rafmacalaba/datause-encoder-data huggingface dataset:
Figure H.2. Rights and Services – Fuzzy RD Plots with Local-quadratic Fitted Polynomial <!-- Start of picture text --> Rights and Services (Index) SISBEN Access<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Subsidized Healthcare Financial Products<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Transfers from Government<br>-200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2<br>4 .5<br>3.5<br>3 .4<br>2.5<br>.3<br>2<br>1.5 .2<br>SISBEN Access<br>1<br>Rights and Services (Index) .1<br>.5<br>0 0<br>.4 .3<br>.35<br>.25<br>.3<br>.25 .2<br>.2 .15<br>.15<br>.1<br>Financial Products<br>Subsidized Healthcare .1<br>.05 .05<br>0 0<br>.25<br>.2<br>.15<br>.1<br>Transfers from Government .05<br>0<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

87
Although the KHDS is a panel of respondents and the concept of a ‘household’ after 1013 years is a vague notion. It is common in panel surveys to consider recontact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to recontact 93% of the baseline households (that is, at least one previous household member was reinterviewed in 2004). This is an excellent rate of recontact compared to panel surveys in low-income countries _and_ high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman _et al_ . (2001) in which the rates ranged from 17.5% attrition _per year_ to the lowest rate of 1.5% per year. Most of these surveys in Alderman _et al._ (2001) covered considerably shorter time periods (two to five years). From an individual respondent perspective, excluding people who died, 82% of all respondents (5,404 total) were located and reinterviewed.

Because people have moved out of their original household, the new sample in KHDS 2004 consists of about 2,700 households which were recontacted from the 832 baseline households. Much of the success in recontacting respondents was due to the effort to track people who had moved out of the 51 baseline communities (mostly villages). One-half of all households interviewed in 2004 were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38% were located nearby the baseline community. Overall, 32% of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important responses to hardship and a strategy to escape poverty. Excluding these households in the sample raises obvious concerns regarding the selectivity of attrition. In particular out-migration from the village, dissolving of 9
# **Figure 1. Inequality across Country Groups, 1980s–2010s**

# _(Market and Disposable Income Gini Coefficients)_

While market-based income inequality has increased greatly in advanced economies since the 1980s, it has been broadly unchanged for emerging market and developing economies.

- a. Advanced Economies

- b. Emerging Market Economies

<!-- Start of picture text --> 55 55<br>50 50<br>45 45<br>40 40<br>35 35<br>30 30<br>Gini market Gini market<br>25<br>25 Gini disposable<br>Gini disposable<br>20<br>20<br>1980s 1990s 2000s 2010s<br>1980s 1990s 2000s 2010s<br>c. Low-Income Developing Countries d. World Average<br>55 55<br>50 50<br>45 45<br>40 40<br>35 35<br>30 30<br>Gini market Gini market<br>25 Gini disposable 25 Gini disposable<br>20 20<br>1980s 1990s 2000s 2010s 1980s 1990s 2000s 2010s<br>Gini coefficient Gini coefficient<br>Gini coefficient Gini coefficient<br><!-- End of picture text --> _Source_ : Authors’ calculations based on data from Standardized World Income Inequality Database (SWIID). _Note_ : Gini market indicates the Gini coefficient before taxes and transfers. Gini disposable indicates the Gini coefficient after taxes and transfers. A higher/lower Gini coefficient indicates greater/less inequality. The average index for the 2010s is up to 2019. The Gini coefficients presented in the figure are regional and global averages of country Ginis (and not interpersonal inequality in these country groups). Country groups are defined according to WEO Methodology. For details see <u>https://www.imf.org/external/pubs/ft/weo/faq.htm#q4b2.</u> 4
Table <u>14: Fixed efects and FGLS estimation results: net FDI inflows</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Variable**||**Coe**|**fcient**||
|GDP|0.899<sup>_∗∗_</sup>|1.208<sup>_∗∗_</sup>|0.899<sup>_∗∗_</sup>|0.899<sup>_∗∗_</sup>|
||(0.017)|(0.107)|(0.017)|(0.017)|
|OPEN|0.941<sup>_∗∗_</sup>|0.705<sup>_∗∗_</sup>|0.920<sup>_∗∗_</sup>|0.943<sup>_∗∗_</sup>|
||(0.083)|(0.162)|(0.081)|(0.083)|
|GDPGRO|0.256<sup>_†_</sup>|0.452<sup>_†_</sup>|0.260<sup>_†_</sup>|0.111|
||(0.142)|(0.260)|(0.143)|(0.127)|
|CPIGRO|-0.008<sup>_†_</sup>|-0.022<sup>_∗∗_</sup>|-0.008<sup>_†_</sup>|-0.011<sup>_∗_</sup>|
||(0.005)|(0.008)|(0.005)|(0.005)|
|RERGRO|-0.336<sup>_∗_</sup>|-0.243|-0.319<sup>_∗_</sup>||
||(0.146)|(0.238)|(0.147)||
|WLDGRO|-0.677<sup>_†_</sup>|-1.711<sup>_∗∗_</sup>|-0.643|-0.386|
||(0.391)|(0.620)|(0.393)|(0.371)|
|WLDFDIadj|0.373<sup>_∗∗_</sup>|0.317<sup>_∗∗_</sup>|0.400<sup>_∗∗_</sup>|0.377<sup>_∗∗_</sup>|
||(0.039)|(0.045)|(0.038)|(0.039)|
|PTAGDP|0.060<sup>_∗∗_</sup>|0.148<sup>_∗∗_</sup>|0.013<sup>_∗∗_</sup>|0.058<sup>_∗∗_</sup>|
||(0.018)|(0.024)|(0.004)|(0.018)|
|DISTA|-0.155<sup>_∗∗_</sup>|-0.488<sup>_∗∗_</sup>||-0.151<sup>_∗∗_</sup>|
||(0.058)|(0.079)||(0.058)|
|N|1541|1541|1541|1541|
|Log-lhd (_R_<sup>2</sup>)|-1716.811|0.721|-1719.664|-1719.765|
|_χ_<sup>2</sup>(F)|3146.643|110.522|3172.543|3138.254|
|Signifcance level<br>_Note:_ Standard <br>is expressed in n<br>mates of Table 4<br>fects, columns (3<br>omitting the dist|s:<br>_†_ 10%<br> errors are in <br>atural logarit<br>, column (2) <br>) and (4) appl<br>ance and RE|_∗_5%<br>_∗∗_<br> parentheses. <br>hms. Column<br> estimates eq<br>y the three-st<br>ER growth v|1%<br> The depende<br> (1) reproduc<br>uation (2) usi<br>ep FGLS estim<br>ariables, respe|nt variable<br>es the esti-<br>ng fxed ef-<br>ator while<br>ctively.|

in Table 4). For equation (1), section B.1 has shown that the choice of estimator has a significant impact on the model results, while sample size is much less important and does not qualitatively change the conclusions. In order to test whether is the former is true for equation (2), column (2) shows the results of estimating this equation with a fixed effects approach. The point estimates vary widely across specifications: for example, the coefficients on inflation, global growth, and PTA extended common market are approximately three times larger when using fixed effects. At the same time, all variables with the exception of REER growth rate have the expected signs and are significant at the ten percent level, and both PTA-related variables are significant at the one percent threshold. This suggests that it is the choice of variables (i.e. model specification) rather than the estimation approach that renders _PTAGDP_ and _DISTA_ statistically significant. In other words, once we account for multicollinearity and endogeneity problems by dropping the _GNIREL_ variable and replacing _WLDFDI_ with _WLDFDIadj_ , the choice of estimation approach becomes 56
**Table A.2: Unemployment and informality**

||**(1)**<br>**Unemployment**|**(2)**<br>**Informal**|
|---|---|---|
|_Demographic variables_|||
|Age|−0.008**|−0.007|
||(0.003)|(0.010)|
|Age squared|0.000*|0.000|
||(0.000)|(0.000)|
|Male|0.051|−0.033|
||(0.040)|(0.087)|
|Secondary|0.001|−0.130**|
||(0.016)|(0.054)|
|University|0.094***|−0.338***|
||(0.034)|(0.098)|
|Other|−0.055**|0.099|
||(0.022)|(0.087)|
|Secondary × Male|0.013|−0.162|
||(0.045)|(0.099)|
|University × Male|−0.128**|−0.154|
||(0.053)|(0.131)|
|Other × Male|−0.028|0.050|
||(0.044)|(0.163)|
|_Geographic characteristics_|||
|Distance to CBD|−0.008**|0.029***|
||(0.003)|(0.008)|
|Distance to CBD squared|0.000|−0.000**|
||(0.000)|(0.000)|
|Adj-R<sup>2</sup>|0.038|0.160|
|Obs.|2,862|1,879|

_Note:_ Standard errors in parentheses.* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

26
-29-

The examples below illustrate the application of this principle and the step-wise adjustments indicated. The effect of different balance sheet structures and the appropriate adjustments are shown for three cases: a) a firm holding monetary liabilities, b) a firm with a depleted stock of inventory, and c) a firm with both depreciable assets and monetary liabilities.

To facilitate comparison with the examples developed earlier, case (a) and (b) use the same data as in table 2 and column 2 of table 3, respectively.

Case a: The firm is assumed to be holding a non-depreciable asset and a balancing amount of a monetary liability.

TABLE 5

|BAL<br>ASSET|ANCE SHEET<br>S AO|End of<br>, Period 0<br>LIABILITIES<br>Lo|INCOME STATEMENT,<br>Period 1<br>Revenue<br>(1000)|1200|
|---|---|---|---|---|
|MA0|0|1000 MLO|Wages<br>(500)|<br>600|
|NMAO|1000|0NWO|Materials<br>(100)<br>Interest<br>(50)|<br>120<br> <br>260|
||||Cash Expenses (650)|<br>980|
|AO|1000|1000 Lo|Cash Flow<br>(350)<br>Pre-tax|<br>220|
||||( )figures indicate<br>values for zero infl|<br>ation.|
analysis we can conduct given that response rates are highly correlated with price paid, particularly so for the BDM sample.

# **4 Results**

## **4.1 Demand for Business Training**

## **BDM Sample**

We first estimate the demand schedule for business training for the BDM sample. Our first measure of demand is whether entrepreneurs purchased the course according to the BDM procedure (i.e., their WTP was at least as high as the offered price) regardless of whether they actually paid. Table 4 shows how the randomly offered price affects this outcome. In Column (1), we use a continuous measure for price offered. Demand is downward sloping, with the point estimate indicating that a J$1,000 increase in price reduces take-up by 3.3 percentage points. In Column (2) we include binary variables for each level of price offered to entrepreneurs. Increases in the price from 0 (the omitted category) to $1,000 or $3,000 do not have statistically significant effects on demand. However, extra increases in price reduce demand significantly. Indeed, we can reject that the reduction in demand is linear with prices.<sup>14</sup> For example, charging a price equal to the cost of the course (J$20,000) would reduce demand by 67 percentage points. While columns (1) and (2) show that raising the price towards the cost of providing the training has a large impact on demand, the coefficients also imply that demand is price-inelastic. For example, doubling the price reduces demand by less than one half. Demand is more inelastic at lower prices: moving from J$1,000 to full price has a price elasticity of -0.04; moving from J$5,000 to full price a price elasticity of -0.22; and moving from half price (J$10,000) to full price a price elasticity of -0.59. Column (3) includes indicators for treatment variations; we do not see any effect from either the credit or the discount frame intervention.

However, as explained above, almost half of participants in the BDM exercise did not pay the agreed amount. In Columns (4)-(6), we use actual payment of the full price offered for the course as our measure of demand. In this case, the decrease in demand with price is even more pronounced: a J$1,000 increase in price reduces demand by 4.7 percentage points. Moreover, while we can still reject linearity of price effects, the pattern of effects is reversed: the decrease in demand is now higher at lower prices than at higher prices.<sup>15</sup>

> 14If we express the coefficients in terms of reductions in demand per $1,000 increase in price, we get: 0, 0.007, 0.01, 0.02, 0.03, and 0.03, respectively.

> 15If we express the coefficients in terms of reductions in demand per J$1,000 increase in price, we get: 0.32, 0.12, 0.12, 0.08, 0.06, and 0.05, respectively. The large decrease from a price of 0 to one of J$1,000 is

15
Panel E of Appendix Table 3 shows summary statistics for the business environment measures. Across industries, it takes between 3 and 5 days to clear customs for imports. The percentage of time spent by managers dealing with regulation and bureaucracy varies from 18.2% in the textiles industry to 27% in the leather/footwear industry. The number of power outages experienced by firms in the year before the survey is very large for firms in all industries. Firms in the food and leather/footwear industries are particularly affected by the weaknesses of the electricity supply, suffering on average 560 and 885 power outages per year, respectively. In face of the frequency of power outages, it is not surprising that more than 80% of firms in all industries own a generator. There is evidence of high levels of corruption in the interactions of Bangladeshi firms with government officials and bureaucracy. More than 85% of firms indicate that in their industry it is necessary to pay bribes to government officials to get things done. Such bribes amount to 1.3% of sales on average in the ready-made garments industry but are only half as large in other industries. Finally, while firms spend relatively little resources on protection payments on average, there is a large degree of variability in those expenses across firms.

# **_3.3. Main Results_**

In this section, we discuss our findings on the determinants of firm TFP. As mentioned earlier, Eq. (2) may suffer from a multicollinearity problem since it includes multiple determinants of TFP that may be correlated. To address this problem, we show in Appendix Table 4 the results from regressions of firm TFP on a _single_ determinant at a time, in addition to basic controls. The sign and significance of the effects in those regressions are similar to those in the regressions including multiple determinants. Thus, the concern of a multicollinearity problem is mitigated.

We focus first on the role of firm size and firm age for TFP. Theoretical models of industrial dynamics with firm heterogeneity predict that more productive firms are larger (Jovanovic, 1982). Also, several stylized facts have been established for developed countries on the impact of the life-cycle on manufacturing firms’ TFP (Bartelsman and Doms, 2000). Studies based on U.S. data find that firms generally enter an industry with a small size and low productivity. The firms that survive grow and converge quickly to 18
TABLE 1. Summary Statistics

||Obs.|**Ethio**<br>Mean S|**pia**<br>td. dev.|Loadings|Obs.|**Sud**<br>Mean|**an**<br>Std. dev.|Loadings|
|---|---|---|---|---|---|---|---|---|
|**1. Firm characteristics**|||||||||
|Firm age (years)|304|17.93|16.1||401|15.21|14.1||
|Education of top manager<sup>(a)</sup>|303|2.71|1.20||399|2.92|1.25||
|Experience of top manager (years)|304|14.5|9.77||395|17.2|12.9||
|Any female owner?<sup>(b)</sup>|304|0.23|||382|0.15|||
|Log(firm employment)|304|3.37|1.66||399|2.61|1.14||
|**2. Innovation and R&D**|||||||||
|Did the firm introduce a new product last year?<sup>(b)</sup>|304|0.35||0.70|391|0.48||0.58|
|Did the firm invest in plants & equipment last year?<sup>(b)</sup>|304|0.52||0.67|400|0.46||0.70|
|Does the firm conduct any R&D?<sup>(b)</sup>|304|0.13||0.72|388|0.23||0.74|
|IT usage (0 = nothing, 1 = email, 2 = website)|304|0.59|0.76|0.48|401|0.45|0.78|<br>0.74|
|**3. Human capital and labor management**|||||||||
|Ratio of nonproduction workers to total employment<sup>(c)</sup>|304|0.27|0.17|0.24|398|0.42|0.30|<br>0.22|
|Any in-house training of staff last year?<sup>(b)</sup>|304|0.28||0.83|397|0.27||0.80|
|Staff sent to formal training course last year?<sup>(b)</sup>|304|0.28||0.84|398|0.12||0.80|
|**4. Contractual practices**|||||||||
|Any direct imports of inputs?<sup>(b)</sup>|304|0.31||0.67|401|0.51||0.74|
|Do you sell on credit?<sup>(b)</sup>|304|0.53||0.65|401|0.64||0.73|
|Does firm subcontract production?<sup>(b)</sup>|302|0.12||0.33|382|0.09||0.22|

35
# **Measuring Ancient Inequality**

Branko Milanovic Peter H. Lindert Development Research Department Department of Economics World Bank, Room MC 3-581 University of California-Davis 1818 H Street NW Davis, CA 095616 Washington D.C. 20433 and NBER bmilanovic@worldbank.org <u>phlindert@ucdavis.edu</u> Jeffrey G. Williamson Department of Economics Harvard University Cambridge, MA 02138 USA and CEPR and NBER <u>jwilliam@fas.harvard.edu</u> We acknowledge help with the data from Carlos Bazdresch, Luis Bértola, David Clingingsmith, Rafa Dobado González, Jan Luiten van Zanden, Jaime Pozuelo-Monfort, Paolo Malanima, Leandro Prados de la Escosura, Martin Ravallion, Jim Roumasset, and Jaime Salgado. The paper has also been improved by the comments of Jan de Vries and other participants at the EHA meetings (Austin, Texas: September 7-9, 2007.) Lindert and Williamson acknowledge financial support from the National Science Foundation (SES-0433358 and SES-0001362) and, for Williamson, the Harvard Faculty of Arts and Sciences.

JEL classification: D3, N3, O1 Key words: Inequality possibility frontier, pre-industrial inequality, history.
many as Brazil. The government is also offering special incentives to attract the Diaspora, rewarding scientific talent, financing for basic research and support for technology innovation and commercialization (OECD, 2008). For this purpose, it has established 18 leading universities and 9 key research organizations. Its national innovation system has shown that China excelled in mobilizing financial and human resources for S&T development on a large scale and has made major progress in R&D indicators (OECD, 2008).

# **6. Brazil: Decline and recovery**

From its position close to or on the Korean technological frontier in many industries in the 1980s, Brazil’s productivity levels slid throughout most of the 1980s and 1990s largely in response to an unstable macroeconomic environment and the absence of healthy competition. Around 2000, labor productivity growth started to pick up (table 2). Even though recovery has been sustained, Brazil still has significant catching up to do.

**Figure 11: Brazil’s petroleum industry was close to the** **<u>Korean technological  frontier</u>**

<!-- Start of picture text --> V.A. per Worker (Korea=100)- product 353 V.A. per Worker (Current '000 US$/worker) - product 353<br>1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005<br>KOR BRA KOR BRA<br>IND CHN IND CHN<br>Product: Petroleum refineries<br>Source: UNIDO Industrial Statistics Database, Rev. 2 and 3<br>Source: COMTRADE, authors’ calculations<br>800<br>150<br>600<br>100<br>400<br>50<br>200<br>0 0<br><!-- End of picture text --> Statistical evidence suggests that the distance of Brazil’s leading industries from Korea’s productivity frontier depends upon the timing of competition and the industry’s distance from the frontier _prior_ to reform.<sup>10</sup> Consider examples from its largest industries (figure 4). In 2000, the Brazilian Food industry’s distance from the frontier was about 25 percent, the same as China’s (figure 4). Even after recovering during 2000-05, this distance has not narrowed mostly because the frontier has moved forward faster. Similarly, the distance has widened in Electrical Machinery. Interestingly, in Industrial chemicals, Transport equipment and Iron and steel, productivity levels are converging albeit only gradually. Most notable is the relative productivity trend in the Iron and steel industry in which Brazil was significantly close to the frontier in 2000. Even better, in the Petroleum refining industry, Brazil’s technological performance exceeded Korean levels in 2000 (figure 11).

> 10 In 1996 and 2004, they were Food products, Industrial chemicals, Transport equipment, Iron and Steel, Petroleum, Electrical machinery and Non-electrical machinery – each had a share of 4 or more percent.

23
would be considered green. A much lower share of broadly green jobs (12 percent) can be classified as high-level occupations, as compared to the overall labor market (24 percent). Thus, while strictly green occupations are highly skilled and concentrated in high-level occupations, there is potential for more employment in mid-level and elementary occupations if green technology and processes are more widely adopted to stem the impact of humans on the environment.

# 3.2 How Prevalent Are Green Jobs in South Africa?

The 46 occupations that can be classified as strictly green account for about 5.5 percent of total employment in the South African labor market that employs 16.4 million people. This share has barely moved over the period 2012-2019 with only a small increase in 2014 (Figure 2). The lack of increase in the strictly green jobs trend could be an indication of a slow implementation of green economy practices. Or it may be a casualty of the data since the task description of occupations is static. In other words, jobs may have adopted technology or processes that require tasks in the strictly green list of the green dictionary, but these tasks may not be reflected in the ISCO-88 occupational task descriptions. Further, strictly green jobs may have added more green tasks over time, but greening within a job is not captured in our data.

Figure 2: Green employment over time (2012-2019) <!-- Start of picture text --> 6<br>33<br>5.5<br>5 32<br>4.5<br>4 31<br>3.5<br>3 30<br>Year<br>Green strict Green broad<br>2012q12012q22012q32012q42013q12013q22013q32013q42014q12014q22014q32014q42015q12015q22015q32015q42016q12016q22016q32016q42017q12017q22017q32017q42018q12018q22018q32018q42019q12019q22019q32019q4<br>Share green strict (%) Share green broad (%)<br><!-- End of picture text --> Source: authors’ calculations using PALMS.

Expanding the definition to capture broadly green jobs, the share of broadly green employment is nearly 33 percent in 2019, up from 31 percent in 2012. A look at employment trends over time shows that there was a visible upward shift in 2013-2014 as a result of an increase in employment in a few mid-level and elementary occupations, specifically extraction and building trades (ISCO code 71), metal machinery and related metal trades (code 72), laborers in agriculture (code 92) and laborers in mining and construction (codes 93). These occupations, especially extraction and building trades and metal machinery and related metal trades, include many broadly green 4-digit ISCO occupations. The increase in broadly green 14