rafmacalaba/datause-encoder-data huggingface dataset:
# 2 Stylized facts on aggregate and sectoral productivity growth

This section documents the main productivity facts in Brazil at the aggregate and sectoral level since the mid-1990s.

## 2.1 Aggregate productivity growth

Economic growth in Brazil since the mid-1990s has been driven largely by factor accumulation, namely an expansion of the employed labor force, human and physical capital, with only a small contribution from improvements in total factor productivity (TFP).<sup>1</sup> An analysis of the decomposition of GDP growth shows that during the 1997-2005 period, factor accumulation had an annual contribution of 3.5 percent to GDP growth, whereas TFP declined 1.1 percent per year (Figure 1).<sup>2</sup> The GDP growth acceleration from 2.4 percent to 4.3 percent per year between 1997-2005 and 2006-2011 was entirely due to TFP, since the contribution of factor accumulation remained around 3.5 percent. Between 2012 and 2019 there was a sharp deceleration of annual GDP growth to 0.3 percent, reecting a decline in the contribution of factor accumulation to 2.5 percent per year and a collapse of TFP (-2.2 percent per year). The reduction of factor accumulation in the recent subperiod reects lower physical capital accumulation and a signicant slowdown in labor force growth. The contribution of human capital has been positive throughout the 1997-2019 period, averaging a little more than 1 percent per year.<sup>3</sup> The negative contribution of TFP during the 2012-2019 period may in part reect the fact that TFP is calculated as a residual, such that the it may appear that the recession of 2014-2016 was caused by a negative contribution of productivity, when in fact it may have been driven by other unobserved factors such as an increase in spare capacity. Nevertheless, the fact that TFP growth was still negative in the 2017-2019 period seems to suggest that its decline in recent years has not only been due to cyclical factors (Figure 2).

International comparisons conrm that Brazil's negative TFP growth stands out in comparison to other emerging and developed economies (Figure 3). Whereas TFP declined at an annual rate of 1 percent per year between 1997 and 2019 in Brazil, it increased around 2 percent per year in China and 2.3 percent in India. Even though there was also a TFP

> 1See Bonelli et al. (2017)for a review and analysis of the productivity evidence in Brazil.

> 2Figures 1to 5 are updated versions of gures presented in Qian et al. (2018)and Dutz (2018).

> 3The data used to perform the growth decomposition exercises was obtained from the Penn World Table 10.0. The human capital index is constructed using the average years of schooling for the population aged 15 and older and rates of return based on cross-country Mincerian wage regressions.

3
Individuals can access their pension savings at age 65 for men and 60 for women, with three withdrawal options: Programmed Withdrawals ( _Retiro Programado_ ), purchase an annuity from an insurance company ( _Renta Vitalicia_ ), or a mix of phased withdrawals for a period of time and a deferred lifetime annuity. The law also allows for early retirement, provided that the worker has pension funds sufficient to generate a pension amount equal to or greater than 110 percent of the minimum pension guaranteed by the state.<sup>13</sup> Prior to the 2008 pension reform, the state provided noncontributory retirement income transfers through two mechanisms. First, a welfare or assistance pension, known as the _PASIS_ pension, equal a little less than a third of the minimum wage was available for program applicants above 65 years of age, irrespective of their contribution history, was allocated based on an index of economic vulnerability, called “ficha CAS”.<sup>14</sup> The second transfer was a minimum pension guarantee (MPG) equal to about twice the PASIS pension. Individuals with more than 20 years of contributions received the MPG if their accumulated contributions could not finance a higher pension. Both of these benefits took the form of a top-up, that is, the benefit was equal to the difference between the guaranteed level and the pension financed by the worker’s account.

# **3.1 Gender pension gaps and the 2008 reform**

In 2008, the pension system underwent significant reforms aimed at alleviating old age poverty and reducing gender gaps in pension accumulations. An analysis of pension contribution histories at the micro level (e.g. Arenas de Mesa et. al. (2007)) showed that most individuals were expected to have low pension accumulations upon retirement.<sup>15</sup> Only 37 percent of women were projected to have a pension above the MPG level, in comparison with 67 percent for men. The average projected replacement rate for women under the pre-reform pension system was 28 percent of the last wage in comparison to 51 percent for men.

An important factor underlying gender gaps in projected pensions is that labor force

> 13The pension must also be equal to or greater than 50 percent of the average taxable income for the last 10 working years.

> 14In August 2007, the minimum wage was 159,000 pesos per month, while the PASIS was 44,186 pesos for retirees between 65 and 70 years of age, 47,103 pesos between 70 and 75 and 51,503 pesos if older than 75.

> 15The micro-level data on pension contribution histories were obtained from a database of the pension fund regulatory agency, the _Superintendency of Pensions_ or SP. These are the same data as used in this paper.

7
A comparison of the youth cohort to older cohorts shows that the former is by far the largest. The age group 35–44 years is about half the size of the youth cohort (Table 3.1). Because of the short life expectancy of only 52 years in Haiti and a high birth rate of 4.2 children per woman in 2003,<sup>8</sup> cohorts are quickly shrinking with increased age in absolute and relative size. Figure 3.2 shows that youth in fact are not the largest cohort. The cohorts of children are larger than youth, suggesting that youth may become an even larger group in the decade to come.<sup>9</sup> Projections show that the pyramid-shaped demographics figure for Haiti, in contrast to other LAC countries such as Brazil, is not likely to change dramatically for at least the next two to three decades (Jiménez 2005). Because youth constitute a large proportion of the population it is clear than any plan for increased economic growth, decreasing poverty, improved health and education, and a generally stable and peaceful society needs to include youth.

Figure 3.2 Population by Gender and Age in Haiti, 2001 <!-- Start of picture text --> 85 +<br>80 - 84<br>75 - 79<br>Male<br>70 - 74<br>65 - 69 Female<br>60 - 64<br>55 - 59<br>50 - 54<br>45 - 49<br>40 - 44<br>35 - 39<br>30 - 34<br>25 - 29<br>20 - 24<br>15 - 19<br>10 - 14<br>5 - 9<br>0 - 4<br>-8.08 -6.06 -4.04 -2.02 0. 0 2.02 4.04 6.06 8.08<br>Percent of population<br>Source: Own calculations based on ECVH 2001.<br>Age cohort<br><!-- End of picture text -->

# **Income Poverty**

Haiti is the poorest country in LAC. Nearly half of all households live in extreme poverty, and because poorer households tend to be larger on average, more than half of the population is extremely poor. Table 3.2 shows household income poverty incidence

> 8 World Development Indicators.

> 9 While Figure 3.2 shows a shrinking population at the very bottom of the pyramid, a 2003 census shows a more constant evolution with the younger cohorts as large as earlier cohorts (Jiménez 2005). This discrepancy may result from the ECVH’s use of a master sample based on an earlier census.

8
voter. To the extent that this is the case, our results should be robust to examining the income shares of the poorest 60 percent of households instead of the poorest quintile.

**Table 3:  Parties and redistribution to the bottom 60%,**

<u>(Dependent variable:</u> _sharegain:_ <u>going from</u> _Market_ <u>(</u> _MarketP_ <u>) to</u> _<u>gross</u>_ income)

||(1)<br>Base specification,<br>_MarketP_income|(2)<br>Base specification,<br>_Market_income|
|---|---|---|
|Share of market income of poorest 60 percent of<br>households|-0.27<br>(0.000)|-0.54<br>(0.000)|
|Continuous years of competitive elections|0.042<br>(0.28)|0.11<br>(0.18)|
|**Largest govt. party is left wing**|**0.50**<br>**(0.025)**|**1.02**<br>**(0.053)**|
|**Age of largest govt. party if left-wing**|**-0.0099**<br>**(0.011)**|**-0.011**<br>**(0.049)**|
|Log of ppp-adjusted per capita income, 2005 US<br>dollars|-1.44<br>(0.12)|-4.59<br>(0.005)|
|Log of total population|2.33<br>(0.36)|-2.52<br>(0.40)|
|Percent of population that is rural|-0.033<br>(0.67)|-0.12<br>(0.11)|
|Percent of population that is 65 and older|-0.29<br>(0.17)|0.66<br>(0.009)|
|Observations|258|263|
|R-squared|0.244|0.575|
|Number of countries|47|49|

Note: All specifications are ordinary least squares, controlling for country fixed effects. Clustered, robust p-values in parentheses. _MarketP_ incomes are market incomes including pensions. _Sharegain_ is the change in income share of the respective quintile after comparing the _MarketP_ ( _market_ ) income share of the quintile with its disposable or gross income share.

Table 3 therefore presents results of regressions that are identical to those in the first and third columns of Table 2, but focus on redistribution to the bottom 60 percent only. The results reveal even stronger effects than in columns one and five of Table 2. The presence of a left-wing government party has a large and positive effect on redistribution to the bottom 60 percent; the effect is larger than in the corresponding regression looking at redistribution to the bottom 20 percent. The maturity of left-wing parties continues to play a significant role in moderating redistributive tendencies, even when focusing on redistribution to the bottom 60 percent.

The results in column two show that, as before, the results are magnified when pensions are counted as redistributive transfers. In fact, the coefficient of 1.02 indicates that a brand new (out of 16
33

# **BIBLIOGR&PBY**

1. Barnum, Howard and Hamid Mohtadi, "A Teaching Model to Simulate the Demographic Effects of Health and Family Planning Programs in Nepal" in Robin Barlow, (ed.), Case Studies in the Demographic Impact of Asian Developaent Projects, University of Michigan, 1982.

2. Barnum, Howard and Daniel Tarantola and I. Setiady, "Cost Effectiveness of An Expanded Program of Immunization in Indonesia", Bulletin of the World Health Organization, Vol. 58, 1980.

3. Barnum, Howard and David Yaukey, "Economic Analysis of an Integrated Rural Health/Family Planning Services Project for Nepal", USAID Project Paper. Washington, D.C., U. S. Agency for International Development, 1979.

4. Birdsall, Nancy, 'Analytical Approaches to the Relationship of Population Growth and Development", Population and Develogment Review, vol. 3, pp. 63-102, 1977.

5. Bongaarts, John and John Stover, The PoRulation Council Target-Setting Model: A User's Manual, The Population Council, Center for Policy Studies Working Paper No. 130, December 1986.

6. Bulatao, Rodolfo A., "Reducing Fertility in Developing Countries: A Review of Determinants and Policy Levers", World Bank, Staff Working Paper No. 680, 1984.

7. Cleland, John, and Zeba Sathar, "The Effect of Birth Spacing on Childhood Mortality in Pakistan", World Fertility Survey, August 1983.

8. Cochrane, Susan H., Fertility and Education: What Do We Really Know, Staff Occasional Paper No. 26, World Bank, Washington, D. C., 1979.

9. Cochrane, Susan H., and K. C. Zachariah, "Infant and Child Mortality as a DetG=r5rn1t of FertilHtv -- The Policy Implications", World Bank Staff Working Papers No. 556, 1983.

10. Easterlin, Richard A., Robert A. Pollack, and Michael L. Wachter, "Toward a More General Economic Model of Fertility Determination: Endogenous Preferences and Natural Fertility", Conference on Population and Economic Change in Less Developed Countries, September 30 - October 2, 1976, National Bureau of Economic Research.

11. ESCAP (Economic and Social Commission for Asia and the Pacific) and Ministry of Health, Indonesia, The Health Situation of Indonesia: (Statistical Profile), ESCAP, health Technical Paper #70/pdh 20, Bangkok, 1984.

12. Gwatkin, Davidson, "Mortality Reduction, Fertility Decline, and Population Growth -- Toward a More Relevant Assessment of the
# **1. Introduction**

Obtaining comparable measures of household incomes across households of different sizes and composition—or converting these incomes on a common (equivalence) scale—is a crucial task for welfare measurement. Indeed, a large body of literature has demonstrated that there are substantial effects of scale adjustments on poverty and profiles of the poor for various countries at different income levels (Lanjouw and Ravallion, 1995; Peichl _et al._ , 2012; Bishop _et al._ , 2014). Equivalence scales are often estimated based on expenditure data; one major disadvantage of this method is that it requires strong identifying assumptions (Deaton and Paxson, 1998).

In this paper, we make several contributions to the literature on equivalence scales and poverty measurement. First, we estimate equivalence scales using an alternative source of data, subjective well-being data. While a growing literature has followed this approach using panel data, these studies mostly investigate data on life satisfaction and income satisfaction.<sup>1</sup> We analyze instead a subjective well-being question where individuals are asked to evaluate their own level of material welfare on a nine-point scale from "poor" to "rich". This question arguably better captures the multidimensional nature of welfare and is more related to household welfare than satisfaction variables (Ravallion and Lokshin, 2001 and 2002).

Second, we offer new and interesting findings regarding the dynamics of poverty given equivalence scale adjustments (scaling) on long-run household panel data from the Russian Longitudinal Monitoring Surveys (RLMS). It is well-known that policies to address short-term

> 1 Two main types of subjective well-being data have been analyzed in the economic literature. The first type asks respondents about a hypothetical minimum income level that is required to reach a specified level of well-being (e.g., Garner and Short, 2004). Since this method assumes that people know what their true minimum income level is, the hypothetical assessment of the situation may lead to interpretation issues of minimum income questions (Steiger _et al._ , 1997). The second type asks respondents to evaluate their level of satisfaction with life or income, and does not have such disadvantage (e.g., Biewen and Juhasz, 2017; Borah _et al._ , 2018). Our paper is more related to the second approach. But we also offer robustness checks using life satisfaction data that are collected in the same household surveys.

2
**Table 4. Mexico: Composition of expenditure by** **<u>population quintile (in %)</u>**

|**Quintile**|**Apparel**|**Education**|**Food &**<br>**Beverages**|**Transport &**<br>**Communications**|**Housing**|**Health**|**Other**|**Total**<br>**Expenditure**|
|---|---|---|---|---|---|---|---|---|
|||||**1984**|||||
|**1**|7.07|4.36|56.49|5.37|15.38|3.00|8.33|6.24|
|**2**|7.06|4.56|54.82|6.93|15.15|3.45|8.05|11.15|
|**3**|7.62|6.57|49.21|8.24|15.64|2.61|10.12|14.72|
|**4**|7.83|6.85|44.55|10.39|16.99|2.18|11.22|21.83|
|**5**|5.73|6.07|24.08|15.40|14.31|2.20|32.22|46.06|
|**All**|6.70|6.04|37.70|11.68|15.25|2.45|20.20|100.00|
|||||**1989**|||||
|**1**|7.82|4.75|55.42|6.04|16.36|4.00|5.61|6.52|
|**2**|7.66|5.84|50.04|7.88|17.01|2.42|9.16|11.14|
|**3**|8.13|6.59|46.39|9.07|17.44|3.24|9.16|15.31|
|**4**|8.44|6.29|39.98|10.46|19.97|2.92|11.93|20.55|
|**5**|6.74|7.74|25.14|14.66|19.14|3.47|23.12|46.48|
|**All**|7.48|6.86|36.19|11.62|18.63|3.24|15.99|100.00|
|||||**1994**|||||
|**1**|7.00|6.26|50.60|9.08|17.63|3.63|5.79|6.28|
|**2**|7.27|7.42|46.42|11.19|17.97|3.31|6.41|11.13|
|**3**|8.14|7.99|41.91|12.74|18.95|3.37|6.91|14.63|
|**4**|7.99|9.07|36.06|14.29|21.08|3.59|7.93|20.61|
|**5**|7.06|14.62|23.16|16.77|23.35|4.14|10.91|47.35|
|**All**|7.43|11.18|32.88|14.56|21.28|3.79|8.89|100.00|
|||||**1996**|||||
|**1**|5.89|7.01|53.97|8.20|16.34|2.89|8.20|6.95|
|**2**|5.87|8.36|47.46|11.56|17.52|2.97|11.56|11.75|
|**3**|5.82|8.87|43.57|13.23|18.57|3.31|13.23|15.61|
|**4**|6.54|10.17|37.39|15.59|19.69|3.22|15.59|21.43|
|**5**|6.27|14.01|23.85|18.98|22.82|3.82|18.98|44.26|
|**All**|6.19|11.23|34.70|15.74|20.41|3.44|15.74|100.00|
|||||**2002**|||||
|**1**|5.85|13.54|45.69|10.31|14.05|2.38|8.18|6.67|
|**2**|5.53|12.41|40.80|14.65|15.07|2.66|8.88|11.32|
|**3**|5.80|14.50|35.28|16.77|15.36|2.89|9.40|14.84|
|**4**|6.19|15.11|31.55|18.14|15.33|2.97|10.71|20.39|
|**5**|5.60|21.57|20.50|20.06|16.34|3.11|12.83|46.78|
|**All**|5.76|17.63|28.93|17.92|15.70|2.95|11.13|100.00|

_Note:_ The table reports the composition of expenditure by population quintile of the expenditure distribution corresponding to seven expenditure categories in each of the surveys analyzed.

20
Chinese local governments have been heavily involved in local firms, especially in SOEs and privatized firms (Cull et al. 2015; Harrison et al. 2019). As discussed earlier, pundits have argued over whether Chinese growth has been due to heavy-handed government guidance or allowing the market to work. To shed light on this key concern, here we examine how the effects of privatization depend on government activism in dealing with firms in general. The more the government remains active in dealing with firms, the less privatized firms act like true private firms, and thus the effects of privatization are likely smaller. Indeed, Harrison et al. (2019) offer evidence that Chinese privatized firms behave like a mixture of SOEs and private firms, still enjoying subsidies from the government, and performing more poorly than true private firms but nevertheless having significantly better performance than SOEs. Thus, where the government is more active, privatized firms are more likely to behave like SOEs, and we expect the effects of privatization to be more muted.

Our proxy of the extent of government activism is based on the World Bank Enterprise Survey data in 120 Chinese cities in 2005. In the survey, the firm is asked to rate “the percent of officials in various government departments that facilitate the development of the firm,” which captures the strength of interactions between the government and the firm. This variable is averaged to the city level to capture regional variations. In Panel A of Table 7, we provide the firm-FE and the IV estimates of the effect of privatization that hinges on our proxy of government activism.<sup>22</sup> Consistent with our expectation, the effects of privatization are significantly lower where local government activism is stronger. Relying on the IV estimate and TFP_IN, the average effects of privatization on productivity is 102 log points at the mean level of local government activism (i.e., 0.407), and it is 180 log points at one standard deviation (i.e., 0.213) below the mean. Government activism thus reduces the effects of privatization.

> 22 Using the same logic as before, the privatization dummy’s instrumental variable is the dummy of the age of the local party secretary is 58 or older, and the instrumental variable for the interaction term of privatization and local government activism is the latter times the dummy of the age of the local party secretary being 58 or older.

``` 21 ```
# Table IV.Z Woveni Textile Samiple: Chtoice of Export Channcls

||Early|Exporters (<br>_N= _5|Before 1977)<br>||||Late Ex|porters (1977<br>N = 25|or Later)|
|---|---|---|---|---|---|---|---|---|---|
||Direct<br>Effort<br>of Firn|Foreign<br>Buyers|Non-Profit<br>Agency<br>Support|Sub-<br>Contracting||Direct<br>Effort<br>of Finn|Foreign<br>Buyers|Non-Profit<br>Agency<br>Support|Sub-<br>contracting|
|Relative Importance (1-5)<br>At First Export||||||||||
|All|2.4|3.2|1.2|4.2|All|2.5|2.4|1.3|4.0|
|Small (< 100 workcrs)'|2.5|3.0|1.0|5.0|<IW10|2.6|2.6|1.3|3.9|
|Large**(.**100 workers)'|2.3|3.3|1.3|3.7|> 100|2.3|1.9|1.3|4.3|
|< 10 yrs. Experience|1.0|1.0|1.0|5.0|C 3 yrs. exp|2.6|2.8|1.3|3.9|
|10 + Experience|3.3|4.7|1.3|3.7|3 + yrs. exp|2.4|1.7|1.3|4.1|
|Relative Importance (1-5)<br>Current"||||||||||
|All|2.4|3.2|1.2|3.2|All|2.6|2.6|1.4|3.9|
|Small Size'|2.5|3.0|1.0|4.5|<100|2.7|2.8|1.4|3.9|
|Large Sizc'|2.3|3.3|1.3|2.3|> 100|2.3|2.0|1.4|4.0|
|<1O yrs. Experience|1.0|1.0|1.0|_5.0_|< 3 yrs. exp|2.8|2.8|1.4|3.9|
|10 + yrs. Experience|3.3|4.7|1.3|2.0|3 + yr s. exp|2.2|2.0|1.4|4.1|
|||All Expo|rters|||||||
|Relative Importance (1-5)||||||||||
|<br>Current|2.5|2.8|1.3|3.7||||||
|Small Exporter<br>(CS billion won)|2.2|2.5|1.3|4.2||||||
|Large Exporter<br>(>5 billion won)|3.1|3.1|1.5|3.2||||||

# 'Size at first export.

The time elapsed since start-up averages 24 years for early exporters and 6.2 years for late exporters.

Note: Use is measured on a scale of I (low) to 5 (high). 'Current' period is 1991.

59
over the three year span from 2002 to 2005, falls in this range, with an average annualized exit rate of 7.6 percent.

A number of papers have looked at the role of exit to understand its contribution to aggregate productivity. While based on individual firm records, the data is used to generate aggregate exit rates by sector, size and/or age. Early work focused on the US, but has since been extended to several OECD countries and to a number of middle income countries in Latin America and Central Europe. Exiting firms are generally found to be less productive than incumbent firms. However, the extent of the contribution of exiting firms to overall productivity or the extent of the productivity gap with continuing firms, various considerably across countries. Thus exiting firms account for over 50 percent of the productivity growth in Chile from 1985-1999, approximately 10 percent in Slovenia (1997-2001) and slightly negative contribution in Latvia (2001-2002). Incumbents’ productivity is on average 63 percent higher than exiters in Colombia (1987-1998) while it is 4 percent lower in Latvia. (Bartelsman et al. (2004)). The literature is now turning to the question of what determines the variation in the extent of firm turnover and its contribution to productivity. The quality of regulations can be important, in particular that more flexible product and labor market regulations can facilitate the reallocation of resources (Loayza et al. (2005), Pavcnik (2002)). This paper takes a different approach. Rather than looking at aggregate exit rates, the paper looks at the exit decision from the perspective of the firm. It covers 27 countries with a broad range of levels of income and institutional development. It also takes advantage of a rich set of disaggregated measures of investment climate. Thus multiple measures are available to capture each of the areas of the investment climate that are of interest. And these measures vary sub-nationally. This approach allows for a detailed investigation into how 5