rafmacalaba/datause-encoder-data huggingface dataset:
Such ethnic inequalities matter for poverty reduction policy and income mobility. Two recent studies examine the interaction between ethnic inequality and poverty in Malaysia based on analysis of tabulated nationally-representative survey data for the period 1970–2016 (Ravallion, 2020a, 2020b). An important insight emerging from this work is that between-group relative inequality decreased over this time period, as a result of Bumiputera households having higher income growth rates than the other ethnic groups. Inequality at the national level also declined. However, Ravallion (2020a, 2020b) observes that the growth differential was not sufficient to reduce absolute differences in income, so that ethnic income inequality in absolute terms is higher now than 50 years ago. The difference is relevant, given that popular perceptions of inequality may well track absolute rather than relative inequality measures.<sup>2</sup> Ravallion (2020a, 2020b) further indicates that only about 10 percent of absolute poverty reduction in Malaysia, and 17 percent of the decline in relative poverty, can be attributed to reduced inequality between ethnic groups over this time. Growth of overall mean income has thus played a much more important role in reducing poverty than the fall in relative inequality between ethnic groups. Nevertheless, Ravallion (2020b) does emphasize the role of reduced ethnic inequality and notes that reducing it further may still matter for reducing poverty as measured by his weakly relative poverty line. Our study will show, however, that Ravallion’s conclusions should be viewed alongside findings about spatially differentiated poverty dynamics and inequality, between rural and urban areas, and between Peninsular and East Malaysia.

Conventional efforts to monitor poverty typically involve tracking measures such as the headcount ratio over time. Such studies provide insights into poverty trends at some aggregate level such as the country, community or sector, but are unable to shed light on the dynamics of poverty at the level of individuals or households. Yet, from both a theoretical and policy perspective, such micro-level poverty transitions are of considerable interest. If we observe chronic poverty, where individuals or households are stuck in poverty over extended periods of time, this would be consistent with theories of poverty traps related to, for example, lack of credit, geographic disadvantages, or discrimination against marginalized groups (Kraay and McKenzie 2014). Focused policies, such as expanding banking services, upgrading transport infrastructure, or introducing affirmative action measures might then be helpful in overcoming these structural barriers. If, on the other hand, people are seen to move in and out of poverty from one period to the next, then poverty is largely a transitory phenomenon that may be due, for example, to fluctuations in the business cycle or weather conditions that affect agricultural yields. In that case,

> 2 See for example Ravallion (2016, Chapter 5) for a discussion.

2
- 25 -

a response to a subsidy increase.<sup>To the rich an increase in the subsidy level</sup> represents a direct income reduction<sup>because it is a lump-sum tax determined only</sup> by the quantity of the ubsidized good demanded by<sup>the poor. It is a purely</sup> redistributive form of tax. The rich give up some of their<sup>own consumption as a</sup> response to this tax. They<sup>also increase their labor supply. The "first-order"</sup> conditions of our Cobb-Douglas<sup>specification of the problem tell us that the rich</sup> are willing to make transfers<sup>to subsidize the poor's consumption up to the point</sup> where the margindl utility<sup>of paying for the poor is equal to the marginal utility</sup> of keeping income for their<sup>own consumption.</sup> 7. **Paternalism with Cash Transfers:**<sup>The next step involves the analysis</sup> of the model when the policy<sup>consists of direct cash transfers, T, from the rich to</sup> the poor, rather than transfers<sup>delivered in the form of price subsidies. We retain</sup> our assumption of paternalistic<sup>behavior. Therefore, we use utility functions, and</sup> a social welfare function similar<sup>to the functions used under the price subsidy</sup> scheme. What differs under this<sup>case is the way in which the scheme affects the</sup> budget constraint of the poor.<sup>This step requires a reformulation of the budget</sup> constraints as follows:

**For the poor:**

# **For the rich:**

The "first-order" conditions<sup>of the problem in ceneral form would tell us that the</sup> rich would make transfers to<sup>the poor up to the point where the marginal utility</sup><sup>**of**</sup> transferring income to the poor<sup>is equal to the marginal utility of keeping it for</sup> their own consumption. Following the same solution<sup>procedure used in the subsidy</sup>
domestic policy response can also avoid the<sup>potentially disastrous</sup> consequences to the poor of their more desperate<sup>later responses, such as</sup> asset sales, and migration. Though individually<sup>rational, these responses can</sup> readily add-up to an snhanced collective<sup>risk of death, through exposure to</sup> diaease, and leave those lucky enough to survive<sup>this time facing longer-term</sup> destitution through a diminished asset<sup>base or greater indebtedness. There is</sup> an evident complementarity here between<sup>the provision of an effective safety</sup> net for the poor and the long.r-term alleviation<sup>of poverty through economic</sup> development.

# **_5_** _Chronic Hunger_

It is one thing to prevent famine, and quite<sup>another to eliminate</sup> chronic hunger. The bulk of the discussion<sup>of the latter problem in HPA</sup> focuses on past country experiences in using direct<sup>policy intervention in</sup> raising nutritional and related capabilities<sup>over the longer-term; the central</sup> question addressed is whether direct intervention<sup>can work.</sup> HPA examines in detail the experiences of a<sup>number of relatively</sup> successful efforts at longer-term public<sup>support in developing countries, as</sup> indicated by rates of improvement in social<sup>indicators, particularly under-</sup> five mortality. In characterizing these<sup>experiences, a distinction is made</sup> between 'prowth-mediated security" and "supDort-led<sup>security"; the former</sup> relies on growth to finance public support,<sup>while the latter does not.</sup> Expanding on earlier work by Sen (particularly<sup>1981b), the authors show how</sup> countries such as China, Costa Rica, Sri Lanka,<sup>Chile and Cuba have used</sup> direct intervention in public provision to<sup>achieve the social indicators</sup> typical of richer countries. For example,<sup>Sri Lanka's life expectancy of</sup> 23
25 Table 14

||89.3-9|2.2|89.3-9|1.3|91.4-|92.2|
|---|---|---|---|---|---|---|
|Firm|Estimate|L-stat|Estinate|t-stat|Estimae|**t-stat**|
|Type|||||||
|All<br>:|-0.014|_-3.04_|-0.012|-2.04|-0.002|-0.28|
|-l~|_ -0.00001|-028|0.0004|0.71|0.0001|1.88|

NP net payables _(=_ payables minus receivables), i.e.. inter-firm borrowing EMPL: Employment PRF: Profit margin CC Cash Constraint (cf. Annex B).

# 4 Role Of Managers

In the pre-reformL socialist era, a manager's worth depended upon the abiliy to bargain with cgovernment for subsidies and falvored allocations of investment and input resources As the previous section showed. those options were by and large foreclosed follr wing the big bang reform program.Another big change this program led to was the cost-price squeeze as a result of trade liberalization and the fixed exchange rate.-' SOEs were no longer able to follow **"cost** plus fixed margin" pricing rules, and were forced to pay attention to efficiency and marketing.

The hardening in the attitude of creditors and government documented in sections 2 and 3 and the compression of margins do not by themselves imply good behavior on the part of SOEs. or more precisely of their managers. Managerial compensation was not designed to encourage managers to :maximize the long-run value of the firm. The legislatively-specified link between profits and bonus for

**The zloty was devalued substantially to** 9500 **per dollar** on **January 1. 1990. at which level it remained for** 17 **months** in **spite of inflation** of 385 **percent during this** period. **Some of this inflation was due to pr.ce-level adjustment as domestic prices drifted up to the quality-adjusted PPP levels implied bv this exchange -ate and import** prices. **See** Pinto. Coriceili **and de la Calle** (1990) **and Wellisz. Kierzkowski and** Okolski (L991).
|**Panel B**|High income|**Life satisfaction a**<br>Upper middle<br>income|**nd Job quality**<br>Lower<br>middle<br>income|<br>Low income|
|---|---|---|---|---|
|Job quality|0.066***|0.047***|0.078***|0.003|
||(0.010)|(0.012)|(0.026)|(0.016)|
|Self employed|0.007|-0.083|-0.048|0.046|
||(0.057)|(0.059)|(0.105)|(0.074)|
|Lower skilled|0.025|-0.169**|-0.189|-0.093|
||(0.053)|(0.071)|(0.147)|(0.086)|
|Part time|-0.156|-0.202***|-0.287**|-0.110|
||(0.097)|(0.070)|(0.126)|(0.080)|
|Farmer|0.087|0.162|-0.067|-0.255**|
||(0.157)|(0.105)|(0.160)|(0.103)|
|Income|0.081***|0.186***|0.347***|0.387***|
||(0.009)|(0.012)|(0.024)|(0.018)|
|Saved part of income|0.345***|0.394***|0.278***|0.464***|
|last year|||||
||(0.040)|(0.047)|(0.083)|(0.071)|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Constant|7.059***|5.567***|4.296***|3.603***|
||(0.242)|(0.267)|(0.566)|(0.370)|
|Observations|8719|13183|2765|7085|

Estimates based on World Value Survey Wave 5. The dependent variable is self-reported life satisfaction measured on a 10pt scale, with higher values denoting higher life satisfaction. Coefficients are calculated with a tobit model to account for possible over reporting on the highest score of the scale. Results were not qualitatively different when we used OLS or ordered Probit models. All estimates control for age, age squared, family status (married, widowed, or divorced), level of education (secondary or higher), number of children, gender, religious attitudes, and work ethics (work is “very” or “somewhat” important). *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are in parentheses.

Differences in the effect of job characteristics are less clearly differentiated across country income groups. However, here also we find that job attributes, as measured by our data, have no independent effect on subjective wellbeing in the poorest economies (Panel B). Also the control for lower skilled workers in the model with unemployment becomes indistinguishable from zero in the poorest countries in our sample (Panel A). Yet, this result may also be explained by the fact that our measure of job characteristics may give less accurate descriptions of the variability of people’s working conditions in lower income countries.

In addition there are other indications that work-related priorities vary across countries at different stages of economic development. Considering the control for household income we see that income contributes increasingly more to life satisfaction with decreasing levels of national income (the effect of income already 16
In terms of poverty reduction, the effect of the fiscal system in Brazil is less favorable (Figure 18). Although it is above average, the reduction is low, and even other countries in the region, such as Argentina, Ecuador, Costa Rica, and the Dominican Republic, achieve better results. A common factor Brazil has with such countries is that poverty reduction comes from direct transfers. As mentioned above, Brazil has the highest amount of direct transfers as a percentage of GDP. When moving to consumable income and incorporating the effects of indirect taxes and subsidies, poverty rises in almost all of them, except Ecuador, where subsidies exceed the effect of indirect taxes.

**Figure 18. Poverty reduction, upper‐middle income countries (percentage points)**

<!-- Start of picture text -->
-8.0<br>-6.0<br>-4.0<br>Brazil (2019), -1.6<br>-2.0<br>0.0<br>2.0<br>4.0<br>6.0 Brazil (2009), 3.7<br>8.0<br>10.0<br>income  (p.p.)<br>Poverty reduction from Market Income plus pensions to consumble<br><!-- End of picture text -->

Source: CEQ Data center. Notes: international comparisons are made using the World Bank international poverty line of USD 5.5 per person per day in purchasing power parity (PPP) 2011.

The results presented here suggest that fiscal policies in Brazil have improved over time in terms of their impacts on poverty and inequality reduction. In 2009, the difference in Gini between MIPP and consumable income was 3.1 compared with 6.4 in 2019. Moreover, the impacts on poverty have gone from being welfare decreasing (i.e. fiscal policies increased poverty in 2009 by about 3.7 p.p. using the $5.50 USD 2011 PPP poverty line) to being welfare increasing and poverty reducing (estimated at -1.6 p.p.). In the 2009 study, the negative effects of indirect taxes on poverty are only partially compensated by the effects of direct transfers. Current estimates suggest that direct transfers (marginally) overcome the negative impacts of indirect taxes. Finally, net cash beneficiaries of fiscal policies belonged to deciles 1 and 2 in 2009 (first quintile), but in 2019 households up to the third decile are net cash beneficiaries. Finally, the fiscal position is favorable up to the seventh decile in both CEQ estimations.

# 6. Conclusions

This paper aims to contribute to the literature on distributional effects of fiscal policies. Using the case of Brazil in the period right before the COVID-19 pandemic, we document a series of important facts about 38
# **_Partial Equilibrium Implications of Population Aging_**

This section will present the partial equilibrium approach to determining the implications of population aging for the household saving rate. The methodology is outlined in, for example, Weil (2000) but is extended to include the important feature of differential saving behavior across income groups.

The advantage of the partial equilibrium approach is to exploit the combination of detailed agespecific _population projections_ and detailed age-specific _saving rates_ available for Brazil — performed across income groups by using scenarios for different inequality levels to determine the projected trajectories for the saving rate. The disadvantage of using this approach is that age-specific saving rates are assumed exogenous, so they cannot change over time when the population experiences lower fertility, lower child mortality, and higher life expectancy.

**Figure 3a Saving Rates are Increasing for all —** **<u>Income Groups Except for the Richest</u>**

## **Figure 3b. Poverty Rates for the Young are** **<u>Higher than for the Elderly</u>**

<!-- Start of picture text --> 100% 25%<br>0% 20%<br>0 10 20 30 40 50 60 70 80 90 100<br>-100% 15%<br>-200% 10%<br>-300% 5%<br>-400% 0%<br>Saving Rate, POF 2002 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90<br>Saving Rate, POF 2008 2008 2025 2050<br>-500%<br><!-- End of picture text --> Source: Author’s estimations based on POF 2002, 2008.

Source: Author’s estimations based on POF 2008; uniform projections when the share below the poverty line falls by 1 percent.

The question is, of course, whether the saving rate is expected to rise or fall in the future when the population ages, since this will affect economic growth in the same direction. Because each age group in the population saves a certain fraction of its income, it matters for the weighted average of the aggregate saving rate which age groups increase and fall in size. Population aging implies that older age groups increase in size and that they possible live longer due to increased life expectancy. Depending on the size of saving rates of the young, the middle-aged and the old people, such demographic changes are likely to change the aggregate saving rate.

# **_The Partial Equilibrium Model_**

The main features of the partial equilibrium model used to analyze the future implications of population aging in Brazil. The model is used for simulating the implications for the household saving rate when the population structure changes over the period 2010 to 2050 — combined with scenarios for the share of poor and rich households. The model combines age-specific saving rates; 11
# **Table 8: Leisure, Time Use Over Activities (weekly hours)**

## **SUR estimates, standard errors in parentheses, p-values in braces Aged 12-17 at Baseline**

## **Aged 18-25 at Baseline**

||**Friends**|**Men**|**Alone**|**Volunteer/**<br>**Church**|**Friends**|**Men**|**Alone**|**Volunteer/**<br>**Church**|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|**Disruption Effect**|.251|1.27***|.641|-.058|.608*|.613|1.15**|.609|
||(.430)|(.444)|(.458)|(.660)|(.354)|(.550)|(.475)|(.401)|
|**Treatment Effect in High Disruption**|-1.42***|-1.86***|-1.55***|-2.48***|-.894***|-1.65***|-1.71***|-2.30***|
||(.403)|(.452)|(.443)|(.585)|(.310)|(.474)|(.479)|(.372)|
|**Treatment Effect in Low Disruption**|-1.11***|-.602***|-1.11***|-2.43***|-.746***|-1.32***|-1.05***|-2.05***|
||(.240)|(.211)|(.233)|(.334)|(.250)|(.327)|(.214)|(.285)|
|**Difference [β3, p-value]**|{.507}|{.012}|{.385}|{.942}|{.696}|{.543}|{.206}|{.580}|
|**Control Mean at BL**|6.57|2.52|6.35|10.8|6.40|7.70|5.77|11.0|
|**Observations**|2,379|2,379|2,379|2,379|2,400|2,400|2,400|2,400|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. Leisure time allocation data was collected both at Baseline and Endline in a similar way. The leisure categories recorded were: "Friends", "Men", "Alone", "Church", "Volunteer" and "Other". The exact phrasing for the "Men" category is, "With boys or men you have a sexual relationship with." Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. The data points were later converted into weekly hours using recorded total leisure. The main results are estimated with an ANCOVA specification. Control variables include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
**<u>Table 11: Vulnerability and poverty across regions</u>**

|**P**|**overty Rate**||<br>**Vulnera**|<br>**bility Rate**||
|---|---|---|---|---|---|
|||**Overall (1)**|**Poor (2)**|**Non-poor (3)**|**(2) / (3)**|
|**Overall**|43.6%|39.2%|65.9%|18.6%|3.5|
|**Urban**|30.7%|15.7%|37.3%|6.1%|6.1|
|**Rural**|50.6%|48.1%|69.9%|25.9%|2.7|
|**Regions**||||||
|**_Kayes_**|26.5%|33.4%|56.5%|25.0%|2.3|
|**_Koulikoro_**|41.7%|41.2%|66.6%|23.0%|2.9|
|**_Sikasso_**|83.8%|73.0%|84.0%|16.5%|5.1|
|**_Segou_**|47.0%|51.0%|69.4%|34.7%|2.0|
|**_Mopti_**|46.6%|31.1%|47.9%|16.4%|2.9|
|**_Tombouctou_**|31.1%|19.9%|35.9%|12.7%|2.8|
|**_Gao_**|26.9%|19.9%|33.2%|15.0%|2.2|
|**_Kidal_**|14.3%|9.0%|16.8%|7.8%|2.2|
|**_Bamako_**|9.6%|1.4%|5.4%|1.0%|5.3|

Source: ELIM 2010 and authors' calculations.

In Table 12 we decompose vulnerability by distinguishing between poverty induced vulnerability (i.e low mean consumption) and risk induced vulnerability (i.e high consumption volatility). This distinction is important because the policies seeking to reduce households’ vulnerability would differ according to which source of volatility is most prevalent. If vulnerability is driven only by high volatility in consumption then designing policies to reduce exposure to risks or to strengthen households’ ability to cope with risks once they occur might be right strategy. If however vulnerability is also driven by low mean consumption, then policies aimed at improving livelihoods such as cash transfers might be necessary to complement risk reduction strategies.

If we focus on this dimension of vulnerability, we see from the table below that in Bamako, vulnerability is mostly driven by high volatility (risk induced). In other cities and also in rural areas however, vulnerability seems to be mostly driven by low mean consumption.

22
empirical evidence that the existence of CBs, but not CRs reduces perceived financing constraint and increases use of bank financing, and find the effect is stronger for smaller firms.

Both papers consider a cross-section of firms and credit information data at one point in time. As such, these estimates report correlations that are not necessarily causal. That is, the results may tell us that banks lend more to firms in countries with information sharing systems than in countries without these systems. However, this finding does not necessarily mean that the implementation of an information sharing scheme caused banks to lend more to firms. Instead, the relationship may be driven by third factors that are not considered in the analysis or are simply unobserved. Essentially, estimates from cross sectional data are more likely to struggle with bias due to omitted variables.

A similar cross sectional analysis for 24 countries in 2002 from Eastern Europe and the Former Soviet Union by Brown, Jappelli and Pagano (2009) is supplemented by firm level panel data from 2002 and 2005. By introducing a time dimension, and also by controlling for unobserved firm heterogeneity with panel fixed effects, their estimate of the effect of credit information sharing on the extent of access to and the cost of finance is more robust, and better explains the causal role of improving the scope and quality of credit information. The sample of countries considered by the authors is, however, narrow, and the dependent variables are subjective survey responses on constraints and cost perceived by the respondent. The nature of their data on credit information also does not allow a distinction between the effect of CB and CR.

A third strand of the literature tries to exploit natural or randomized experiments to estimate the causal effect of credit information sharing on firm credit and default (Luoto, McIntosh, and Wydick, 2007; de Janvry, McIntosh, and Sadoulet, 2010, Hertzberg, Liberti, 5