rafmacalaba/datause-encoder-data huggingface dataset:
As profits are given in the data as gross profit margin for manufacturing firms, we reduce it by subtracting the statutory corporate income tax rate, 15 percent dividends and 10 percent depreciation expenses.<sup>4</sup> For the services sector, where total costs are unobservable due to data restrictions, an assumption of 9 percent net profit margin is imposed.

The channels through which businesses finance their working capital indicates the reliance on profits. In Ukraine, for example, about 88 percent of the day-to-day operations of an average firm are financed through retained earnings. Firms in Kazakhstan, Greece and Portugal also finance their operations out of retained earnings (Figure 1). In contrast, firms in Colombia and Peru rely substantially on external financing. On average, retained earnings finance about twothirds of working capital (Table A2).

<!-- Start of picture text --> Figure 1: Percent of Working Capital Financed Through Retained Earnings on<br>Average<br><!-- End of picture text --> <!-- Start of picture text --> Peru<br>Colombia<br>Morocco<br>Kenya<br>Italy<br>Jordan<br>Russia<br>Turkey<br>Greece<br>Portugal<br>Kazakhstan<br>Ukraine<br><!-- End of picture text --> <!-- Start of picture text --> 10%<br><!-- End of picture text --> <!-- Start of picture text --> 20%<br><!-- End of picture text --> <!-- Start of picture text --> 30%<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br><!-- End of picture text --> <!-- Start of picture text --> 50%<br><!-- End of picture text --> <!-- Start of picture text --> 60%<br><!-- End of picture text --> <!-- Start of picture text --> 70%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br><!-- End of picture text --> <!-- Start of picture text --> 90%<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br><!-- End of picture text --> <!-- Start of picture text --> 0%<br><!-- End of picture text --> We use these data to expand the numerator, by taking the ratio of internal to external financing of working capital as constant over the period of extreme economic distress. In reality, financing may dry up if banks are unwilling to lend. Alternatively, government-sponsored

> 4 The data on the statutory corporate income tax rate are taken from the PWC’s _Worldwide Tax Summaries_ .

8
16 Figure 1 charts the relative wages of full-time; year round workers by educational group, using those with less an an elementary degree as the base'<sup>5</sup> . Lch ratio is normalized to be one in 1987 so that changes in the height of the ratio are interpretable as percentage changes in relative earnings for the group. The figure shows that relative earnings for the most educated were rising slightly in 1988, but then changed dramatically after the transition began in 1989. Those with four year university degrees gained 27 percent relative to those with less than an elementary education. Equally remarkable is that the proportional changes in relative earnings in Slovenia over this period are larger than the dramatic increases in relative returns to college graduates in the United States observed over the same period.<sup>1 6</sup> Returns to Experience. Consistent wit the results on education, retums to the most experienced rise relative to those with the least experience. As with returns to education, returns to expenence change in similar ways for men and women. The linear term becomes less positive and the quadratic term becomes less negative, meaning that marginal returns to a year of experience fall for the least experienced but rise for the most experienced. In fact the wage-experience profile for women tins convex in 1991. This outcome is not just a fiction of the quadratic approximation. In Figure 2, the coefficients on. a series of dummy variables representing progressive four-year experience increments pooled across men and women reveals the same pattern. Relative to 1987, the wage-experience profile is flatter over the first eight years, but is steeper thereafter. There is a IPa-year workers were paid very different nominal wages dependig upon the timg of their employment wihin the year. Use of the sample of year-round workers removes the artificial inrease i wa vanation caused by inflton. 16 Using CPS data for the U.S., over the 1984-1991 period, relative annual eanings for those with 16 years of education rose 25 perent relative to elementary school graduates. In fact, real salaries for U&S college graduates were decitmug over the period, but salaries for the elemeLtary school graduates were declining even more. More detailed analysis of changes in inequality of earnings across skill groups in the U.S. are in Juhn, Murphy, and Pierce (1993) and Katz and Murphy (1992). Levy and Mumnane (1993) provide an extensive review of the topic.
and wheat) grew at a slow pace of 1.0 percent per annum as compared with consumption growth of 2.3 (Table 5). South Asia’s production growth has been slower than world production while consumption growth rate has much exceeded the world consumption growth rate. This is partly owing to a faster pace of expansion of South Asia’s population, but also reflects a positive income elasticity.

There are significant differences at the country level in terms of production and consumption behavior, but all countries share the common result that on average food consumption has exceeded production during 1999-2008. In India, South Asia’s largest grain producer for both rice and wheat accounting for 71 and 76 percent of production respectively, wheat production has barely expanded during 1999-2008 although rice production has shown a healthy trend. In Pakistan, a primarily wheat consuming country and accounting for 21 percent of South Asia’s total wheat production, the production has fluctuated widely on a year-to-year basis but has been basically flat in 2000-2008, like in India. The other large foodgrain producer, Bangladesh, which is primarily a rice consuming country and accounts for 20 percent of South Asia’s rice production, has registered a production growth of 2.0 percent per year in rice. In sharp contrast to the production pattern, the rate of growth of food grain consumption in all three large producing countries have outstripped production.<sup>3</sup> The story is similar in the smaller countries of Afghanistan, Nepal and Sri Lanka. The data are not very reliable for Afghanistan, but it is a hugely food deficit country and relies mostly on wheat imports from Pakistan. Nepal’s rice production has stagnated but wheat production has expanded; overall foodgrain consumption growth exceeds production. In Sri Lanka, which is primarily a rice eating country, production of rice has been somewhat higher than production of wheat, allowing Sri Lanka to reduce its reliance on wheat imports.

**Table 5: Annual growth rates in production and consumption of food in South Asian Countries, 2000-2008 (percent)**

|Countries|Production gro|wth|<br>Consumption growth|Population|
|---|---|---|---|---|
||Wheat Rice|Total|<br>Wheat Rice Total|growth|
|Bangladesh|-8.82.0|1.5|0.02.62.3|1.6|
|India|0.31.5|1.0|1.92.62.3|1.7|
|Nepal|2.10.2|0.8|2.20.40.9|2.3|
|Pakistan|0.31.8|0.8|1.2 -1.01.0|2.6|
|Sri Lanka|0.02.2|n.a.|2.0 0.01.6|0.9|
|South Asia|1.6 0.2|1.0|2.2 2.4 2.3|1.9|
|World|1.8 1.0|1.5|1.3 1.0 1.2|1.1|

_Source: Production, Consumption growth rates calculated from Tables 3 and 4. Population data from World Bank World Development Indicators_

> 3 Surprisingly, however, Pakistan’s consumption growth for foodgrain is substantially lower than the population growth rate suggesting negative income elasticity.  This could alternatively reflect data problems.

8
64 major change in policy was initiated. The govemment increased controlled prices on food, utility and rail tariffs and increased electricity prices 95 percent over a period of two years, 1983-84.

# The Energy **Sector**

# _Structure of Energy Demad_

Net nery consunption in Zimbabwe at the time of the first domestic electricity price increase in 1982 was estimated to be 5056 thousand toe. Non-conmnercial fuels accounted for more than half (55%) of net energy consumption. Total commercial energy (coal, petroleum and electricity) consumption was 2303 toe, of which coal accounted for nearly half (47%), the rest being provided by petroleum (26%) and electricity (27%). Users of commercial energy were industiy (54%), transport (24%) and households (22%), which includes the commercial sector. The main fuels for industry were coal (53%) and electricity (34%), which together accounted for nearly 87 percent of commercial energy consumption in this sector, the rest being provided by petroleum products. The transport sector was highly dependent on oil. For the household sector, which also includes the consumption of the commercial sector,<sup>42</sup> the main commercial fuels were coal (54%) and electricity (40%).

Zimbabwe has large domesfic reserves of coal. There is also ample access to electricity both from domestic hydro and thermal sources and via imports from Zambia There are no reserves of oil and the landocked location of the country imposes high traportaion costs for imported oil. All these factors were reflected in domestic consumption where domestic resource and imports of electricity from Zambia provided most of commercial energy. Table 5.1 gives the sttre of commercial energy demand in 1982.

_Table 5.1: Zimbabwe: Primary Energy Consumption by Major Sectors, 1982 (000' toe)_

||Solid fuels|Oil|Electricity|Non-commercial<br>fuels|Total|
|---|---|---|---|---|---|
|Total<br>|1095<br>|596<br>|612<br>|2790<br>|5056<br>|
|Industry<br>|648<br>|161<br>|414|_450_|1673<br>|
|<br>Transportadon<br>|144<br>|403<br>|-<br>|-<br>|547<br>|
|Other sectors<br>|267|32|198|2340|2836|
|Other non-specified|-|-|-|-||

**_Source:_** lEA (I954).

a Coninmial sector refers to a heterogenous mix of govermnnt institutions, school, govenument offices, large and small retail and wholesale establishments and services.
|**Table 6: All i**|**nteraction te**|**rms included**|**simultaneou**|**sly (Log odds**|**ratios)**||
|---|---|---|---|---|---|---|
|Dependent variable: Woman<br>top manager Y:1 N:0|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Bank credit (ratio of|3.384***|3.461***|3.492***|3.213**|3.234**|3.306**|
|<br>GDP)*Financial dependence|(1.120)|(1.132)|(1.270)|(1.314)|(1.306)|(1.303)|
|Bank credit (ratio of|-3.945***|-3.895***|-6.302***|-6.238***|-7.564***|-6.950***|
|GDP)*Credit bureau present<br>Y:1 N:0|(1.111)|(1.080)|(1.033)|(1.028)|(1.251)|(1.324)|
|<br>Bank credit (ratio of GDP)*5-|0.084***|0.087***|0.128***|0.149***|0.151***|0.140***|
|<br>bank asset concentration|(0.019)|(0.018)|(0.025)|(0.027)|(0.035)|(0.040)|
|Financial dependence|-0.411|-0.436|-0.444|-0.302|-0.307|-0.348|
||(0.515)|(0.523)|(0.574)|(0.623)|(0.618)|(0.623)|
|Credit bureau present Y:1 N:0|2.538***|2.516***|3.757***|3.614***|4.719***|4.254***|
||(0.797)|(0.785)|(0.648)|(0.642)|(0.813)|(0.891)|
|5-bank asset concentration|-0.031***|-0.030***|-0.031***|-0.043***|-0.050**|-0.043|
||(0.009)|(0.008)|(0.011)|(0.014)|(0.021)|(0.026)|
|GDP per capita (logs)||-0.502|-1.684|-1.249|-2.034|-2.447*|
|||(0.750)|(1.149)|(1.170)|(1.254)|(1.278)|
|Number of workers (logs)||-0.197***|-0.226***|-0.202***|-0.198***|0.037|
|||(0.052)|(0.067)|(0.065)|(0.066)|(0.161)|
|Age of firm (logs)|||-0.080|-0.116|-0.117|-0.359|
||||(0.115)|(0.122)|(0.122)|(0.327)|
|Labor productivity (logs)|||-0.026|0.002|-0.014|0.092|
||||(0.049)|(0.050)|(0.049)|(0.099)|
|Women owners Y:1 N:0|||<br>2.622***|<br>2.720***|<br>2.719***|<br>2.728***|
||||(0.146)|(0.160)|(0.160)|(0.161)|
|Women workers (proportion)|||1.451***|1.453***|1.496***|1.511***|
||||(0.220)|(0.215)|(0.220)|(0.221)|
|Exports (direct, share of sales)|||-0.100|-0.114|-0.119|-0.090|
||||(0.257)|(0.254)|(0.253)|(0.251)|
|Foreign ownership|||0.093|0.139|0.137|0.128|
||||(0.270)|(0.267)|(0.266)|(0.264)|
|Quality certification Y:1 N:0|||0.044|0.077|0.071|0.081|
||||(0.143)|(0.143)|(0.143)|(0.143)|
|Overdraft Y:1 N:0||||<br>-0.117|<br>-0.120|<br>-0.126|
|||||(0.126)|(0.127)|(0.125)|
|Multi establishment firm Y:1||||<br>0.065|<br>0.044|<br>0.053|
|N:0||||(0.189)|(0.190)|(0.187)|
|Obtaining license is a major||||0.094|0.094|0.093|
|obstacle Y:1 N:0||||(0.196)|(0.198)|(0.198)|

46
Other incomes and wages (for comparison and illustrative purposes):

|Position|(1)<br>Official wage<br>(taels p.a.)|_(2)_<br>_Yang lien_(taels<br>p.a.)|(1)+(2) in<br>terms of the<br>estimated<br>overall income<br>mean|Source|
|---|---|---|---|---|
|District magistrate|45|1000|~160|Chang, p.13|
|Governor|150|12000|~1900|Chang, p.13|
|Highest level military<br>rank*|605||93|Chang, p.13|
|Seventh level military<br>rank*|36||5.5|Chang, p.13|
|Highest level court|307||47||
|official*||||Chang, p.35|
|Ninth level court official*|54.4||8.3|Chang, p.35|

*/ Wages include income in kind. Note: _Yang lien_ is an allowance paid on top of the official wage.

**Population and area** : Population from Maddison (2004). Area:  Current area of the People’s Republic of China plus Taiwan.

**Urbanization rate:** From Bairoch, _De Jéricho à Mexico_ , NRF, Gallimard, 1985, p. 462. Based on population living in towns that are greater than 5,000 inhabitants.

**Mean income in $PPP** .  From Maddison (2004).

# **REFERENCES**

Maddison, Angus (2004), _World population, GDP and per capita GDP, 1-2001 AD_ , available at http://www.ggdc.net/Maddison/content.shtml.

Bairoch, Paul (1985), _De Jéricho à Mexico_ , NRF, Paris: Gallimard.

Chang, Chung-li, _The Income of the Chinese Gentry_ , University of Washington Press, Seattle 1962, pp. 326-333.

49
With the increasing importance of personal pension plans, replacement rates have become a more common measurement of pension coverage. Even though the categorization does not strictly adhere to the pillars of the pension system, figure 1 shows that net replacement rates will surpass 90 percent over the next 15 years and it will all be due to occupational and personal savings as the relative income from the social pension will diminish. The increasing replacement rates and growing savings are partly driven by a change in the general view of what retirement should be. People are retiring earlier and living longer, which in itself raises the financial requirements. In addition, the prospect of healthier and more active years makes for a demand for not only security, but financial prosperity in retirement, which can only be attained by personal savings.

_Figure 1: Expected development in replacement ratios, 2000 - 2050_ <!-- Start of picture text --> Pct<br>100<br>90<br>80<br>70<br>60<br>50<br>Occupational Pensions and SP<br>40<br>Social pensions and ATP<br>30<br>20<br>10<br>0<br>2000 2010 2020 2030 2040 2050<br><!-- End of picture text --> Source: The Ministry of Economic and Business Affairs

# _1.3 THE DANISH PENSION INSTITUTIONS_

Life insurance companies are the most important group of pension institutions with over 40 % of total assets (Table 2). They are established either as shareholderowned joint-stock companies or as policyholder-owned mutual companies. Insurance companies typically manage employer-specific plans, which are negotiated with the employers concerned and cover all people employed by them. They are also active in offering personal pension plans. Multi-employer pension funds are created as memberowned mutual companies and cover industry-wide plans. Although they have different ownership structures, multi-employer pension funds and life insurance companies are 6
plants that are likely to the main suppliers of inputs to the organized sector. Tables 2 (a) and 2(b) show the differences between these two segments of the unorganized sector.

In order to measure changes in industrial policy, we use the detailed dataset of industrial policy in India constructed by [Sharma (2006a), Sharma (2006b)] and used in [Chamarbagwalla and Sharma (2011)]. Data on nominal tariffs are obtained from [Harrison et al. (2013)] for the period 1985-2007. A dummy variable indicating which 4-digit industries were FDI-deregulated in 1992 is taken from [Sivadasan (2009)] and combined with the ASI data to calculate the proportion of employment in a 3-digit industry exposed to FDI.

# **b. Plant-level Estimation of the Solow Residual**

Before aggregating to the state-industry cell level, we estimate a simple formulation of plantlevel multifactor productivity. We estimate the following simple production function for each state-industry cell separately ( _i_ is the index for plants).

Using the log-log formulation and controlling for year fixed effects we extract the (Solow) residual from this regression. This residual is exponentiated and the weighted mean across plants forms our measure of cell-level multifactor productivity where the weight is the plants’ share of cell output in each year.

# **c. Summary Statistics**

The analysis is motivated by several compelling yet puzzling facts that emerge from recent research by [Ghani et al. (2011a)], [Mukim (2011)], [McMillan and Rodrik (2011)], [Kathuria et al. (2010)] and [Nataraj (2011)]. A few of them are listed below.

- a. The unorganized sector in India is extremely large in absolute and relative terms compared to the organized sector and compared to other countries at a similar stage of development. The presence of unorganized plants is more pervasive in services than in manufacturing. Yet the numbers for manufacturing are still extremely large – in 2005, 99% of establishments and 81% of employment in manufacturing were in the unorganized sector.

13
<!-- Start of picture text --> Figure 20. Cumulative change in weighted average<br>log labor productivity, 2014-2021<br>(transportation and storage, constant 2010 prices)<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database <!-- Start of picture text --> Figure 21. Cumulative change in weighted average<br>log labor productivity, 2014-2021<br>(financial services, constant 2010 prices)<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Note: Averages are weighted by firms’ share of labor.<br>Source: World Bank and ZRA staff calculations based on CIT<br>database<br>Log-changes<br><!-- End of picture text --> The most notable exception to this overall trend is the accommodation and food service subsector, which has experienced overall productivity growth over the 2014-2021. Further study is needed to assess the drivers of the sector’s strong labor productivity performance, but the broad trend may be related to recent high-profile foreign direct investment (FDI) in the sector, including the acquisition of the Protea Hotels brand by Marriott and subsequent expansion of the Protea Hotel in Lusaka, the acquisition of the InterContinental Hotel Lusaka by Mauritius-based QG Africa Hotel, and acquisition of a portion of Sun International’s portfolio (including the Royal Livingstone Hotel) by the Thailand-based Minor Hotels. The sector has also experienced steady growth in its share of formal firms’ labor (from 1.8 percent in 2014 to 3.1 percent in 2021), although its aggregate share of formal firm employment remains modest (see Figure 22).

Figure 22. Cumulative change in weighted average log labor productivity, 2014-2021 (accommodation and food service, constant 2010 prices) <!-- Start of picture text --> Cumulative contribution to labor productivity growth (log)<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Note: Averages are weighted by firms’ share of labor.<br>Source: World Bank and ZRA staff calculations based on CIT database<br>Log-changes<br><!-- End of picture text -->

# 4.3.2. Productivity trends in agriculture, forestry, and fishing

In contrast to trends in other segments of the economy, formal firms in agriculture, forestry, and fishing have actually experienced steady increases in labor productivity over the 2014-2021 period, driven by within-firm growth (see Figure 23). The divergence between the formal firms in the CIT dataset and broader agricultural trends in Zambia reflects how the firms in the CIT dataset are often large-scale commercialized farms. Such farms have a relatively greater ability to leverage Zambia’s strong resource 13
An important difference between these two mechanisms is that they have different implications for how inequality should behave between and within demographic groups. Most of the papers in this literature focus on changes in inequality that are between demographic groups: skill, education, age, gender, and so on. On the other hand, models with firm-level heterogeneity predict that observationally identical workers can earn different wages and, so, any change in inequality predicted by such models would be _within_ demographic groups.<sup>3</sup> To illustrate the potential of relative price movements in explaining inequality, we study Mexico over the 1990-2014 period. Mexico is one of the most studied Latin American countries because it is both one of the most unequal countries and a liberalization leader, having embarked on ambitious trade liberalization programs over the last twenty years. We first briefly present evidence on changes in the relative price of Mexico’s skill-intensive goods. We show that those changes help explain the initial rise in inequality but not the subsequent sustained decline that motivates the Salter-Swan model. The Salter-Swan model generates several predictions that we then evaluate using both census and household labor market data. We show that the data are consistent with the predictions of the model, which implies a potentially very important role for firm-level heterogeneity in explaining changes in wage inequality.

The paper is organized as follows. The next section presents some stylized facts about laborincome inequality in Mexico and motivates the Salter-Swan model with heterogeneous firms. Simulating a calibrated version of the model generates several predictions that we then evaluate using both population census data and household survey data. Section V concludes.

II. Wage Inequality in Mexico: 1990 to 2014

> 3 Notably, it is well documented that observable demographic factors (age, education, and gender), occupation, and industry explain far less than half of the individual-level wages.  The residual may be due to worker-specific characteristics or firm-specific characteristics (such as match quality).  Recent literature focuses on match quality and firm-level heterogeneity as explanations for wage inequality.  Prominent examples include Helpman et al. 2014, DixCarneiro 2014, and Dix-Carneiro and Kovak 2014.   These models illustrate that trade-related changes in inequality are likely due to firm-level heterogeneity.  Empirical evidence from Brazil supports predictions from these models.

3
Glass Barriers World Bank. _Lao PDR - Mapping the gender dimensions of trade: A preliminary exposition._ Vientiane: World Bank, 2012b.

World Bank. _Lao PDR Trade and Transport Facilitation Assessment._ Vientiane: World Bank, 2014a.

World Bank. _The Republic of Zambia Diagnostic Trade Integration Study (DTIS)._ Washington, D.C.: World Bank, 2014b.

World Bank. _Toward Gender Equality in East Asia and the Pacific: A Companion to the World Development Report._ Washington, D.C.: World Bank, 2012c.

- —. _World Development Indicators._ 2005 and 2009. http://databank.worldbank.org/ (accessed June 6, 2014).

Cambodia and Lao PDR 24