rafmacalaba/datause-encoder-data huggingface dataset:
**Cumulative**

|Table 4: Bankruptcies a|nd Liquidat|ions:**1992419**|**94.2**||
|---|---|---|---|---|
||**1992**|**1993**|**1994.1-2**|**Cumu**|
|Bankruptcies|||||
|Total|4169|987|40|5196|
|of which;|||||
|self-filed (%)|_25%_|14%|57%|23%|
|mandatory (%)|75%|86%|43%|77%|
|Court announced||||66%|
|No proceedings||||41%|
|Settled out-of-court||||25%|
|Admijiistratively closed||||27%|
|Liquidations|||||
|Filed at court|9891|7242|917|18050|
|by creditors (%)|82%|81%|-50%|80%|
|by debtors<br>(%)|18%|19%|50%|20%|
|Court announced||||29%|
|Administratively closed||||_50%_|
|Completed||||10%|

Source: Ministry of Finance

**Table** 5: Firms in bankruptcylliquidation (loss-makers over 0.1 **bnHUF):** 1992

|**Type**|Number|In Bankruptcy/Liquidation|Share|
|---|---|---|---|
|Value|113|60|53%|
|Subtractors||||
|Near Value|242|140|58%|
|Subtractors||||
|Negative<br>operating<br>profit|189|83|440/o|

Source: Ministry of Finance and World Bank 11
|<br>|TT<br>|C|Int. S|tress|
|---|---|---|---|---|
|BankingSystem/Sensitivity Parameter<br>|—_=_—=a"al2cn_ kk__<br>kappa=5%|kappa=10%|kappa=10%|kappa=20%|
|Effective Capital Buffer (%) - Asset-Weighted Average|10.9%|10.3%|0.7%|0.4%|
|Effective Capital Buffer (%) - Median|11.3%|10.8%|0.7%|0.4%|
|Effective Capital Buffer (%) - St.Dev.|2.7%|3.1%|4.4%|4.7%|
|Effective Undercapitalization (% of Profits)|4%|14%|105%|129%|
|# of Banks with Effective Capital Buffer < 8%|1|2|9|9|
|# of Banks with Effective Capital Buffer < 2%|0|0|6|6|
||rho(LGD,PD)=10% =|rho(LGD,PD)=20%|=rho(LGD,PD)=30% ~|— rho(LGD,PD)=50%|
|Effective Capital Buffer (%) - Asset-Weighted Average|10.7%|10.3%|0.4%|0.4%|
|Effective Capital Buffer (%) - Median|11.1%|10.8%|0.4%|0.4%|
|Effective Capital Buffer (%) - St.Dev.|2.9%|3.1%|4.8%|4.7%|
|Effective Undercapitalization (% of Profits)|7%|14%|128%|129%|
|# of Banks with Effective Capital Buffer < 8%|1|2|9|9|
|# of Banks with Effective Capital Buffer < 2%|)|0|6|6|
||c_cap=99.5|c_cap=99.9|c_cap=99.5|c_cap=99.9|
|Effective Capital Buffer (%) - Asset-Weighted Average|10.3%|7.2%|0.4%|0.2%|
|Effective Capital Buffer (%) - Median|10.8%|7.6%|0.4%|0.3%|
|Effective Capital Buffer (%) - St.Dev.|3.1%|1.9%|4.7%|3.3%|
|Effective Undercapitalization (% of Profits)|14%|79%|129%|167%|
|# of Banks with Effective Capital Buffer < 8%|2|6|9|10|
|# of Banks with Effective Capital Buffer < 2%|0|0|6|8|
||corp.hedging=50%|corp.hedging=30%|corp.hedging=50%|_corp.hedging=30%|
|Effective Capital Buffer (%) - Asset-Weighted Average|10.3%|10.3%|0.4%|0.0%|
|Effective Capital Buffer (%) - Median|10.8%|10.8%|0.4%|0.0%|
|Effective Capital Buffer (%) - St.Dev.|3.1%|3.1%|4.7%|4.7%|
|Effective Undercapitalization (% of Profits)|14%|14%|129%|145%|
|# of Banks with Effective Capital Buffer < 8%|2|2|9|9|
|#ofBankswithEffectiveCapitalBuffer<2%|0|0|6|8|
market clearing and suggests that at the peak season in agriculture there was at that time practically speaking no unemployment in this sense, but, of course, does not exclude surplus labor in the normative sense even at the peak season. Granted market clearing around the year we should expect the same result from eventual winter surveys. Indeed:, winter surveys might disclose zero unemployment ;accompanied by huge surplus labor. Market clearing requires highly flexible nominal and real (product) wages and we should expect wages to be relatively low during the slack winter months. There is little information about rural wages in Turkey. The little we have is from the SII, Social Insurance Institute, seasonalities are averaged out, it covers only a few recent years, and it applies only to a few insuredworkers in in agriculturewho most certainly are not representative of landless farm hands.

Let us return now to the LFS for 1985 and the impiicit unemployment estimate for rural areas, computed by me as the difference between national and urban (10,000 and above) LFS for that year. Table VI.Ap.1.6 shows the breakdown by urban and rural areas and by completed education. The latter breakdown w:ith a distribution of registered unemployed by age groups for 1979-86 (shown in Table 1.8)and distributions by age, marital status, work experience and length of unemploymentin 1985 (I?, table 1.9 help us somewhat to come to grips with the nature of unemployment in Turkey.

As already mentioned the implicit unemployment rate for

-8-
_Table 9. Estimation results for the electronics sector_

||||ln(C|A)|||||ln(rev|enue)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS||Rand|om effe|cts||OLS||Rand|om eff|ects|
||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||
|woman|-0.18|0.17|0.32|-0.17|0.23|0.45|-0.25|0.24|0.29|-0.29|0.28|0.31|
|can read|0.40|0.24|0.10|0.39|0.32|0.22|0.51|0.30|0.09|0.49|0.37|0.19|
|education>2|0.37|0.24|0.13|0.36|0.32|0.26|0.42|0.29|0.16|0.40|0.37|0.28|
|age 30-39|-0.14|0.11|0.19|-0.15|0.14|0.28|-0.19|0.14|0.17|-0.21|0.17|0.22|
|age 40-49|-0.35|0.19|0.06|-0.35|0.25|0.16|-0.56|0.24|0.02|-0.60|0.29|0.04|
|age>49|-0.63|0.49|0.20|-0.64|0.65|0.32|-1.18|0.79|0.14|-1.22|0.83|0.14|
|age unknown|0.36|0.15|0.02|0.36|0.20|0.07|0.33|0.18|0.08|0.37|0.23|0.11|
|exp. 5-10|0.25|0.15|0.10|0.26|0.20|0.20|0.26|0.19|0.17|0.32|0.23|0.18|
|exp.>10|0.62|0.19|0.00|0.61|0.25|0.01|0.86|0.23|0.00|0.90|0.29|0.00|
|Abidjan|0.44|0.16|0.01|0.42|0.21|0.05|0.51|0.20|0.01|0.50|0.25|0.04|
|PB|0.25|0.12|0.03|0.27|0.12|0.02|0.10|0.15|0.49|0.15|0.15|0.32|
|PA|0.32|0.11|0.01|0.32|0.12|0.01|0.39|0.15|0.01|0.41|0.15|0.01|
|NPB|0.09|0.10|0.40|0.09|0.05|0.08|-0.10|0.13|0.44|-0.06|0.10|0.53|
|constant|10.42|0.29|0.00|10.43|0.38|0.00|9.74|0.36|0.00|9.69|0.44|0.00|
|PB-NPB=0|2.08||0.15|2.36||0.12|2.00||0.16|2.06||0.15|
|PA-PB=0|0.29||0.59|0.58||0.45|3.59||0.06|5.46||0.02|
|PA-PB=1|0.91||0.34|2.93||0.09|0.85||0.36|1.81||0.18|
|B&P LM-test||||18.40||0.00||||35.12|||
|Hausman-test||||0.00||1.00||||5.43||0.07|
|N|422|||422|||422|||422|||

# _4.4.  Presentation of results from estimations by number of employees_

Behavior in different kinds of establishments may also effect the earnings formation in different ways. Therefore models similar to the ones previously presented are estimated for different sizes of firms, splitting up firms by the number of employees.

21
This is consistent with our model which predicts that the incidence of the policy would be heterogeneous among incumbent workers (who have a job offer and stay or are poached) but not among new entrants (who come from unemployment and have low bargaining power).

Furthermore, to make sure that our exposure measure _TCRit−_ 1 is not contaminated by the policy itself, we only use one post-policy year 2013 and one pre-policy year 2012 in the benchmark regression. Later we perform a robustness check where we define the tax cut rate based on wages two years before, formally _TCRit−_ 2 = 14 _,_ 500 _/wit−_ 2, and then we use data from 2014 and 2012. In the benchmark specification we also focus on full-time, full-month workers, to minimize measurement error in wages, and present robustness checks which include part-time workers.

The results of the wage regressions are reported in Table 6. Column (1) estimates wage effects for new entrants using equation (11). The change in the wages of new entrants is economically small and statistically insignificant. This is in line with the prediction of the model that suggests that the effect of the policy on new entrants should be limited.

Table 6 also shows the estimates for the incumbent workers for whom we can calculate the tax cut rate. Column (2) suggests that the average impact of the tax cut on wages among incumbent workers is positive. The coefficient showing the treatment effect post policy in relation to the tax cut rate ( _β_ 1) is 0.22 (s.e. 0.09). This implies that a $1 increase in the tax cut would result in a 22 cent increase in wages on average, or that average pass-through is 22% with firms capturing 78% of the tax cut on average.

We also examine heterogeneity in this treatment effect. We estimate the following equation, using the notation of equation (12): where we interact all coefficients in equation (12) with _Qj_ ( _i,t_ ), the productivity of firm _j_ where individual _i_ works at time _t_ . To check that our estimates are not simply driven by transitioning to high-productivity firms, in Appendix Table B11 we show that the estimated treatment effects are robust to using the firm productivity in the previous year.

Column (3) of Table 6 shows the main estimates of treatment effect heterogeneity. The estimates show that the wage effects are driven by high-productivity firms. In high28
the consumption distribution.<sup>16</sup> We scale this consumption value by the total population. Our benchmark budget is set to equal 5% of this total value, and we experiment with a higher budget of 10% of this figure as well.

Gains from targeting also vary with the choice of poverty line. In this study, we select as benchmark poverty line, that line which yields a 20% headcount rate in each of our three countries.<sup>17</sup> For comparison we experiment as well with a higher poverty line corresponding to a headcount rate of 40%.

# **IV. Results**

# _Optimal targeting_

Tables 2a-2c present the basic results from our simulations with the optimal targeting scheme in Ecuador, Madagascar and Cambodia. There are five main observations. First, in all three countries, the availability of disaggregated data on poverty can help to improve on a uniform lump-sum transfer across the entire population. Targeting transfers to poor localities, in accordance with the optimization scheme outlined above, yields lower values of the national FGT2 than when the budget is transferred as a uniform lump-sum transfer to the entire population. Second, the more disaggregated the poverty map, the greater the improvement over the uniform lump-sum transfer. Traditional household surveys are generally able, at best, to provide estimates of poverty at the first administrative level. The simulations here suggest that with estimates of poverty at the 2<sup>nd</sup> or 3<sup>rd</sup> administrative level, further improvements in terms of impact on the FGT2 with a given budget are attainable, and are non-negligible. Third,

> 16 The consumption distribution is constructed on the basis of the average, across _r_ replications, of household-level predicted percapita consumption in the population census.

> 17 Within each replication _r_ , the predicted per-capita consumption level associated with a 20% headcount rate is identified.  The average across the _r_ replications of this predicted consumption level is then taken as poverty line.  It is clear that this poverty line will

23
|**Table 9. Regressions of au**<br>**controls**|**toregressive**<br>**baseline**|**coefficient on siz**<br>**w/**<br>**developing=1 **|**e: baseline and**<br>**w/**<br>**all controls**|**various**<br>**w/**<br>**types of FR**|
|---|---|---|---|---|
|GDP|-0.01|-0.00|-0.00|0.00|
|GNI|-0.00|-0.00|-0.01|-0.01|
|Employment|0.02|0.02|0.01|0.02|
|**Aggregate demand:**|||||
|Private cons|0.01|0.01|-0.01|-0.01|
|Gov cons|0.00|0.00|-0.01|-0.01|
|Aggregate inv|0.02**|0.02***|0.01|0.01|
|Priv investment|0.02|0.02|0.05*|0.04|
|Gov investment|0.02|0.02|0.00|-0.03|
|Exports|-0.01|-0.01|-0.02|-0.02|
|Imports|-0.00|-0.00|-0.01|-0.01|
|**External sector:**|||||
|Trade balance/GDP|0.01|0.01|0.00|0.00|
|Current account/GDP|0.02***|0.03***|0.03***|0.02**|
|Terms of trade|0.01|0.01|0.01|0.00|
|**Monetary/Fiscal policy:**|||||
|Inflation|0.02**|0.02***|0.03***|0.03**|
|Real int rate|0.01|0.01|0.03**|0.03*|
|Fiscal balance/GDP|0.02|0.03|-0.01|-0.02|
|Public expenditures|0.03|0.04*|0.01|0.01|
|Public revenues|0.00|0.01|-0.02|-0.02|
|**Source:**|Table A4|Table A8|Table A12|Table A15|

Note: * p<0.10, ** p<0.05, *** p<0.01 Several results are worth pointing out:

1. The coefficient on the measure of country size in the cross-country regressions of current account persistence on (log) population is positive and significant, indicating that current account dynamics are less persistent in smaller countries.

2. We also find that the persistence of aggregate investment and inflation is lower in smaller countries.

3. For all other variables, the persistence does not vary significantly with country size.

# **5. Robustness**

# **Country Size and the Level of Development**

The results above suggest that country size matters for macroeconomic volatility, persistence and cyclicality. We next ask to what extent the regressions above truly pick up the relationship between business cycle moments and size rather than reflect the association between country size and other factors, for instance, the level of development. We investigate this proposition in detail. Our hypothesis is that if size is just a stand-in for the developmental status of a country, 26
# **7. Conclusion**

This paper analyzes the effects of demographic change, measured by changes in age structure, on GDP per capita growth, savings and poverty. A range of alternative econometric specifications and techniques are applied to examine the impact of demographic change on growth, savings, and poverty reduction, while also addressing potential endogeneity between demographics and development outcomes. The analysis suggests that, on average, an increase of 1 percentage point in the share of working-age population is associated with an increase of 1.5 percentage points in GDP per capita growth, an increase of 0.78 percentage point in savings, and a decrease of about 0.75 percentage point in the poverty rate. Of these results, the growth and savings impacts are found to be the most robust across different specifications. The results also suggest a positive association on the interaction between changes in the share of working-age population and years of schooling with GDP per capita growth.

An important policy implication based on these results is that demographic transition may provide an important opportunity for countries to boost their welfare, by increasing per capita GDP growth and savings, and reducing the poverty rate, while child dependency ratios are shrinking. This may provide opportunities particularly for countries in Sub-Saharan Africa and South Asia that expect an increase in the share of working-age population, as they continue to undergo demographic transition and as their fertility rates continue to fall. However, additional policies that could affect labor participation and labor productivity may be necessary in order to guarantee the potential gains from an increase in the share of working age population for these countries. In addition, these results may not provide sufficient guidance on the effects of reduction in the share of working age population in aging countries, as this effect could be non-linear, when compared to an increase in the share of working-age population driven by reduction in the share of children.

# **REFERENCES**

- Ahmed, S.A., M. Cruz, D. Go, M. Maliszewska, and I. Osorio-Rodarte. 2016. How Significant is Sub-Saharan Africa’s Demographic Dividend for Its Future Growth and Poverty Reduction? _Review of Development Economics_ . DOI 10.1111/rode.12227

- Aiyar, S., and A. Mody. 2011. "The Demographic Dividend: Evidence from the Indian States.” International Monetary Fund.

- Attanasio, O., and M. Székely. 2000. "Household Saving in East Asia and Latin America: Inequality Demographics and All That _." Annual World Bank Conference on Development Economics_ . pp. 393-438.

24
To obtain quantitative estimates, we rely on a survey of Armenian firms undertaken for the study by Maliszewska _et al_ ., (2008) and data from the Cost of Doing Business study of the World Bank. We estimate that the costs of importing from and exporting to the EU will fall by 2.5 percent of production costs, while the costs of importing into Armenia from non-EU countries will fall by 2.3 percent of production costs and the cost of exporting to non-EU countries from Armenia will fall by 2.2 percent of production costs. Details of the estimation are available in appendix D.

# **Standards costs**

The EU devoted considerable resources to assisting its new member states with standards and, similarly it is allocating resources to this problem for the countries with which it may potentially have a DCFTA. Consequently, we assume these costs will fall as a result of a Deep and Comprehensive Free Trade Agreement. That is, for firms who will sell in the EU, after adaption by the firms and Armenian development of the National Quality Infrastructure, we assume the production costs of selling in the EU by Armenian firms will decline. We rely on a survey by Jakubiak et al., (2006) and adapt it for Armenia. We estimate that the costs of compliance with EU standards as a percentage of production costs will fall for Armenian exports to the EU of agricultural (manufacturing) products from 15.8 (21.6) percent of production to 11.8 (16.2) percent of production. But there are costs of facilities to meet EU standards and in development of the National Quality Infrastructure that we estimate will diminish the cost reduction by about 2 percent. Since the CIS market is predominantly regulated by ―GOST‖ regulations, we do not assume that production costs for Armenian exporters would fall on exports to any market other than the EU. Details are in Appendix E.

An important policy caveat to the above paragraph is that we do not recommend that Armenia adopt all EU technical regulations and sanitary and phyto-sanitary (SPS) measures. On the one hand, facilitating but not compelling voluntary harmonization to standards in goods should be beneficial and is the core of what we estimate. Armenian firms could continue to produce according to Gosstandard (GOST) standards for the CIS market or at home if they choose to do so. On the other hand, requiring Armenian firms to adopt EU technical regulations in goods and especially all SPS requirements will likely impose very high costs. It does not appear that the benefits of these mandatory requirements are justified in all cases without actual 14
trade flows in the network between 3,403 city pairs. The model incorporates economic scale for origin and destination cities (proxied by their estimated total incomes), the network road distance between them, and road quality in each transshipment country. We compute trade volumes for the current road network, and for an upgraded network with road quality comparable to current levels in The Gambia and Zimbabwe. Our results suggest that coordinated upgrading of the network would expand overland trade by about $250 billion over 15 years.

Using the World Bank’s ROCKS database, we also estimate country-specific unit costs for upgrading and maintaining the 83-city road network. Our results indicate total expenditures of approximately $35 billion for upgrading and maintenance over 15 years, of which about $12 billion would directly generate jobs and income for about 8.4 million rural construction workers in the upgrade period, and 365,000 annual jobs for maintenance workers. We estimate additional 15-year costs of $12 billion for administration, monitoring of road conditions, and programs that compensate abutting settlements for loss of revenue from barricades. Over 15 years, the implied annual implementation cost is $5.9 billion for the first five years, and $1.8 billion thereafter. This is well within the range of aid programs that are currently proposed for Sub-Saharan Africa.

We acknowledge that our network perspective incorporates a broad vision, and in closing we would like to address the question of feasibility. It is natural to ask whether an integrated trans-African road network is feasible, given Africa’s many disappointments during the past several decades. We believe that a coordinated road upgrading program can succeed, for several reasons. First, the growing development gap 45
Table B11: Access to basic amenities and location

|||Water|||Sanitation|||Electricity||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Panel A: DOU**||||||||||
|Urban center<br>Urban cluster|0.229***<br>(0.005)<br>0.131***<br>(0.006)|0.202***<br>(0.006)<br>0.120***<br>(0.006)|0.147***<br>(0.006)<br>0.098***<br>(0.006)|0.210***<br>(0.006)<br>0.102***<br>(0.006)|0.163***<br>(0.006)<br>0.083***<br>(0.006)|0.124***<br>(0.006)<br>0.067***<br>(0.006)|0.384***<br>(0.006)<br>0.148***<br>(0.006)|0.288***<br>(0.006)<br>0.108***<br>(0.006)|0.179***<br>(0.006)<br>0.063***<br>(0.006)|
|Log of real exp.||0.058***<br>(0.003)|0.030***<br>(0.004)||0.100***<br>(0.003)|0.093***<br>(0.003)||0.206***<br>(0.003)|0.160***<br>(0.003)|
|**Panel B: DB**||||||||||
|Core|0.203***|0.176***|0.128***|0.181***|0.139***|0.104***|0.331***|0.244***|0.150***|
||(0.006)|(0.006)|(0.007)|(0.005)|(0.005)|(0.006)|(0.006)|(0.006)|(0.006)|
|Suburb|0.147***|0.128***|0.093***|0.168***|0.138***|0.115***|0.228***|0.168***|0.098***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.009)|(0.009)|(0.008)|
|Town|0.073***|0.065***|0.056***|0.064***|0.051***|0.043***|0.088***|0.061***|0.042***|
||(0.006)|(0.006)|(0.006)|(0.005)|(0.005)|(0.005)|(0.006)|(0.006)|(0.006)|
|Log of real exp.||0.066***|0.035***||0.104***|0.095***||0.217***|0.165***|
|||(0.003)|(0.004)||(0.003)|(0.003)||(0.003)|(0.003)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|No|Yes|No|No|Yes|No|No|Yes|
|Education|No|No|Yes|No|No|Yes|No|No|Yes|
|Employment|No|No|Yes|No|No|Yes|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.073|0.080|0.097|0.165|0.189|0.225|0.204|0.277|0.341|
|Panel B Adj.R<sup>2</sup>|0.065|0.074|0.094|0.159|0.186|0.223|0.186|0.270|0.338|
|Nr. of countries|16|16|16|16|16|16|16|16|16|
|Nr. of hh|134196|134196|134196|134196|134196|134196|130866|130866|130866|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. GHSPOP 1km is used for the DOU and DB method. The dependent variable is a dummy variable taking the value 1 if a household has access to a respective amenity. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

69