rafmacalaba/data-use-mentions-extended huggingface dataset:
using data from the base year, we only require an explicit calibration of four parameters. Namely: the DNWR parameter _δ_ , the growth in world nominal GDP in dollars _γ_ , the inverse elasticity of mobility across sectors _ν_ , and the trade elasticity 1 _− σ_ . We normalize _δ_ to one, indicating that nominal wages cannot fall, without loss of generality. We then set _γ_ to 4%, in line with the relatively high nominal growth rate observed in the post-pandemic period, but we explore the robustness of our results to different values. Finally, we obtain _ν_ directly from RUV, and we take _σ_ from the trade literature.

Our model-based analysis shows a temporary yet long-lasting decrease in U.S. labor force participation due to the shock. During the high trade cost period, engaging in the home production sector (which provides a constant utility flow) becomes more appealing, resulting in a decline in labor force participation. Once the shock dissipates, there is negative pressure on nominal wages as the economy adjusts to the lower trade costs, which in the presence of DNWR can generate unemployment. The manufacturing sector, where increase the most the trade cost the most unem- wages during high period, experiences ployment when the shock dissipates.

The impact of the trade cost shock on the labor market varies by sector. There is a temporary employment increase in manufacturing, a sector that is very tradable and where the United States is a net importer. By contrast, the service and agriculture sectors experience temporary employment reductions. We also find that the effect of the shock varies by state. States with larger service sectors (such as Alaska, Nevada, and Hawaii) tend to experience a more significant decline in labor force participation than those with larger manufacturing sectors (such as Ohio, Pennsylvania, and Wisconsin), but additional factors such as the exposure to other countries and states also play an important role in determining the extent of the impact.

The impact of the shock on labor force participation varies internationally and
. It assesses whether the laws contain specific good practice provisions or regulatory attributes, thereby conveying something about the quality of the regulatory environment. In doing so, it builds on the conceptual framework described above.

# **3. Methodology**

Within the trust framework, the term _safeguards_ stands for norms and legal frameworks that aim to protect the rights of individuals and entities participating in the data economy by addressing misuse of data, or data breaches. Cybersecurity requirements imposed on data processors or controllers are typical safeguards. The term _enablers_ refers to norms and laws that facilitate the use and reuse of data, such as data portability mechanisms, open data legislation, and so on. Although safeguards and enablers include both formal legal frameworks as well as social norms, the diagnostic assesses only formal regulatory environments. While the diagnostic goes beyond identifying the presence or absence of legislation, by examining different attributes of the regulatory framework, it is not possible to assess the implementation or enforcement of the regulatory frameworks.

Moreover, according to the WDR21, a legal framework with enablers and safeguards to build trust in the data economy needs to be multidimensional, covering different types of data, actors, and transactions. Personal data—data directly provided by an individual that involved personally identifiable information— as well as nonpersonal data have different levels of sensitivity, thus requiring tailored safeguards. According to international law, individuals have fundamental rights regarding their personal data, such as the right to object to the data usage, file complaints, and seek redress. Safeguarding nonpersonal data instead requires protection of intellectual property rights. Depending on the “domain” of the data—whether data are generated and/or controlled by the public or private sector—enablers would also vary. Government can directly adopt regulatory or policy reforms to mandate access or use of public data, while in the case of private data, the government’s role is more limited to creating incentives and removing barriers to facilitate 4
in **Figure 9** , but this time using data from Colombian plants.<sup>19</sup> The graphs plot the relations between the log of TFP at the plant level and the log of the different measures of activity at the industry level after partialling-out plant, industry, and year fixed effects. The conclusion that emerges from the three figures is similar, and suggests that the scale factor is independent of these aggregate variables. The linear specifications show a clearly flat relation between the log of firm level TFP and the aggregate variables, and the non-linear specifications are undistinguishable from the linear ones. Again, although these results should be taken with caution because of the caveats mentioned above, the 18 Results are similar using other values for the capital share. Note also that we make no effort to adjust for the quality of labour input, as is often done in development accounting exercises. It could be that the nonlinearity in productivity comes precisely from sharp improvements in worker skills at some level of development, and so we would like our estimates of productivity to capture this.

19 The data on Colombian plants comes from Fernandes (2003). Industry level data was obtained from UNIDO (2002).

30
mark>,</mark> _<mark>97</mark>_ <mark>(4), 1135–1156.</mark>

- <mark>Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions.</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>,</mark> _<mark>103</mark>_ <mark>, 102360.</mark>

- <mark>Knox, J., Hess, T., Daccache, A., & Wheeler, T. (2012). Climate change impacts on crop productivity in Africa and South Asia.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(3), 034032.</mark>

- <mark>Missirian, A., & Schlenker, W. (2017). Asylum applications respond to temperature fluctuations.</mark> _<mark>Science</mark>_ <mark>,</mark> _<mark>358</mark>_ <mark>(6370), 1610-1614.</mark>

- <mark>Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 014010.</mark>

81
, at 75 percent in Tanzania and 84 percent in Sri Lanka when benchmarked. This is comparable to the 76 percent coverage rate in both countries when using standard direct estimates. In Tanzania, the estimates from the unit level model are roughly as accurate and moderately more efficient than those from the area-level Fay-Herriot model. In Sri Lanka, where the poverty rate is low and the Fay-Herriot model is less predictive, the estimates from the unit-level model are substantially more accurate and efficient than the Fay-Herriot estimates.

These results are robust to a variety of robustness checks that explore alternative implementation options, including the absence of benchmarking and the use of a noisier welfare measure. When using the noisier welfare measure, the gain in efficiency is not as large in Sri Lanka, on the order of doubling the size of the sample, but the predictions remain accurate and coverage rates remain high. Finally, to further probe the robustness of the results, we implement both a model-based simulation and a designbased simulation of 250 randomly selected samples covering three regions in Northeast Tanzania. The small area estimates perform exceedingly well, while the design-based simulation also shows that the small area estimates substantially improve on the direct estimates in terms of efficiency and accuracy while maintaining high coverage rates.

> 7 See Fay and Herriot (1979).

> 8 While the main efficiency improvements occur by incorporating information from non-sampled sub-areas, there are also minor efficiency improvements from combining sample data with synthetic predictions in sampled subareas.

> 9 This is based on a comparison of the average coefficient of variation between the standard direct estimates and the household level model reported in Table 10.

4
Evaluations of the PSNP have shown that the program has significantly reduced food insecurity and distress sales of assets. Data collected for the 2014 PSNP highlands impact evaluation<sup>25</sup> show that food security has improved significantly in PSNP localities: the average PSNP beneficiary household reported a food gap of approximately 3 months between 2006 and 2010. This has dropped to 2.04 months in 2012 and 1.75 months in 2014. Moreover, the PSNP has led to a significant decrease in distress asset sales. While in 2010, 54% of public works beneficiary households made distress sales to meet food needs, by 2014, this has dropped to 25%. It has also been found that livestock holdings by the poorest PSNP public works participants have increased markedly, from 0.5 Tropical Livestock Units (TLU) in 2006 to 1.65 TLU in 2014.

Drought response through the PSNP is conducted through the use of the PSNP’s contingency budgets. Under the PSNP III program, which was in effect between 2009 and 2015, drought response was managed through the use of _woreda_ and regional contingency budgets, as well as the Risk Financing Mechanism (RFM). Thereby, _woreda_ contingency budgets were designed to address inclusion and exclusion errors<sup>26</sup> as well as unexpected needs of chronically food insecure households. Regional contingency budgets were designed to address unexpected needs of the chronically food insecure population, and transitory food insecurity. Regional contingency budgets were to be used prior to the release of funds under the RFM, unless the severity of the shock was beyond the capacity of regional budgets<sup>27</sup> . The PSNP III’s RFM was designed specifically to respond to drought shocks. In particular, the mechanism both extends the transfer amount to existing beneficiaries, and includes additional beneficiaries that suffer from food insecurity as a result of drought.

Under the PSNP IV, amendments have been made to the mechanisms to respond to drought. The PSNP IV came into effect
intervention site rose at approximately the same rate as access in the surrounding region. In Ghana, access at the MV intervention site rose more rapidly than in the surrounding region. (MVP 2010c does not report this statistic for the MV in Kenya.) For this and all subsequent statistics, the exact numbers and standard errors are presented in Tables 1–3.

> 7 The indicators shown in the figures as “MV region, rural” are for rural households in the region in which the Millennium Village is located. This is rural Ashanti in Ghana, rural Nyanza in Kenya, and the rural Northwest Region in Nigeria. The Nigeria DHS does not provide state-level data in 2003. 8 Standard errors are not reported in the interim MV report, so we are not able to report standard errors for the indicators for the MV sites, the trends at the MV sites, or the differences between the trends at MV sites and the surrounding areas.

> 9 For example, Figure 6 shows a higher linear slope at the intervention sites than in the surrounding region, but all points in the graph could hypothetically lie on roughly the same exponential growth curve.

12
5% and 13.6%, respectively. Both of these events increased SNAP benefits at the federal level in October 2008 and April 2009, respectively, through the intensive margin (SNAP benefits per recipient) as shown in Figure 2a, with benefits per recipient ranging from $ 80 to $ 150. This led to state-level variation in the percentage change of average SNAP benefits per population, as shown in Figure 3, ranging from around 6% to 25%. SNAP participation rates—ranging from 5% to 25%—were growing over this period, contributing to the smooth rise in the time series through the extensive margin (SNAP recipients per

> 13 County-level benefits are available at the bi-annual level for 33 states (the remaining states do not report them). County-level participation counts are available for all states at the annual level. Data at a higher spatial resolution is not available.

6
.6<sup>∗∗∗</sup>|(1.7)|
|**5. Aid received in the last year**|||||||
|MASAF or Education aid|||||3.7<sup>∗∗</sup>|(1.7)|
|**6. Geography**|||||||
|Rural Household|-53.6<sup>∗∗∗</sup>|(3.7)|-53.5<sup>∗∗∗</sup>|(3.7)|-9.8<sup>∗∗∗</sup>|(3.4)|
|Distance to town (km)|0.0|(0.0)|0.0|(0.0)|0.0|(0.0)|
|District FE|Yes||Yes||Yes||
|IHS round FE and Season FE|Yes||Yes||Yes||
|Observations|35755||35755||35627||

Note: The coefficients capture the percentage change in per capita consumption without aid (food and cash). Standard errors are clustered at the district level. Standard errors in parentheses:<sup>∗</sup> _p <_ 0 _._ 10,<sup>∗∗</sup> _p <_ 0 _._ 05,<sup>∗∗∗</sup> _p <_ 0 _._ 01 Households’ exposure to droughts decreases their consumption per capita, after accounting for any increase in food aid and cash support in response to such a disaster (Table 2). Drought reduces the consumption of households, on average, by 3 percentage points, compared to those that are not exposed to drought. Of course, the magnitude of the impact varies
the NHAI portfolio has come from public sector banks in which the government owns more than 50 percent of capital shares. The large number of cancellations in the NHAI portfolio have contributed to the rising number of nonperforming assets in India’s banking sector. The State Bank of India, which holds the greatest nominal amount of Indian highway-related debt, reported that about 20 percent of loans to ports and highways were in nonperforming status by the end of 2016, with the trend increasing in 2016 (ADB and others 2018).

The likelihood of cancellation increases with the length of the highway. The finding that longer highways are more likely to be canceled is consistent with the observation that many of the cancellations have been related to the government having problems with securing the right of way.

# **6. Concluding remarks and policy recommendations**

The fiscal risks from the current infrastructure PPP programs in South Asia are not negligible for some countries. Under a severely adverse scenario, the fiscal risks from early termination in 2020– 2021 amount to 2.9-4.0 percent of the government revenues in Bangladesh, India, Nepal, and Pakistan. The pipeline of PPP projects in South Asia is quite large, particularly in Bangladesh, which has the same number (30) of infrastructure projects in the pipeline as it has active projects. Such an expansion of the PPP program can lead to a significant increase in fiscal risks. Therefore, an important agenda in South Asia is improving the design and management of infrastructure PPPs to mitigate the corresponding fiscal risks while ensuring financially responsible and timely implementation of infrastructure protects to address the infrastructure deficit.

The analysis identifies five overarching lessons from the global PPP data. First and foremost, several factors that increase the risk of early termination of PPP projects are related to the financing risk of the project. Governments may alleviate some of the financing risk of PPPs by providing support through capital grants, revenue subsidies or in-kind transfers, which lead to reduced rates of early termination. Second, the sector in which a
nificant. Personality traits - Big Five, Non-Cognitive, PSM and Integrity - are all strongly and significantly correlated with each other. The correlation of PSM/Big Five is 0.35 and PSM/Non-Cognitive is 0.39.<sup>19</sup> This suggests that these measures, captured in different questionnaire modules, do capture some underlying behavioral traits of the interviewees.<sup>20</sup>

# **2.2 Measure of productivity of district bureaucracies**

These survey data on bureaucrats and politicians are matched to government data on the productivity of districts in implementing national programs. The Ministry of Health (MoH) in Uganda compiles detailed information on the performance of districts in delivering nationally mandated public health programs. Using the Health Management and Information System (HMIS), the MoH compiles the data reported by local health facilities into Health District League Tables (DLT) that are intended to capture the relative performance of service delivery across districts (See Figure 12 for details). The DLTs are part of the Annual Health Sector Performance Report by the MoH. The closest year to our survey for which the DLT data are available is FY 2014/2015. The bureaucrats and politicians we interview in September-December 2015 were the presiding officials during FY 2014/2015 when the health performance indicators were compiled by MoH .

Table 4, top panel, presents descriptive statistics on the quality of health services across districts. Whereas there is little variation in some of the individual indicators (the median coverage of DPT3 vaccination is 98 percent), other indicators vary widely between Districts: the average share of women having 4 or more ante natal care visits is only 34 percent (s.d.: 10.9 ) and the average share of pregnant women receiving preventive malaria treatment in pregnancy (IPT2) is 53 percent (s.d.: 11.6). The DLT total score is a weighted average of 14 quality care and management practice indicators ranging from 0 to 100 - the average
. However, the latter is only possible if data on cargo characteristics is made available to terminal operating companies and used to segregate full containers according expected dwell time. In that respect this study has only used very partially the potential of data available in ASYCUDA and Cameroon customs are encouraged to further the investigation of databases.

Further research is needed to better address the long cargo dwell time issue in Sub-Saharan African ports:

- Further research is needed to investigate governance issues, for example to see if long delays between arrival and lodging can be interpreted because of ongoing negotiations with agents with a view to lower overall import cost for shipment. Likewise, long time between duties payment and exit may be related to redundant controls.

29
billion allocation (2.8% of 2008 GDP) to support HSR during 2009 - 2011 (4), and government expenditure on health increased to 1.4% of GDP in 2009. (5) This is still low compared to government expenditure on education (3.5% of GDP in 2008) (6), but brings China closer to a WHO estimate that government spending on health of 1.5-2.0% of GDP could guarantee primary health care (public health and a modest package of essential clinical care) for all in China. (7) The announcement of the priorities for and content of the reforms by national government, along with the expectation of their implementation at all administrative levels, has placed a great burden on provincial and sub-provincial health authorities. Moreover, ensuring that the concomitant injection of new fiscal resources contributes effectively to the accomplishment of HSR objectives is a challenge, since about 90 percent of public resources for health care (as well as other public services) is generally allocated at the provincial and sub-provincial levels. (8) Promoting allocative and operational efficiency in public expenditure at these levels is a particular challenge that has yet to receive appropriate attention nation-wide.

In this paper, we explore whether the government’s actual expenditure on health across levels support China’s HSR objectives. We analyze the available national, provincial and sub-provincial statistics to explore recent trends in relation to equity in certain key health outcomes and public 2
**Table ‎2.3:  Percent of Households Engaged in HE, by Area, 2006**

|1.3|1.4||
|---|---|---|
||HE as primary activity*|All HE*|
|Dar|58.4|67.9|
|Other urban|49.1|74.2|
|Rural|13.0|63.5|
|Total|24.4|65.9|

_* Households where HE is the primary activity of at least one member.** Households with HE as a primary or secondary activity of at least one member. Source: Calculations based on the ILFS 2006 data._

**Table ‎2.4:  Percent of Households Engaged in HE, by Asset Quintile, 2006**

||HE non-agriculture*|HE non<br>-agriculture**|
|---|---|---|
|Quintile 1|9.4|66.1|
|Quintile 2|15.3|59.3|
|Quintile 3|20.4|69.2|
|Quintile 4|30.6|73.1|
|Quintile 5|51.1|61.9|
|Total|14.5|65.9|

_Note: * refers to HE as a primary activity ** refers to HE as a primary or a secondary activity Source: Calculations based on the ILFS 2006 data._ 16. Does running an HE reduce poverty? The evidence in Tanzania is not clear. The ILFS 2006 data showed that both poor and non-poor households participate in HEs. The one panel study for Tanzania which analyzed this question found that in rural Kagera, adding an HE to a farm-based activity is indeed a successful route out of poverty, but only for those with access to towns and markets (De Weerdt, 2008). What we can see
||**Source**|
|---|---|
|Voice and Accountability||
|Quality of the Bureaucracy|Kaufinann, Kraay and Zoido-Lobaton (1999a).<br>International Country Risk Guide (ICRG), average|
|Civil Liberties|1984-1998.|
|Property rights and rule-based<br>governance<br>Government Effectiveness|Freedom House, average 1972-1998.<br>Country Policy and Institution Assessment (CPIA), the<br>World Bank 1998.|
|Graft<br>Law and Order Tradition|Kaufinann, Kraay and Zoido-Lobaton (1999a).<br>Kaufmnann, Kraay and Zoido-Lobaton (1999a).<br>International Country Risk Guide (ICRG), average|
|Political Instability and|1984-1998.|
|Violence<br>Political rights|Kaufinann, Kraay and Zoido-Lobaton (1999a).|
|Regulatory Burden|Freedom House, average 1972-1998.|
|Rule of Law|Kaufmann, Kraay and Zoido-Lobaton (1999a.<br>Kaufmann, Kraay and Zoido-Lobaton (1999a).|

The quality of political institutions will clearly also be an important factor for growth. As the recent literature on corruption (e.g. La Porta et al, 1997) has shown, arguments that corruption "greases the wheels" of growth simply do not stand up to empirical scrutiny (Tanzi and Davoodi, 1997). A central hypothesis emerging from our social cohesion framework is that strong social cohesion makes it easier to improve the quality of institutions..

# **4. New Evidence on Social Cohesion, Institutions and Economic Performance**

The central story of economic growth over the last 50 years has been the contrast between the years 1950-74 and 1975-2000. The former was a time of general prosperity, in which all strategies yielded positive outcomes; rich and poor countries, open and closed economies, temperate and tropical countries-everyone did well. The twenty-year period between 1974 and 1994, however, was disastrous for virtually everyone except the