rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Adult mortality has a significant effect on national economies, both through the direct loss of productivity among those of working age and by altering fertility, incentives for risk-taking behavior, and investment in human and physical capital. Tuberculosis (TB) is the most important cause of adult death due to infectious disease after HIV/AIDS. Roughly 8.8 million new TB cases and 1.7 million TB-related deaths were reported in 2003 (Corbett, Watt et al. 2003), including 229,000 deaths of individuals who also were infected with HIV/AIDS. Almost all of those who died from the disease lived in low- and middle-income countries (Lopez, Mathers et al. 2006) and most were between age 15 and 49 (Styblo and Rouillon 1991). Table 1 provides estimates of TB-related deaths from the World Health Organization (WHO) in the 22 countries with the highest burden of TB in 2004.

TB places an extraordinary burden on those afflicted by the disease, their families, and communities and on government budgets. The greatest burden of TB falls on productive adults who, once infected, are weakened and often unable to work. The burden of taking care of sick individuals usually falls to other family members and, in addition to putting them at greater risk of infection, can lower their productivity. Besides loss of productivity, the cost of treating TB also can be significant. Mean household spending on TB can account for as much as 8–20 percent of annual household income, varying by region (Russell 2004). Children also are affected. Each year, a significant proportion of children from families in India in which the primary breadwinner has TB are forced to drop out of school or seek employment (Rajeswari, Balasubramanian et al. 1999). However, the most devastating impact of TB is death; without treatment, two-thirds of smear-positive cases die within five to eight years, with most dying within 18 months of being infected (Styblo and Rouillon 1991).

## <u>Impact of adult mortality on economic growth</u>

Adult deaths place an especially high economic burden on societies. The loss of working-age adults represents a loss of human capital and has a profound effect on household economic well being. A cross-sectional study of the effects of adult mortality 1
agricultural production of the main export crop, cocoa, which was more easily shipped to the coast. In turn, agricultural development bolstered urbanization. These effects are still present today as districts close to these rail lines are more developed.

In line with some of the arguments advanced above about the importance of transportation infrastructure for the decentralization of manufacturing activity away from large metropolises, Baum-Snow et al. (2013) underscore the importance of railroads in the decentralization of manufacturing production in China.<sup>32</sup> Rothenberg (2011) shows similar effects regarding highways in Indonesia.

Storeygard (2011) provides evidence about the importance of inter-city transportation costs for inland African cities. Using new roads data for Africa and satellite data (‘lights at night’) to estimate economic activity, he assesses the effect of higher transportation costs. To circumvent the endogeneity of transportation costs (roads may be built to access growing cities), he uses arguably exogenous variations in oil prices. He finds an elasticity of economic activity with respect to transportation costs of approximately -0.2.

All of these findings are suggestive of the profound and long-lasting effects of major transportation infrastructure. However, it is necessary to remember that major transportation networks are extremely costly investments.

# _Urban favoritism_

The last key difference between cities in developing and more advanced countries involves the effects of the favoritism by governments toward the largest cities. Although the reasons for primate city favoritism are debated (Ades and Glaeser 1995; Henderson 2005), there is little doubt that such favoritism occurs in many different ways. As argued in Duranton (2008), primate 38
- 7 -

Tanzania National Roads Agency (TANROADS), are generally well-maintained. About 12,000 km of roads are paved, of which half or 55 percent are maintained in good or fair condition. On the other hand, most rural roads remain unpaved, and nearly 90 percent are in poor or very poor condition.

Rail transport has the general advantage of long-haul freight transport. Tanzania has about 3,557 km of rail lines, which are operated by two rail companies: Tanzania Railways Limited (TRL) and Tanzania Zambia Railway Authority (TAZARA). The TRL lines were constructed during the colonial era in the early 20th century. The construction of the Central Railway was started in 1905, to Kigoma, a port town situated on Lake Tanganyika, which was reached in 1914 (e.g., Amin, Willetts and Matheson, 1986). The current TRL was established as a parastatal company jointly owned by Reli Assets Holding Company (RAHCO) on behalf of the Government of Tanzania (49 percent) and an Indian private operator RITES (51 percent) in 2007. However, the anticipated increase in performance under RITES management did not materialize.<sup>1</sup> Since the completion of negotiations in 2011, the company has fully been owned by the government.

The TRL network is based on a 1,000 mm narrow-gauge standard and extends more than 2,500 km, connecting Dar es Salaam and large inland cities, such as Mwanza, Kigoma and Arusha. These inland areas are more than 1,000 km away from the coast. Historically, rail transport has been playing an important role to provide affordable access to the global market to Tanzania, which is a large country with a land area of about 900,000 km<sup>2</sup> .

TAZARA is another major rail line connecting Dar es Salaam to Mbeya and New Mposhi in Zambia. Unlike the TRL, which is primarily a national network with the ends of lines located at Dar es Salaam and Tanga on the Indican Ocean, Mwanza on Lake Victoria and Kigoma on Lake Tanganyika, TAZARA is a binational rail network, extending 1,860 km, with 975 km in Tanzania and 885 km in Zambia ( **Table 3** ). TAZARA is a statutory body established under

> 1 Based on the TRL website and TRL (2014). TRL performance review: JTSR 2014.
data in Appendix Table A3, which displays the obstacles separately for all the countries covered in the Enterprise Surveys, shows that the difference in the perception of macroeconomic instability as an obstacle is driven by the observations from Ecuador and El Salvador. In these two countries, female-owned firms are about 20 percent more likely than male-owned firms to report that macroeconomic instability is an obstacle. These differences persist even after controlling for industry fixed effects.

The other two variables that a higher percentage of female-owned firms perceive to be an obstacle than male-owned firms are both related to child care and household obligations. Unfortunately, information on these obstacles is only available for the Bolivian data. 59.5 percent of female-owned firms in the Bolivian data report that having to care for small children or family is an obstacle to their firm’s operation and growth. Similarly, 71.1 percent of femaleowned firms in the Bolivian data perceive family obligations or household chores as an obstacle. Both numbers are about 23 percentage points higher than for male-owned firms. In absolute and in relative terms, these are by far the greatest differences in perceived obstacles across femaleand male-owned firms.

**<u>Table 6: Obstacles to Firm Operation and Growth</u>**

||Average|<br>for Fema<br>Firms|% that perceive ...<br>le-Owned<br>|as an obstac<br>Differenc|le<br>e (Female|-Male)<br>|
|---|---|---|---|---|---|---|
||Bolivia|Peru|Enterprise<br>Surveys|Bolivia|Peru|Enterprise<br>Surveys|
|Access to land|59.5||45.1|-6.5||3.9|
|Labor regulation|31.4|61.9|65.2|-2.6|4.4|-2.5|
|Skills and education of available workers|55.4|62.5|77.4|-3.0|-9.0**|2.1|
|Business licensing and operating permits|||53.3||-2.7|-3.6|
|Access to finance (e.g. collateral)|71.1|75.6|76.2|-10.9**|-2.7|1.0|
|Cost of finance (e.g. interest rates)|68.6|79.8|82.8|-19.6***|-6.0*|-0.7|
|Economic and regulatory policy<br>uncertainty|83.5|91.1|88.4|-6.1|-0.5|-1.2|
|Macroeconomic instability|89.3|94.6|92.2|-1.7|-0.8|4.1**|
|Corruption|80.2|92.3|88.1|-9.4**|1.0|3.1|
|Crime, theft and disorder|91.7|93.5|81.5|4.9|-2.7|1.1|
|Anti-competitive or informal practices|||84.3|||3.2|
|Legal system/conflict resolution|32.2|54.2|65.2|-7.4|0.8|-2.0|
|Having to care for small children or family|59.5|||23.4***|||
|Family obligations or household chores|71.1|||21.8***|||

_Note_ : Significance levels: *** 1 percent, ** 5 percent, * 10 percent. _Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, Bolivian World Bank Micro Enterprises Survey, 2007, Peruvian World Bank Micro Enterprises Survey, 2008, World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua, 2003.

The data on perceived obstacles to firm operation and growth in this section does not suggest that female-owned firms face more severe obstacles than male-owned firms in most areas. The only 17
difference in hourly earnings is statistically significant in six of the countries, with the exception of Kenya, where the mean hourly wage for men is US$2.58 and for women is US$2.50 (2011 PPPadjusted US dollars). The small wage differences in Kenya have been documented by previous research (Schultz, 1991) and are explained by the greater involvement of women in market production. For countries other than Kenya, the raw (unadjusted) wage gap ranges from 22 percent in Vietnam to 39 percent in Armenia.<sup>14</sup> In Kenya, where the difference in the average wages of men and women is not significant, the wage gap is a modest 3 percent.

**Table 2. Average Hourly Earnings among Men and Women, by Country** (in 2011 PPP-adjusted U.S. dollars)

||Me<br>|n<br>|Wom<br>|en<br>|||
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|N|_t_value|
|Armenia|3.33|2.00|2.02|1.18|530|8.83|
|Bolivia|4.11|4.12|2.92|3.25|653|4.52|
|Colombia|4.03|5.14|2.79|2.19|832|4.98|
|Georgia|4.12|3.27|2.76|2.67|481|4.56|
|Kenya|2.58|2.88|2.50|3.15|1160|0.87|
|Ukraine|4.14|1.72|2.88|1.71|730|7.62|
|Vietnam|3.66|3.57|2.87|3.43|1394|4.01|

_Note_ . Earnings have been converted to 2011 PPP-adjusted U.S. dollars. The sample includes 25- to 64year-olds in self-employment or wage work. Employers, unpaid workers, part-time workers, and the top 1 percent of earners are excluded from the sample.

_Source_ : STEP Surveys (2014).

We examine the unconditional wage gap at different percentiles of the distribution. These are presented in Figures 1, 2, and 3. The distributions show substantial variation throughout the wage distribution. Further, the pattern is not systematic across countries. In Bolivia and Kenya we observe higher gaps at lower ends of the wage distribution, whereas in Armenia and Colombia we find the biggest gaps at the top of the wage distribution, implying that women do well in the labor market up to a point, beyond which their prospects and growth are limited (Albrecht, Bjorklund, and Vroman, 2003). In Ukraine, the second and fourth quartiles of the wage distribution show the biggest gaps. These patterns will be further explored in the decomposition analysis.

> 14 It must be noted that the observed wage gap is for the urban population only and could be an over- or underestimate of the overall wage gap in the country.

13
Table 6: The impact of the net in-migration rate on the employment probability of the non-migrants. Second stage of an IV probit analysis.

||Employment probability|
|---|---|
|Instrumented value of the net in-migration rate|-0.782***<br>(0.033)|
|Economic development in 2001|-0.108***<br>(0.002)|
|SPI (yearly average between 1997 and 2002)|0.002<br>(0.002)|
|Male|0.232***<br>(0.001)|
|Age|0.204***<br>(0.001)|
|Age<sup>2</sup>|-0.009***<br>(0.000)|
|Education|0.000<br>(0.001)|
|Urban area|-0.067***<br>(0.002)|
|Observations|530,827|

Notes: Source: Authors’ analysis based on data described in the text. The table reports marginal effects, based on the second stage of an IV probit analysis. The unit of observation is the non-migrant. Robust standard errors are bootstrapped at the regional level. *, ** and *** indicate significance at the 10, 5 and 1% levels.

Table 7: The impact of the net in-migration rate on the employment probability of the non-migrants in regions with low and high road density. Second stage of an IV probit analysis.

||Employment<br>Below-median road density<br>|probability<br>Above-median road density<br>|
|---|---|---|
|Instrumented value of the net in-migration rate|-1.031***<br>(0.067)|-0.527***<br>(0.037)|
|Economic development in 2001|-0.155***<br>(0.003)|-0.057***<br>(0.003)|
|SPI (yearly average between 1997 and 2002)|-0.038***<br>(0.003)|0.018***<br>(0.003)|
|Male|0.225***<br>(0.002)|0.236***<br>(0.002)|
|Age<br>|0.199***<br>(0.001)|0.208***<br>(0.001)|
|Age<sup>2</sup>|-0.009***|-0.009***|
||(0.000)|(0.000)|
|Education|-0.003|-0.001|
||(0.002)|(0.002)|
|Urban area|-0.067<br>(0.004)|-0.074***<br>(0.002)|
|Observations|225,490|305,337|

Notes: Source: Authors’ analysis based on data described in the text. The table reports marginal effects, based on the second stage of an IV probit analysis. The unit of observation is the non-migrant. Robust standard errors are bootstrapped at the regional level. *, ** and *** indicate significance at the 10, 5 and 1% levels.

46
# Comparison of different types of disaster management investments

<!-- Start of picture text --> GDP compared to baseline<br>—— Only shocks<br>5 —— Shocks and 100% of adaptation<br>——— Shocks and 100% of insurance<br>0 —— Shocks and 100% of contingency fund<br>-5 —— Shocks and 100% of fiscal space<br>-10<br>-15<br>-20<br>-25<br>-30<br>2020 2030 2040 2050 2060 2070 20380 2090 2100<br>Consumption compared to baseline<br>—— Only shocks<br>5 ——— Shocks and 100% of adaptation<br>—— Shocks and 100% of insurance<br>0 —— Shocks and 100% of contingency fund<br>5 —— Shocks and 100% of fiscal space<br>-10<br>-15<br>-20<br>-30<br>-35<br>2020 2030 2040 2050 2060 2070 20380 2090 2100<br><!-- End of picture text --> <!-- Start of picture text --> Potential GDP compared to baseline<br>—— Only shocks<br>5 ——— Shocks and 100% of adaptation<br>——— Shocks and 100% of insurance<br>0 < i —— Shocks and 100% of contingency fund<br>5 “WER, —— Shocks and 100% of fiscal space<br>-10 oe4 ‘ (aa>< iLra 4 F4 (Zz~ I<br>V \VA YY<br>v<br>-20<br>25<br>-30<br>-35<br>2020 2030 2040 2050 2060 2070 20380 2090 2100<br>Debt/GDP<br>—— Only shocks<br>160 —— Shocks and 100% of adaptation<br>—— Shocks and 100% of insurance<br>140 —— Shocks and 100% of contingency fund<br>—— Shocks and 100% of fiscal space<br>120<br>100<br>80<br>40<br>20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
estimates across transitional economies although inequality remains very marked even including this measure of output.

**Table 4.2 The size of the shadow economy 1979-2000 (% of GNP)**

||1979(1)|1989(1)|1995(1)|1995(2)|1999/2000(3)|
|---|---|---|---|---|---|
|Azerbaijan|50.0|33.0|69.9|60.6|60.6|
|Georgia|50.0|33.0|71.4|62.6|67.3|
|Moldova|43.0|29.0|47.8|35.7|45.1|
|Uzbekistan|50.0|33.0|28.5|6.5|34.1|
|**CIS-7**|**48.3**|**32.0**|**54.40**|**41.35**|**51.78**|
|Belarus|43.0|29.0|34.5|19.3|48.1|
|Kazakhstan|50.0|33.0|49.8|34.3|43.2|
|Russia|27.0|18.0|45.6|41.6|46.1|
|Ukraine|38.0|25.0|56.5|48.9|52.2|
|**Other CIS**|**39.5**|**26.3**|**46.60**|**36.03**|**47.40**|

Source: (1) Alexeev and Pyle (2002); (2) Johnson et Al (1997); (3) Schneider and Klinglmair (2004)

**Table 4.3 The size of the shadow economy 2000**

||GNP at market<br>prices (current<br>US$ bn) 2000|Shadow<br>economy in<br>% of GDP<br>1999/2000|Shadow<br>economy<br>(current US$ bn) 2000|Shadow<br>economy<br>GNP per cap-<br>ita (current<br>US$)|GNP per<br>capita 2000<br>Atlas method|
|---|---|---|---|---|---|
|Armenia|19.3|46.3|8.9|240.8|520|
|Azerbaijan|49.2|60.6|29.8|363.6|600|
|Georgia|30.5|67.3|20.5|424|630|
|Kyrgyz|12.2|39.8|4.9|107.5|270|
|Moldova|13.6|45.1|6.1|180.4|400|
|Uzbekistan|74.2|34.1|25.3|122.8|360|
|**CIS 7**|**33.2**|**48.9**|**15.9**|**239.9**|**463**|
|**Other CIS**|**815.8**|**47.4**|**381.0**|**767.3**|**1630**|
|**Non-CIS**|**468.9**|**29.6**|**129.0**|**991.7**|**3549**|

Source: Schneider, F. and Klinglmair, R. (2004) Thus, the shadow economy seems to be a natural compensating mechanism of the formal economy performance. But is this an economics law and applicable cross-country? And, is the shadow economy a good or a bad thing, i.e. does it constrain further growth (formal and informal) or is simply a natural survival mechanism in the absence of which many people would face poverty and possibly starvation?

Schneider and Klinglmair (2004) report a somehow puzzling result. In their regressions of growth in the formal sector, they find that the shadow economy positively contributes to growth in transitional and industrialized economies and negatively in developing economies over the 1990s. They also review the literature on the causes of the shadow economy highlighting tax and social security contributions burdens (higher taxes => higher shadow economy), the degree of regulation (higher regulation => higher shadow economy) and the vicious cycle of state revenues (higher taxes =>higher shadow economy=>higher taxes on the formal sector). This would seem consistent with their regression findings given that industrialized and transitional economies share a higher degree of taxes and regulation in relation to developing economies. Industrialized countries with significant taxes and regulations turn to the informal sector for further growth while in prevalently agricultural societies, where taxes and regulations are either few or poorly enforced, the informal sector is almost a natural status mostly related to subsistence activities.

27
overlapping quinquennial averages of data over the same two periods as for the time series model (although for a somewhat smaller sample of countries as dictated by data availability), and then generate forecasts for the remaining years in the sample which can be compared to actual outcomes. In order to benchmark the forecasts generated by these models against current practice, we also make some comparisons with long-term forecasts produced by the World Bank's Unified Survey in 1990. However, our primary interest is in the relative performance of the time series and growth models. **2** We assess the out-of-sample forecast performance of these models using standard summary statistics which capture their bias and mean squared error. These statistics suggest small median (across countries) differences in forecast performance of the alternative models, which vary with the forecast horizon. For example, there is some evidence -- consistent with our priors -- that the mean squared error of growth regression based forecasts is smaller at long forecast horizons (five years or more). However, these differences in median forecast performance are typically very small relative to the cross-country dispersion in forecast performance, casting doubt on the significance of observed "typical" differences. The relative performance of the altemative forecasting models is also very unstable over time within countries. We test for and do not reject the null hypothesis that the past relative performance of the growth model and the time series model in a particular country is independent of the future relative performance of the two models in that country.

These results indicate that neither forecasting model dominates, both across countries and within countries over time. Rather than attempt to choose a single "best" forecasting model, we instead ask whether there is value in combining the forecasts of alternative models. We implement forecast encompassing tests and find evidence that these approaches can "learn from each other", in the sense that the forecasts from both models are jointly significant in explaining actual outcomes. This is especially true at shorter horizons, and it suggests that there are potential benefits from combining these forecasts in some way to arrive at a superior overall method.

> **2** For a more systematic assessment ofthe quality of World Bank forecasts, see Ghosh and Minhas (1993), and Verbeek (1999). Artis (1996) does the same for the IMF's short-term forecasts.

2
40

**Table 6**

# **Robustness: measure of remittances**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ED4<br>median|ED4<br>mean|ED4<br>75th|BP5<br>median|BP5<br>mean|BP5<br>75th|
|CA|-2.798<br>[0.229]***|-2.797<br>[0.227]***|-2.785<br>[0.226]***|-1.772<br>[0.214]***|-1.765<br>[0.213]***|-1.763<br>[0.213]***|
|REM|-0.181|-0.326|-0.620|0.076|-0.097|-0.311|
||[0.535]|[0.570]|[0.607]|[0.389]|[0.396]|[0.414]|
|RES|-0.966<br>[0.434]**|-1.053<br>[0.395]***|-0.971<br>[0.382]**|-1.242<br>[0.486]**|-1.216<br>[0.442]***|-1.155<br>[0.426]***|
|EXTD|0.166|0.199|0.151|0.164|0.181|0.154|
|SHORTD|[0.085]*<br>-0.024|[0.085]**<br>-0.001|[0.090]*<br>-0.009|[0.069]**<br>-0.021|[0.072]**<br>-0.001|[0.073]**<br>-0.003|
||[0.144]|[0.145]|[0.144]|[0.106]|[0.108]|[0.108]|
|CREGRO|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||[0.002]|[0.002]|[0.002]|[0.002]|[0.001]|[0.001]|
|PPPGDP|-0.020|-0.016|-0.017|-0.008|-0.005|-0.006|
||[0.026]|[0.026]|[0.026]|[0.025]|[0.026]|[0.026]|
|OPEN|0.192|0.204|0.185|0.216|0.222|0.212|
||[0.107]*|[0.108]*|[0.107]*|[0.096]**|[0.097]**|[0.096]**|
|OT|-0.686|-0.719|-0.698|-0.463|-0.483|-0.488|
||[0.349]**|[0.342]**|[0.348]**|[0.303]|[0.298]|[0.302]|
|CONCD|-0.011|-0.003|-0.010|0.026|0.034|0.032|
||[0.112]|[0.113]|[0.112]|[0.102]|[0.102]|[0.102]|
|RES^2|1.586|1.534|1.490|2.132|2.036|1.951|
||[0.826]*|[0.796]*|[0.796]*|[1.043]**|[1.015]**|[1.004]*|
|EXTD^2|-0.027|-0.038|-0.029|-0.036|-0.046|-0.041|
||[0.024]|[0.022]*|[0.025]|[0.015]**|[0.016]***|[0.017]**|
|RES*DREM|0.380|0.692|0.605|0.576|0.731|0.736|
||[0.317]|[0.316]**|[0.308]*|[0.305]*|[0.291]**|[0.293]**|
|EXTD*DRE|-0.053|-0.077|-0.001|-0.072|-0.072|-0.029|
|M|||||||
||[0.048]|[0.049]|[0.058]|[0.039]*|[0.041]*|[0.049]|
|Observations|1214|1214|1214|1191|1191|1191|
|R-squared|0.30|0.31|0.30|0.22|0.22|0.22|

_Notes_ : see footnote to Table 5. Here remittances are given by the sum of workers’ remittances and compensation of employees for all countries except Argentina, Azerbaijan, Barbados, Belize, Benin, Brazil, Cambodia, Cape Verde, China, Cote d’Ivoire, Dominican Republic, Ecuador, El Salvador, Guyana, Panama, Rwanda, Senegal, Seychelles, Turkey and Venezuela. * significant at 10%; ** significant at 5%; *** significant at 1%
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