rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Figure 4:  Poverty Gap (P</u>**<sup>**1**</sup> **<u>) and Squared (P</u>**<sup>**2**</sup> **<u>), regions in Rio Grande do  Norte (1991)</u>**

<!-- Start of picture text --> 0.70<br>P(1) P(2)<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Agreste Potiguar AngicosBaixa VerdeBorborema PotiguarChapada do ApodiLitoral NordesteLitoral Sul Macau NatalPau dos Ferros Serra de Santana Umarizal<br><!-- End of picture text --> _Data Source:_ Atlas do Desenvolvimento Humano no Brasil (1998) – IBGE.

# **3.2. Income, inequality and poverty in the 1990s.**

In this section we use the PNAD data to sketch income poverty in the 1980s and 1990s. We mainly use the indigence poverty line in the analysis. The findings are presented in Appendices B and C<sup>14</sup> .

The period from 1994 and forward can be roughly characterized as a period of disinflation, which affected mean incomes and poverty in the Northeast region as well as in Brazil as a whole. The disinflation is associated with a reduction in poverty. Furthermore, in general, the poverty indexes are more affected by high inflation than are mean incomes. Hence, the poor accounted larger losses than the nonpoor during the high inflation period prior to the Real Plan launched in 1994. However, in the stabilization period that followed, the poor also experienced higher gains than the nonpoor.

As can be seen from Figure 5, at the beginning of the 1990s, poverty was at a record high in Brazil, the Northeast region, Pernambuco (the richest state of the Northeast), Maranhão (the poorest state of the Northeast) and the other northeastern states<sup>15</sup> . The major

> 14 The coverage of Rio Grande do Norte (1,000 observations) is not as good as, for example, for Pernambuco (ca. 6,000 observations) or the Northeast as a whole (ca. 20,000 observations).

> 15 Though, in Pernambuco and Maranhão this peak in poverty seemed to have developed a little bit later, in 1993.

12
# 4.4. Family allowances

Finally, family allowances are a small percentage of GDP and fall mainly outside the scope of this study. The relevant facts are the following. A large part of family allowances go to old people, as an additional form of income support rather than in proportion to the number of children they raise. A number of studies of income distribution in Poland and in Central Europe show that financial hardship is more frequent among young households with children than any other population group (see Grootaert 1995). The share of family allowances in total social spending and the ratio of the average family allowance to the average pension have declined slightly. They reached their lowest level in 1991, when wages and pensions peaked. This suggests either a diversion of spending in favor of more powerful groups or inefficient rules for allocating social spending.

# **5. POLICY EVALUATION AND IMPLICATIONS**

# 5.1. From profit taxes and subsidies to payroll taxes and benefits

The above analysis of government revenue and expenditure suggests that a structural shift occurred in the distribution of tax revenue-away from profit taxes and into payroll taxes-and in the distribution of income-away from profits into wages. It may even be tempting to claim that by taxing these higher wages at a higher rate, it was also possible to shift part of the labor force away from earning labor income, and toward earning benefits. This change could facilitate the enterprise restructuring initiated by the elimination of most subsidies.

In this context the economic transformation can be seen as a change of social contract. The old contract was between firms and the government. It basically implied that profit taxes financed subsides, which in turn justified that firms were in charge of full employment and some benefits. The new contract is between workers and the government. It implies that payroll taxes finance replacement income. The government stops intervening in most production decisions, and it stops 25
MINIMUM WAGES IN CAMBODIA 7 to differences in hours worked. Workers in the garment sector, particularly women, work longer hours per day, and work more days in a month.<sup>8</sup> In addition to the differences in the levels, Figure 2 illustrates that that are strong trends in participation rates, as well as in earnings. Between 2007 and 2015, monthly wages increased by 7.7 percent per year in the garment sector and by 8 percent per year in non-garment sector, slightly lowering the earnings gap. The gap in hourly earnings also seems to be narrowing over time during this period. Furthermore, monthly wages in the other private sectors seems to have been adversely affected by the global recession and the resulting currency appreciation during July 2008 - November 2010.<sup>9</sup> This shock flattened the monthly wages in the garment sector, but hourly wages in both of these sectors seem to have been negatively affected by the shock.

This suggests that any impact of minimum wage change will be affected by these sectoral trends in wages and earnings as well as trends in participation in various sectors of the economy. The empirical specification used in this paper allows the identification of the impact of minimum wage hike even after controlling for the sector specific time trends and shocks.

# 3. Theoretical framework

The dual sector model of the economy presented in Harris and Todaro (1970) provides a useful framework to understand the impact of minimum wage hikes on employment and wages in Cambodia.<sup>10</sup> In this model, wages are artificially high in one sector (urban sector in the original model, garment and footwear in the current sector) due to minimum wage policies. This implies that while workers can move from the garment sector to other sectors, mobility in the opposite direction is difficult due to reduced demand at higher wages. In such a context, a hike in minimum wage in the garment sector will have the obvious impact of a fall in employment and an increase wages in the garment sector. However, the excess labor shed from the garment sector can freely move to non-garment sector, pushing out the labor supply. This will reduce wages and increase employment in the other sector.

The magnitudes of these changes depend on the elasticities of the respective labor demand curves. The fall in employment in the garment sector will be smaller if labor demand in the garment sector is relatively inelastic to price changes. This could also lead to higher wagebill (wage earnings) in the garment sector. Similarly, if the labor demand is elastic to price changes in the non-garment sector, the fall in wages (and wage earnings) will be higher.

> 8On average, women in garment sector work 45 minutes more per day, and work 0.84 additional days in a month compared to women in the other private sectors who work 7 hours per day, 26.4 days per month.

> 9The Cambodian Riel is ‘softly-pegged’ with the USD at a rate of about 4040 KHR/US$ (The World Bank, 2017). Following the global financial crisis, the exchange rate rose to an average of 4150 KHR/US$.

> 10See Ham (2018) for a recent empirical examination studied using this framework.
|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
||data, 5 with WC<br>data)||||(−0.41 kg/m2;−0.46,−0.36) and WC<br>(−0.43 cm; −0.52,−0.33)|
|Nguyen et al. (2023)<br>†|48 articles (40<br>cohorts)|Children, median age<br>10 y, range 6 mo to 17<br>y (n=91,713)|SSBintake|BMI|Each serving/day increase in SSB<br>intake was associated with higher<br>BMI (0.07 kg/m<sup>2</sup>; 95% CI 0.04, 0.10)<br>in children|
|Abbasalizad<br>Farhangi et al.<br>(2022) †|SRM of 33 studies<br>(23 cross-<br>sectional, 1 case-<br>control, 4 cohort, 6<br>longitudinal),<br>included Argentina<br>(3), Mexico, China<br>(7), the Islamic<br>Republic of Iran,<br>Lebanon|Children 2-18 y<br>(n=121,282)|SSBintake|BMI(n=19),body fact<br>percentage(n=5),waist<br>circumference(n=15)|High SSBs intake was associated with<br>0.75 kg/m<sup>2</sup>increase in BMI in children<br>and adolescents (WMD: 0.75; CI<br>0.35–1.15), higher WC (WMD:<br>2.35 cm; 95% CI, 1.34, 3.37; p =<br>0.016) and BFP (WMD: 2.81; CI 2.21–<br>3.41; p < 0.001).|
|Jakobsen, Brader,<br>and Bruun (2023)|SRM of 60 studies<br>(51 cross-<br>sectional, 9<br>longitudinal),<br>includes mix of<br>HICs and LMICs|Children and<br>adolescents 5-18 y<br>(n=242,061)|Consumption of 14<br>differentindividual food<br>or beverage categories<br>_(excluded dietary_<br>_patterns)_|Overweight and/or obesity<br>(based on age- and sex-<br>specific BMI cut-offs)|Higher intake of<br>SSB (OR 1.20; p <0.05, n=26),<br>fast food (OR 1.17; p <0.05, n=24),<br>meat (OR 1.02, p < 0.05, n=7),<br>and refined grains (OR 1.28, p<0.05,<br>n=3) associated with<br>overweight/obesity;<br>Conversely, higher whole grain (OR<br>0.86, p = 0.04, n:5) and sweet bakery<br>(OR 0.59, p < 0.05, n:3) intake<br>associated with lower risk.|
|Poorolajal et al.<br>(2020)|SRM of 199 studies|Children and<br>adolescents 5-19 y<br>(n=1,636,049)|Diet-related risk factors|Overweight/obesity|Factors associated with child<br>OW/OB:<br>sufficient consumption of<br>fruits/vegetables 0.92 (0.84, 1.01)<br>breastfeeding <4 months 1.24 (1.16,<br>1.33); eating sweets≥3 times/week<br>0.78 (0.71, 0.85); eating snack≥4<br>times/week 0.84 (0.71, 1.00); drinking<br>SSB≥4 times/week 1.24 (1.07, 1.43);<br>eating fast-food≥3 times/week 1.03<br>(0.89, 1.18); eating fried-food≥3<br>times/week 1.09(0.90,1.33)|

34
The experimental groups were compared to a control group where respondents were first shown the statement: “There are many popular search engines in the world. Search engines are used every day by over 1 billion people worldwide.” and then were asked “Many people say they are annoyed by all the advertising on search engines. Does this apply to you?” with the answer options of yes and no available.<sup>1</sup> These treatments allow us to compare the effectiveness of a standard descriptive norms treatment where the reference group is relevant to the citizen, with treatments of the descriptive behavior of salient outgroups.

In this study we are interested in two outcomes related to tax morale: 1) a survey measure of tax morale “If a taxpayer does not report all of his income in order to pay less income taxes do you feel it is: Not wrong, A bit wrong, Wrong, and Seriously wrong” and 2) a survey measure of how much respondents would fine tax cheats: “If a taxpayer does not report all of their income in order to pay less income taxes, what percentage of their income should they pay as a penalty? None (0%), 1-10%, 11-20%, More than 20%.”

# Riwi survey platform

We conducted the survey using the Riwi survey platform between 2017-06-21 and 2017-06-29. Riwi recruits respondents by presenting the survey to people who accidentally type an incorrect URL into their browser. Normally these websites will display advertisements to people but Riwi buys temporary space on these pages to display the survey instead. Respondents are first asked their age and gender (with respondents below the age threshold removed from the survey) and their location is determined based on their IP address. Because Riwi respondents are not expecting to take a survey, Riwi surveys have high attrition rates as the survey goes on. As a result, we take care to check for differential attrition and covariate imbalance (see appendix). We found no evidence of either problem in this study.

# Statistics

The social norm study was conducted between 2017-06-22 and 2017-06-29. A total of 14,499 subjects answered Q1 (after the age & gender selector) and 6,698 answered Q8, a completion rate of 46.2%.

Descriptive Statistics from the subjects that completed the study are shown in table 1.

**Table 1 Descriptive statistics**

|Statistic|N|Mean|St. Dev.|Min|Pctl(25)|Pctl(75)|Max|
|---|---|---|---|---|---|---|---|
|Age|6,055|32.48|14.20|16|21|40|65|
|Female|6,055|0.35|0.48|0|0|1|1|
|Business Owner|6,054|0.06|0.23|0.00|<br>0.00|0.00|1.00|

1 An initial positive sign that respondents actually read the messages is that around 70% of respondents answered yes to the question in each treatment condition, whereas only 55% of the control group answered ‘yes’ that they are annoyed by advertising in search engines.
# Table 19 Explaining Changes in Poverty

||Hea|d Count Rad|o|Pov|ery Gap Rato||
|---|---|---|---|---|---|---|
|Yews|Tota Change<br>In Povety|Dueto<br>_Growth<br>I|Dus to<br> Inequalty|Total Change<br>In Povety|Due to<br>Growth|DO to<br>IInequality|
||||Po|or|||
|1980 to 1985|-9.67|-8.69|-0.98|-3.82|-2.3|-1.52|
|1985 to 1989|-10.44|-6.99|-3.45|-3.37|-1.57|-1.8|
|1989 to 1990|-6.4|-4.04|-2.36|-1.89|-0.84|-1.05|
|1990 to 1991|-2.77|-0.94|-1.83|-0.96|-0.2|-0.76|
|1991 to 1992|20.99|17.13|3.86|5.53|4.01|1.52|
|1980 to 1992|-8.29|-3.53|-4.76|.4.51|-0.9|-3.61|
||||Ultra|Poor|||
|1980 to 1985|-8.82|.4.51|-4.31|-2.63|-1.69|-0.94|
|1985 to 1989|-5.85|-3.06|-2.79|-2.37|-0.96|-1.41|
|1989 to 1990|-3.07|-2.38|-0.69|-1.29|-0.38|-0.91|
|1990 to 1991|-2.29|-0.59|-1.7|-0.58|-0.08|-0.5|
|1991 to 1992|13.68|9.94|3.74|3.02|1.97|1.05|
|1980 to 1992|-6.35|-0.6|-5.75|-3.85|-1.14|-2.71|

As we noted earlier, poverty in Ukraine increased sharply in the 1991-92 period. On the basis of the head-count rado, the total increase in poverty was 20.99 percent. The quesfion we ask is: how much of the total increase in poverty can be explained in tms of changes in the mean income and income inequality. It can be seen that 17.13 percent of the increase in poverty was due to a decrease in the per capita income and the remaining 3.86 pacent due to e increase in income inequality. During the same period ultra poverty increased by 13.68 pement of which 9.94 percent was due to decrease in per capita income and 3.74 percent due to an increase in income inequality. This shows that the increase in inequality has a greater effect on ultra poverty than that on poverty.

Looking at the enfire period 1980 to 1992, it can be seen that the poverty in Ukraine decreased quite substantially. A larger proportion of this decline came about as a result of redistribution of income in favor of poorer families. For instance, the poverty gap ratio shows that the poverty decreased by 4.51 points; the growth in per capita income contributed only 0.9 40
40 <!-- Start of picture text --> Country Ownership links between banks and corporations Banks' and non- Accounting framework 16<br>financials' capital (Countries are scored on a scale from 1 to 4 for<br>adequacy positions each variable with 4 Indicating best practice<br>and 1 indicating furthest away from best<br>Framework Practice practice.)<br>Banks allowed Non-financial Significant Significant links Financial and<br>to own non- companies ownership links between non- non-fnancial Loan Loan loss Interest Averge<br>financial finns allowed to own between banks and banks and companies linked classification provisioning accrual<br>financial firmns corporations corporations through family<br>ownership<br>Thailand Yes Yes; statutory No. No. Yes 29 Many weakpublic and I 1 l<br>(1997- linit 5%; private conumercial<br>ongoing) banks.<br><!-- End of picture text -->

**29** It hasbeen estimnated, forexarrple, thatthetop 10 famniliescontrolled46.2percentofthenuarketcapitalization of listed fimisasofend-1996,ofwhichholdings in financial institutions accounted for 18.3 percent of their wealth and in corporations for the rest (World Bank 2000b).
<!-- Start of picture text --> “a argh oO<br>Co Pa<br>o 7<br>fi»% a<br>& &. Main See nt Patches<br>Pa % rr ayat<br>ae Cs) anchaya send Pumps<br>a —<br>‘ “s ee<br>at ee” } 7<br>Ease age s<br>} r mat<br>0 180360 720) 61,080 1,440<br>aees | leters<br><!-- End of picture text -->
own. We have also included in the analysis an indicator variable for workers below or at the national minimum wage in a given year. Minimum wage information is collected from the ILOSTAST Database. The description of trends in earnings and other variables in the next section is based on this dataset and variable definitions.

# **3. Falling Inequality in Brazil and Its Five Potential Drivers**

As noted earlier, Brazil has experienced a non-trivial reduction in income inequality since the macroeconomic stabilization of 1994-1995. This decline was particularly pronounced since 2003, a period during which average incomes also grew relatively rapidly (Figure 1, Panel A) and poverty fell sharply.<sup>9</sup> Brazil was not alone: similar trajectories were observed in a number of other Latin American countries over the same period. A comprehensive discussion of the trends in Argentina, Brazil, Mexico and Peru can be found in López-Calva and Lustig (2010).<sup>10</sup> Much of the popular discourse on falling inequality in Latin America has stressed the role of growing fiscal redistribution, through new social protection instruments such as conditional cash transfers (CCTs). Indeed, Brazil’s federal government launched a conditional cash transfer program named _Bolsa Família_ in 2003, which has since reached over 50 million people, and become one of the world’s largest CCT programs. Although _Bolsa Família_ and other fiscal redistribution programs did contribute to the reduction in household income inequality, the best available estimates put this contribution at between 30-40 percent of the overall decline (Barros et al., 2010; Azevedo et al. 2013). Another 10 percent or so has been attributed to demographic factors, chiefly the rapid decline in family sizes, which has been most pronounced among poorer households. The remaining 50-60 percent of the decline in inequality in household incomes has been attributed to changes in the distribution of labor earnings. It is these changes that are our subject here.

Figure 1 provides a visual description of the basic income trends in Brazil over this period. Panel A depicts trends in levels, rather than in dispersion: real labor earnings and (per capita) household incomes behaved similarly in the 1995-2012 period, but their behavior differed markedly across two sub-periods. From 1995 to 2003, both earnings and household incomes were either stable or declining: median labor earnings and average household per capita incomes were roughly constant, while mean labor earnings fell by 18 percent. The

> 9 This section draws in part on our earlier paper (Ferreira, Firpo and Messina, 2016).

> 10 In addition to these four in-depth case studies, this volume contains thematic chapters looking at the Tinbergen race between educational upgrading and technological change, the political economy of redistribution and trends in top incomes.

7
Page 75

- Hossain, M. 1987. "Credit for Alleviation of Rural Poverty: The Experience of Grameen Bank in Bangladesh." Washington, D.C. and Dhaka: International Food Policy Research Institute and Bangladesh Institute of Development Studies.

- Hossain, M. and R. Afsar. 1988. "Credit for Women: A Review of Special Credit Programmes in Bangladesh." Dhaka: Bangladesh Institute of Development Studies.

- Humana, C. 1986. "The Economist's World Human Rights Guide." London: Hodder and Stoughton Ltd.

- IPPF (International Planned Parenthood Federation), Medical Bulletin, 15-2, London.

- Jamison, D. and P. Moock. 1984. "Farmer Education and Farm Efficiency in Nepal." World Development 12(1).

- Jiggins, J. 1987. "Outline of a Programme to Strengthen Women's Economic Contribution." Washington, D.C.: Background Paper for the World Bank: Women in Development Division, Washington, D.C., processed.

- Joekes, S. 1988. "Gender and Macro-Economic Policy." Institute of Development Studies, University of Sussex, Brighton, England.

- Joekes, S., M. Lycette, L. McGowan, and K. Searle. 1988. "Women and Structural Adjustment." International Center for Research on Women, Washington, D.C.

- Jones, C. 1985. "The Mobilization of Women's Labor for Cash Crop Production: A Game Theoretic Approach." In Women in Rice Farming, International Rice Research Institute, Manila: Gower Publishing Co., pp. 445-454.

- Khandker, S. 1987. "Women's Time Allocation and Household Non-Market Production in Rural Bangladesh." The Journal of Developing Areas, 22.

- Khandker, S. 1988. "Determinants of Women's Time Allocation in Rural Bangladesh." Economic DeveloDment and Cultural Change.

- King, E. and R. Evenson. 1983. "Time Allocation and Home Production in Philippine Rural Households." In Women and Poverty in the Third World, Buvinic and others, eds., Baltimore: Johns Hopkins University Press.

- Kurrien, J. 1988. "The Education of Girls and Women in India." Background Paper for World Bank Study, "India: Gender and Poverty", World Bank: Women in Development Division, Washington, D.C., processed.

- Kwast, B. 1987. "Roads to Maternal Death: Case Histories, Including Comments on Preventive Strategies." World Health Organization, Informal Background Paper prepared for Safe Motherhood Conference, Nairobi, Kenya, February, 1987.
recalibrate it to fit a developing country (or multiple developing countries). Given the necessary data, this would be very straightforward. And the bulk of the necessary data – industry-level inputs and outputs – is certainly available (and has been used for models like ENVISAGE). The biggest potential difficulty here is the availability of labor market data (such as job turnover by industry), although in the absence of such data, one could certainly extrapolate from the data that are available (and try a range of different extrapolations to see how robust the model’s conclusions are).

# _ii. Longer-Term (Development of New Models)_

The best answer would come from a model specifically built to address the job effects of climate policy in developing countries. Such a model would include not just search frictions, but also some representation of key features that make developing country labor markets different from those in developed countries, especially labor market segmentation. That segmentation could include explicit modeling of the informal sector and/or a rural-urban split. We know that such segmentation slows an economy’s adjustment to new policy and leads to substantial divergence in how different industries, occupations, and regions are affected. A good model could provide useful information about how big those effects are, and will help to evaluate policies, which could address the resulting divergence in economic outcomes. The major challenge here will be to capture key features of the market, while still keeping the model simple enough to be tractable and easily understood.

To the extent possible, it would be useful to parameterize such a model to match developing country–specific empirical evidence about the labor market effects of policy. This could be taken from existing studies, or could represent new empirical work.

Building such a model will take substantially more time and effort than making straightforward modifications to an existing model. Thus, this would be more of a longterm effort. But it would provide substantially better quantitative results than what we currently have.

41