rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: GDP and population exposed to storm surges under current climate and future climate change effects (GDP in thousand US$ in 2005; Population in number of persons)

||Exposu<br>current cl<br>mangro|re under<br>imate and<br>ve cover|Exposure<br>climate cha<br>SLR,<br>intensificat<br>of some m|under all<br>nge impacts:<br>storm<br>ion and loss<br>angroves|Perce<br>unde<br>chan|nt increase<br>r climate<br>ge effects|
|---|---|---|---|---|---|---|
|Region|GDP|Population|GDP|Population|GDP|Population|
|Sub‐saharan Africa|724|31,037|805|34,236|11%|10%|
|East Asia & Pacific|286,211|2,757,953|1,015,435|5,726,135|255%|108%|
|Latin America and<br>Caribbean|84,748|275,198|280,265|617,656|231%|124%|
|South Asia|33,498|487,176|52,957|832,433|58%|71%|
|**Total**|**405,181**|**3,551,364**|**1,349,461**|**7,210,461**|**233%**|**103%**|
|Top10 Mangrove cou<br>|ntries<br>||||||
|Indonesia|123,281|1,519,155|148,176|1,877,974|20%|24%|
|Mexico|33,120|70,801|199,557|325,256|503%|359%|
|Myanmar|1,888|110,040|5,854|298,858|210%|172%|
|Papua New Guinea|1,337|33,464|1,576|40,311|18%|20%|
|Bangladesh|922|30,052|1,762|62,613|91%|108%|
|Cuba|5,872|17,512|6,207|18,632|6%|6%|
|India|27,585|376,498|43,127|656,620|56%|74%|
|Venezuela,RB|21,813|53,750|35,057|83,693|61%|56%|
|Mozambique|497|21,446|528|22,771|6%|6%|
|Philippines|28,819|447,748|106,925|1,355,247|271%|203%|
|**Subtotal**|**245,134**|**2,680,466**|**548,769**|**4,741,975**|**124%**|**77%**|
|All other countries|160,047|870,898|800,693|2,468,486|400%|183%|
|Total|405,181|3,551,364|1,349,461|7,210,461|233%|103%|

*GDP estimates value of production in constant US dollar for the year 2005; population

estimates are for 2005 Source: authors’ estimates as described in the text.

The change in vulnerability of GDP and population to storm surge across countries depends on many local factors, especially on the extent of coastal development. Although the increase in the storm surge area in a changing climate is relatively similar for all regions – between 50 and 23
appear to be a relationship between perceptions of the helpfulness of donors and non-state actors and the willingness to defer to the police and to the courts. Individuals who believe that donors and non-state actors exert too much, rather than, too little influence over one’s government is associated with the willingness to defer to the court and to the police. Findings also suggest that citizens who believe non-state actors are responsible for providing law and order are less likely to be willing to defer to the police and to the courts than respondents who believe the state is responsible for providing law and order.

# **7.4 Conclusion**

This paper demonstrates that the logic of the fiscal contract is relevant to a wide variety of contemporary African states. Findings from a cross-national — analysis of survey data from Africa link citizens’ legitimating beliefs indicated by a willingness to defer to the tax department, the police and the — courts to a government’s fulfillment of a fiscal contract. Citizens who are satisfied with their government’s provision of services and goods are more likely to be willing to defer to the tax department, courts and police than citizens who disapprove of government service provision. There is also evidence that the more competent, honest and fair a government is in serving its citizens, the more likely it is to produce a virtuous cycle of citizen compliance with its extractions, leading to better government and greater compliance. There is little evidence that the presence of infrastructure in neighborhoods is itself linked to citizens’ legitimating beliefs.

In Sub-Saharan Africa, NGOs, donors, churches, traditional leaders, and other non-state actors provide a significant portion of essential goods and services. The provision of services by non-state actors and donors is not unique to Africa and occurs throughout the world in both OECD and non-OECD countries. The growth of non-state actor service provision has sparked a debate in political science and sociology and in the aid community over the conditions under which non-state actors are likely to affect citizens/state relationships (Bratton and van de Walle, 1997; Brown, 1998; Cammett and Lynch, 2008; Moore, 2007; Smith and Lipsky, 1993; White, 1999). I offer some preliminary answers as to whether donors and non-state actors are undermining or strengthening citizen/state relationships. Findings suggest that across a wide range of African countries, including fragile states like Liberia and stronger states like Botswana and South Africa, donors and non-state 28
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE A major issue has been the country‘s rampant inflation causing frequent tariff changes to ICT pricing (POTRAZ 2009). The dollarization of the economy in 2009 brought this to an end by introducing stability to tariffs, which are now expressed in U.S. dollars. Zimbabwe‘s fixed-line and mobile retail pricing is similar to peer averages. Fixed broadband is expensive, almost twice as much as the Sub-Saharan average, which is already high by international standards.

Internet-access penetration in Zimbabwe is average for the southern African region (figure 17). There are no comprehensive official data on the number of Internet users in Zimbabwe, though the Zimbabwe All Media and Products and Services Survey found that 22 percent of urban dwellers aged 15 years and older used the Internet in mid-2010 (Ndlela 2010). This amounts to some 645,000 people or 5.1 of the total population.<sup>11</sup> International connectivity is a challenge for Zimbabwe due to its landlocked situation. Much of Zimbabwe‘s international connectivity has been through satellite. Activities are under way to build out national fiber networks to obtain cross-border connectivity to undersea fiber cable. For example, Powertel, a subsidiary of the Zimbabwe Electricity Supply Authority, inked an agreement with the Botswana Telecommunications Corporation (BTC) for supply of international bandwidth via its links to undersea cables (Smarts 2010). Tel-One is building a fiber-optic cable to Mutare on Zimbabwe‘s eastern border where it will connect to a submarine cable in Mozambique ( _Technology Zimbabwe_ , June 24, 2010). Econet is also busy laying a nationwide fiber-optic cable backbone with spurs planned for connection to undersea fiber systems in Mozambique and South Africa (Econet Wireless Zimbabwe 2010). If these initiatives achieve fruition, they would create competing sources of international bandwidth, driving down bandwidth costs and retail broadband prices. Wireless broadband will also create greater competition with Econet having launched 3G and WiMAX<sup>12</sup> networks while Powertel and Tel-One have deployed CMDA<sup>13</sup> 2000-based high-speed wireless networks.

> 11 As in most low-income countries, Internet access is estimated to be negligible in rural areas in Zimbabwe. The 2010 estimate for Internet users is derived from applying the ZAMPS **[[expand acronym?]]** survey data to the urban population 15 years and older using statistics for Zimbabwe from the United Nations. See: unstats.un.org/unsd/demographic/products/socind/.

> 12 Worldwide interoperability for microwave access.

> 13 Code division multiple access.

39
**<u>Table 2. Determinants of the Probability of Product Innovation by Incumbent Firms (Marginal Effects from the Probit Estimator)</u>**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
||<br>**Dependen**<br>|<br>**t Variable:**|<br>**If firm deve**<br>|<br>**loped new p**<br>|<br>**roduct=1 **<br>|
|Dummy=1 if firm has invested in R&D|0.184<br>(10.69)**||0.181<br>(11.10)**|0.181<br>(11.09)**|0.179<br>(10.37)**|
|Dummy=1 if firm uses licensed technology||0.111<br>(4.30)**|0.101<br>(4.32)**|0.101<br>(4.31)**|0.101<br>(4.28)**|
|log of total number of last year's employees|0.075<br>(6.28)**|0.086<br>(6.58)**|0.073<br>(5.92)**|0.073<br>(5.88)**|0.075<br>(5.59)**|
|log of total number of last year's employees squared|-0.004|-0.004|-0.004|-0.004|-0.004|
||(2.32)*|(2.33)*|(2.24)*|(2.23)*|(2.19)*|
|Dummy=1 if foreign owned (>=10 of assets)|-0.012|-0.022|-0.020|-0.020|-0.022|
||(0.87)|(1.54)|(1.40)|(1.39)|(1.45)|
|Average capacity utilization over the last year|0.001|0.001|0.001|0.001|0.001|
||(2.55)*|(2.37)*|(2.56)*|(2.56)*|(2.62)**|
|Dummy=1 if exporter (exports over sales >=10)|0.049<br>(3.34)**|0.053<br>(3.53)**|0.048<br>(3.30)**|0.048<br>(3.23)**|0.046<br>(2.88)**|
|log of average years of education of the enterprise's labor force|0.055|0.052|0.054|0.054|0.053|
||(2.20)*|(2.12)*|(2.17)*|(2.17)*|(2.18)*|
|Index of average applied tariffs by sector and their dispersion||||0.008<br>(1.05)|0.009<br>(1.15)|
|Index of incidence of non-tariff barriers by sector||||-0.046<br>(0.71)|-0.049<br>(0.68)|
|Year, Industry (eight), and Country Dummies Included|Yes|Yes|Yes|Yes|Yes|
|Manufacturing Industries|8|8|8|8|8|
|Countries|68|68|68|68|58|
|Observations|25806|25806|25806|25806|24362|
|Observed average probability|0.475|0.475|0.475|0.475|0.477|
|Predicted probability with average of explanatory variables|0.471|0.470|0.471|0.471|0.474|

* significant at 5; ** significant at 1

Absolute value of robust z statistics in parentheses. Estimation errors are clustered by countries. Intercepts are not reported. Sample in model (5) excludes firms from countries with a GDP per capita (USD$ PPP) above $14,000. Note: The results in Table 2 include the average number of years of education of workers employed by each firm. The China and Indonesia surveys do not have these data. We imputed this variable for the missing observations based on the observed correlation between labor education and the other firm characteristics. Table 1 presents the summary statistics for these variables with and without the imputed values, which have very similar means and standard deviations.

19
# **Using Gross Trade Data to Map Archetypal GVCs**

Michael J. Ferrantino and Gabriela Schmidt

**_JEL:_** _F63, L62, L63, L67_

**_Keywords:_** _exports, Global Value Chains, inter‐regional comparisons_

_This paper was supported by Strategic Research Project P155715,”GVC Participation and Streamlining NTMs,” funded through the World Bank’s Development Economics group under TF#0A0973. The authors are grateful to Ronald Jansen and his colleagues at the U.N. Statistical Division, Ganesh Sathiyamoorthy, and Siddhesh Kaushik for invaluable aid in developing the intertemporal concordances used in this work; to Karly Dairabayeva, Lara Loewenstein and Esteban Rojas for sustained efforts in working out operational applications, including the dashboard approach; to Stacey Frederick, Tim Sturgeon, and Aldo Bortoluzzi for expert advice and for catching many anomalies, and to Daria Taglioni and Deborah Winkler for initial guidance and ongoing inspiration in the study of global value chains. All errors and omissions are the sole responsibility of the authors_ .
on S3 plots is reduced in households with a greater dependency ratio, likely through labor channels; and that on the use of extension services. Uncertainty around rainfall patterns, proxied by the coefficient of variation in flowering season rainfall, has no significant relationship with technical efficiency in the majority of specifications. It does, however, exhibit a positive and statistically significant (at the 10 percent level) coefficient for AFSIS S2 plots, suggesting that increased uncertainty results in decreased technical efficiency.

The lack of predictive power of observable household and manager characteristics on technical inefficiency is consistent with the results of separate productivity analysis conducted via ordinary least squares regression (available in Annex Table A4), in which none of the household or manager characteristics has a statistically significant relationship with productivity, with the exception of agricultural asset counts. It is conceivable that the use of crop-cutting-based production measurement, rather than farmer-estimated production as is most commonly utilized in smallholder agricultural analysis, results in a reduced effect of these manager characteristics. By using objectively measured production data, we eradicate the noise and/or bias associated with the plot manager’s estimate of production, which may be correlated with manager’s education, experience, exposure to extension services, etc. However, there is little empirical evidence to support this theory, at least within this particular dataset, as Gourlay et al. (2019) find that observable manager characteristics have very little explanatory power in yield overestimation (measured as self-reported production-based yield minus crop-cutting-based yield).

Stochastic frontier analysis was also executed on subpopulations of interest, including plots with female managers, plots with male managers, and pure stand plots. Results of these analyses are available in Table 5. As the sample sizes of these subpopulations are small, it is difficult to draw conclusions on the S1 and S3 classifications so only the overall and S2 classifications are reported. Output elasticities across plot manager gender are similar, but male managed plots experience greater returns to soil suitability and only male managed plots have positive and significant returns to inorganic fertilizer application on average. Additionally, the impact of weather-related uncertainty varies by plot-manager gender, with only male-managed plots exhibiting a positive and significant relationship between uncertainty and technical inefficiency (while the converse is true with respect to current season rainfall inputs in the production function). Output elasticities on pure stand plots are in line with those observed across the full sample, with the exception that inorganic fertilizer application has greater impact on the production of pure stand plots.

20
_Table 6 Cost and length of interview for each arm; Note: The entry time corresponds highly with the response time, though if proxying for response time, would be a lower bound for both the one-line recall and diary arms. The FAFH module entry time includes the bounding portion, the 24-hour recall module, in the second row._

||(1)<br>T1: 7-day<br>one-line<br>recall|(2)<br>T2: 7-day<br>individual<br>diary|(3)<br>T3: 7-day<br>individual<br>recall|(4)<br>T4: 7-day HH<br>bounding/<br>salience|(5)<br>T1<br>vs.<br>T2*|(6)<br>T2<br>vs.<br>T3*|(7)<br>T2 vs.<br>T4*|(8)<br>T3<br>vs.<br>T4*|(9)<br>p-value from<br>joint<br>orthogonality<br>test of<br>treatment<br>arms|
|---|---|---|---|---|---|---|---|---|---|
|Total cost per<br>||||||||||
|survey (upper<br>bound)|$102.10|$160.04|$106.19|$121.42||||||
|Field cost per<br>survey (excl.<br>fixed costs)|$57.93|$115.87|$62.02|$77.25||||||
|FAFH entry<br>||||||||||
|time (in<br>minutes)|1.30|27.00|8.27|7.92|0.00|0.00|0.00|0.34|0.00|
||(0.05)|(0.92)|(0.31)|(0.21)||||||
|Instruction<br>time|0|15|0|7.5||||||
|FAFH entry<br>time<br>(including<br>instruction<br>time)|1.30|42.00|8.27|15.42|0.00|0.00|0.00|0.00|0.00|
||(0.05)|(0.92)|(0.31)|(0.21)||||||
|FAFH entry<br>time<br>(including<br>instruction<br>time) without<br>24-hour<br>bounding in<br>T4 (in<br>minutes)|1.30|42.00|8.27|8.37|0.00|0.00|0.00|0.74|0.00|
||(0.05)|(0.92)|(0.31)|(0.05)||||||
|N|478|473|477|476||||||

*p-values

In light of our results on both the measurements and the cost of each method, we are left with topics for future exploration to further refine the best methodology for FAFH collection. An analysis of which component of the bounding/salience arm worked best could help us understand further how to help improve measurements with minimal cost. For example, the bounding component contributed greatly to both the time and actual costs since it required two separate visits to each household. Is the 24-hour recall section necessary, or would the first visit alone be enough to mark the recall period? Elkin (2002) conducts a similar cost analysis for a U.S. survey, recommending elimination of the bounding interview (to which he attributes up to 12 percent of costs and time). Additionally, is it necessary to have a visit with tracking sheet, or can 31
**Figure 4:Sigma-convergence Across Wealth Groups in the Mean Quantity of Selected Durable Goods, 1995** **<u>vs. 2001 vs. 2003</u>**

|Panel (A): Au<br>|tomatic washing machines<br><br>||
|---|---|---|
|1995|2001<br>2003||
|0|.1<br>.2|.3|
|Panel (B): Co|lor TVs||
|1995|2001<br>2003||
|0|.1<br>.2|.3|
|Panel (C): Pe|rsonal Computers||
|1995|2001<br>2003||
|0|.1<br>.2|.3|
|Panel (D): Ca|rs||
|1995|2001<br>2003||
|0|.1<br>.2<br>sigma (std. dev.)|.3|

Source: Author’s calculations.

Note: Sigma is the standard deviation in the mean quantity of the durable good across wealth groups.

- 22 -
were born 1 to 3 years before each survey because our focus is on child nutrition outcomes which are especially crucial at these ages. The numbers of children in the sample in each country and survey year are shown in Table 1. The earliest survey was conducted in 2001 and the most recent was completed in 2010. For both Ghana and Nigeria, we pick only the regions or states in the northern parts of the countries because they are the ones with the closest resemblance to the Sahel environment. The final sample size is 55,772 observations.

Table 1: Sample sizes of children between 1-3 years, by country and survey

||2001|2003|2005|2006|2008|2010|Total|
|---|---|---|---|---|---|---|---|
|Burkina Faso|0|5,733|0|0|0|8,421|14,154|
|Ghana|0|970|0|0|739|0|1,709|
|Mali|6,076|0|0|7,727|0|0|13,803|
|Nigeria|0|2,212|0|0|10,837|0|13,049|
|Senegal|0|0|5,498|0|0|7,559|13,057|
|Total|6,076|8,915|5,498|7,727|11,576|15,980|55,772|

Source: Authors’s calculation using DHS, various years Although maternal and child health are the primary concerns of the surveys, information on household demographics, nutrition, asset holding, migration and employment is also collected. Table 2 contains summary statistics for all variables used in the regressions. The top part of the table shows the anthropometric Z-scores, and for rounds one and two surveys. Note that the survey rounds are different from country to country, so that e.g. 2005 may be round 1 in one country and round 2 in another. The second part of the table summarizes the shocks, the construction of which is described in detail in the subsection that follows. The summary statistics for some of the control variables which explain a large part of the variation in standards of living are shown in the third section, while the bottom part shows the distribution of observations among countries in the database. About 30 percent of the sample is from Northern Nigeria, 10
- [6] Behrman, Jere, Fernald, Lia and Patrice Engle (2013). ”Preschool Programs in Developing Countries.” in P. Glewwe, _Education Policy in Developing Countries_ . Chicago: University of Chicago Press.

- [7] Besamusca, Janna, Tijdens, Kea, Keune, Maarten and Steinmetz, Stephanie (2015). ”Working Women Worldwide. Age Effects in Female Labor Force Participation in 117 Countries.” _World Development_ , 74, 123-141.

- [8] Brollo, Fernanda, Nannicini, Tommaso, Perroti, Roberto and Tabellini, Guido (2013). ”The political resource curse.” _American Economic Review_ , 103(5), 1759-96.

- [9] Carneiro, Pedro and Heckman, James, J. (2004). ”Inequality in America: What role for human capital policies?” In J. J. Heckman and A. B. Krueger (Eds.), _Human Capital Policy_ . The MIT Press.

- [10] Cascio, Elizabeth U. (2009). ”Maternal labor supply and the introduction of kindergartens into American public schools.” _Journal of Human Resources,_ 44, 140–170.

- [11] Cohen, Andrew, Freeborn, Beth and McManus, Brian (2013). ”Competition and crowding out in the market for outpatient substance abuse treatment.” _International Economic Review_ , 54(1), 159–184.

- [12] Cunha, Flavio, James Heckman and Lance Lochner (2006). “Interpreting the Evidence on Life Cycle Skill Formation.” in Eric Hanushek and Finis Welch (Eds.), _Handbook of the Economics of Education_ . North Holland: Amsterdam, pp. 697–812.

- [13] Cutler, David and Gruber, Jonathan (1996). ”Does public health insurance crowd out private insurance?” _Quarterly Journal of Economics,_ 111, 391–430.

- [14] Engle, Patrice, Black, Maureen, Behrman, Jere, Cabral de Mello, Meena, Gertler, Paul, Kapiriri, Lydia, Martorell, Reynaldo and Young, Mary (2007). “Strategies to Avoid the Loss of Developmental Potential in More Than 200 Million Children in the Developing World.” _The Lancet_ 369 (9557), 229-42.

28
shows a positive and statistically significant estimated coefficient: a larger market potential is associated with greater employment. Instrumentation shows that the effect remains large: A 10 percent increase in market potential is associated with a 2.9 to 6.5 percent increase in total employment. Interestingly, more urbanized locations benefited less from increases in market whereas areas with more educated more. These results are at potential, population benefited odds with what we found in the market access regression. This indicates that market access and market potential are not completely substitutable. One potential channel to explain the positive market potential impact on employement growth in more skilled localities may be that greater market potential (which is a function of surrounding incomes) increases the demand for goods which have stronger skill contents.

Table 7: The effect of market potential on employment

||||Total emp|loyment||
|---|---|---|---|---|---|
|Variables|mponly|ols|<br>iv (Doughnut)|<br>iv (Road count)|iv (Effcient roads)|
|Market Potential (mp)|0.192***|0.351***|0.322***|0.292**|0.645***|
||(0.00324)|(0.0401)|(0.0484)|(0.148)|(0.0342)|
|mpx Metropolitan dummy||-0.0180***|-0.00188|-0.00954|-0.0680***|
|||(0.00652)|(0.00822)|(0.0232)|(0.0144)|
|mpx Education||0.0168***|0.0188***|0.0195***|0.000723|
|||(0.00207)|(0.00286)|(0.00705)|(0.00270)|
|mpxnaftadummy||-0.0591***|-0.0557***|-0.0424|-0.141***|
|||(0.0127)|(0.0140)|(0.0423)|(0.00928)|
|mpx Capital city||-0.00822*|-0.0113**|-0.0108**|-0.0109*|
|||(0.00430)|(0.00450)|(0.00427)|(0.00646)|
|mpx Oil dummy||0.0227|0.0168|0.0248|0.0135|
|||(0.0157)|(0.0190)|(0.0172)|(0.0229)|
|Above median education||0.187***|0.151***|0.185***|0.203***|
|||(0.0417)|(0.0404)|(0.0374)|(0.0359)|
|Above median population||0.406***|0.219**|0.302|0.973***|
|||(0.0872)|(0.101)|(0.267)|(0.171)|
|Constant|3.749***|2.134***||||
||(0.0382)|(0.334)||||
|Observations|10229|10229|8168|10098|9248|
|Adj. R-squared|0.463|0.503|0.511|0.503|0.471|

_Note_ : * denotes significance at the 10% level, ** denotes significance at the 5% level, and *** denotes significance at the 1% level. The doughnut iv is calculated by excluding all localities within a 25km circle. The road count IV is the number of roads intersecting a circle of 10km radius. The efficient roads doughnut iv is obtained by calculating the road efficiency units in a doughnut within two circles of 25 and 100 km radii.

# **.2 5 Spatial and sectoral heterogeneous effects**

Market access and market potential have heterogeneous effects on employment in different sectors and different economic regions. We report the estimated results for the three main sectors, namely services, manufacturing, and commerce. The results from the iv estimations 22