rafmacalaba/data-use-mentions-extended huggingface dataset:
competitiveness using 114 indicators in 137 economies.<sup>8</sup> It provides a score of between 0-100 for aggregate indicators ranking countries as well as for each of the areas of competitiveness. Another tool using a similar approach is the _Export Competitiveness Diagnostic Toolkit_ from the World Bank (Reis and Farole, 2012) which uses quantitative analysis to assess constraints to export competitiveness in 13 broad policy areas. It assesses a country’s overall basket of exports and/or specific traded sectors and identifies the main constraints to improved trade competitiveness and related policy responses.

Investment Climate Surveys are conducted at the firm level and thus focus primarily on investigating firm characteristics and how different firms relate to specific policy issues.<sup>9</sup> Conducted by the World Bank since the 1990s, _Enterprise Surveys_ provide firm-level surveys of a representative sample of an economy’s private sector.<sup>10</sup> While methodologically different to benchmarking approaches, they offer the same possibility of cross-country panel study as surveys share identical questions across the various countries in which they have been implemented. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. The available information now includes information on 139 countries gathered from 131,000 firms. Constraints facing firms can be ranked and the data can be parsed by size of firm and to a lesser extent by sectors.<sup>11</sup> The data consists of a mix of perception/recall data and actual revenue and cost information provided by firms. Enterprise surveys are periodically repeated allowing for a review of how constraints evolve over time and selectively for the use of panels. Based on the Enterprise Surveys, Investment Climate Assessment reports have been often produced which in addition to an in-depth analysis of the survey data also undertake a deeper analysis of investment climate constraints and potential solutions.

- Identification Frameworks

Already implicitly present in tools such as Doing Business and World Economic Forum approaches,
is, taxpayers outside Dar es Salaam might have been ‘lying to the taxman.’ The lack of a behavioral response to this visit supports the notion that the visits were viewed as possible enforcement actions without much credibility to support them. In the absence of further presence, reporting behavior did not change or (in the case of the Southern Zone) actually worsened (frustration over the visit combined with a low credibility of enforcement actually backfired).

Meanwhile, the finding that compliance improved in areas where tax morale did not improve could reflect a higher (albeit short-lived) credibility of enforcement caused by the intervention. Businesses in the Eastern zone of the country are more used to seeing heavier presence of the tax officials in their wards, so the presence of the tax officials at the moment of the survey did not influence their tax morale (stated, of course). However, as it was different from other local presence (surveying rather than conducting enforcement actions), it might have shifted the credibility of enforcement on the margin or alternatively increased the sense of trust, since the local presence was purely observational and no enforcement or quasi-voluntary actions were being take. The endline survey seems to support the second hypothesis, but this is not entirely clear from our analysis of the data.

Despite the lack of clarity in our findings, this intervention was meaningful in that it shows the promise and potential challenges in conducting experimentation through a revenue authority that involves face-to-face interaction and steps outside the traditional “nudge” framework. Further research is needed to understand how and why local presence in highly centralized revenue administration environments can serve to improve compliance; in particular, to understand whether increased presence interpreted as enforcement actions versus facilitation of trust-building actions can serve to increase compliance and collection, particularly among SMEs where there is limited cost-effectiveness from heavy handed enforcement interventions.

26
high as to classify most of the population as poor. We have shown further that the benefits from targeting are most clearly discerned when expressed in terms of budgetary savings of achieving a given rate of poverty reduction. We have noted, however, that despite the availability of reasonably precise estimates of poverty at the level of communities with populations of perhaps only 5,000 households, transfer schemes targeting such communities are still unable to achieve the kind of success that would be attainable if household-level income or consumption data were available. We suggest therefore that a potentially useful way forward is to combine fine geographic targeting on the basis of a poverty map with withincommunity targeting based on either self-selection or alternative targeting mechanisms.

Our assessment of targeting performance has been based on an optimal use of estimates from poverty maps. There might be grounds for concern that the design of transfer schemes based on such optimized routines suffers from lack of transparency and would be difficult to describe in simple terms. We have considered, therefore, an alternative transfer scheme, based on a naïve, non-optimal use of the poverty map. We have found that while this naïve scheme does not achieve the same success in our three countries as the optimal targeting scheme, its performance remains surprisingly good in the case where the overall budget and poverty line are both relatively low. On the other hand, when the budget and/or poverty line is relatively high, the naïve scheme we implemented can perform less effectively than even a uniform lump-sum transfer. To the extent that policy makers are concerned about issues of communication and transparency, our results suggest that there are conditions under which even simplistic targeting schemes based on the poverty map can yield encouraging results.

We would be remiss not to restate the important caveats that attach to these results. First, we have assumed in this paper that the budget available for distribution is exogenously 29
cases, further validation either using physical patient counts or repeat measurements can be used to improve accuracy in the estimated variation in the underlying distributions.

# **7 Conclusion**

The World Health Organization argues that 90% of LMICs suffer from critical health worker shortages.<sup>32, 33</sup> Using insights from country-level estimates for the WHO African Region combined with nationally representative facility-level survey data in 10 countries, we submit that the consensus of a human resource crisis in low-income countries needs a fundamental reassessment. The problem appears not to be so much a general “shortage” of providers as it is a complex issue of unequal caseloads across unequal providers, which results in under-utilization of high-competence providers while low-competence providers manage large caseloads.

Combining novel data on these two dynamics, we estimate that, in half of the countries in this study, the productivity losses due to misallocation are quite large. We emphasize that these calculations are intended as a sufficient statistic to estimate productivity losses in primary health care quality due to systematic excess capacity among highcompetence providers, not as a policy prescription. A policy of provider reallocation is potentially realistic only to the extent that (public) health systems can post or transfer individual providers to different facilities. In practice, our results suggest that a wide variety of allocative strategies, such as triage and patient-transfer, may be worth further investigation at the local and subnational levels, in addition to centralized provider reassignment.

In the other half of the countries, the potential gains from reallocations are much lower. The inconsistent quality of LMIC medical education appears to be a significant constraint as there are so few high-competence providers that reallocating them does very little. In these countries, there is no easy way out of the quality crisis. Their problem is still not a general shortage of health care providers – as in other systems, there is plenty of excess capacity among the workforce. Instead, the inability of these countries to train and retain highly competent providers leads us to believe that a total overhaul
# **1 Introduction**

Air transport is increasingly important to the global economy. Be it for exports of perishable goods, such as Kenyan cut flowers, or the movement of highly specialized component parts within transnational production networks, the ability to move goods quickly, reliably, and at a reasonable price is a crucial ingredient in the trade performance of a wide range of countries. The economic importance of air transport was highlighted by the eruption of Iceland's Eyjafjoell volcano in April 2009, which severely disrupted air traffic—including freight—into and out of Europe. Press reports indicate that Kenyan farmers were forced to dump stocks of fresh food and flowers destined for European markets, at a cost of some $3.8m per day.<sup>4</sup> Our aim in this paper is to analyze one aspect of the global air transport system that has received relatively little attention from economists and policy experts to date: its character as a network industry, and thus the importance of _connectivity_ as an overall measure of the level of service—frequency of flights, reliability, and diversity of destinations—available to end users. Connectivity is increasingly emerging as a key policy concept, particularly in international forums such as ASEAN and the Asia-Pacific Economic Cooperation, yet it currently lacks a rigorous empirical framework that would allow policymakers to undertake cross-country comparisons, or track progress through time. Our approach aims to help fill that void, using air transport as an example.

This paper builds on and extends two recent efforts at measuring connectivity in economically important sectors. Whereas UNCTAD (2007) adopts an intuitive but ad hoc approach to developing a connectivity indicator for liner shipping based on a weighted average of capacity and utilization data, we embed our approach in a rigorous network analysis framework. In addition to being more consistent, our approach also has the advantage of taking account of the hub-and-spoke nature of the global air transport

> 4 <u>http://news.bbc.co.uk/2/
**_<u>________________________________________________________</u>_**

indicators that are closely followed in the specialized transport and economic press and by transporters who need to follow the various national trends affecting their operations. GDP forecasts are based on the latest forecasts of the EU statistical office (Eurostat) in its "Quarterly National Accounts ESA". In those countries where forecasts are not available, trend analysis and average growth of neighboring countries are used to make the forecasts. Registration forecasts are based on time series analyses, taking into account other factors such as the development of transport volumes and GDP. Transport volumes forecasts are made using NEAC Transport Simulation System which quantifies the relationship between production sectors and the transport they generate, using various economic scenarios, to estimate future transport volumes in and between 176 regions.

# _<u>United Nations Economic and Social Commission for Asia and The Pacific (UNESCAP)</u>_

The report _Transit Transport Issues in Landlocked and Transit Developing countries (2003)_ includes average and maximum waiting times at the border for several landlocked Asian countries: LAO PDR, Mongolia, Nepal, Uzbekistan and Kazakhstan. The intention is to extend the study to border crossings along the Trans Asian corridors.

For individual countries, the _<u>Moroccan Government</u>_ provides detailed data for several years on clearance through Customs. Recent DTIS studies and Trade and Transport Facilitation Audits carried out in select countries are also good sources of information.

“ _–_ _<u>Trade and Transport Facilitation An Audit Methodology</u>_ ”, published by the World Bank in April 2000, the toolkit has incorporated changes to reflect new developments such as recent antiterror security requirements, important trade facilitation initiatives. The toolkit seeks to link audit results, through careful analysis, to appropriate remedial Action.

_<mark>World Bank Trade and Transport Facilitation Audits (TTFAs)</mark>_ are being carried out to identify the obstacles associated with the cross-frontier movements of
|3,676|1,389|1,364|1,377|1,359|
|Adjusted R2|0.070|0.080|0.062|0.077|0.080|0.082|0.074|0.087|0.064|0.076|0.051|0.075|

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies.  The instrument in the IV columns is once-lagged (CF/lagged K).  Other controls in “more control” columns include: loss of sales due to electricity, loss of sales due to transportation, road density, autonomy in investment, CEO schooling, CEO also board chairman, CEO wage/worker wage, CEO has incentive plans, the property rights protection index,

53
Chinese local governments have been heavily involved in local firms, especially in SOEs and privatized firms (Cull et al. 2015; Harrison et al. 2019). As discussed earlier, pundits have argued over whether Chinese growth has been due to heavy-handed government guidance or allowing the market to work. To shed light on this key concern, here we examine how the effects of privatization depend on government activism in dealing with firms in general. The more the government remains active in dealing with firms, the less privatized firms act like true private firms, and thus the effects of privatization are likely smaller. Indeed, Harrison et al. (2019) offer evidence that Chinese privatized firms behave like a mixture of SOEs and private firms, still enjoying subsidies from the government, and performing more poorly than true private firms but nevertheless having significantly better performance than SOEs. Thus, where the government is more active, privatized firms are more likely to behave like SOEs, and we expect the effects of privatization to be more muted.

Our proxy of the extent of government activism is based on the World Bank Enterprise Survey data in 120 Chinese cities in 2005. In the survey, the firm is asked to rate “the percent of officials in various government departments that facilitate the development of the firm,” which captures the strength of interactions between the government and the firm. This variable is averaged to the city level to capture regional variations. In Panel A of Table 7, we provide the firm-FE and the IV estimates of the effect of privatization that hinges on our proxy of government activism.<sup>22</sup> Consistent with our expectation, the effects of privatization are significantly lower where local government activism is stronger. Relying on the IV estimate and TFP_IN, the average effects of privatization on productivity is 102 log points at the mean level of local government activism (i.e., 0.407), and it is 180 log points at one standard deviation (i.e., 0.
10.8|10.8|12.0|11.2|10.2|9.8|12.3|10.8|
|Latin America & Caribbean|3.3|9.6|9.4|7.4|0.6|12.0|8.8|7.0|
|Europe, Middle East & North Africa|-4.1|5.9|13.4|4.8|0.4|3.6|14.4|6.0|
|Sub-Saharan Africa|-1.3|3.3|13.7|5.1|-1.4|3.6|14.2|5.2|
|**Memo:**|||||||||
|BRICS 5 countries|10.8|9.1|17.8|12.5|7.8|9.3|18.7|11.8|
|OPEC 12 members|-4.6|5.9|13.2|4.6|-2.0|3.9|15.0|5.4|

Source: Based on UN COMTRADE database.
type of government intervention in the economy. We are interested in the link between officials' desire for personal financial gain and the creation of opportunities for rent seeking.

Development economists have long recognized that government policies and institutions matter to growth (Pack 1988, pp. 343-358, Olson 1996), and recent cross-country research relates institutional and political variables to measures of economic growth and to other macroeconomic variables of interest.<sup>2</sup> The study of corruption and rent seeking is a natural next step for the institutionally-oriented economist whatever the empirical difficulties involved. Rather than treating the imperfections of government as only the result of laziness or incompetence, one views govermnent failures as also stemming from the self-interest of politicians, bureaucrats, and private individuals and firns. The central issue for research is whether payoffs are a way around inefficient rules or a source of inefficiency on their own. Are they an explanation for some of the evident distortions obvious to an observer of government policies, or are they an efficient response to these distortions?

Although data on corruption will always be difficult to obtain since the practice is illegal and secret, recent cross-country studies shed some light on these questions. Research has been made possible by data from proprietary firms that provide information to companies trying to decide where to invest.<sup>3</sup> Statistical studies using these data series indicate that strong legal and governmental institutions and low levels of corruption have beneficial effects on investment, growth, and other economic variables. Other work provides evidence that more competitive economies have less corruption because they have fewer economic rents available for capture by corrupt agents. The studies suggest that countries that have poorly functioning government institutions tend to be relatively corrupt, and that payoffs to public officials are seldom sufficient 4
Economic Outlook (WEO) (available since 1980).<sup>47</sup> We then compute our spending variable as follows: ≡[G ) −G ]⁄ yy where iitt iitt is the 3-year moving average of NGDP and iitt tt iitt−1 iitt−1 iitt = ΔGG PPYY x NNGGDDMM /(1 + ππ PPYY x NNGGDDMM MMAANNGGDDMM ⁄ −1 is the inflation rate. iitt tt MMAANNGGDDMM ππ The primary data source to construct the commodity resource revenue variable, tt tt−1 CCMMII CCMMII , is the ICTD/UNU-WIDER Government Revenue Dataset 2018 (GRD), yy which provides data on “Total Natural Resource Revenue, General Government, iitt ΔCCRRC Percent of GDP” (CREVY), from 1980 to 2016. We also use data from the IMF World Commodity Exporters Dataset (WCED), which provides data on general government resource revenues (% of GDP) from 1980 to 2013.<sup>48</sup> In both datasets, resource revenue includes tax and non-tax receipts from natural resources that include a significant component of economic rent, primarily from oil, natural gas, and mining activities. For each country in the panel, we use the dataset (GRD or WCED) that provides the longest time series.<sup>49</sup> Like for the spending variable, we define the resource revenue variable as, yy iitt ⁄ ) ]⁄ ΔCCRRC iitt iitt tt iitt−1 iitt−1 iitt ≡[CCRRPPCCYY xNNGGDDMM (1 + ππ −CCRRPPCCYY x NNGGDDMM MMAANNGGDDMM 47 GGEY consists of general government total expenses and the net acquisition of nonfinancial assets (public investment). Conceptually, the measure should exclude public investment, but this decomposition is not available for many country-years in the WEO database.

48 GRP is a comprehensive dataset that contains data on resource revenue for 69 countries from five different sources: OECD Revenue Statistics, OECD Latin American Tax Statistics, IMF Government Finance Statistics, IMF Article IV Staff Reports, and CEPALSTAT Revenue Statistics in Latin
and monthly profits of 907.73 USD PPP.<sup>20</sup> Only 20% of the businesses report having a tax ID and 18% has applied for a loan in the last 12 months. Finally, the average adoption rate of business practices included in the handbook is 35% at baseline, compared to 18% for practices not covered in the handbook.

## **B Survey Attrition**

There were two sources of survey attrition. Some shops closed down and a few others refused to be part of the midline or endline survey. Table 2 presents regression analysis on survey attrition. Columns (1) and (2) study differential attrition based on refusals both at midline and endline, respectively. Columns (3) and (4) repeat the analysis for attrition based on the shop being closed, also at midline and endline.

Overall, attrition is very low. We were able to reach 92% of the sample at midline and 81% of the sample at endline, and the small attrition rates are not correlated with treatment status. Moreover, columns (1) and (2) show that there was no differential attrition by treatment, as all the

> 18Appendix Table A10 shows the list of the business practices measured in the surveys.

> 19Risk attitude is measured with the answer to the question: “Some people usually avoid taking any risk, others are generally fully prepared to take risks. Please imagine a yard stick from 0 to 10. This time 0 means you usually avoid taking any risk and 10 means you are generally fully prepared to take risks.” Time preferences are measured in a similar way: “Some people usually want to have things now rather than later, others are generally willing to wait a long time. Now, please imagine a yardstick from 0 to 10. 0 means you usually want things now rather than later and 10 means you are generally willing to wait.”

> 20The profit and sales figures are winsorized at 5%
of the representative household’s problem.

We estimate the model using a time-series cross-section dataset comprising 50 industrialized and developing countries over the period 1970-2010. We find an average coefficient of partial risk sharing of around 0.5. In terms of the model, this means that the average country contributes half its income to the income pool. The estimates, however, vary systematically between industrial and developing countries, with the former exhibiting, on average, higher degrees of risk sharing than the latter (centered around 0.59 and 0.43, respectively). Re-estimating the model over rolling time samples, we find evidence that the degree of risk sharing has been on the rise for the average country during most of the sample period. When we split the sample between industrial and developing countries,

> 4Crucini’s paper reports a Monte Carlo experiment to check if this procedure leads to major biases in the risk-sharing estimate.

> 5Moreover, even if we had written the estimating equation in terms of a weighted consumption average, the nonlinearity of the model would break the linear relation between right and left-hand side variables inherent to the linear regression.

3