rafmacalaba/datause-encoder-data huggingface dataset:
subnational level and across groups. The questions were grouped into several modules: dimensions of trust (which institutions or entities are being trusted), performance of the public sector, public communications and transparency, corruption and integrity, behaviors linked to trust, impact of COVID-19, and socio-demographic questions.

# **2.3 Sample Description**

Many existing surveys covering Morocco have previously measured some aspect of trust, such as the Arab Barometer, the Afro-Barometer, the Gallup World Poll, and the Moroccan Institute for Policy Analysis (MIPA) surveys. An important added value of our survey, compared to existing opinion surveys, is the size of its sample which allows for sub-national inference. The international and regional instruments used to assess trust have shortcomings linked to their coverage which is often uneven both across countries and over time. Most of the other survey data come from a small sample (i.e., 1,000 to 1,500 respondents per country), precluding intra-country analysis and sometimes failing to be representative of the whole population.

The 5,916 respondents selected created a nationally representative sample of the population, age 18 and above. They were randomly selected using the quota method (age, gender, socio-professional category), following a stratification by region.<sup>15</sup> This allowed for each region, age, gender, and income groups to contain a relatively high level of respondents compared to other surveys described above. Selected sample statistics can be found in Table 1.<sup>16</sup> In addition, the sampling strategy ensured representativity between urban and rural populations. According to the Haut Commissariat au Plan’s 2014 population census, 41.12 percent of the population was rural. In the sample, 41.5 percent classified as rural. This addresses a common criticism of other widely used surveys in the region which disproportionately represent urban populations.

# **2.4 Timing of the Survey**

The survey took place at a specific time of the COVID-19 crisis, and it is important to contextualize this period and to verify whether our data was not overly impacted by events that took place around the time of the survey. As mentioned above, the survey was implemented during the months of June and July 2021 close to the timing of Wave 6 of the Arab Barometer. As shown in Figure 1 using Arab Barometer data, trust in the government benefitted from an initial increase in third quarter of 2020 following the decrease in 2018 mentioned in the introduction. By 2021, when the study’s survey took place, trust was back to levels more in line with the historical average. The increase 15Regions covered are Oriental, Fes-Meknes, Rabat-Sale-Kenitra, Beni Mellal-Khenifra, Grand Casablanca-Settat, Marrakech-Safi, Draa-Tafilalet, and Souss-Massa.

> 16To stratify the sample and determine the weights for each group, the sampling methodology used the HCP’s latest available population census dating from 2014.

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<!-- Start of picture text --> Figure 17: Tradeoffs between distance to the frontier and competition<br>1300<br>Time dimension<br>Present Past<br>1100<br>High Competition<br>900<br>700<br>500<br>Brazil<br>300<br>India<br>China<br>100<br>Low Competition<br>-100<br>Log of Distance to the Frontier<br>Low Competition High Competition<br>Source: Figure 17 is a representation of the estimates from the empirical model that is discussed in section 8.<br>Note: lines for Brazil, India, and China are provided for exemplification purposes and do not necessarily represent the behavior<br>of all industries in these countries.<br>Value Added per Worker<br>-1.2 -1 -0.9 -0.7 -0.5 -0.3 -0.1 0.07 0.25 0.44 0.62 0.81 0.99 1.18 1.36 1.55 1.73 1.92 2.1 2.29 2.47 2.66 2.84 3.03<br><!-- End of picture text -->

# **_Empirical methodology_**

According to the Schumpeterian model, there is a positive relationship between larger firms and innovation. Scherer’s (1965) empirical research did not substantiate the Schumpeterian paradigm. Empirically, there is no _a priori_ evidence to support or reject the view that competition adversely affects innovation. The economic environment, distance of industries from the technological frontier, and other variables play an important role in affecting the impact of competition on innovation. Nickell (1996), for example, found a strong and positive relationship between competition and innovation measured by firm level TFP.

An investigation of the Schumpeterian paradigm needs to incorporate several factors. The first is a correction for the correlation between the industry characteristics such as size and innovation. We deal with this by controlling for industry fixed effects. Second, the analysis must address reverse causality: whether growth in labor productivity which is our dependent variable affects the distance to the Korean technological frontier or a change in distance affects the growth and level of labor productivity. Panel data and an instrumental (exogenous) variable are used to 34
[21] Randel, J., German, T. 2002. Trends in the financing of humanitarian assistance. in J. Macrae (ed.), 2002. The new Humanitarianism: a review of trends in global humanitarian action. HPG Report nr.11. London: ODI.

[22] Round, J., Odedokun, M., 2004. Aid Effort and its Determinants. International Review of Economics and Finance 13, 293—309.

[23] Stock, J. H. and M. Yogo (2005). Testing for weak instruments in linear IV regression. Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg. D. W. K. A. a. J. H. Stock. Cambridge, Cambridge University Press.

[24] Tarp, F., Bach, C. F., Hansen, H., Baunsgaard, S., 1999. Danish Aid Policy: Theory and Empirical Evidence, in Gupta, K. (ed.): Foreign Aid: New Perspectives. Norwell MA, 149-69.

[25] The Economist. 2005. More generous than thou. http://www.economist.com. January 8th, 2005.

[26] United Nations, 1991. General Assembly Resolution A/RES/46/182, ttp://www.un.org/documents/ga/res/46/a46r182.htm.

[27] United Nations, 2004. Reducing disaster risk: a challenge for development. UNDP report.

[28] Walker, P., Wisner, B., Leaning, J., Minear, L., 2005. Smoke and Mirrors: Deficiencies in Disaster Funding. BMJ 2005. 330: 247-50.

[29] Wooldridge, J., 2002. Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, MA.

[30] World Bank (2006). World Bank Development Indicators 2006 CD-ROM.

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of political budget cycles, which relied on highly aggregated data on total expenditures and revenues, current and capital expenditures, or public deficits (Castro and Martins 2018; Akhmedov <u>and Zhuravskaya 2004; de Haan and Klomp 2013). These new data distinguish expenditures on</u> social assistance that involve direct transfers to citizens (such as cash and in-kind transfers) from expenditures on programs focused on job retention that are usually channeled through firms (such as wage subsidies or waivers or reductions in social insurance contributions).

The results of our empirical analysis demonstrate that the electoral cycle affects the composition of the social protection response. The longer the time since the last election in a country—and thus the sooner the next election date—the larger the share of the social protection pandemic budget allocated to social assistance and income protection and the lower the share allocated to job retention schemes. In contrast, the electoral cycle has no impact on the overall size of the budget allocated to social assistance, except in countries with high political competition. In these countries, the electoral cycle appears to affect the size of the social assistance budget by crowding out the resources allocated to job protection programs. The maturity of democracy, the intensity of political competition, and the rule of law in a country all seem to affect how the electoral cycle influences the composition of the social protection stimulus budget.

We identify a potential source of bias in our estimates – the postponement of elections due to COVID-19 restrictions. We address this by re-estimating our model, excluding countries that postponed elections. We find that this potential bias does not affect our main empirical results.

This paper contributes to the literature on the effects of electoral cycles on public expenditure in several ways. To our knowledge, it is the first study to analyze the effect of political budget cycles on the size and composition of social protection expenditures in the COVID-19 stimulus. Our analysis shows that the predictions of the political budget cycles theory apply even during systemic shocks like a pandemic. Also, our study relies on cross-country, exogenous variation in the electoral cycle, a strategy that has not been used in the literature so far. The large and highly granular cross-country data sets on which the paper is based allow us to analyze compositional changes in the stimulus budget.

The paper is organized as follows. The next section describes the literature on the causes and main drivers of political budget cycles. Section 3 discusses the sources of data and presents descriptive 3
revenues to cover their expenses and depend fully on government transfers to pay for maintenance and road construction. Maintenance of roads can have predictable components that can be budgeted for, but emergency maintenance expenses can also be large and unpredictable. These increases are often caused by weather shocks or sudden deterioration of infrastructure that requires immediate attention (Heggie 1995).

For power companies, fuel inputs are the main source of volatility in operating profits and, therefore, the main source of fiscal risk. For most years, fuel cost averaged about half of revenues in the power sector and about 20 percent in the airlines sector. In these sectors, sudden increases in the price of oil can destabilize profitability and create a need for further fiscal transfers. Airlines not only have relatively high fuel expense ratios, they also have large employment expenses, which can also increase the volatility of profits.

To get an idea of the fiscal support SOEs receive in each sector and the different instruments used by governments, we focus on fiscal injections. Panel a of figure 4 shows that on average, the airport and airline SOEs in our sample received the largest annual fiscal transfers as a percentage of assets (29 percent), followed by railways (27 percent), roads (21 percent), and power (8 percent) SOEs. These transfers are so large relative to assets that they look more like frequent bailouts than small cash flow increases.

# **Figure 4 Average fiscal injections, by sector**

# **a. As share of total assets**

# **b. As share of GDP**

<!-- Start of picture text --> 30 0.25<br>0.20<br>20<br>0.15<br>0.10<br>10<br>0.05<br>0 0.00<br>Power Airlines and airports Roads Railways Power Airlines and airports Roads Railways<br>Operations subsidies Government equity injections Operations subsidies Government equity injections<br>Deferred tax liabilities Government loans Deferred tax liabilities Government loans<br>SOE loans SOE loans<br>Fiscal injections as percent of assets Fiscal injections as percent of GDP<br><!-- End of picture text --> _Source_ : Authors, based with data from the World Bank Infrastructure SOE Database. _Note_ : Data are for 2009–18. Figure was constructed by adding operational subsidies, government equity injections, deferred tax liabilities, government loans, and SOE loans and subtracting changes in assets from the previous year to determine whether there was a fiscal injection. Government loans and SOE loans capture long-term debt or loans. Deferred tax liabilities, government loans, and SOE loans show increases since the previous year.

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the wake of the debate over the Labor Contract Law, many employers also proactively began to change practices to comply with some aspects of the new law in order to reduce the risk of worker-initiated complaints or lawsuits. The survey of manufacturing firms revealed that on average firms spent 6.5 days and over 12,000 RMB on training managers about the new Law’s provisions. After China began to recover from the financial crisis in 2009, the central government also began to monitor local compliance with the new law by sending out research teams to measure local enforcement efforts (Wang 2008).

While the survey cannot distinguish between the types of enforcement that may have affected the respondents’ perception of enforcement levels, the importance of education may indicate that employees with high levels of education are more aware about their options for resolving disputes and/or more capable of pressing for enforcement of new protections in the Labor Contract Law. For example, a knowledgeable employee with a second short-term contract about to end may request that his employer now enter into an open-ended contract. The employer may comply, perhaps in order to retain a highly educated employee. In this case, the employee may feel positive about the level of enforcement or compliance, but his high level of education partly determines why enforcement was possible.

**_Reversing the trend toward greater informality_** . Although previous labor regulations already mandated that workers sign labor contracts, passage of the 2008 Labor Contract Law was accompanied by a renewed emphasis on formalizing the employment relationship with workers by signing written contracts, made credible by explicit, costly penalties for noncompliance. As noted above, the new Law also required that workers completing two fixedterm contracts are issued open-ended labor contracts to provide greater job security. In the late 1990s and early 2000s, the number of “missing” urban workers, defined as the difference in total urban employment estimated from labor force surveys and from reports by employers increased significantly, reaching a peak of 39% in 2002 and 2003 before declining to 36% by

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the six cycles is 1.6%) and the average share of participating lower-middle-income countries is about 13%. The seven<sup>6</sup> countries participating in PISA for Development include two low-income and four lower-middle income countries; if they all complete the assessment on schedule, then the share of low-income country participations would increase to 5.5% and the share of lower-middle income country participations would increase to 18.7%.

The low rate of participate for low-income and middle-income countries may also reflect the deterrent effect of the actual costs of participation. At a minimum, participation in PISA requires payment of an international participation fee of 182,000 Euros, payable at 45,500 Euros annually for four years. This amount is a very small cost for upper-middle-income and highincome countries, but may appear greater for lower-middle-income and low-income countries, particularly when added to other direct costs (such as for travel to required meetings) and the incountry costs for implementing the assessment. In the past, the participation of low- and middleincome countries in various international large-scale assessments has been facilitated by the economic support of donors such as the World Bank and the United Nations Development Program (Liberman & Clarke 2012; Lockheed 2010).

**Does participation in other types of assessment build the capacity for participating in an international large-scale assessment such as PISA?** Here, the answer appears much clearer. A much higher share of countries that have participated in another international largescale assessment (TIMSS) go on to participate in PISA. Moreover, countries that have an established national assessment system also go on to participate in PISA at higher rates than countries lacking national assessments. This higher rate of participation can be explained by the increased capacity for assessment that such participation develops, which can be both for the administrative demands of the assessment and its technical requirements.

**Third, is the growth in international large scale assessment driven by the data needs of economists?** The results indicate that – at a minimum – country members of a significant organization dominated by economists, the OECD (which sponsors PISA), are far more likely to participate in PISA than non-members. The participation rate of OECD member countries is 100% in all years, whereas the participation rate of non-OECD countries averages around 15% across the six PISA cycles. OECD membership predicts PISA participation perfectly in all years. Research suggests that economists disproportionately use the results from ILSAs in general and PISA in particular in their research on human capital and economic development (Lockheed

> 6 The countries participating in PISA for Development are: Cambodia (low income), Ecuador (upper-middle-income), Guatemala (lower-middle-income), Paraguay (lower-middle-income), Senegal (lower-middle-income), Tanzania (low income), Zambia (lower-middle-income)

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# **2. Data**

We gather information from several sources. This section presents the essential features of the data, focusing on our novel NTM dataset. Summary statistics of all variables used in the analysis are presented in Table 1. Details on all sources and variables’ construction are presented in Appendix B.

# _2.1. NTMs in Indonesia: Institutional Background_

The overall responsibility to determine rules over products sold in Indonesia lies with the Ministry of Trade (MoT). However, different government institutions are also responsible to determine NTMs for specific products. For instance, agricultural products are usually subject to NTMs applied by the Ministry of Agriculture and the Ministry of Health, including the food and drug agency (BPOM). These NTMs typically aim to protect consumer safety from risks such as plant and animal diseases through sanitary and phyto-sanitary (SPS) measures. Similarly, the Ministry of Industry regulates NTMs aiming to ensure consumer protection from risks linked to the use of manufacturing goods through technical barriers to trade (TBT).

Overall, 13 government institutions (ministries and agencies) have issued regulations on NTMs in Indonesia during our period of analysis (2008-15). As a result many products are subject to multiple NTMs. In our data, an average of 2.6 NTMs are applied to the universe of HS-10 digit products in 2008-15; and 5.4 for products with at least 1 NTM applied. These span a wide range of NTMs. Using the most refined NTM classification of UNCTAD at the 3-digit level, Indonesia applied 56 individual NTM types to the universe of its products in the 2008-15 period.<sup>5</sup> The Indonesian government has been increasing the use of NTMs over the period of analysis through relatively frequent changes in their application across HS-10 digit products. To the extent that NTMs mainly fulfill the objective of addressing market failures in trade, such changes should be less subject to the kind of regulatory capture typical of import tariffs (Grossman and Helpman, 1992). This would allow to use this

> 5UNCTAD classifies NTMs by assigning codes according to different levels of aggregation. The first level corresponds to six macro-categories: i) SPS; ii) TBT, iii) pre-shipment inspections and other formalities (INSP); iv) non-automatic licensing, quotas and other quantitative controls (QC); v) price controls (PC), and vi) other NTMs (OTH). The next categories are defined according to digits, with 2- and 3-digits.

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numerical gradient simulation procedure sketched out in Elbers et al (2002). Columns 2- 4 present estimates based on the “parametric bootstrapping” (Pfeffermann and Tiller, 2005) procedure outlined in section 3 and are computed using the POVMAP2 software that has been purpose-written by Qinghua Zhao in the Research Department of the World Bank.<sup>16</sup> The parametric bootstrapping results vary depending on whether disturbances are drawn from the empirical distribution (Column 2) or from parametric distributions (Column 3). The estimates in column 4 are based on a program written in SAS, based also on application of the procedure outlined in section 3 (with disturbances drawn from a parametric distribution), and are presented to illustrate that simulation based results do vary depending on different random number generating algorithms as well as seeds. Finally the results presented in Column 5 are based on an alternative, non-parametric, scheme outlined in Appendix 1.<sup>17</sup> Point estimates differ only slightly across different simulation approaches. In Table 2, while the true headcount rate for target population 1 is 60.5% the estimated rate for this target population varies between 60.9% and 61.6% across the different estimation approaches. The approaches are more clearly at odds in terms of the estimated standard errors. In particular, standard errors deriving from the “parametric bootstrapping” procedure described in Section 3 and summarized in Columns 2-4, tend to be somewhat smaller than those based on the numerical gradient method described in ELL(2002) – Column 1 - and the non-parametric approach of Appendix 1 (Column 5). In the case of pseudosurvey 2 the distinction is not of great significance: irrespective of methodology, the 95% confidence interval around each target population’s estimated headcount rate encompasses the true poverty rate in 19 out of 20 cases. However, with other pseudo surveys the distinction does matter. In Table 3, results are presented based on a model of consumption estimated from pseudosurvey 3. With this survey, the “classical” approach (Elbers et al, 2002) and the alternative approach outlined in the appendix yield three

> 16 POVMAP2 can be freely downloaded at <u>http://iresearch.worldbank.org.</u>

> 17  Note that these estimates do not show significant differences in poverty between target populations. This reflects both the relative homogeneity of the group of PROGRESA households, the random composition of target populations, and  the small sizes of the target populations, about 1000 households. On the other hand, discriminating between poverty of the target populations is not the subject of the current paper and all standard errors are about the same size as one would get from survey-based estimates at the aggregate level..

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