rafmacalaba/datause-encoder-data huggingface dataset:
EAST AFRICA

# **1.** Estimates and Projections

|**Yl**r|**PpAlatfn**|**Dp.**<br>rt to|Period|Growth<br>rate|Cm|CDR|TF9|*0<br>_N_**lt**|**so**<br>F_ml|in|q5<br>x1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|195<br>|230286<br>|94.8|1985-89|3.09|46.3|15.3|6.43|50.3|54.0|107|167|
|1990<br>|268723<br>|96.1<br>|1990-94<br>|3.05<br>|44.3<br>|17 8|6.23|51.8|55.8|98|152|
|1995<br>|312978<br>|95.5<br>|1995-99<br>|3.02<br>|42.6<br>|12.3<br>|5.93<br>|53.5|57.7|**88**|134|
|2000<br>|364079<br>|93.9<br>|2000-04<br>|2.92<br>|40.2|10.9|5.49|55.3|5V.A|77|116|
|2005<br>|421409<br>|°0.0<br>|2005-09<br>|2.78<br>|37.7<br>|9.9<br>|5.01<br>|56.9|61.5|72|106|
|2010<br>|484228<br>|85.4<br>|2010-14<br>|2.63<br>|35.3<br>|9.0<br>|4.54|58.4|63.2|67|96|
|2015<br>|552294<br>|80.1<br>|2014-19<br>|2.46|32.8|8.1|4.09|60.1|65.0|61|87|
|2020<br>|624722<br>|74.8<br>|2020-24<br>|2.31<br>|30.5|7.4|3.72|61.8|66.9|55|76|
|2025<br>|701369<br>|70.0|2025-49|1.72|23.0|6,1|2.74|66.4|71.8|37|48|
|2050<br>|1076497<br>|50.0<br>|2050-74<br>|0.94<br>|16.1<br>|6.7<br>|2.09<br>|72.2<br>|77.9|16|20|
|2075<br>|**136564S**<br>|51.6<br>|2075-99<br>|0.46<br>|13.5<br>|8.9<br>|2.06<br>|76.3<br>|82.2<br>|8|10|
|2100<br>|1532778<br>|63.0<br>|2100-24<br>|0.18<br>|12.4<br>|10.6<br>|2.04|79.6|85.9|3|4|
|2125<br>2150|160386o<br>1637662|68.6<br>71.3|2125-49|0.08|12.0|11.2|2.04|80.9|87.4|3|4|

### 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65.89<br>80-64<br>55-59<br>50-54<br>45-49<br>a a 1 ~~~40-44<br>= ! ~~~~35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9 2EEEf EZR<br>EExmxxa FEEEEZ=ES] c0-4 ox 7m<br>10<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> P4Th C21 |4Ip g /<br>EASY (.' 6%)<br><!-- End of picture text -->

<!-- Start of picture text --> /<br>EAST (42 n)<br>.T (36 n<br><!-- End of picture text --> 6
it must be concluded that fiscal imbalances (both explicit and implicit) were prevalent during the whole decade.

Yet the fact that thle reform led to a higher measured fiscal deficit still carries a lesson. From the economic, as opposed to accounting, perspective, the higher deficit had existed all along, and the ' lifting of the veil' just put it in the open. But the conversion of the implicit into explicit debt did impact on two dimensions, however. First, public sector financing needs were raised by the amortization of the newly-recognized debt -- as measured by the benefits that the public sector had to keep on paying -- and this entailed additional demands on domestic and/or foreign financial markets. Second, perceptions of Argentina's fiscal position may have been affected as well, to the extent that markets did not see fully through the veil separating explicit from implicit government debt.

The lesson is that extra care is needed regarding the consequences for financial markets of revealing and floating hidden pension liabilities. Even if doing so improves the long term fiscal position, it must be accompanied by further fiscal adjustment in the short term (to absorb at least part of the increased medium term cash deficit) and appropriate long-term dormestic debt instruments. With the benefit of hindsight, the boom years from the end of 199:5 to mid 1998 were a major lost opportunity to tackle the fiscal imbalances revealed by the pension reform.

**Figure 4.8. The Social Security Deficit in Perspective (Percentage of GDP)**

<!-- Start of picture text --> 2-<br>0~ ~~ _<br>-1<br>19 ~~~~~1995 1996 1997 1998 1999 2000 2001<br>-2<br>-3<br>.4 \A<br>-*--Consolidated Public Sector Primary Balance<br>-Soclal 0 Security Elalance<br>-7 -* Consolidated Public Sector Overall Balance<br>Source: Ministero de Economia de la Republica Argentina and Intemational Monetary Fund.<br><!-- End of picture text --> 39
# **Figure 5: Healthy life expectancy has been increasing across the globe**

<!-- Start of picture text --> Healthy life expectancy at age 60<br>20.0<br>19.0<br>16.8 16.7<br>15.0 14.5<br>12.8 13.8 13.4<br>12.0<br>10.0<br>5.0<br>0.0<br>Male Female Male Female Male Female Male Female<br>HIC UMIC LMIC LIC<br>2000 Addition 2000-2015 2015<br>Years<br><!-- End of picture text --> Source: Estimates based on WHO statistics.

# **Working longer can in fact create a virtuous circle of health and work at older age, but the outcome**

**likely depends on the nature of work and employment conditions** . Age-related physical and psychological changes can be moderated with physical and intellectual activity and other lifestyle factors.<sup>46</sup> Work can bring cognitive stimulation and social interaction, which contribute to continued mental and physical agility. Social interactions at work, which are likely to demand constant adaptation, have been shown to be particularly important to maintain brain capacity.<sup>47</sup> However, prolonging work in (predominantly low-skill) jobs that provide limited stimulation, jobs that cause undue stress, and jobs that are physically straining with high risk of occupational disorders, is bad for healthy longevity.<sup>48</sup> The overall employment situation also matters, as job insecurity in old age can in fact lead to deteriorating mental health.<sup>49</sup>

**Beyond health, skills investments are also critical as demographic patterns shift, since the nature of jobs is also changing, raising the importance of continuous skills development** . Across the globe, there is substantial evidence that jobs are becoming more intense in cognitive, analytical tasks. Technological change is resulting in higher demand for technology-complementary skills, both higherorder cognitive skills as well as socio-emotional skills.<sup>50</sup> Accessing productive work, hence, increasingly requires a more complex set of skills and adaptive capacity. Evidence from the EU shows that the productivity decline often observed among older workers is more likely due to skills obsolescence than to age itself; in Mexico, wages of college-educated workers have fallen due to a fall in wages of mature rather than young workers, pointing to a similar potential effect.<sup>51</sup>

**The aging process need not prevent older people from working productively and learning new things; depending on the task, aging may even be helpful** . Aging inevitably involves some cognitive decline. However, both the timing and extent of decline vary significantly across individuals, and the

> 46 Crawford et al. 2009.

> 47 Staudinger et al. 2016.

> 48 Staudinger et al. 2016, Prinz et al. 2018.

> 49 Gutierrez and Michaud 2019.

> 50 World Bank 2019.

> 51 Mayhew and Rijkers 2004.

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Table 17: IV estimates of the effect of ICT use on other outcomes: Commerce

||∆ln(numbe|r of workers)|∆ln(|sales-per-w|orker)|∆ln(Val|ue added-per|-worker)|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|∆Share of labor that uses computer|0.0018***|0.0019***|0.0002|-0.0004|0.0040***|0.0089***|0.0085***|0.0109***|
||(0.0002)|(0.0002)|(0.0005)|(0.0005)|(0.0006)|(0.0005)|(0.0005)|(0.0007)|
|F- frst stage|1041.78|1008.71|1041.78|1008.71|576.59|1041.78|1008.71|576.59|
|Sargan p-value|0.8630|0.7107|0.4121|0.9509|0.4809|0.6244|0.9946|0.8315|
|∆Share of labor that uses Internet|0.0016***|0.0018***|0.0002|-0.0004|0.0039***|0.0082***|0.0078***|0.0107***|
||(0.0002)|(0.0002)|(0.0004)|(0.0004)|(0.0006)|(0.0005)|(0.0005)|(0.0007)|
|F-frst stage|1395.40|1358.95|1395.40|1358.95|645.52|1395.40|1358.95|645.52|
|Sargan p-value|0.8749|0.7106|0.4124|0.9525|0.4453|0.6421|0.9530|0.7401|
|∆ln(stock of computer equipment/worker)|0.3197***|0.6073***|0.0506|-0.1112|-1.8585*|1.6305***|2.7020***|-6.0131*|
||(0.0639)|(0.1496)|(0.0841)|(0.1515)|(1.0491)|(0.2308)|(0.5890)|(3.2325)|
|F-frst stage|28.66|12.32|28.66|12.32|2.11|28.66|12.32|2.11|
|Sargan p-value|0.1905|0.4532|0.2617|0.2060|0.0995|0.9982|0.8023|0.3185|
|**Controls**|||||||||
|∆ln(capital/worker)|No|Yes|No|Yes|Yes|No|Yes|Yes|
|Age dummies|No|No|No|No|Yes|No|No|Yes|
|Size dummies|No|No|No|No|Yes|No|No|Yes|
|Observations|8,333|8,333|8,333|8,333|8,333|8,333|8,333|8,333|

Standard errors in parentheses * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.
_chs-pd_ is the default probability, calculated as in Campbell, Hilscher and Szilagyi (2008).<sup>90</sup> _Merton-DD_ is the Merton (1974) distance-to-default measure. The computation of _merton-DD_ is described in detail in Anginer and Yildizhan (2010). _Rating_ is the S&P 500 rating, where the rating has been converted to numeric values (AAA = 1, ..., C = 13). Finally, _maturity_ is the average maturity of the bonds for a firm and is measured in years.

Table 1 reports that, along most of the financial characteristics described above, unionized and non-unionized companies are similar. The results indicate that, relative to the non-unionized companies, the unionized companies are less profitable, have bonds with slightly longer maturities, and have slightly lower-ranked ratings. Unionized companies are also larger and have more liabilities relative to assets. But with respect to other financial ratios and credit risk measures, we do not see statistically significant differences. That is, unionized and non-unionized companies are the same in terms of aggregate volatility, idiosyncratic volatility, default probability, distance to default, and default score. These results suggest that the two set of companies have similar creditriskiness characteristics.

# **VI. Results**

In this section, we examine yields and returns on corporate bonds over the 2008 and 2009 period. We then conduct an event study of abnormal bond returns on key event dates relating to the Chrysler bailout.

Figure 2 shows the yields on bonds of unionized companies and non-unionized companies. Results are shown separately for high-yield and investment-grade bonds. For investment grade bonds, the yield on bonds of unionized companies tracks the yield on bonds of non-unionized companies for most of 2008. Around September, 2008,

> 90 _chs-pd_ is defined as:

_CHSPD_ = 1 æççççè1 + exp æççççè-+ 9.1641.411 - _SIGMA_ 20.264 _t_ - _NIMT AAVG_ 0.045 _RSIZEt_ + _t_ -1.416 2.132 _T LMT ACASHMT At_ - 7.129 _t_ + 0.075 _EXRET AVGMB t_ - 0.058 _t PRICE t_ öö÷÷÷÷÷÷÷÷÷÷øø where _NIMTAAVG_ is a geometrically declining average of past values of the ratio of net income to the market value of total assets, _TLMTA_ is the ratio of total liabilities to the market value of total assets, _EXRETAVG_ is a geometrically declining average of monthly log excess stock returns relative to the S&P 500 index, _SIGMA_ is the standard deviation of daily stock returns over the previous three months, _RSIZE_ is the log of the ratio of market capitalization to the market value of the S&P 500 index, _CASHMTA_ is the ratio of cash to the market value of total assets, _MB_ is the market-to-book ratio, and _PRICE_ is the log of the price per share truncated from above at $15.

25
# **Table 5: The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012 Arab Spring Effect**

**The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012** _Dependent Variable: Log GFI inflows (in millions of USD) by broad sector and source_

||LSD|VC Estimates|||
|---|---|---|---|---|
|_Sector_|Resources a|nd Energy|Non-resource|Manufacturing|
||Arab Spring Effect|Full Specification|Arab Spring Effect|Full Specification|
||(1)|(2)|(3)|(4)|
|Arab Spring Dummyt-1|0.08|0.10|-0.76*|-0.04|
||(0.59)|(0.66)|(0.44)|(0.48)|
|Political Instabilityt-1||-0.05||-1.44***|
|||(0.56)||(0.42)|
|Economic Controls|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|604|604|604|604|
|Number of Countries|17|17|17|17|
|_Sector_|Tradable|Services|Non-tr|adables|
||Arab Spring Effect|Full Specification|Arab Spring Effect|Full Specification|
||(5)|(6)|(7)|(8)|
|Arab Spring Dummyt-1|-0.34|0.30|-0.72|-0.52|
||(0.38)|(0.42)|(0.48)|(0.53)|
|Political Instabilityt-1||-1.27***||-0.39|
|||(0.36)||(0.46)|
|Economic Controls|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|604|604|604|604|
|Number of Countries|17|17|17|17|

Bootstrapped standard errors in parentheses ***p<0.01, ** p<0.05,* p<0.10 30
income gradient in the _SAH_ of adults, although he did find that _SAH_ tends to be higher for men, to fall with age, to rise with education and to be higher for the employed.

We use data from two rounds, 2002 and 2000, of the Russian Longitudinal Monitoring Survey (RLMS).<sup>10</sup> This is a comprehensive socio-economic survey for a nationally representative sample. The 2002 round of the RLMS included an unusually detailed module on health. The sampled households included 9,100 adults (3,900 men and 5,200 women). For aspects of the analysis we also exploit the less complete data available for the 6,000 adults (2,600 men and 3,600 women) in the 2000-02 panel that can be formed from the RLMS. The RLMS sample is re-designed at each survey round to assure that it remains representative.

The survey included the following _SAH_ question, asked of each adult in the sampled household: “ _How would you evaluate your health? Very good, good, average (not good, not ” bad), bad, very bad, don’t know/refuse_ . Figure 1 gives distributions of the answers for 2002. We find that 16.5% of adults rated their health as “very bad” or “bad;” this was true of 20.0% of women and 11.7% for men. Men rate their overall health higher than women. However, this is not reflected in some other health indicators. Women tend to live much longer: in 2001, male life expectancy was 59 years, versus 72 for women (Heleniak, 2002). And Russian men tend to have worse “health lifestyles” (higher alcohol consumption, smoking more, fattier diets).<sup>11</sup> The gender difference in _SAH_ appears to reflect a psychological difference.

The 2002 survey included questions on functional abilities, specific ailments and recent disease histories. The questions covered the level of difficulty incurred in performing various physical activities (running, walking, lifting, crouching etc), weight, height, reported ailments (problems in chest, lungs, liver and so on) and diseases diagnosed (diabetic, heart attack,

> 10 The RLMS data are obtainable from: http://www.cpc.unc.edu/projects/rlms/rlms home.html.

> 11 See Cockerham (2000). Also see the gender differences in vital statistics for Russia reported DaVanzo and Adamson (1997) and Brainerd and Cutler (2005).

12
nization of European standards, for instance through a political economy process, the estimators are not longer unbiased or consistent. I argue that _NHt_<sup>_i_is exogenous to trade flows for two reasons:</sup> First, the decision making process to create or to harmonize an European standard does not involve any international consultation with trade partners.<sup>27</sup> Second, U.S. firms have raised concerns on their inability to participate in the formation of EU standards, these claims are documented in the 2010 Report on Technical Barriers to Trade.<sup>28</sup>

# **4.2 The Extensive Margin of Trade**

While harmonization of product standards are important to increase U.S. export value to the EU, the theoretical model predicts that the extensive margin of trade (the number of exporting firms) accounts for this variation (hypothesis 1). I estimate the impact of falling trade costs on the probability that non-exporting firms to the EU become exporters to the EU via a logistic regression on my measure of changing trade costs, firm productivity and other firm characteristics. I use firms’ information from the 1992, 1997, and 2002 U.S. Censuses of Manufactures. I define the change in trade costs for census year _t_ as the log difference in tariffs over the preceding five years ( _△τt_<sup>_i_</sup> +5<sup>)andasthelogdifferenceonmymeasureofprod-</sup> uct standard heterogeneity over the preceding five years ( _△NHt_<sup>_i_</sup> +5<sup>).These regressions are given by:</sup> where _Et_ +5 is a dummy variable equal to one if a firm does not export to the EU in year _t_ and becomes an exporter to the EU in year _t_ + 5; _PRt_ is the firm’s revenue based labor productivity, and _Zt_ is a set of additional firm characteristics. Additional firm controls include size, capital intensity, wage level, and multi-plant dummies. In specification four, I include the dummy variable _OR_ firm, which is equal to one if the firm is an exporter to a developing country and not to the EU in year t. I also include industry ( _γi_ ) and time ( _γt_ ) fixed effects and cluster the standard errors at the industry level.

Results are reported across four columns in Table 6, with the first column focusing on my trade costs measures and subsequent columns including additional firm characteristics. Across all specifications, I find a positive and statistically significant association between product standards harmonization and the probability that a non-exporting firm to the EU becomes an exporter to the EU across Census years. The probability of becoming an EU exporter is higher in industries with greater harmonization of product standards. Surprisingly, EU tariff changes do not affect the

> 27See Portugal-Perez et al. for the European institutional standard setting mechanism in Electronics.

> 28http://www.ustr.gov/about-us/press-office/reports-and-publications/2010-0

15
deficiencies (for details see Grootaert, 1992). One was the observation that average household size, as recorded in the survey, dropped from about eight persons to about six persons over the four-year period. It was clear that this did not correspand to a demographic reality and the culprit was found to be a flawed listing procedure, before the start of the 1985 data collection. Indeed in 1984, a listing operation was undertaken within each of the 100 retained primary sampling units (PSU). Contrary to customary survey practice, these PSU's were not listed exhaustively (because the units selected were too large). The field staff responsible for the listing was instructed to start at a random point in the PSU and to list every fourth househola until 64 households were listed. In practice, true randomness was not achieved and larger dwellings with larger households turned out to have been over-listed and hence were over-represented in the final sample. This flaw in the listing could easily have been corrected at the time of field operations, had design and supervision been better. However, it required a significant effort at the analytic stage to compensate for this error through the construction of ex-post corrective sampling weights (see Demery and Grootaert, 1992). In 1987, the listing techniques were revised. Each PSU was now completely listed: this eliminated the earlier problem and the 1987-88 household size corresponds closely to that recorded in the 1988 population census.

It is clear that, in principle, one should avoid making ex-post corrections to data and this experience underlines the importance of close supervision of the field work underlying the selection of the sample, and of keeping the field work simple. An exhaustive listing of small PSU's is simpler and less error-prone than the requirement that enumerators undertake random selections during the field work. Any error made at the sampling stage will inevitably require often time-consuming ex-post corrections at the analytic stage. (In addition to the correction for household size, corrections were also necessary for over-sampling of high income PSU's in Abidjan in 1985-86, and under-sampling of rural PSU's in 1987-88 - see Grootaert, 1992.) The CILSS was designed with a self-weighting sample. One reason offered for this design was that self-weighting data are easier to analyze. In practice, it did not work 13
differenced in order to eliminate the country fixed effects (Griliches and Hausman, 1986). Second, identification might be confounded by unobserved factors that determine both economic growth and aid. Third, growth itself could also affect aid. In response to these potential problems, previous studies have introduced different instrumental variables to identify the causal effect of aid on growth, in some cases using cross-sectional data and in others panel data. In this section we briefly review the two major identification strategies previously exploited in the literature.

Studies that use cross-sectional data often rely on population size, economic policies and donor-recipient political connections as instruments for aid (e.g., Boone, 1996; Burnside and Dollar, 2000; Rajan and Subramanian, 2008). These cross-country instruments are likely to violate the exclusion restriction since they are correlated with observable and plausibly also unobserved country-level characteristics that also contribute to economic growth. For example, population size can affect economic growth through channels other than aid (Bazzi and Clemens, 2013). Donor-recipient ties (e.g. colonization, trade or migration) that are correlated with aid flows can also affect growth indirectly through the institutional environment (Acemoglu, Johnson, and Robinson, 2001) or other channels.

Other studies relying on panel data rule out the impact of time-invariant determinants of economic growth by either first differencing the data or by conditioning on country fixed effects. These studies tend to focus more on the short term effect of aid on growth. Many studies in this category adopt a dynamic panel model and employ difference GMM or system GMM estimators. They instrument for current aid with lagged values of income and aid, as well as with other standard cross-country regressors (e.g., Hansen and Tarp, 2001 and Rajan and Subramanian, 2008). However, recent studies show that GMM estimators of dynamic panel models using all mechanical instruments are unstable and potentially biased in finite samples, due to the problem of many and weak instruments (Roodman, 2007, 2009a, 2009b; Bazzi 6
**Table 8: Panel Regression Results, 1962-2007**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Fertility<br>Rate|Fertility<br>Rate|Fertility<br>Rate|Female<br>Labor<br>Force Par-<br>ticipation|Female<br>Labor<br>Force Par-<br>ticipation|Female Ed-<br>ucational<br>Attainment|Female Ed-<br>ucational<br>Attainment|
|(Log) Female Labor|-0.39***|-0.27***|-0.23***|0.20***|-0.02|0.39***|-0.05|
|Need of Exports<br>(FLNX)|(0.031)|(0.030)|(0.028)|(0.045)|(0.026)|(0.067)|(0.060)|
|(Log) Openness|-0.08***|-0.10***|-0.02|-0.04**|-0.04**|0.16***|-0.07**|
||(0.017)|(0.017)|(0.017)|(0.021)|(0.015)|(0.037)|(0.032)|
|(Log) GDP per capita|-0.37***|-0.26***|-0.19***|-0.07***|0.01|0.62***|-0.06|
||(0.009)|(0.020)|(0.022)|(0.011)|(0.018)|(0.021)|(0.035)|
|(Log) Population|-0.08***|-0.43***|-0.08**|-0.04***|0.20***|0.09***|0.99***|
||(0.008)|(0.021)|(0.036)|(0.011)|(0.049)|(0.016)|(0.087)|
|Country Fixed Efects|No|Yes|Yes|No|Yes|No|Yes|
|Year Fixed Efects|No|No|Yes|No|Yes|No|Yes|
|_R_<sup>2</sup>|0.609|0.929|0.938|0.113|0.968|0.508|0.949|
|Observations|1,247|1,247|1,247|819|819|1,102|1,102|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. _Fertility Rate_ is (log) total births per woman; _Female Labor Force Participation_ is (log) female labor participation rate (% of female population ages 15+); _Female Educational Attainment_ is the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals). All of the variables are five-year averages over the period 1962-2007, except for _Female Labor Force Participation,_ which is averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.