rafmacalaba/datause-encoder-data huggingface dataset:
Beyond these average results for the continent as a whole, it is also useful to provide graphical representations of the results for different countries. This is done in Figures 1 through 6. In each Figure, we have a scatter plot with the neighborhood access rate in the country in urban areas on the horizontal axis, and the estimates along the econometric method for the proportions of deficit coverage due respectively to demand-side factors, supply-side factors, and combined factors on the vertical axis. The curves through the scatter plots have been simply fitted in Excel for visual purposes.

Clearly, pure demand-side factors are much more important in countries where access is already high, as expected. Pure supply-side factors appear not to depend as much on access rates. This may at first seem surprising, but one should remember that even in countries where access is high, there are significant neighborhoods or parts of neighborhoods that remain unserved. In addition, supply-side factors are expressed in the Figures in percentage terms of the lack of coverage, so that the share of the unserved population due to supply-side issues should not necessarily be smaller where neighborhood access and thereby supply are higher. As to combined demand and supply-side factors, they are lower in percentage terms where there are higher access rates, essentially because when the access rate is higher, demand-side issues tend to show up more in the pure demand-side component of the decomposition than as mixed problems. Overall, given substantial differences in the nature of the obstacles to coverage between countries at different levels of neighborhood access, when thinking of policy options, it is clearly important to look at the specific estimates obtained for a given country. In fact, ideally, it would be even better to look at a lower level of disaggregation, for example for the capital city as opposed to other urban areas, if the data so permit. Census data would be useful, as the type of work conducted here is based on variables that are typically available in censuses.

# **4. Conclusion**

As part of the efforts needed to reach the Millennium Development Goals, many countries in sub-Saharan Africa are aiming to improve coverage of network-based infrastructure services such as piped water and electricity in the population. Yet in order to inform policies necessary to do so, it is important to first understand whether lack of coverage is due primarily to demand-side or affordability issues, or to a lack of supply. Indeed, some households may live in areas where access to piped water and electricity is available, but may not be able to pay for
<!-- Start of picture text --> Remaining poor Moving out of poverty<br>=-<br>o<br>SS (oe)<br>© © ———_—<br>~ ) st<br>nN N oo<br>fo) °<br>s* s* s s * s * s- s* s7 < s s s<br>Moving into poverty Staying out of poverty<br>_© SeSS<br>© ©<br>st ~t<br>N LS .<br>o> lo)<br>sf FS GS SC SP SP SS SM Se<br>hac tyare basedgive the on syntheticconditional panel estimatesprobability. The forpoverty household line used headsis agedRM 527. between 25 and 60 in the first round of each interval.<br><!-- End of picture text -->
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 3.5 . Net imports as a share of domestic demand (percentage)**

<!-- Start of picture text --> 100<br>50<br>0<br>-50<br>-100<br>-150<br>-200<br>-250<br>Trade expansion Trade stagnation<br>domestic consumption<br>Net imports of power as percentage Ethiopia Rwanda Sudan Uganda Tanzania Kenya Egypt Burundi<br><!-- End of picture text --> Source: Derived from Rosnes and Vennemo 2009.

It is important to consider the implications for EAPP if hydropower were affected by climate change. There is some evidence that climate change will produce unstable and unpredictable weather patterns, including droughts, storms, heavy rainfall, and floods. Several sources argue that East Africa is likely to experience decreased water flow, while water flow will increase in southern Africa. The possible impact of climate change throughout Africa ranges from a 15 percent decrease to a 5 percent increase in water flows by 2050 compared to the 1961–90 baseline.

AICD simulated a scenario in which climate change reduced by 5 to 25 percent so-called firm hydropower production from both existing and new capacity . The results indicated that investments in natural-gas-fired power plants in Egypt and Tanzania would replace investments in the less-reliable hydropower in EAPP. Because natural-gas-fired power plants produce more energy (in gigawatt-hours) per megawatt installed, the _total_ investment would be reduced from that of the non-climate-change scenario. But because the gas resources of Egypt and Tanzania are almost fully exploited, geothermal power from Djibouti would become a more profitable option. In rural areas, diesel-fired power plants would become an alternative to small-dam hydropower.

Lower capacity investment and a shift away from hydropower under the climate-change scenario would reduce total overnight (short-term) investment costs. Additionally, the lower investment in hydropower would reduce the need for transmission lines, since thermal power plants can be built in the vicinity of load centers. On the other hand, as hydropower resources are increasingly constrained, substitutes become increasingly expensive. Ultimately, _overnight_ investment costs are only slightly reduced.

On the other hand, when taking into account the higher operating costs and shorter economic lifetime of gas-fired power plants, _annualized_ costs increase steadily as hydropower investments are replaced by other technologies. Results indicate that annualized costs increase 9 percent when hydropower availability is reduced 25 percent.

43
WPS3945 over the use of household resources. These contrary effects could, in principal, have gender differentiated impacts and lead to higher or lower net investments in human capital among children in migrant households.

In this paper, we examine the impact of temporary economic migration by a household member on investments in child schooling. We are particularly interested in disaggregating the role of the potentially contrary channels through which migration could in fl uence schooling decisions. The data come from rural Pakistan. Temporary economic migration is quite substantial, with more than one in four rural households reporting at least one migrant. School enrollment rates also remain relatively low and the rural gender gap in schooling is large. This makes it a particularly useful context for examining the impact of migration on gender inequalities in human capital investment. School age children are also routinely engaged in home or market production so that foregone income from such activity is the appropriate opportunity cost of time spent in school. We examine this tradeoff directly by looking at the labor market activity of school age children. A substantial number of migrant households are also female headed. We can therefore examine how variations in household structure among migrants in fl uences schooling choices, and in particular, how it in fl uences the gender allocation of labor and schooling among migrant children.

The literature on migration and human capital investment in origin communities is small and focused largely on the impact of migration on accumulated schooling (see, for example, Cox Edwards and Ureta (2003), Lopez Cordoba (2004), Yang (2004), Hanson and Woodruff (2003), deBrauw and Giles (2005) and McKenzie and Rapoport (2005)). Most of these studies fi nd some positive impact of migration on school attainment, but none looks at the impact of migration on gender inequalities in schooling carefully. Hanson and Woodruff (2003) fi nd that the gains in schooling are greatest for girls in the age group 13-15. McKenzie and Rapoport’s (2005) fi nding that 16-18 year old boys in migrant households have signi fi cantly fewer years of schooling effectively reduces the gender gap in schooling among 16-18 year old children, but only via a net reduction in schooling for boys. We are also not aware of any studies that have looked at the effects of migration on schooling and labor market outcomes for children simultaneously, or on the effects of migration induced changes in household structure on child schooling and labor market participation.<sup>3</sup> Since migration can affect child schooling through multiple channels, the paper focuses

> 3 The one possible exception is Joshi (2004), who looks at the effects of household structure in Bangladesh on child schooling, but does not focus speci fi cally on migration.

2
**<u>Table 7. Matching results—direct taxes</u>**

|**Treatment Variable:**<br>**Non-Cooperation**|**1-Nearest**<br>**Neighbor**<br>**Matching**|**2-Nearest**<br>**Neighbor**<br>**Matching**|**3-Nearest**<br>**Neighbor**<br>**Matching**|**Ra**|**dius Match**|**ing**|**Local**<br>**Linear**<br>**Regression**|**Kernel**<br>**Matching**|
|---|---|---|---|---|---|---|---|---|
|||||**r=0.005**|**r=0.01**|**r=0.05**|**Matching**||
||||**Depend**|**ent variab**|**le: Incom**|**e Taxes**|||
|[1] Average Treatment on the Treated (ATT)|-0.617|-0.371|-0.437|-0.304|-0.329|-0.413|-0.469|-0.412|
||(0.585)|(0.497)|(0.502)|(0.419)|(0.372)|(0.295)|(0.291)|(0.302)|
|Number of observations|484|484|484|484|484|484|484|484|
|Number of Treated observations|100|100|100|100|100|100|100|100|
|Number of Control observations|384|384|384|384|384|384|384|384|
|Rosenbaum bounds sensitivity tests|1|1|1|1|1|1.1|1.2|1.1|
|Standardized biases (p-value)|0.73|0.99|0.99|0.99|0.99|0.99|0.73|0.99|
|Pseudo R2|0.02|0.005|0.005|0.005|0.005|0.004|0.02|0.004|
||||**Depende**<br>|**nt variabl**<br>|**e: Individu**<br>|**al Taxes**<br>|||
|[2] Average Treatment on the Treated (ATT)|-0.482|-0.566*|-0.391|-0.303|-0.248|-0.168|-0.114|-0.182|
||(0.350)|(0.330)|(0.296)|(0.261)|(0.240)|(0.213)|(0.216)|(0.233)|
|Number of observations|424|424|424|424|424|424|424|424|
|Number of Treated observations|86|86|86|86|86|86|86|86|
|Number of Control observations|338|338|338|338|338|338|338|338|
|Rosenbaum bounds sensitivity tests|1.2|1.3|1.2|1.2|1.1|1|1|1.1|
|Standardized biases (p-value)|0.82|0.99|0.98|0.99|0.99|0.99|0.82|0.99|
|Pseudo R2|0.02|0.004|0.008|0.005|0.005|0.004|0.02|0.004|
||||**Depend**|**ent variabl**|**e: Propert**|**y Taxes**|||
|[3] Average Treatment on the Treated (ATT)|-0.0613|-0.0669|-0.0764|-0.119|-0.136|-0.0864|-0.0967|-0.0923|
||(0.134)|(0.115)|(0.109)|(0.0962)|(0.0866)|(0.0719)|(0.0743)|(0.0768)|
|Number of observations|426|426|426|426|426|426|426|426|
|Number of Treated observations|96|96|96|96|96|96|96|96|
|Number of Control observations|330|330|330|330|330|330|330|330|
|Rosenbaum bounds sensitivity tests|1|1|1.2|1.3|1.6|1.3|1.5|1.4|
|Standardized biases (p-value)|0.43|0.92|0.97|0.81|0.98|0.99|0.43|0.99|
|Pseudo R2|0.03|0.01|0.008|0.01|0.006|0.002|0.03|0.002|

Note: Standard errors in parentheses computed using 500 bootstrap replications. * _p_ <0.10, ** _p_ <0.05, *** _p_ <0.01.

20
to capture a large part of the value of the increased market for manufacturing goods in emerging economies. And at the same time the domestic value added content of their own production declined. Both trends can be interpreted as a loss of competitiveness. International competition is not a zero-sum game however. And the declining shares in global GVC do not necessarily mean an absolute decline in GVC income. On the contrary, in real terms world GVC income on manufactures (deflating by the US CPI index) has increased by about one-third over the period 1995 to 2008.

**Figure 3 Regional shares in world GVC income for all manufactures (%)**

<!-- Start of picture text --> 35<br>30<br>EU27<br>25<br>NAFTA<br>20<br>China<br>15 BRIIAT<br>10 East Asia<br>5<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Note: Value added by regions in the production of final manufacturing goods. East Asia includes Japan; Korea; and Taiwan, China. BRIIAT includes Brazil, Russia, India, Indonesia, Australia, and Turkey. EU27 includes all European countries that have joined the European Union. NAFTA includes Canada, Mexico and the US. Shares do not add up to 100% as the remainder is the share of all other countries in the world. _Source:_ Author’s calculations based on World Input-Output Database, April 2012, updated to 2011.

Aggregate EU27 performance hides substantial variation within the European Union. In Table 1 we present the change in GVC income for individual EU countries. Throughout the paper, we will only present results for the 20 major EU countries to save space. Results for the remaining 7 small European countries<sup>7</sup> are available upon request from the authors. The first two columns in Table 1 indicate that real GVC income has increased in all EU countries. About one third of the increase in the overall EU27 GVC income was earned on the EU12 territory which is much higher than their share in EU27 GDP (7.8% in 2008). This testifies to the importance of the new member states for growth in European production capacity. In contrast, the competitive position of all major EU countries dwindled over this period. The most important industrial economy of Europe, Germany, contributed more than a quarter to EU27 GVC income since 1995 (29.8% of total EU27 GVC income). But the German share dropped at the end of the 1990s and did not significantly improve afterwards (26.4% in 2008). Also shares in other major old EU countries declined. The French decline was slow but steadily, and the share in the UK dropped severely after an initial increase in the late 1990s. But even in this country, the absolute level of

> 7 Bulgaria, Cyprus, Estonia, Latvia, Lithuania, Malta and Slovenia.

20
# **References**

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74
# **TABLE 11. Wage gap skilled‐unskilled**

||F<br>|E<br>|FE‐<br>|2SLS<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|‐0.0039|‐0.015|0.059*|0.061**|
||(0.0086)|(0.0090)|(0.031)|(0.031)|
|ICT|‐0.0055|‐0.015|0.053|0.058|
||(0.011)|(0.011)|(0.037)|(0.037)|
|ICT * High Productivity|0.0026|0.00038|0.0051|0.0023|
||(0.012)|(0.012)|(0.019)|(0.019)|
|ICT|‐0.0071|‐0.016*|0.052|0.055*|
||(0.0092)|(0.0093)|(0.032)|(0.031)|
|ICT * Skills|0.012|0.0072|0.021|0.018|
||(0.014)|(0.014)|(0.018)|(0.018)|
|Observations|2,294|2,284|2,294|2,284|

Dependent variable: change in log average wage of skilled workers relative to unskilled workers. Regressors: ICT investment dummy, and ICT investment dummy interacted with firm‐level indicator variables defined as in Table 5. Instruments defined as in Table 3. Robust standard errors in parenthesis.

35
pre-crisis levels by December 2010.<sup>11</sup> Moreover, during the rebound, emerging economies witnessed growth rates in IP sometimes even larger than pre-crisis rates, a phenomenon not observed in advanced economies. For example, using the data in Figure 3, we observe that IP in advanced economies was over sixteen percentage points below its geometric trend by December 2010, but in emerging economies it was about nine percentage points below trend by that date.<sup>12</sup>

# _2.3. Volatility or resilience?_

One important difference between emerging and developed economies is their degree of volatility in economic activity. To the extent that typical business-cycle fluctuations are more volatile in emerging economies, we expect larger declines and recoveries in these economies. To address this issue, we report in Table 1 (middle panel) statistics on yearly changes in GDP growth during crisis and non-crisis years over 1980–2010.<sup>13</sup> We first observe that GDP growth in emerging economies is twice as volatile as that of advanced economies during tranquil periods: the average positive (negative) change in GDP growth is 3.2 (-3.1) percentage points in the former and 1.5 (-1.5) in the latter. This difference becomes more acute during crisis years (excluding the 2008–2009 crisis) as the average decline in GDP growth is -5.9 and -1.7 percentage points in emerging and advanced economies, respectively. In contrast, in 2009, the average change in GDP growth was -5.9 and -4.4 percentage points, respectively. These numbers show that the 2008–2009 crisis was not a typical business-cycle fluctuation: the average crosscountry decline in GDP growth during 2009 was almost twice as large as that during tranquil

> 11 Alternative measures capturing the length of the recession, displayed in the bottom panel of Table 1, confirm that emerging economies started to recover earlier than advanced economies.

> 12 In making this comparison, we use the emerging economies’ geometric trend over the period January 2002-April 2008.

> 13 The crisis years are defined using a composite crisis indicator that takes into account banking, currency, and domestic and external debt crises on an annual basis. The beginning of a crisis event is considered as the first year with any of these crisis events, provided that no other crisis has been observed in the preceding two years. For more details, see Broner et al. (2011).

14
- 43 -

# **B. Resuts**

73. Table 16 contrasts the sectoral gross output and Income multipliers that arlse from growth In Irrigated and riinfed agriculture. A 100 rupee Increase in irrigated agricultural output generates 105 rupees of additional output In manufacturlng, 114 rupees of additional tertiary output and 45 rupees of additlonal nontradable agricultural output. This amounts to a total nonfarm output multiplier of 2.19, and a total output multipiler of 2.64. In contrast, rainfed agriculture, because of Its less Intense use of manufactured and tertiary Inputs (Table A.19), generates nonfarm gross output multipliers that are about five to ten percent smaller.

74. An Income multipiler gives the amount of Income (vaIue added) generated in a particular sector as a result of a one rupee Increase In Income (value added) In either Irrlgated or rainfed agriculture. These multipliers are relatively small for manufacturing because of the low value added to gross output ratios In that sector. They are also relatively large for the tertlary sector. Table 16 demonstrates that Irrigated agriculture has larger nonfarm Income multiDliers than rainfed agriculture, and that the latter are about 20-25 percent smaller.

75. The figures In parentheses In Table 16 show the percentage of the gross output multiplier, that Is attributable to household consumptlon linkages rather than Inter-industry production linkages. For the total nonfarm economy, only 6 percent of the gross output multiplier Is attributable to productlon linkages when the expansion Is driven by rainfed agriculture. The share Increases to 18 percent with Irrigated agriculture because of Its higher dependence on purchased Inputs.

76. Taking a base-year welghted average of the Irrigated and ralnfed agricultural multipliers, Table 16 Indicates that a one rupee Increase In agricultural output will generate about 2.11 rupees In gross output of nonfarm goods and services. This corresponds to an Income multiplier of 1.35, nearly twice as large as the 0.66 Income