rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Trade in services is important for countries across the world, especially small economies, which often derive a significant share of their foreign exchange revenues from services exports. According to data from the WTO, between 2010 and 2018, trade in commercial services grew by 42% for developed economies, by 60% for developing and emerging economies and by 49% for LDCs, while global trade in commercial services grew by 48%. In fact, exports of commercial services alone witnessed a 90% increase for LDCs over this period.

Services matter not just because they are a potentially important source of foreign exchange revenue and associated employment and household income. Many services are important for economic growth and development by virtue of their role as inputs into production in all sectors of economic activity. Realization of many of the Sustainable Development Goals (SDGs) also depends on the performance of a range of specific services sectors (Fiorini and Hoekman, 2018).

The quality, price and availability of services inputs is determined by a mix of factors, including infrastructure connectivity network investments, the restrictiveness of trade and investment policies for goods and services, and the investment climate/business environment. There is substantial empirical evidence that services trade and FDI in services foster productivity growth by inducing greater competition in domestic markets and providing firms access to higher-quality, more varied, and cheaper services inputs, which benefits producers of both goods and services. However, trade costs for services are higher than trade costs for goods, and the rate of decline that has been observed in services trade costs since the early 2000s has been much less than for trade costs for goods (Miroudot and Shepherd, 2016).

These costs are especially salient for services delivered by the temporary movement of natural persons (MNP) or Mode 4 trade in GATS parlance, which inter alia explains the low share of Mode 4 trade in total trade in services.<sup>2</sup> Barriers to trade in services in general do not take
# **_Limitations_**

Although this research has considerable promise, there are a number of limitations which should be acknowledged, including most importantly that this analysis only claims to be an _initial_ assessment of the relationship between the variables. While it would be ideal to have more years of data—and more observations included in the data set—the analysis uses data that are _currently_ available, as OGD only started to be measured in 2012/2013. In this regard, OLS regressions are instructive in teasing out initial associations between variables; however, as more data become available, increasingly more sophisticated estimations can be performed in the future. For instance, it would be ideal to include a country fixed-effects estimation as it would demonstrate that the results are not driven by omitted variable bias, which are country-specific and do not change over time. Unfortunately, including country fixed-effects estimation at this point in time would lead to a significant loss of degrees of freedom to estimate the parameters of interest.<sup>130</sup> As such, there is an inherent trade-off between unbiasedness/consistency that would be provided by the fixed effects estimation versus being able to detect parameters using this initial OLS estimation.

Second, given the fact that OGD is a rather recent phenomenon, it is difficult to suggest a purely _causal_ relationship between variables. While a lag of nine months has been introduced between the availability of OGD statistics and changes in accountability and service provision, it is likely that these changes may take more time to materialize. Accordingly, the present analysis only purports to provide evidence of associations between variables, to help support or refute existing theoretical propositions. Relatedly, challenges exist with certain indicators and it is necessary to ensure that there are no significant issues of endogeneity and simultaneous causality. For instance, more accountable governments _may_ be more inclined to support an OGD initiative in the first place, which would reverse the causal relationship. As such, it is difficult to argue at this early stage whether OGD proceeds accountability or accountability proceeds
br>|GCI 11.01-11.09);<br>– innovation (WEF GCI 12.01-12.07)<br>•<br>Extent of staff training (WEF GCI 5.08)|
|•<br>•|Creation of clusters and other task bundling efforts<br>Changes in production (methods and equipment) towards more<br>efficient and sustainable use of natural resources (e.g. water)<br>and energy|•<br>Labor statistics – activity rates, unemployment, male/female,<br>etc. (ILO, WDI)<br>•<br>Innovation indicators and surveys – public and private R&D<br>expenditure, high and medium-high technology manufacturing,<br>knowledge intensive services (OECD)<br>•<br>Production capacities – sector output – andproductivity statistics<br>(national statistics,WIOD)|

Source: OECD, 2012a

# **_Preserving participation in GVCs_**

It is not enough to join GVCs: business strategies are very flexible and tend to adjust quickly to changes in demand (consumers’ tastes and spending) and supply (competition among input providers). Participation in GVCs thus contains a number of risks and raises sustainability issues that should be addressed by trade and competitiveness policies.

# _(1) Identifying the threats and opportunities_

SWOT (strengths, weaknesses, opportunities, threats) analyses are common in business literature. They allow the assessment of a country’s endogenous (strengths and weaknesses) and exogenous (opportunities and threats) competitiveness factors, and in the context of a value chain analysis, the ability of a country to join and remain part of GVCs.

Exogenous factors of particular relevance to GVCs include:

- Variations in the length of GVCs (e.g., the consolidation of a GVC that reduces the number of participants and the geographical spread of the production);

- Shift in end markets (e.
data on the occurrence and impacts of over 22,000 mass disasters worldwide since 1900 (Yamamura, 2015; Cappelli _et al._ , 2021). Another source of disaster data is DesInventar, which provides records of disaster at the country level from 1980 (Rodriguez-Oreggia _et al._ , 2012; Keerthiratne and Tol, 2018). The advantage of such databases is their wide coverage and the standardization of disaster data, enabling cross-country comparisons. However, they often lack detailed local-level data, which can be crucial for understanding variations in disaster impacts within countries. Moreover, these databases predominantly focus on large-scale disasters, potentially excluding smaller, localized disasters that can have a substantial cumulative impact on poverty and inequality.<sup>8</sup> The reliance on official reports or media coverage for disaster validation may also underrepresent disasters in areas with less robust reporting systems. These limitations highlight the importance of incorporating additional data sources to provide a more comprehensive understanding of disaster impacts.

Another nuanced approach in the study of disaster impacts combines traditional survey techniques with geospatial and localized risk information (i.e., footprint data). Geo-referencing is employed to further validate self-reported exposure data, thus combining ground truth with computational models. Such methodologies aim to generate a more intricate understanding of localized risks and household experiences, and therefore bring a multidimensional perspective that blends traditional and high-tech methods. Specifically, recent studies use community flood maps and satellite imagery to identify flood risks or use self-reported survey data that were validated with a newly developed flood map, offering a more complex understanding of households’ experiences during flood events (Erman _et al._ , 2019; Erman _et al._ , 2020). A particularly significant development is the use of reanalysis data, which combines satellite observations, ground measurements, and statistical models to offer near-global coverage of

> 8 To be included in EM-DAT, a disaster must meet at least one of the following criteria: 10 or more people reported killed, 100 or more people reported affected, declaration
where i is the log of the unit price of a food product in LGA _j_ at wave _t_ and D i is the share of people who live in the LGA covered by 3G/4G for different lengths of time (e.g., one year or more; 2 years or more; 3 years or more). The key food products include the five most consumed products PP among the poor who live under the $1.90 poverty line: rice, palm oil, beef, white beans, and groundnut oil. i includes the same set of time-varying household control variables as included in Equation 1 but now aggregated to the LGA level. XX Another way in which mobile can drive improvements in individual and household welfare is by enabling access to financial services, especially mobile money. Studies have shown that mobile money reduces transaction costs for users and helps households to better manage their cash-flows more effectively, enabling them to smooth consumption, manage risk and build working capital. It can also provide for higher incomes through remittances.<sup>23</sup> However, the adoption of mobile money in Nigeria was limited during the 2010-2016 period. Evidence from the World Bank Findex survey shows that the proportion of adults using mobile money in Nigeria was 2% in 2014 and 5% in 2017.<sup>24</sup> Given such low levels of usage, it is very unlikely that any impact of mobile broadband coverage on consumption or poverty was driven by mobile money access.

In terms of the labor market and price mechanisms, we run each of our regressions on several subsamples to explore heterogeneous effects by: gender; initial level of consumption of each household; region; age; area (urban/rural) of residence; education and literacy status of the household head or respondent, and the presence of children under the age of three in any of the three waves.

# Pre-treatment trends

By focusing on mobile broadband coverage and not mobile broadband usage (which is a household choice)
-2-

new economic policies, for example, by vegrina production towards exports, or to leave the market.

On the macroeconomic side, the most important developments in 1980-82 were export orientation and disinflation.' The comprehensive stabilization/liberalization program included policies aimed at trade liberalization, real depreciation of the exchange rate and reduction of domestic absorption. The economy responded quickly. The Gross National Product (GNP) growth rate, which was negative in 1979 and 1980 picked up and reached 4.1% in 1981 and 4.6% in 1982; although below the 1973-77 average of 6.5%, the increase in growth rates did reflect a recovery. In the meantime, the composition of demand changed drastically: The rate of growth of domestic absorption, averaging 8.2% in 1973-77, was only 1.6 and 2.8% in 1981 and 1982. While the contribution of foreign balance to GNP growth was negative between 1973-77, it was positive in the 1980's--with the exception of 1983.2 Exports grew by 47 and 25% in constant Turkish lira prices in 1981 and 1982 respectively. The decline in the rate of inflation was even more dramatic. The annual rate of change of the wholesale price index declined from around 107% in 1980 to 37% in 1981 and 25% in 1982 (see Figure 1). In short, 1980-82 were years of drastic realignments in major macroeconomic variables, which were bound to affect corporate performance.

The deregulation episode in the financial markerc lasted two and a half years. The objective of increasing deposits and financial savings in general 1 For a detailed overview of macroeconomic policies and performance in the 1970s and 1980s, see Celasun and Rodrik (1987).

**2** Data from OECD, Economic Survey: Turkey, various issues. Let Y, A and B stand for GNP, domestic absorption and the current account surplus respectively, all in constant prices. Then, Y - A + B and Y - aA
instance, the requirement that foreign providers completely re-do the university degree, practice and exam in the domestic country) as well as the administrative, financial and economic costs of compliance with such measures (e.g. the costs of applying for a business visa). While minimizing the latter costs forms an obvious part of a trade facilitation in services (TFiS) agenda on Mode 4, eliminating excessively burdensome regulation or at least making it less onerous is more of a trade liberalization agenda. Meanwhile, another key issue in a Mode 4 TFiS agenda is reducing the heterogeneity in necessary regulation and the administration of regulatory measures, though regulatory heterogeneity also imposes trade costs on suppliers, accounting for 21 percent of total trade costs in services along with trade policy barriers (WTO, 2019). Finally, the emphasis should be on the extent to which a TFiS agenda can enhance predictability and certainty for service suppliers, since both these attributes reduce services trade costs. According to WTO (2019), information- and transaction-related costs form the largest component of bilateral trade costs, accounting for 25 percent of total services trade costs.

The rest of the paper is structured as follows. Section 2 conceptualizes a TFiS agenda as it would apply to Mode 4 trade. Section 3 discusses costs of restrictive barriers to Mode 4 trade by providing an analysis of the OECD’s STRI data on MNP. Section 4 provides suggestions on facilitating Mode 4 trade by drawing on the OECD’s STRI data on regulatory transparency applicable to MNP. Section 5 concludes.

# **2. Conceptualizing a TFiS agenda for Mode 4**

In trying to conceptualize a TFiS agenda on MNP, which is the main objective of this paper, it is useful to clarify at the outset what constitutes services trade facilitation in Mode 4 and what does not (see Table 1 for examples). The distinction in this context is between liberalizing regulation that may be unnecessarily restrictive or burdensome and which can be streamlined, if not eliminated in its entirety, and between facilitating
government’s response. As such, we are able to examine whether government bailouts of unionized firms have an impact on bond prices.

# **V. Data & Methodologies**

In this section, we briefly describe the data sources used in this study. Firm-level accounting and price information are obtained from COMPUSTAT and CRSP. Bond trade prices are obtained from the Trade Reporting and Compliance Engine (TRACE)

> 80 _See_ , e.g., Lewis (1986).

> 81 _See_ , e.g., Chen, Kacperczyk, and Ortiz-Molina (2010a).

> 82 _See_ , e.g., Hirsch (1991) and Abowd (1989).

> 83

> _See_ , e.g., Connolly, Hirsch, and Hirschey (1986).

22
WPS3784 poverty.<sup>4</sup> The analysis concentrates on coffee, the main exportable crop and the main vehicle for raising cash incomes in rural areas and for stimulating a switch from subsistence to more market oriented agriculture. Beyond coffee farmers, reductions in rural transport costs will benefit all producers and consumers in rural areas and will be integral to a policy that seeks to increase the role of markets and market oriented production in rural areas. Thus, the impact of policy and investment initiatives to improve access to rural transport will by far exceed the more narrow simulations on poverty that we investigate here.

As a small coffee exporter, Rwanda is a price taker in the world markets. Hence in the analysis below we take world prices as exogenous and assume that changes in coffee supply in Rwanda have no impact on the world price. For all coffee farmers, regardless of the quality of their crop, one of the key determinants of the producer price that they receive in Rwanda will be the transport cost.

Transport costs incurred by exporters both within Rwanda and on the main corridor routes to ports in neighboring countries are very high. It is estimated that the transport cost from the farm-gate in Rwanda to the port in Mombassa is about 80 percent of the producer price. Rural transport costs alone, that is, of transport from the farm gate to the capital, Kigali, is estimated at 40 percent of the farm gate price!<sup>5</sup> These costs represent an implicit tax on coffee (and other exportable) producers.

Porto (2005) shows how reducing trade costs in Moldova could have a substantial impact on poverty. We follow a similar approach simulating lower rural and international transport costs leading to higher prices for farmers, which encourage greater production and raise the incomes of rural farmers. Here, we look specifically at the effect of a 50 percent cut in rural transport costs on coffee farmers’ income. This is not unrealistic. Most of the roads in rural areas are unpaved.
Review_ , _14_ , 693–709.

- OECD/SWAC. (2020). _Africa’s urbanisation dynamics 2020: Africapolis, mapping a new urban geography_ . West African Studies, OECD Publishing, Paris.

- OECD/UN ECA/AfDB. (2022). _Africa’s urbanisation dynamics 2022: The economic power of Africa’s cities_ . West African Studies, OECD Publishing, Paris.

- Ravallion, M. (2018). An exploration of the changes in the international comparison program’s global economic landscape. _World Development_ , _105_ , 201–216.

- Ravallion, M., Chen, S., & Sangraula, P. (2007). New evidence on the urbanization of global poverty. _Population and Development Review_ , _33_ (4), 667–701.

- Ravallion, M., Datt, G., & van de Walle, D. (1991). Quantifying absolute poverty in the developing world. _Review of Income and Wealth_ , _37_ , 345–361.

- Ravallion, M., & Bidani, B. (1994). How robust is a poverty profile? _The World Bank Economic Review_ , _8_ (1), 75–102.

- Roberts, M., Blankespoor, B., Deuskar, C., & Stewart, B. (2017). Urbanization and development: Is Latin America and the Caribbean different from the rest of the world?, World Bank Policy Research Working Paper 8019.

- Satterthwaite, D. (2007). _The transition to a predominantly urban world and its underpinnings_ . London: International Institute for Environment and Development (IIED).

- Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely sensed and ancillary data. _PLOS ONE_ , _10_ (2), e0107042.

- Uchida, H., & Nelson,
|`227`|`47.59`|
|`Very well informed`|`44`|`9.22`|

# **Climate-related Hazards during the Past 10 Years**

25. Sixty-two percent of respondents in Tajikistan reported having encountered extreme hot/cold seasons that were harmful for health during the past 10 years; 42 percent experienced unpredictable and extreme weather that harmed agricultural production, while 40 percent reported having suffered a decline in fresh water availability for drinking (Figure 8).

**Figure 8: Respondents affected by climate-related hazards during past 10 years**

<!-- Start of picture text --> 62<br>Extreme hot/cold seasons that were harmful for health<br>23<br>Unpredictable and extreme weather that harmed 42<br>agricultural production 9<br>40<br>Decline in fresh water availability for drinking<br>12 Tajikistan<br>36 Kazakhstan<br>Decline in fresh water availability for irrigation<br>10<br>23<br>Natural disasters (e.g. floods, fires, mudslides, etc.)<br>1<br>0 20 40 60 80<br>Percent of respondents<br><!-- End of picture text -->

**Source:** Authors calculations based on P-LITS 2 data.

14
**Hypothesis 3:** The security situation will be better in villages that have received a development program.

According to the “hearts and minds” theory, improved attitudes towards the government should decrease support for insurgents, which should in turn lead to a decrease in security incidents. Hypothesis 3 is also consistent with the “opportunity cost” theory, but is inconsistent with the “greed” theory and the “bargaining” theory, both of which predict that projects should increase insecurity, at least in the short run.

# **V. Data**

Data for the study come from three sources: a baseline survey, a follow-up survey, and events data on security incidents gathered by ISAF. The following sections provide further details on these data sources.

## **V.1. Baseline Survey**

Data from the baseline survey were collected during August and September 2007 and prior to the introduction of the development program in the 250 treatment villages. The survey consisted of four different instruments: (a) a male household questionnaire administered to ten randomly-selected male heads-of household in each village; (b) a male focus group questionnaire administered to a group of village leaders in each village; (c) a female focus group questionnaire administered to a group of important women who tended to overwhelmingly be wives or other relatives of the village leaders; and (d) a female individual questionnaire. In total, the survey covered 13,899 male and female villagers as well as village leaders across the 500 sample villages.

## **V.2. Follow-Up Survey**

Data from the follow-up survey were collected between May and October 2009. The follow-up survey was administered following CDC elections (which occurred between October 2007 and May 2008) and project selection (which occurred between November 2007 and August 2008), but before projects were completed.<sup>14</sup> The timing of the survey following project implementation but prior to the delivery of project benefits enables the isolation of
more likely there is a quantifiable economic benefit. The second category of gains are less easily measured; arguably social cohesion is a welfare improvement in itself but from the standpoint of economic gains, the benefits come from increased investments or more effective policy choices prompted by this social capital. While there is evidence that SNs may contribute to social cohesion in some circumstances (and be divisive in others) it is beyond the scope of this paper to review the literature on how such social capital augments economic growth.

# _V. MEASURING NET IMPACT_

This review has illustrated a range of impact on economic growth or on asset accumulation that can be attributed to safety nets. However, even if these examples are typical of the wider set of studies – and, as mentioned, the review is not intended as an exhaustive survey – there is still a major obstacle to quantifying the economic return to safety sets as an investment. The measurement of single dimension of the outcomes of a safety net program - say increased enrollment - as is often done in cost effectiveness studies biases the economic contribution of SNs downwards since it ignores the full range of outcomes.

Even if one could reasonably estimate the returns to the type of investments attributed to a safety net, transfer programs often have a dual objective of raising current consumption while simultaneously promoting investments. This makes it difficult to compare outcomes of a safety net with direct investments. The full social value of transfers does not easily aggregate with outputs in a benefit cost assessment. For one thing, such a summation requires a quantification of the weight society puts on consumption of the poor relative to that of the average citizen. Absent this calculation, a direct comparison of direct investments, say in education or health, with a SN does not put both categories of expenditures on the same metric. A cost benefit analysis or a cost effectiveness comparison within a sector generally assumes away the 18