rafmacalaba/datause-encoder-data huggingface dataset:
8

> **Table 5: Consumption inequality(') during the 1990s in selected African countries**

|Ginicoefficient|_YearI _|_Year2 _|_Change_|
|---|---|---|---|
|_Ethiopia()_||||
|1994-1997(rural)|0.43|0.42|-0.01|
|1994-1997 (urban)|0.44|0.48|0.04|
|_Ghana1992-98_||||
|Rural|0.34|0.37|0.03|
|Urban|0.34|0.35|0.01|
|All|0.37|0.39|0.02|
|_Madagascar1993-99_||||
|Rural|0.42|0.36|-0.06|
|Urban|0.41|0.38|-0.03|
|All|0.43|0.38|_-0.05_|
|_Mauritania1987-95_||||
|Rural|0.43|0.37|-0.06|
|Urban|0.40|0.36|-0.04|
|All|0.43|0.39|-0.04|
|_Uganda1992-2000_||||
|Rural|0.33|0.32|-0.01|
|Urban|0.39|0.40|0.01|
|All|0.36|0.38|0.02|
|_Zambia1993-98_||||
|Rural|0.46|0.52|0.06|
|Urban|0.40|0.48|0.08|
|All|0.52|0.53|0.01|
|_Zimbabwe1991-96_||||
|Rural|0.58|0.57|-0.01|
|Urban|0.60|0.59|-0.01|
|All|0.68|0.64|-0.04|

> () Real expenditures per adult equivalent - real per capita expenditures for urban Ethiopia.

**(2)** Puiposively sarnpled villages and urban centers; not nationally representative. Source: Country Studies under Dynamics of Poverty study.

# **_Trends in poverty during the 1990s_**

If growth episodes were not associated with significant changes in inequality, did they lead to poverty reduction? Table 6 reports poverty estimates for the countries covered by the _Poverty Dynamics_ study. As with the inequality measures, real household consumption per adult equivalent is taken as the central economic welfare measure. Poverty lines in all cases (except Mauritania) are derived from a food consumption basket, estimated to yield a minimum caloric intake, with adjustments made for essential non-food consumption. We reiterate that because of differences in survey design, and in the specifics of how the welfare measure and poverty lines are derived, the data in Table 6 are not comparable across countries. But the research has been designed to ensure comparable estimates over time.
The findings in Table 5 are consistent with the predictions from trade models with heterogeneous firms whereby growth in the number of firms contributes relatively little to total exports as compared with growth in average firm size because entrants tend to be marginal firms.<sup>41</sup>

**_Stylized Fact 3: Across countries, entry and exit rates are strongly positively correlated with each other and both are negatively correlated with entrant survival rates, average exporter size, and diversification at the exporter level (in terms of products and destinations)._**

Table 6 shows the matrix of correlation coefficients among several measures in the Database at the country-year level (averaged over the 2006-2008 period) and the corresponding p-values.<sup>42</sup> The table presents also the correlations between the dynamics and diversification measures with total exports. Exporter entry and exit rates are strongly positively correlated in Table 6, implying that countries with high entry rates also have high exit rates. This evidence is similar to that based on industry data for individual countries such as Peru by Freund and Pierola (2010). In a typical year there are naturally high levels of experimentation (entry) accompanied by similar levels of failure (exit) in all countries. This intuition is reinforced by the strong negative correlations of both entry and exit with one-year survival of new entrants into export markets. Entry and exit rates are also strongly negatively correlated with average exporter size. This is not surprising since export markets are more contestable in countries where exporters are relatively smaller, observed churning is likely to be higher.

> 41 This finding differs, however, from cross-sectional evidence provided by Mayer and Ottaviano (2008) and our stylized fact 5 below on the correlation between the number of exporters and standard gravity variables. We discuss this in more detail below. 42 Kuwait and Portugal are part of the sample used to compute the correlations in Table 6 despite their data covering a period other than 2006-2008. However, similar correlation patterns are obtained if they are excluded from the sample. Moreover, similar patterns are obtained if we use data for individual years within or outside the 2006-2008 period.

28
# Table 4

||Real|Pension F|und Returns||
|---|---|---|---|---|
||US|UK|Netherlands|Switzerland|
|1996-70|-5.4|4.2|1.7|0.8|
|1971-75|-0.8|-2.8|-1.4|-0.5|
|1976-80|-1.9|4.9|2.0|4.0|
|1981-85|8.1|12.4|10.5|3.0|
|1986-1990|11.2|10.1|6.3|-0.2|
|1966-90|2.2|5.8|4.0|1.5|

Source: Davis (1992)

**4.4** Demographic pressures and funded and pay-as-you-go **schemes**

The question of the adequacy of capital productivity and investment returns tor fully funded schemes with universal coverage is also related to the issue of whether funded schemes can cope better than pay-as-you-go schemes with aging populations and the resulting demographic pressures. As shown in Tables 3a and 3b, a pillar demographic structure (zero population growth) implies a higher level of pension fund assets than a pyramidal structure.

This issue can be addressed by comparing the simple algebra of defined contribution plans with that of pay-as-you-go schemes. The required contribution rate for a funded scheme, when pensions are indexed to wages, is given by where "G", "D" and **"F"** are defined as above. If "g" is equal to "r", equation (43) simplifies to where the fraction m/n can be seen as the passivity ratio, i.e. 5 the ratio of the retirement life over the working life". The level of this ratio will depend on the age at which workers start _15_ The passivity ratio is the inverse of the active worklife ratio. The passivity ratio corresponds to the dependency ratio in social security parlance, while its inve.se, the active worklife ratio, corresponds to the support ratio.

31
Table 2: Disruptiveness and Elasticity of Funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded|Last Funded|Ever Funded|Last Funded|
||(1)|(2)|(3)|(4)|
||Panel|a. All Firms|||
|Disruptive|2.634***|2.090***|2.847***|2.297***|
||(0.284)|(0.195)|(0.257)|(0.178)|
|Disruptive_×_Africa|-2.239***|-1.683***|-2.505***|-1.878***|
||(0.300)|(0.204)|(0.294)|(0.210)|
|Disruptive_×_LAC|-1.635***|-1.245***|-1.776***|-1.374***|
||(0.387)|(0.249)|(0.456)|(0.300)|
|_R_<sup>2</sup>|0.241|0.208|0.242|0.209|
|Observations|25464|25464|25464|25464|
|Countries|33|33|33|33|
|P|anel b. Start-u|ps (10 years old|or less)||
|Disruptive|<br>2.172***|<br>1.795***|<br>2.417***|2.029***|
||(0.107)|(0.080)|(0.074)|(0.100)|
|Disruptive_×_Africa|-1.891***|-1.451***|-2.150***|-1.609***|
||(0.173)|(0.126)|(0.144)|(0.145)|
|Disruptive_×_LAC|-1.297***|-1.024***|-1.407***|-1.114***|
||(0.225)|(0.173)|(0.364)|(0.260)|
|_R_<sup>2</sup>|0.229|0.193|0.230|0.195|
|Observations|15634|15634|15634|15634|
|Countries|33|33|33|33|
|Pa|nel c. Older fr|ms (11 years ol|d or more)||
|Disruptive|2.944***|2.161***|3.095***|2.328***|
||(0.397)|(0.135)|(0.411)|(0.151)|
|Disruptive_×_Africa|-2.339***|-1.616***|-2.526***|-1.840***|
||(0.489)|(0.241)|(0.485)|(0.246)|
|Disruptive_×_LAC|-2.137***|-1.461***|-2.294***|-1.643***|
||(0.504)|(0.243)|(0.533)|(0.284)|
|_R_<sup>2</sup>|0.162|0.128|0.163|0.129|
|Observations|9830|9830|9830|9830|
|Countries|33|33|33|33|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, and the number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is the IHS transformation of the amount of funding in US Dollars received in the last funding round. Standard errors clustered at the country level.

21
-18-

<!-- Start of picture text --> 4&A-IRE<br>0.70 Z 120%<br>A 210%<br>0.60<br>1100%<br>0.50 90%<br>N ~~~~~~80%<br>0.40<br>0.30 -. 60%<br>-50%<br>0.20 - -- _ 40%<br>010 - ~~~~~~~~~~309%<br>M2-ml/gdp 20%<br>0.00 10%<br>73 74 75 76 77 78 79 80 81 82 83 84 85 86<br>M1/GDP _M2/GDP<br>(M2-Ml)/GDP -INFLAION<br><!-- End of picture text --> FIGURE 2c
(at 1993 PPP) looks similar to Figure 1. But the RDV lines are notably lower; the gap diminishes as consumption falls, but still persists amongst the poorest countries. For the poorest 15 countries ranked by consumption per capita at 1993 PPP, the mean poverty line in the RCS data set is $44.19 ($1.45 a day<sup>18</sup> ) versus $33.51 ($1.10 a day) using the old (RDV) series for eight countries with consumption below upper bound of consumption for those 15 countries.

This might suggest that there was an upward drift in the national poverty lines of poor countries between the 1980s and the 1990s and 2000s. However, this seems unlikely, given that it appears to be quite rare for developing countries to increase the real value of their poverty lines over time. The more plausible explanation lies with the aforementioned differences between the two samples of national poverty lines. There are two salient differences. First, recall that the RDV sample used rural poverty lines, which are invariably lower than urban lines (Ravallion et al., 2007); this will lower the schedule of poverty lines at given national mean consumption.

Second, the original RDV sample under-represented SSA. Three of the eight countries in the RDV sample that qualify to be in our reference group of countries (with consumption per capita less than $60 per month) are in SSA, yet 22 of the 29 countries with consumption less than $60 per month in the set of 115 countries for which we measure poverty are in SSA. In other words, the proportion of SSA countries in the RDV sample is about half what it should be to be considered representative of poor countries. This is not such a problem for the RCS sample; as can be seen from the list of countries above used to set the $1.25 a day poverty line, 13 out of the 15 countries are in SSA; if anything this appears to slightly over-represent SSA.

This difference between the RDV and RCS samples is relevant to understanding why the RCS lines are higher at given consumption, as seen in Figure 2. National poverty lines at 1993 PPP tend to be higher in SSA than other regions at given consumption. The difference between SSA and non-SSA poverty lines amongst the reference group of countries cannot be reliably estimated given that there are so few non-SSA countries in the reference group. In other words there is very little common support at very low consumption levels. A reasonable definition of the region of common support is the 37 countries with consumption over $50 and under $250 per month at 1993 PPP. (Only one country in the sample, Mauritius, has consumption $250, and it is well above it at $426.) On regressing the poverty line at 1993 PPP on consumption per capita for the sample in the region of common support, with a dummy variable for SSA countries, one

> 18 Note that this is at 1993 PPP; $1.45 in 1993 prices represents $1.96 a day at 2005 US prices.

12
The results show that the reduction in tobacco consumption and the expected increase in years of potential work have positive impacts on welfare. In the first scenario, the gains are evenly distributed across income deciles. However, elasticities vary across deciles, generating an important impact on lower-income groups (figure 5).

**Figure 5. Income Gains, Production during Years Lost**

# **d. Net effects: Total distributional impacts**

Once the effects of the tobacco tax policy on prices, medical expenditures, and increased years of employment are calculated separately, one may examine the bigger picture. Based on a lower-bound elasticity, the results show a mixed effect of tobacco tax policy. The effect is progressive in that it has a smaller impact on the lowerincome groups of the population relative to the higher-income groups, but the overall effect is negative (table 12; figure 6). Apparently, a population that is not as sensitive to tobacco price changes will not reduce consumption sufficiently to allow health and work benefits to offset cost increases. This is exactly what happens in Ukraine.

**Table 12. Total Net Effect (%)**

|_Price shock scenario_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|0.11|−0.08|−0.12|−0.22|−0.26|−0.29|−0.29|−0.27|−0.32|−0.29|
|Medium elasticity|0.48|0.28|0.19|0.05|−0.01|−0.06|−0.08|−0.07|−0.15|−0.16|
|Upper-bound elasticity|0.86|0.63|0.49|0.31|0.24|0.16|0.14|0.12|0.02|−0.02|

_Source:_ Proportion of household budget. Based on data of the 2013 household budget survey. _Note:_ The table shows the share of total consumption for each decile.

17
EU) of preferences, even when there are restrictive ROOs, and the short run e¤ects in Table 8. Back of the envelope calculations that ignore the role of entry like Mattoo et al. (2003) as well as more sophisticated calculations based on models with the ...xed mass of entrants,<sup>51</sup> could easily underestimate these long run e¤ects, or even get the e¤ect on welfare reversed.

# 6.2 Subsidizing Fixed Costs

Which ...xed costs should be subsidized? Is there a di¤erence? Table 9 looks at these questions in terms of promoting exports. It compares the e¤ectiveness of a given dollar value ($1,500,000) of a subsidy to di¤erent kinds of ...xed costs. In this, it follows Das, Roberts and Tybout (2007). The results suggest the export e¤ects vary considerably depending on where the subsidy is applied. A policy maker wanting to raise exports would get up to a $25 ($81) increase in export revenue for every dollar spent reducing ...xed costs when the quota price is endogenous (exogenous). In general, applying the subsidy at a later stage so that it is not wasted on ...rms that end up exiting produces greater results. Thus, subsidies to market entry are the least e¢ cient (with $.4 increase in exports per dollar spent), while compensating ...xed costs of production raises exports the most (by $24.8 per dollar spent). Also, subsidizing market entry costs for markets, where the market share is lower, gives more leverage as ...rms can “steal” business from a greater fraction of competitors. Thus, subsidizing US entry with the Bangladeshi market share around 4% gives $11.4 per dollar spent, while doing the same for the EU with Bangladeshi market share around 16% gives only $5.5.

We also ...nd that cross market e¤ects are large: if the EU market entry is subsidized, the US exports (and tari¤ revenue) rise but only when the quota price is exogenous. As expected, these e¤ects are much more muted when the quota price is endogenous. Thus, policies have large cross market e¤ects, though quotas dilute these e¤ects considerably.

# 6.3 The Responsiveness of Trade Flows to Trade Barriers

It is worth explaining how we get such a large e¤ect on exports given there is free entry.

> 51Recall this assumption is made in Eaton, Kortum and Kramarz (2011) and Chaney (2008).

37
<!-- Start of picture text --> 1969 4.1 5.2<br>1968 2.9 100 4.9 100<br>1967 2.8 4.6<br><!-- End of picture text --> SPO estimates chained in 1972.

Judging from these conjectures, the (chained) SPO estimates overestimate the national rate of unemployment by some **1⁄2-1** percentage point in the early 1980s and by some 1-2 percentage point in the late 1960s with the result that the increase in unemployment may be underestimated considerably, the estimated increase from 1967-69 to 1982-85 being about 149 percent as as compared with the conjectured increase of 253 percent, at the least.<sup>4</sup> Comparison with the population census data for 1965, 1975 and 1980, as already explained indicates overestimate of both level and increase of the unemployment rate. Although population census and LFS data are not directly comparable there is something to indicate that SPO overestimates the increase of unemployment during the 1960s and thereafter underestimates the increase. This impression is corroborated by the placement office statistics to which we now turn.

The remaining labor market indicators are placement office data for applications and placements (apparent6Y flow data), registered unemployed (stock data) and new vacancies (flow data). Labor placement data are always difficult to interpret because

- 16 -
Table 12: Extensive margin II: Probability of export exits

|**Dependent Variable:**Probability|of Export Exit_f pjt_||||||
|---|---|---|---|---|---|---|
||PCA|Baseline||Robus|tnesstests||
||||Lags|w/o large firms|w/o concentrated sectors|Placebo|
||(1)|(2)|(3)|(4)|(5)|(6)|
|NTM Principal Component_i_,_j_,_p_,_t_|0.1995<br>(0.031)***||||||
|Log (Tariff_i_,_j_,_p_,_t_)||-0.0545|0.0382|-0.0614|-0.0493|-0.0390|
|||(0.057)|(0.055)|(0.059)|(0.058)|(0.070)|
|SPS_i_,_j_,_p_,_t_||0.1098|0.0390|0.0860|0.1316|0.0898|
|||(0.087)|(0.084)|(0.094)|(0.088)|(0.093)|
|TBT_i_,_j_,_p_,_t_||0.1939|0.2936|0.2148|0.2012|0.0538|
|||(0.072)***|(0.077)***|(0.083)***|(0.074)***|(0.080)|
|Pre-Shipment_i_,_j_,_p_,_t_||0.0516|0.1018|-0.0357|0.0777|-0.1040|
|||(0.096)|(0.079)|(0.101)|(0.093)|(0.099)|
|QuantityControl_i_,_j_,_p_,_t_||-0.2712|-0.1944|-0.3128|-0.3292|-0.1175|
|||(0.114)**|(0.101)*|(0.125)**|(0.114)***|(0.132)|
|PriceControl_i_,_j_,_p_,_t_||-0.0495|-0.1269|0.1028|-0.0229|-0.0480|
|||(0.087)|(0.118)|(0.108)|(0.090)|(0.092)|
|OtherNTMs_i_,_j_,_p_,_t_||-0.1053|-0.1477|-0.1053|-0.0639|-0.0706|
|||(0.074)|(0.060)**|(0.077)|(0.076)|(0.078)|
|Log (Import Demand_f _,_p_,_j_,_t_)|-0.0282|-0.0211|-0.0210|-0.0310|-0.0218|-0.0322|
||(0.006)***|(0.008)**|(0.008)**|(0.010)***|(0.008)***|(0.010)***|
|Observations|17221|17221|17221|13455|16961|13354|
|Adjusted_R_<sup>2</sup>|0.13|0.18|0.18|0.18|0.18|0.18|
|Fixed Effects|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_<br>_f _|−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 1 for the extensive margin II of Colombian firm-level exports. Columns (2-6) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Table 5 shows the distribution of antidumping actions and their final determinations per group of products, divided according to categories of use. It should be noted that of 193 cases, 107 resulted in an affirmative determination (52.8%), 102 of which were settled through antidumping duties and 5 through price undertakings, while the other 86 were rejected (44.6%).

Table 5 Share of Antidumping Cases per Group of Products, according to Categories of Use and Final Determination: Brazil, 1988-2002

|Category of use|Cases %|of total case|s Accepted|(%)|Price undertaking|(%)|Rejected|(%)|
|---|---|---|---|---|---|---|---|---|
|1. Consumer durables|6|3.1|5|83.3|0|0.0|1|16.7|
|2. Soft goods|23|11.9|18|78.3|2|8.7|3|13.0|
|2.1 Food|20|10.4|16|80.0|2|10.0|2|10.0|
|2.2 Other|3|1.6|2|66.7|0|0.0|1|33.3|
|3. Raw materials|154|79.8|71|46.1|3|1.9|80|51.9|
|3.1 Food|5|2.6|0|0.0|0|0.0|5|100.0|
|3.2 Rubber|10|5.2|7|70.0|0|0.0|3|30.0|
|3.3 Pharmaceuticals|12|6.2|4|33.3|2|16.7|6|50.0|
|3.4 Metals|44|22.8|22|50.0|0|0.0|22|50.0|
|3.5 Plastics|19|9.8|8|42.1|0|0.0|11|57.9|
|3.6 Non-metal Minerals|6|3.1|4|66.7|0|0.0|2|33.3|
|3.7 Paper|1|0.5|0|0.0|1|100.0|<br>0|0.0|
|3.8 Chemicals|45|23.3|20|44.4|0|0.0|25|55.6|
|3.9 Textiles|12|6.2|6|50.0|0|0.0|6|50.0|
|4. Spares & components|10|5.2|8|80.0|0|0.0|2|20.0|
|Total|193|100.0|102|52.8|5|2.6|86|44.6|

Source: DECOM, SECEX, MDIC Reports, several years. Customs, Compendium of Laws on Foreign Trade. Author's calculations.

23
27

**Figure 18. A Measure of “lost time” in economic development by individual components of the Proust index**

<!-- Start of picture text --> Lost time in terms of GDP per capita Lost time in terms of unemployment<br>(Number of countries out of a sample of 180) (Number of countries out of a sample of 79)<br>4% 5% 13%<br>19%<br>11%<br>47% 13%<br>39%<br>33%<br>16%<br>Lost time in terms of consumption per capita Lost time in terms of equity prices<br>(out of 134 sample countries) (out of 74 sample countries)<br>2% 5% 7% 3% 1%<br>11%<br>19%<br>53%<br>29%<br>70%<br><=0 years Between 0-5 years Between 5-10 years<br>Between 10-15 years More than 15 years<br><!-- End of picture text --> _Source_ : Authors’ computations based on data from Bloomberg (equity prices); World Bank WDI for GDP and consumption per capita; and WDI and ILO for the unemployment rate.

financial links with them. The youth has been hit particularly hard, with an estimated 74 million young people out of jobs in 2012 globally. The jump in the youth unemployment rate has been most pronounced in advanced economies, reaching alarming levels in the peripheral Europe with more than half of the youth labor force unemployed in Greece and Spain. Among those employed worldwide, 49 percent on average are in vulnerable employment, taking poorly-paid jobs in the informal sector that offer limited insurance or compensation.

What is more, the ongoing financial crisis has disproportionately hurt the poor. While developing countries have continued to make progress on poverty reduction, over a billion people still live in extreme poverty. The progress in poverty reduction has been uneven across regions, and the pace has slowed since 2007 across the board. The crisis has also posed challenges to attaining other development goals, including reducing the prevalence of malnutrition and mortality rates, and improving gender equality in education and access to clean water and sanitation. Progress toward meeting the millennium development targets for undernourishment, primary education, maternal