rafmacalaba/datause-encoder-data huggingface dataset:
countries but within industries. If industry characteristics affect the results, then the panel and pooled estimates will be quite different, and the factor endowments perspective will be shown to contribute to our understanding of discovery. The results using both HS 4-digit and HS 6-Digit data are shown in Table 6.

**<u>Table 6: Testing for Structural Transformation</u>**

||**HS 4**<br>|**-Digit**<br>|**HS 6**<br>|**-Digit**<br>|
|---|---|---|---|---|
||**Discoveries by**<br>**Industry**|**Discoveries**<br>**Pooled**|**Discoveries by**<br>**Industry**|**Discoveries**<br>**Pooled**|
|Coefficient on ln<br>GDPpercapita|13.5666<br>(7.02)|15.6518<br>(5.68)|11.9700<br>(9.56)|15.7203<br>(5.89)|
|95% Confidence<br>Interval|<br>9.7769 to<br>17.3562|10.2503 to<br>21.0532|9.5152 to<br>14.4248|10.4869 to<br>20.9535|
|Coefficient on ln|-.8109|-.92803|-.6970|-.9114|
|GDP per capita<br>squared|(-7.29)|(-5.92)|(-9.83)|(-6.03)|
|95% Confidence<br>Interval|-1.02 to -.5929|-1.2353 to -.6208|-.836 to -.5581|-1.2074 to -.6154|
|Maximum Point|$4295|$4595|$5358|$5564|
|CountriesinSample|50|50|50|50|

Note: parentheses indicate z-statistics. Source: Author’s calculations The coefficient estimates of the data grouped by industry fall within the 95% confidence intervals for the pooled data at both the 4- and 6-digit levels. Furthermore, the maximum points occur at very similar levels. That is, the data suggest that the observed relationship between discovery activity and income per capita is not significantly different across industries. This is illustrated in Figure 3, which shows the estimated relationship between expected discoveries and growth (the discovery curves) estimated individually for each of the 16 HS industry groups with discoveries (using the 4-digit data).

27
Table 7. Distribution of location characteristics by tier

||**Bottom**<br>**H0: Top**|**locations**|**Averag**<br>**H0: Top**|**e locations**|**Catch**<br>**H0: Top**|**ment areas**||
|---|---|---|---|---|---|---|---|
||<br>**locations**<br>**have larger**<br>**values**|**H0: Top**<br>**locations have**<br>**smaller values**|<br>**locations**<br>**have larger**<br>**values**|**H0: Top**<br>**locations have**<br>**smaller values**|<br>**locations**<br>**have larger**<br>**values**|**H0: Top**<br>**locations have**<br>**smaller values**|**Unit of**<br>**observation**|
|_Population density_||||||||
|Population density (people/sq. km, in log)|0.0367|-0.339***|0.0146|-0.2843***|0.0755|-0.2959***|District|
|_Type of jobs_||||||||
|Main workers (% of total workers )|0|-0.5681***|0|-0.3889***|0.102|-0.149|District|
|Regular wage workers (% of total workers)|0|‐0.5143***|0.001|‐0.2788***|0.0591|‐0.114|Place|
|_Sectoral structure_||||||||
|Agriculture share (% of total workers)|0.3401***|-0.0088|0.1966***|0|0.0778|-0.0561|Place|
|Manufacturing share (% of total workers)|0.0058|-0.3722***|0.0163|-0.211***|0.1209|-0.0226|Place|
|_Skills_||||||||
|Secondary education (% total working age population)|0.0083|-0.4464***|0|-0.3621***|0.0991|-0.0791|Place|
|Tertiary education (% total working age population)|0|-0.2818***|0|-0.1853***|0.0874|-0.1235|Place|
|_Infrastructure and services_||||||||
|Road density (km/ sq. km)|0.0101|-0.7051***|0.0128|-0.5121***|0.0857|-0.2224*|District|
|Access to electricity (% of total households)|0|-0.7317***|0|-0.5419***|0.0143|-0.151|District|
|Access to cellphone (% of total households)|0|-0.773***|0.008|-0.6619***|0.0143|-0.1918|District|
|Access to banking services (% of total households)|0.0081|-0.5176***|0.006|-0.3446***|0.0469|-0.0755|District|
|_Social and religious affiliations_||||||||
|Scheduled Tribes (% total population)|0.3842***|-0.0179|0.2143***|-0.009|0.073|-0.0491|Place|
|Scheduled Castes (% total population)|0.0239|-0.1312*|0.0053|-0.1074|0.0878|-0.0565|Place|

Note: The numbers report the Kolmogorov–Smirnov two one-way test results comparing the distribution of other tiers with that of top locations. The significant levels are reported as well: *significant at 0.1 level, ** significant at 0.05 level, *** significant at 0.01 level.

34
Policy Research Working Paper 7374

# **Abstract**

This paper examines China’s rural minimum living standard guarantee (dibao) program, one of the largest minimum income cash transfer schemes in the world. Using household survey data matched with published administrative data, the paper describes the dibao program, estimates the program’s impact on poverty, and carries out targeting analysis. The analysis finds that the program provides sufficient income to poor beneficiaries but does not substantially reduce the overall level of poverty, in part because the number of beneficiaries is small relative to the number of poor. Conventional targeting analysis reveals rather large inclusionary and exclusionary targeting errors; propensity score targeting analysis yields smaller but still large targeting errors. Simulations of possible reforms to the dibao program indicate that expanding coverage can potentially yield greater poverty reduction than increasing transfer amounts. In addition, replacing locally diverse dibao lines with a nationally uniform dibao threshold could in theory reduce poverty. The potential gains in poverty reduction, however, depend on the effectiveness of targeting.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at numapathi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 12: Domestic Travel in Belize**

<!-- Start of picture text --> 1,400,000 30<br>27<br> 1,200,000<br>24<br> 1,000,000 21<br> 800,000 18<br>15<br> 600,000 12<br> 400,000 9<br>6<br> 200,000<br>3<br> - 0<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats<br>Domestic Airports Served<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> _Source: Authors’ calculation based on data provided by Diio (2014)._ In relative terms, the level of domestic air traffic in Belize is surprisingly high. In 2013, Belize’s domestic traffic was almost 3 times higher than Jamaica’s and 20 times higher than DR’s. This might be partially explained by Belize being a tourist destination that relies on its off-shore islands. For instance, the island of Ambergris Cay is a very popular diving destination.

# <u>Fragile State: Haiti</u>

Haiti’s domestic services are very limited, and have experienced large swings between 2001 and 2013. Since 2011 only one airport pair has been served, between Cap Haitien in the North and Mais Gate in Port-Au-Prince. Previous pairs included Cap Haitien with Port De Paix, Mais Gate with Jeremie on the far western tip, and Mais Gate with Port de Paix, on the peninsula north of Jeremie. No data was found for the period 2006-2009 (Figure 13), which corresponds to the period between when Tropical Airways d’Haiti ceased operations and when a new airline, Salsa d'Haïti, brought domestic scheduled services back to Haiti.<sup>14</sup> Tourism in Haiti is a very small sector and is generally limited to one isolated resort in the North owned by Royal Caribbean and accessed through its cruise ships, (Beaubien, 2013). Due to the high level of poverty (the highest in the region), most Haitians cannot afford domestic air transport.

> 14 These figures might be an underestimate. For instance, Tortug’ Air, Haiti’s flag carrier, operates a fleet of three turboprop aircraft, but their schedules do not appear in any international database. The current annual capacity provided by the airline is estimated to be 90,000 seats.

37
Using Probabilistic Models to Appraise and Decide. Ley-Borrás and Fox

**Figure 2. An example of decision model (an influence diagram) for sovereign disaster risk decisions.**

<!-- Start of picture text --> Infrastructure<br>characteristics Population<br>distribution<br>DRM strategy Severe<br>natural event<br>Economic Overall<br>SDRFI strategy<br>impact impact<br>Fiscal cost<br>Available funds<br>Response<br>effectiveness<br><!-- End of picture text --> Source: R. Ley‐Borrás and D.J. Clarke.

In turn, the _Available funds_ have an effect on _the Fiscal cost_ for the country and the very important (but nevertheless uncertain) _Response effectiveness_ : the ability of the government to help the population in distress and recover from direct government losses of infrastructure and revenue). The uncertain event _Response effectiveness_ is also influenced by the _DRM strategy_ because the strategy may include preventive actions than enhance the logistical and technical capabilities, among others, critical for effective response. This node is also influenced by _SDRFI strategy_ because when a country contracts specific forms of insurance or re‐insurance, it also commits to precise rules for loss adjustment methodology and funds spending, and that framework is believed to have a positive influence on post disaster response (Clarke 2013a, Dana and von Dahlen 2014, De Janvry et al. 2014). The diagram also shows that _Response effectiveness_ has a direct influence on _Human impact_ and _Economic impact_ , expressed by arrows pointing to those nodes.

This influence diagram includes a _Severe natural event_ uncertainty node that has no incoming arrows (because the occurrence of the event is not influenced by decisions) and its outgoing arrows show its impact on the three types of consequences being considered, as well as on the _Available funds_ node (as mentioned). The magnitude of a disaster is a consequence of the severity of the natural event and of factors that the country can influence with its decisions: infrastructure characteristics, population distribution, available funds and response effectiveness.

**11**
Table 1: First Stage Regressions

||(1)<br>ln Market Access<br>OLS|(2)<br>ln Market Access<br>OLS|(3)<br>Checkpoints 10-25km<br>OLS|
|---|---|---|---|
|Checkpoints 10-25km|-0.0381***<br>(-8.59)|-0.0380***<br>(-8.60)||
|ln(1+Pal.Fat._<_5km)||-0.0175<br>(-1.11)|0.0639<br>(0.56)|
|ln(1+Isr.Fat._<_5km)||0.0006<br>(0.02)|-0.0132<br>(-0.04)|
|Year FE|Yes|Yes|Yes|
|Locality FE|Yes|Yes|Yes|
|_N_|1928|1928|1928|
|adj. _R_<sup>2</sup>|0.671|0.671|0.478|

Notes: * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01. Heteroscedasticity robust _t_ -statistics clustered by locality in parentheses, _Pal.Fat.<5km (Isr.Fat.<5km)_ denotes the number of Palestinian (Israeli) fatalities within a 5km radius in a given calendar year (Source: B’Tselem). _Checkpoints 10-25km_ is the number of checkpoints deployed in a radius between 10 and 25km away from the locality’s center (Source: UNOCHA). _Market Access_ is calculated using: _MAit_ =<sup>�</sup> _j_<sup>_h_(</sup><sup>_Tijt_)</sup><sup>_Pjt_,</sup> where _P_ denotes destination population, _T_ denotes travel time, and _h_ ( _T_ ) = exp ( _−_<sup><u>1</u></sup> 2<sup>_T_2</sup><sup>_/θ_2)isadistancedecayfunction</sup> with _θ_ = 40 (see Section 3 for details).

located in a radius between 10 and 25km of the locality (columns 3 and 4), the association between lights and market access strengthens substantially. According to our preferred IV estimates that control for local violence, presented in column 4, a 10 percentage points increase in market access leads to a 1.9 percentage point increase in lights emissions (or a 0.6 percentage point increase in local GDP). OLS estimates of the relationship between market access and lights are thus downward biased (a finding which is consistent with existing studies, see e.g. Jedwab and Storeygard (2017), Redding and Turner (2015)). We speculate that this may reflect attenuation bias due to measurement error in our market access measure. Whatever the reason may be, the IV regressions lend credence to a causal interpretation of the positive association between market access and economic performance.

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# **G Detailed acknowledgments**

The preparation of the Firm-level Adoption of Technology survey questionnaire involved the contribution of several sector experts within and outside the World Bank.

First, we would like to thank the following World Bank Group colleagues: Erick C.M. Fernandes (Lead Agriculture Specialist), Holger A. Kray (Manager, Agriculture), Michael Morris (Lead Agriculture Economist), Madhur Gautam (Lead Agriculture Economist), Robert Townsend (Lead Agriculture Economist), Ashesh Prasann (Agriculture Economist), Aparajita Goyal (Senior Economist, specialized in Agriculture), Harish Natarajan (Lead Financial Sector Specialist), Erik Feyen (Lead Financial Sector Economist), Laurent Gonnet (Lead Financial Sector Specialist), Arturo Ardila Gomez (Lead Transport Economist), Victor A Aragones (Senior Transport Economist), Edson Correia Araujo (Senior Health Specialist), Irina A. Nikolic (Senior Health Specialist), Brendan Michael Dack (Chief Industry Specialist at IFC), Emiliano Duch (Lead Private Sector Specialist), Blair Edward Lapres (Economist), Kazimir Luka Bacic (Operations officer), Justin Hill (Senior Private Sector Specialist), Etienne Raffi Kechichian (Senior Private Sector Specialist), Justin Yap (Private Sector Specialist), Austin Kilroy (Senior Economist).

Similarly, we would like to thank several external experts. From Embrapa-Brazil, we would like to thank Alexandre Costa Varella (Head of Embrapa research unit in the South region, expert on livestock), Fl ́avio Dessaune Tardin (Researcher, Maize and Sorghum), Edison Ulisses Ramos Junior (Researcher, Soybean), Isabela Volpi Furtini (Researcher, Rice and Beans), Alberto Duarte Vilarinhos (Researcher, Cassava and Fruits), Carlos Estev ̃ao Leite Cardoso (Researcher, Cassava and Fruits, Technology transfer), and other participants of the internal seminars to validate the sector specific questionnaire for agriculture and livestock. We also would like to thank a group of senior consultants from Patina Solutions, who contributed with the preparation and validation exercise for several sectors, including Daren Samuels (Consultant, Manufacturing), Christ Baughman (Consultant, Food Processing), Sandra Aris (Consultant, Wearing Apparel and Lather), Steve Zebovitz (Consultant, Pharmaceutical sector), Shelly Wolfram (Consultant, Retail and Wholesale), James M. Keding (Consultant, Transport and Logistics), as well as Justin Barners (Consultant, Automotive). Finally, we would like to thank Sudha Jayaraman (Associate Professor, Department of Surgery, Virginia Commonwealth University), Christina Kozycki (Infectious Disease Fellow, NIH), Jonathan Skinner (Health Economist, Dartmouth College), and Elizabeth Krebs (Assistant Professor of Emergency Medicine at the Jefferson Center for Injury Research and Prevention), for their comments and feedback on health services.

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Appendix

# Money-Metric Social Welfare

## June 1, 2007

When we work with a general equilibrium model in which there are multiple households, we can define a social welfare metric which reflects aversion so inequality, e.g.

in which _nh_ is the number of individuals represented by household type _h_ and _uh_ () is the utility function for household _h_ . In this social welfare function _ν_ is a measure of inequality aversion. When _ν →_ 0, the social welfare function is _Rawlsian_ , and the only changes which matter are those of the least well off household. When _ν →_ + _∞_ , the social welfare function is _utilitarian_ , and equivalent money-metric welfare impacts on poor and right households are valued identically in terms of social welfare.

One challenge involved in working with a social welfare function is defining appropriate utility indices for individual households. If household utility functions are not identical, the definition of _SWF_ () is problematic. In order to deal with this logical problem we express household welfare using _money-metric_ welfare indicies. This is only possible with household welfare indices are homothetic or quasi-homothetic. For example, if welfare for household _h_ is a constant-elasticity aggregate, e.g.

then utility is a linear function of income. That is, if we solve then the indirect utility function has the form: where _σh_ = 1 _/_ (1 _− ρh_ ). Notably, a one percent change in _Mh_ is equivalent to a one percent change in _Vh_ .

1
and analyze China’s rising domestic content in exports, or the ratio of domestic value added in exports to gross exports (DVAR). Our transaction-level data cover the universe of Chinese exporters during the period of 2000-2007, allowing us to construct ...rm, industry and aggregate DVARs over time to study their evolution. The recent burgeoning literature on measuring industry and aggregate DVARs relies on input-output (IO) tables. While using IO tables has the advantage of capturing IO linkages within and across countries, the presence of ...rm heterogeneity may result in signi...cant aggregation biases in the estimates of the DVAR. Our ground-up approach embraces ...rm heterogeneity by measuring industry and aggregate DVARs as the weighted averages of the underlying ...rms’DVARs.<sup>2</sup> This unique methodology further allows us to compute bootstrapped standard errors for our aggregate estimates, which are then used to perform statistical tests on the rising trend. Finally, we use the customs data merged with manufacturing ...rm survey data to examine whether changes in export composition, ...rms’production costs and material shares are responsible for China’s rising DVAR.

Our DVAR estimates con...rm existing studies that China’s DVAR has been rising, but are higher than previous estimates. Speci...cally, we ...nd that the DVAR of China’s aggregate exports increased from 65% to 70% between 2000 and 2007, with similar magnitudes of increases in the country’s bilateral exports to its major trading partners. The increase in the aggregate DVAR is statistically signi...cant and con...rms the upward trend found in Koopman, Wang, and Wei (2012) (KWW12 hereafter), which adopts an IO table-based approach. However, our DVAR estimate for processing exports is signi...cantly higher than those of KWW12.

The ...nding that our DVAR estimates are higher than those of KWW12 exempli...es

> 2Ahmad et al. (2014) also allow ...rm heterogeneity to a¤ect the estimates of a country’s aggregate DVAR. They use ...rm-level data to generate indicators by exporter status, which are then used to re...ne IO-table based estimates of domestic value added in exports from Turkey.

2