368|0.368|
|Obs total|780,653|780,653|780,653|1,439,689|1,439,689|1,439,689|

_Source_ : Firm census panel data from 2010-2018 matched with public procurement transaction data. _Note_ : Second, we restrict the sample only to firms that obtained (any type of) public procurement contracts to estimate if GPP contracts are more often allocated to more productive firms than non-GPP contracts. Again, we test if that relation depends on the corruption risk involved in awarded GPP and other contracts.

Table 6 shows that firms awarded GPP contracts tend to be more productive than same sector, fully privately-owned firms with a comparable size and age that did not obtain any GPP contract (columns 1 and 5). The positive relation between GPP and firm productivity is stronger and significant at conventional levels for both productivity measures (TFP and labor productivity) when GPP carry a low risk of corruption (columns 2 and 4); that is, 24
4

# II. **HEALTH CARE FINANCE**

How much do Japan and the Tigers spend on health care? How do they finance their spending? Who bears the financing burden? Do the poor pay proportionately more or less for their health care than the better off? And how far are people protected from catastrophic health payments? These are the questions addressed in this section.

# **_How much is spent on health care?_**

Across health systems, it is well known that the share of GDP devoted to health tends to rise with GDP per capita. Japan—a good deal richer than the richest Tiger economy (Hong Kong (China))—actually spends somewhat more than is predicted from a cross-section regression of total health expenditure (as a share of GDP) on per capita income (Figure 2). The Tigers, by contrast, spend less than predicted by the regression, the biggest ‘under-spender’ by a long way being Singapore, whose share of GDP devoted to health is barely half the predicted figure. (The other three Tigers all spend 80-90% of their ‘expected’ shares.) _Figure 2: Health spending and GDP_<sup>_*_</sup> <!-- Start of picture text --> 16<br>14<br>Actual<br>Predicted<br>12<br>10<br>8 Japan<br>6 Taiwan, China<br>Hong Kong, China<br>Korea<br>4 Singapore<br>2<br>0<br>0 10,000 20,000 30,000 40,000 50,000<br>GDP per capita ($US)<br>Total Health Exp as % GDP<br><!-- End of picture text -->

> * Data refer to 2001 the latest year for which complete data are available. The predicted figures are from a regression of the natural logarithm of total health expenditure (expressed as a share of GDP) on the natural logarithm of GDP per capita. Data are from the 2005 World Health Report annexes in the cases of Japan, Korea and Singapore. The per capita income data for Hong Kong (China) and Taiwan (China) are from the World Bank’s World Development Indicators. Data on health spending are from <u>http://www.hwfb.gov.hk/statistics/download/dha/en/table1.pdf in the case of Hong Kong (China) and from http://www.doh.gov.tw/ufile/doc/200411_Statistic%20of%20Expenditure%20for%20Health,%201991-2003.pdf</u> in the case of Taiwan (China).
# HOUSEHOLD PRODUCTION, TIME ALLOCAnlON, AND WELFARE IN PERU

# 1. **Introduction**

This paper uses the Peruvian Living Standard Survey (PLSS) data to analyze (a)<sup>inequality in the distribution</sup> of Income, (b) labor market participation of men and women and the variations<sup>in hours of work, and (c) the relation-</sup> ship between variations In labor supply and Income Inequality. We<sup>use a decomposing method to analyze income</sup> Inequality. Furthermore, we utilize a structural neo-classical model to analyze<sup>household production, consumption,</sup> time allocation and welfare. The purpose Is to study the effect on<sup>production, consumption, and time allocation of</sup> changes in education and wage rates. For example, how many men and women<sup>would participate in wage work</sup> if education were Increased? And how would policy changes affect the mean<sup>level and the degree of inequality in</sup> the distribution of economic weffare?

Most of the available information on economic Inequality in developing countries refers<sup>to the distribution</sup> of Income among eamers. Although this Information constitutes an important<sup>element for understanding the labor</sup> market and the related distribution of income, it is less helpful In the analysis of inequality<sup>as a welfare issue. A more</sup> relevant indicator of welfare is per capita (or per adult equivalent) household<sup>income or consumption. This paper</sup> uses this Indicator in an analysis of economic inequality. Our methodological<sup>approach is based on a summary</sup> measure of inequality which is closely related to the Gini coefficient.<sup>The essential difference is that our proposed</sup> measure of inequality gives more weight than the Gini coefficient to transfers related<sup>to the veiy poor.</sup> Based on the estimates of an econometric model of production, consumption<sup>and time allocation, we have</sup> examined the Impact of changes in wage rates and education on economic inequality.<sup>In particular we demonstrate</sup> how female labor and education affect economic inequality among households.

The structural econometric model we develop and estimate<sup>Is convenient for simulating certain types of</sup> policy experiments. It is of particular interest to apply empirically<sup>founded behavioral models to assess the labor</sup> supply response and the corresponding Impact on economic welfare from<sup>various policy measures. SpecHfically,</sup> given similar economic cond-tions in Peru as of 1985, our study suggests what we<sup>may be able to achieve, and how,</sup> for example, different measures would affect economic inequelity.

The theoretical model Is based on the neoclassical model for consumption and time<sup>allocation. Provided</sup> the data are not corrupted by measurement error, this framework is useful since:

- No one can spend more than his or her income. (In other words the budget constraint<sup>plays a</sup> role.)

- There is also a time constraint of 24 hours a day.
**Table S3. Results from missing data analysis for anemia.** Effect of the IST intervention at 12 and 24 months follow-up on the primary health outcome of anemia for study children combined using a longitudinal, random effects regression modeling approach. Results presented (i) for all children with either 12 or 24 months follow-up measurements of the outcome (unadjusted), (ii) for those with baseline measurements of the outcome and accounting for age, sex and stratification effects as the primary pre-specified analysis, and (iii) for those additionally with baseline measures of parental education, SES and baseline educational level (measured by baseline spelling) as further predictors of missingness.

|**Prevalence of**<br>**i**<sup>**a**</sup>||**Control**|**I**|**ntervention**|**Odds ratio**<sup>**c**</sup>|**p-value**<sup>d</sup>|**ICC (9**|**5% CI)**|
|---|---|---|---|---|---|---|---|---|
|**anema**||**(50 schools)**|**(**|**51 schools)**|**(95% CI)**||||
|||**n (%)**<sup>**b**</sup>||**n (%)**<sup>**b**</sup>|||School|Child|
|**Unadjusted**|||||||||
|12-month|2146|837 (39.0%)|2297|920 (40.1%)|1.08 (0.74,1.43)||||
|24-month|2027|809 (39.9%)|2173|910 (41.9%)|1.12 (0.77,1.48)|0.758|0.07 (0.05,0.10)|0.50 (0.45,0.54)|
|**Adjusted**|||||||||
|12-month|2048|788 (38.5%)|2142|858 (40.1%)|1.09 (0.79,1.40)||||
|24-month|1935|765 (39.5%)|2027|842 (41.5%)|1.11 (0.80,1.42)|0.890|0.06 (0.04,0.08)|0.38 (0.33,0.43)|
|**Adjusted for pred**|**ictors of**|**missingness**|||||||
|12-month|1998|768 (38.4%)|2083|832 (39.9%)|1.05 (0.77,1.34)|0789|||
|24-month|1889|747 (39.5%)|1969|820 (41.7%)|1.09 (0.79,1.38)|.|0.05 (0.03,0.07)|0.34 (0.32,0.42)|

> **a** Age-sex specific anemia was defined using age and sex corrected WHO thresholds of hemoglobin concentration: <110g/l in children under 5 years; <115g/l in children 5 to 11 years; <120g/l in females 12 years and over and males 12 to 14.99 years old; and <130g/l in males ≥ 15 years. All female adolescents are assumed to not be pregnant

> **b** Number and percentage with outcome c Odds ratios (intervention/control) presented for anemia are obtained from random effects logistic regression analysis accounting for school-level clustering and repeated measures of children for the comparison of the intervention effect at 12 months to 24 months d p-value for the comparison of the intervention effect at 12 months to 24 months

**Unadjusted** : All children with outcome measures, not adjusted for any baseline or study design characteristics.

**Adjusted** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available.

36
**Figure 7: High school completion in Mexico, 18-24 age cohort, 2000**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Income quintile<br>Completion of high school (%)<br><!-- End of picture text --> Figures 5-7 give an impression on the accessibility of higher education per income category. Figure 5 shows higher education enrollment (as a percentage of the 18-24 age cohort) per income quintile. The inequalities are very large. University enrollment exceeds 25% in the richest quintile of the population. In contrast, university enrollment in the poorest quintile is only 3%. To study the causes of these inequalities, we look at university enrollment per income quintile among high school graduates in Figure 6. This figure gives an impression of transition probabilities from high school into tertiary education. Though the differences across income quintiles are less pronounced, there are still important inequalities in university entrance. Specifically, high school graduates from the richest income quintile have a 50% larger probability of university enrollment than high school graduates from the poorest quintile. Figure 7 shows high school completion per income quintile, and reveals that the inequalities with regard to university entrance are equally strongly caused by inequalities in high school completion. Effective government action to promote access to higher education thus not only calls for an appropriate student finance program (to reduce differences in transition probabilities from high school into tertiary education as illustrated in Figure 6), but also requires an improvement in high school completion rates among the poor and middle income groups (Figure 7).

# _5.2 Econometric model and results_

ENIGH 2000 provides information on educational attainment, age, income, and also on student support. This allows us to estimate the effect of financial support on accessibility to higher education. To that end, we can estimate the following PROBIT-model: 14
**Table 12a: Predicted change in the probability of selecting each animal from AOGCM climate scenarios for large farms**

|||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|---|
|Baselin|e probability|24.5%|33.4%|15.1%|20.8%|6.1%|
|2020|||||||
||CCC|-3.9%|3.2%|-0.7%|2.6%|-1.2%|
||CCSR|-1.5%|1.5%|-0.1%|1.9%|-1.9%|
||PCM|-2.8%|-2.0%|2.4%|2.1%|0.3%|
|2060|||||||
||CCC|-2.0%|-1.7%|1.5%|3.6%|-1.3%|
||CCSR|-2.1%|5.8%|-0.8%|-0.8%|-2.0%|
||PCM|-6.2%|4.9%|-2.7%|7.3%|-3.2%|
|2100|||||||
||CCC|-11.0%|8.3%|-7.2%|15.3%|-5.4%|
||CCSR|-6.9%|3.6%|4.1%|1.4%|-2.2%|
||PCM|-3.1%|0.2%|0.0%|4.3%|-1.4%|

# **Table 12b: Predicted change in net income per animal from AOGCM climate scenarios for large farms (US$/animal)**

|||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|---|
|<br>Baseline|income|146.01|120.18|6.04|12.08|1.68|
|2020|||||||
||CCC|-4.50|-3.11|-1.01|-3.20|-0.35|
||CCSR|-14.93|-4.89|-0.99|-3.30|-0.39|
||PCM|-7.63|23.16|-0.59|-1.84|0.07|
|2060|||||||
||CCC|-11.50|23.35|-1.01|-3.35|-0.03|
||CCSR|7.02|-12.61|-1.76|-5.25|-0.48|
||PCM|-13.73|-12.36|-1.69|-5.75|-0.48|
|2100|||||||
||CCC|-20.75|-46.00|-2.26|-8.27|-1.02|
||CCSR|1.40|63.36|0.23|3.43|-0.28|
||PCM|-12.67|28.40|-1.23|-3.22|-0.08|

38
countries where transport costs seem to be particularly high because of location and poor infrastructure.<sup>9</sup> International transport in Sub-Saharan Africa also suffers from low competition, reflecting the regulations of African governments intended to promote national shipping companies and airlines. For example, as described by Collier and Gunning (1999), many African governments (especially in West Africa) have adopted “cargo reservation schemes,” which allow privileged liner operators to set inflated freight rates.

Studying primary international corridors in Africa,<sup>10</sup> Teravaninthorn and Raballand (2008) argue that the costs backed by transport-service providers are not excessively high in Africa. Nevertheless, the transport prices charged to end-users in Africa are relatively high compared with prices in developed countries and most developing countries. This finding is notable given the low level of wages for truckers in Africa compared with wages elsewhere, as illustrated in Table 2.

**Table 2**

**Median Monthly Wages for Truckers (U.S. dollars)**

|**Country**|**Median monthly wages**|
|---|---|
|**France**|3,129|
|**Germany**|3,937|
|**Chad**|189|
|**Kenya**|269|
|**Zambia**|160|

_Source_ : Teravaninthorn and Raballand (2008).

Teravaninthorn and Raballand suggest that the trucking market structure and environment in West and Central Africa are characterized by strict market regulation leading to low transport quality. By contrast, in East Africa, the trucking environment is more competitive and the market is more mature. Trucking operators from landlocked countries, especially in West and Central Africa, have benefited from formal and informal protection for decades. The result is higher transport prices and lower quality of services. Trucking surveys also find the presence of a large mark-up and profit margin for transport providers. This is due, in part, to regulation leading to high transport prices along international corridors, such as those in West and Central Africa. By contrast, Teravaninthorn and Raballand also find that major corridors in Southern Africa are the

> 9 Faze, McArthur, Sachs, and Snow (2004) present a detailed appendix with regional overviews outlining key challenges facing the landlocked countries in each region of Africa.

> 10 The study focuses on four corridors covering Africa’s four sub-regions and including 13 countries. These corridors carry more than 70 percent of the international trade of the selected landlocked countries.  The 13 countries served by the corridors are:

> West Africa: Ghana, Niger, Burkina Faso, Togo Central Africa: Cameroon, Chad, CAR East Africa: Kenya, Uganda, Rwanda Southern Africa: South Africa, Zimbabwe, Zambia

16
The original values of WTP for the morbidity endpoints are based on report from the Centre for Social and Economic Research on the Global Environment (Pearce and others 1999), U.S. EPA (1999), and ExternE (1999).i9 Table 6.4 shows the values derived for Mexico using different income elasticities.

**Table 6.4 WTP Estimates for Morbidity Impacts Obtained with CVM** (in US$, 2010 values in 1999 prices)

|||_Incomeelasti_<br>|_city_<br>|
|---|---|---|---|
|_Healthendpoint_|0|0.4|1|
|_Hospitaladmission_||||
|Respiratory|550|330|153|
|Cardiocerebrovascular|550|330|153|
|Congestiveheartfailure(elderly)|550|330|153|
|_Emergencyroomvisits_||||
|Respiratory|284|170|79|
|_Restrictedactivity days_||||
|Totala,b|49|35|21|
|_Minor restrictedactivitydaysc_||||
|Total(adults)|49|35|21|
|_Effectsinasthmatics_||||
|Asthmaattacks|52|31|15|
|Coughwithoutphlegm(children)c|49|35|21|
|Cough withphlegm(children)c|49|35|21|
|Somerespiratorysymptoms(children)c|49|35|21|
|_Chronicmorbidity_||||
|Chronic bronchitis,newcases|422,991|253,899|118,074|
|Chroniccough,prevalence(children)|287|199|116|

a. All RADs are valued using WTP. The work-loss days, a subset of RADs, are not valued separately to prevent double counting.

b. We value restricted activity days (RADs) as a cough episode and thus equal to a minor restricted activity day. This underestimates the WTP for RADs. However, Pearce and others (1999) found the value of a bed day, which might be seen as an overestimate of the RAD as not all RADs are bed days, to be only 30% higher than WTP to avoid cough.

c. Following ExternE (1999) we value most cases of effects in asthmatics, cases of respiratory symptoms, and minor restricted activity days (MRADs) as a cough case (episode). For MRADs this give the same value as used by U.S. EPA (1999).

In estimating the WTP for premature mortality it is important to realize that the number of life-years lost due to acute and chronic exposure to air pollution is limited. Because we intend to value only the reduction in the number of life-years lost, the "years of life lost" (YOLL) approach has been applied.<sup>2 0</sup> The YOLL approach is particularly recommended for deaths arising from exposure to air pollution. The value will depend on a number of factors, such as how long it takes for the exposure to result in an illness and eventually death. In this

> 19 See Cesar and others (2000) for a more detailed discussion

> 20 An alternative for the YOLL approach is the "value of a statistical life" (VSL) approach. A comparison of the two approaches is provided in the Cesar and others (2000).

_35_