rafmacalaba/datause-encoder-data huggingface dataset:
Transport and professional services remain a bastion of protectionism in high-income countries and are also subject to high barriers in developing countries. In maritime transport, even though international shipping is today quite open, entry into cabotage and auxiliary services such as cargo handling is in many countries restricted. In professional services, even though there is increased scope for international trade through electronic means, there remain restrictions on foreign presence, particularly of individual service providers. In general, accounting and the practice of international law tend to be more open than auditing and the practice of domestic law. In the sectors and modes covered here, the restrictions on foreign investment are generally less stringent than the restrictions on cross-border trade, and far less stringent than the presence of foreign professionals (Figure 4).

**Figure 1: Restrictiveness of Services Trade Policies by GDP per capita, 2005**

<!-- Start of picture text --> IND<br>PHL<br>TUN<br>IDN<br>EGY THA<br>CHN<br>MYS<br>SAU<br>JOR<br>RUS<br>LKA VEN<br>TZA MAR KOR<br>PAK BRA<br>ARG ITA<br>KEN MEX PRT FRA<br>GHAKHMSENNGA UKRCOLPERECUZAF CHL HUN CZE ESP JPNBELSWEDNKAUT IRL<br>MNG POLLTU TTO GRCFINDEUGBRAUS NLDCAN USA<br>NZL<br>0 10000 20000 30000 40000<br>GDP per capita PPP, 2006<br>Note: GDP per capita, PPP is in constant 2005 international USD<br>60<br>50<br>40<br>30<br>20<br>Restrictiveness of services trade policy<br>10<br><!-- End of picture text --> Note 1: There are in total 56 countries, of which 32 are developing and 24 are OECD countries and the following sectors are covered: financial, telecommunications, retailing, maritime, air passenger transport<sup>7</sup> , and professional services.

> 7 For air transport, the policy information for mode 1 (BASA) came from the QUASAR database of the WTO.

5
informative if the gap is consistently and measurably positive or negative, and can be linked to specific policy interventions.

The data requirements for both local and reference prices can be demanding:

- Calculating adjusted reference prices may require dedicated studies. Even with petroleum products—the only energy sources for which international benchmark prices applicable to all countries are available—situation-specific costs of transportation (both international and domestic), storage, distribution, and retailing need to be added. Coal and natural gas are traded much less frequently across national borders, and electricity and district heating seldom so, making identification of reference prices even more challenging.

- As for local prices, where there is pan-territorial pricing—whether the territory is the country or a province or a city—official prices can be used, although if different prices apply to different territories, consumption in each territory may be difficult to obtain. If price competition exists, a price survey is needed, and the prices so collected would ideally be volume-averaged to arrive at weighted average prices paid by consumers. Further, frequent price surveys may be needed, and are in fact essential in times of rapidly changing world energy prices. If, for example, only the prices collected in the global price survey conducted by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) over a few days in mid-November every other year (GIZ 2014) were used to compute unit subsidies and annualized for 2008, the subsidies for that year would be grossly under-estimated.

Price gaps do not capture many forms of subsidies (Koplow 2009):

- If there are gross inefficiencies somewhere along the supply chain—such as large technical and non-technical losses in energy delivery (see World Bank 2011 for a detailed discussion on such losses)—local prices may not be lower than reference prices and there could be subsidies to energy suppliers.

- By definition, any subsidy that does not result in net changes in end-user prices is not captured. Because oil is a global commodity usually unaffected by production costs in a single country, subsidies provided to oil producers are typically not accounted for. Policies that boost domestic prices, such as market price support and priority purchase of more expensive forms of energy, may not be captured if price-gap calculations focus only on underpricing, as is often the case with conventional energy. Conditional cash transfers to consumers that leave prices untouched, such as India’s direct benefit transfer program for LPG purchase (India 2014) or the Low-Income Heating Assistance Program in the United States, are also not captured.

- Many other forms of subsidies may not lower prices measurably enough to be captured by a price gap. Examples include corporate tax concessions, statutory limits on commercial liabilities, and underpricing of government assets during privatization.

15
# **Table 1: IPC Acute Food Insecurity Phase Descriptions**

For more details we refer to IPC 3.0 Area Phase Classification (IPC Global Partners, 2019). The left-most column describes the classification used in this article. The rationale being that IPC3+ phases are identified as requiring “urgent action” (IPC Global Partners, 2019) and individual high IPC stages lacking sufficient historical observations.

|IPC1|Phase 1|Minimal|Households are able to meet essential food and non-food needs without engaging in atypical<br>and unsustainable strategies to access food and income.|
|---|---|---|---|
|IPC2|Phase 2|Stressed|Households have minimally adequate food consumption but are unable to aford some essential<br>non-food expenditures without engaging in stress-coping strategies.|
||Phase 3|Crisis|Households either have food consumption gaps that are refected by high or above-usual acute<br>malnutrition, OR, are marginally able to meet minimum food needs but only by depleting<br>essential livelihood assets or through crisis-coping strategies.|
||||Households either have large food consumption gaps which are refected in very high acute|
|IPC3+|Phase 4|Emergency|malnutrition and excess mortality, OR, are able to mitigate large food consumption gaps but<br>only by employing emergency livelihood strategies and asset liquidation.|
||Phase 5|Famine|Households have an extreme lack of food and/or other basic needs even after full employment<br>of coping strategies. Starvation, death, destitution, and extremely critical acute malnutrition<br>levels are evident.|

# **Table 2: Exogenous variables and transformations**

This table shows the collected exogenous variables in different categories and the transformations we apply (feature engineering). The order of transformations and aggregations follow the vertical structure of the table. Spatial aggregations are applied to the district-level data to compute country-level values. Temporal aggregations are computed on a monthly frequency and we then select the values that are contemporaneous with the reported IPC values.

|Category|Argonomic stress and weather|Confict|Economic|
|---|---|---|---|
|Variable|Rainfall<br>Normalized diference vegetation<br>index (NDVI)<br>Evapotranspiration (ET)<br>Evaporative stress index (ESI)|Number of violent events<br>Number of fatalities|Consumer price index (CPI)<br>Gross domestic product (GDP)<br>Staple food price index|
|Transformations|Average<br>Anomalies (anom)|Count<br>Inverse-distance weighted (IDW)||
|Spatial aggregation||Average over regions<br>Standard deviation over regions||
|Temporal aggregation|<br>Rollin|Percentage changes (%Δ)<br>Rolling average over 3- or 6-months<br>g standard deviation over 3- or 6-mon|ths|

6
- 20 -

Table 5. Potential consequences of enterprise housing divestiture as assessed by enterprise managers

|**_Issues_**|**_Total_**<br>|**_Assessment of_**|**_consequences (numb_**|**_er of responses)_**|
|---|---|---|---|---|
||**_number of_**<br>**_responses_**|**_decrease_**|**_increase_**|**_no influence_**|
|Financial burden|21|16|2|3|
|Managerial burden|21|19|0|2|
|Opportunities to hire<br>workers|21|7|2|12|
|Prices|20|9|0|11|

**Source:** Enterprise survey

Table 6. Sources of resistance to enterprise housing divestiture as assessed by enterprise managers

|_Resistance_<br>**_from the side_**_of._|_YES_<br>**_(number of responses)_**|_NO_<br>**_(number of responses)_**|
|---|---|---|
|local adminiistration|12|9|
|enterprise workers,|3|18|
|specifically:|||
|enterprise managers|0||
|trade union|I||
|workers of housing<br>maintenance unit|2||

Source: Enterprise survey

**Total number of responses:** 21
# **Chart 5**

<!-- Start of picture text --> Mortgage Debt as Percent of GDP Selected Countries<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Source: World Bank country<br>Denmark United Kingdom United States Spain Germany Hong Kong SAR Japan Malaysia France Korea Panama Italy Thailand Morocco Chile China Lithuania Mexico Poland Colombia Turkey Kazakhstan Ukraine Indonesia Guatemala Senegal Peru Argentina Russia Uganda Azerbaijan Cameroon Pakistan Nigeria<br><!-- End of picture text -->

# **Emerging financial markets have been damaged by losses from opaque off-balance**

**sheet entities.** Weaker reporting regimes reduce market discipline. For instance, Guatemala’s Bancafe failed in 2006 as a result of levered investments by an offshore unit based in Barbados (Fitch, 2007). In transition countries, including Russia and Poland, domestic legal shortcomings have led banks to securitize leases and mortgages using offshore vehicles under the laws of other countries, such as Great Britain and the Netherlands. To the degree that any of these securitizations permit recourse to the issuing institution, the domestic financial systems may bear more risk than is widely understood.

# **Smaller economies have relied on foreign capital to fund growth in domestic**

**financial markets.** These countries have suffered in the international credit crunch, as international investors fled to high quality government bonds from large economies, including Europe, and somewhat ironically, the United States. Kazakh banks incurred external debt worth 46 percent of GDP by the end of June 2007, then saw their access to international capital markets curtailed two months later as a result of the international credit crunch. The government has had to tap its reserve funds to help banks to meet foreign currency commitments and maintain liquidity in the banking system. Other countries without Kazakhstan’s oil wealth lack the capacity to compensate against such exposures (Fitch, 2007).

32
for passenger transport over the period 1990 to 2004 (see Figure 2(d)). Rail’s share of passenger transport activity has been steadily declining over the same time frame.

<!-- Start of picture text --> 96<br><!-- End of picture text --> <!-- Start of picture text --> 98<br><!-- End of picture text --> <!-- Start of picture text --> 04<br><!-- End of picture text --> <!-- Start of picture text --> 06<br><!-- End of picture text -->

**Figure 2: Modal Mix for Passenger Transport**

<!-- Start of picture text --> Railway Roads Aviation Shipping Subway<br>(a) China (1990-2006) (b) Korea (1994-2006)<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>(c) Vietnam (1990-2006) (d) Pakistan (1990-2004)<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>96 98 00 02 04 06<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 1994 1996 1998 2000 2002 2004 2006<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Source: National Bureau of Statistics of China (2007); Korea National Statistical Office (2008); General Statistical Office of Vietnam (2005); Accountancy (2007); United Nations Statistics Division (2008a) Whereas road is the chief mode of passenger transport in all four of these countries, the modal mix for freight transport is rather different, except in Pakistan.

Figures 3(a)-3(d) show changes in the modal mix for freight transport activity in China, 9
A Road to Trust only 8.4 percent of Filipinos declare trusting people in general.<sup>3</sup> Among the countries for which similar data is available, only two countries (Brazil and Lesotho) have lower levels of generalized trust. For comparison, in neighboring Indonesia 41.7 percent of individuals declare trusting others.

Our analysis shows that road building is associated with an increase in generalized trust. The most conservative estimate indicates that building a road is associated with a 4.5 percentage point (62.5 percent of the baseline mean) increase in generalized trust. Those results are robust to allowing for selection on observables. There is, however, no relationship between road construction and bonding trust ( _i.e._ , towards community members), presumably because roads increase interaction with outsiders, but they have no effect on the ease of interaction with community members. While we cannot directly test if increased interactions led to more trust, we show that access to markets improved in villages in which a road was built. Further, we find that, after road construction, individuals most likely to engage in exchange have larger increases in trust levels.

Our evidence suggests that the current literature on the positive impact of trust on growth might have overlooked the potential endogeneity of trust. Indeed, road construction (which generates reductions in transaction costs) promotes both growth and trust-building interactions. This is consistent with findings from Indonesia where, over the period 19851997, industrialization was associated with stronger social networks (Miguel et al. 2006).

> 3 Data retrieved from http://www.worldvaluesurvey.org (accessed on 04/02/2008).

4
1 Introduction In Africa, as in many other parts of the world, trees on farms are often overlooked in research and policy making. In forestry, the focus is mostly on trees in forests rather than outside them (Barton, 2002; Fay and Michon, 2005). In agriculture and livelihood studies, the focus is typically on annual crops and their effects on household income. When perennials (such as coffee trees) are considered, it is mostly from a value chain perspective. The organization of extension and other services reflects this division, with agriculture and forestry typically separated in different institutions (de Foresta et al., 2013). As a result, trees on farms are often left out of forest-related, agricultural and livelihood statistics and little remains known about their prevalence and economic contribution, particularly at the national scale.

Yet, trees on farms are often a vital component of agriculture-forest landscapes. They perform important ecological functions, including the provision of soil nutrients, habitat for animals, and greater structural connectivity (Manning et al., 2006) and serve as a key basis for biodiversity conservation (Bhagwat et al., 2008; Schroth et al., 2013) and climate change adaptation and mitigation (Mbow et al., 2014a). At the same time, sub-national case studies further suggest that on-farm trees often also play an important role in rural livelihoods, whether directly as a source of income (from timber or non-timber products such as fruit) (Degrande et al., 2006; Kalaba et al., 2010; Mbow et al., 2014b), or indirectly for the ecological services they provide such as nitrogen fixing, prevention of soil erosion, or shade (Place and Garrity, 2015). Roughly a third of the agricultural land in Sub-Saharan Africa is estimated to have had at least 10% tree cover during 2008-2010 (Zomer et al., 2014). Trees and agricultural activities therefore often co-exist not only in larger landscape contexts but also in single landowner holdings.

The available research on trees on farms has so far largely focused on case studies within particular countries (e.g. Dewees, 1995b; Godoy, 1992; Pouliot and Treue, 2013). Region-wide aggregated approaches have also shed light on the prevalence of on-farm trees (Zomer et al., 2014), but because they are based on remotely sensed data such studies have not directly accounted for household perspectives and practices. Cross-national (Wunder et al., 2014) and global (Agrawal et al., 2013) syntheses of forest and broader environmental income also exist, but systematic comparative information on the prevalence and economic contribution of trees on farms remains missing. This is especially problematic given intensifying competition for land in Africa (Peters, 2013) and the challenge of simultaneously advancing human development and environmental protection goals.

2
Barriers to Growth-Enhancing Structural Transformation: The Role of Subnational Differences in Intersectoral Productivity Gaps<sup>∗</sup> Saumik Paul<sup>1</sup> Dhushyanth Raju<sup>2</sup> _Keywords_ : Structural transformation, misallocation of resources, labor productivity, nonfarm enterprises, subnational regions, informal and formal sectors, Ghana _JEL codes_ : D24, F15, F43, N10, O11, O14, O47

> ∗ We are grateful to Elwyn Davies, David Elmaleh, Katherine Stapleton, and Michael Weber for helpful comments, and to Mpumelelo Nxumalo for research assistance inputs.

> 1 Newcastle University and IZA, <u>saumik.paul@newcastle.ac.uk.</u>

> 2 World Bank, <u>draju2@worldbank.org.</u>
- 19 -

**<u>Table 3. Probit regression with sample selection</u>**

||Probit w|ith sample sel|ection||Probit||
|---|---|---|---|---|---|---|
||Health a<br>|ccess (_Y_)<br>|Selectio<br>|n (_D_)<br>|Health a|ccess (_Y_)<br>|
||Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|
|Travel time to HF|-0.563|(0.155) ***|||-0.585|(0.153) ***|
|Bike/Motorcycle|0.097|(0.042) **|||0.107|(0.042) **|
|Car|-0.008|(0.083)|||0.024|(0.090)|
|Type of HF:|||||||
|Urban health center|0.265|(0.077) ***|||0.264|(0.083) ***|
|Hospital|0.303|(0.094) ***|||0.302|(0.100) ***|
|Health post|0.036|(0.078)|||0.031|(0.083)|
|Remoteness of HF|-0.113|(0.028) ***|||-0.117|(0.027) ***|
|Age|-0.002|(0.002)|-0.004|(0.001) ***|-0.003|(0.001) ***|
|Female|0.020|(0.039)|0.066|(0.016) ***|0.042|(0.036)|
|HH size|0.003|(0.009)|-0.019|(0.004) ***|-0.004|(0.009)|
|Female HH head|-0.011|(0.042)|0.069|(0.018) ***|0.008|(0.041)|
|Age of HH head|-0.001|(0.001)|0.003|(0.001) ***|0.000|(0.001)|
|No of bedrooms|-0.014|(0.022)|-0.022|(0.010) **|-0.021|(0.021)|
|Wealth index|0.119|(0.020) ***|-0.001|(0.013)|0.119|(0.028) ***|
|Access to power|||-0.165|(0.030) ***|-0.019|(0.067)|
|Piped water|||-0.056|(0.029) *|0.086|(0.064)|
|Flush toilet|||-0.090|(0.033) ***|-0.152|(0.079) *|
|Solid roof|||0.002|(0.030)|-0.019|(0.066)|
|Mosquito net|||0.021|(0.027)|0.061|(0.060)|
|Mosquito net for kids|||0.044|(0.018) **|0.019|(0.041)|
|t=2018|0.073|(0.092)|0.255|(0.024) ***|0.151|(0.055) ***|
|constant|0.794|(0.452)*|-0.931|(0.089)***|0.246|(0.221)|
|No. of obs.|38115||||5776||
|No. of selected|5778||||||
|No. of district|||||||
|dummies|113||||||
|Wald chi|13373||||669.47||
|_ρ_|-0.366|(0.317)|||||

Note: The dependent variable is _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

Since it is found that the sample selection bias is not severe, the standard probit regression can also be applied ( **Table 4** ). Not surprisingly, the results remain broadly similar to the above. The coefficient of travel time to visit a health facility is -0.59, which is statistically significant. Owning bikes or motorcycles is found to be useful to promote people’s access to health care services. Health facilities in rural and remote areas are less attractive: The quality of health care services provided may be low. Younger people, essentially children, are more
# **Figure 19: Monitoring and evaluation features**

## **19a. Success indicator By type**

<!-- Start of picture text --> Commercial Development<br>Loan volume Clients/Products served<br>Number of new clients Profitability<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **19b. Reporting Success Indicator By type**

<!-- Start of picture text --> Commercial Development<br>Annual Report Periodic statement<br>To shareholders None<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **Across granular regions**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>Loan volume Clients/Products served<br>Number of new clients Profitability<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **Across granular regions**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>Annual Report Periodic statement<br>To shareholders None<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **19c. Impact evaluation undertaken By type**

<!-- Start of picture text --> Commercial Development<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **Across granular regions**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> 40
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20