rafmacalaba/datause-encoder-data huggingface dataset:
# **_3. Data and Variable Construction_**

# _3.1 The Dataset_

The dataset used in this analysis is a census of manufacturers conducted annually by the Moroccan Ministry of Industry, Commerce and Productivity (formerly the Ministry of Industry and Telecommunications) from 1985-2001. The survey provides information for approximately 90% of firms operating in the manufacturing sector.<sup>8</sup> Components of this dataset have been used by Haddad (1993), Haddad and Harrison (1993), Haddad, de Melo and Horton (1996), Harrison (1996), Currie and Harrison (1997), Clerides, Lach and Tybout (1998) and Fafchamps, El Hamine and Zeufack (2002) to explore questions related to trade liberalization, foreign direct investment, exporting and/or productivity; and by Bun and El Makhloufi (2004), Fafchamps and El Hamine (2004), Fafchamps (2004) and Mengistae and Thompson (2006) to study subnational location effects.

This data set has several advantages. First, it is a census dataset that is carried out every year. Second, it does not have a floor or minimize size threshold for respondents to be included. Having a threshold (e.g. of 10 or 20 employees) would make it almost impossible to distinguish true exiting firms from those whose employment has slipped below the cut-off but who remain in operations. Likewise the measures of entry are cleaner and do not reflect a period (potentially of years) when firms operated, but below

> 8 Only very small or informal firms are not captured by the sample. The coverage of medium and large enterprises is virtually universal. The Ministry of Industry, Commerce and Productivity imputes values for all non-responding firms, usually based on the previous year’s information. These imputed observations are used in the construction of the agglomeration variables and for measuring overall rates of entry and exit. The coefficients used to construct TFP were estimated excluding the imputed observations, but TFP was then estimated using all firms.  All results were run with and without the imputed observations.  Results are extremely robust to their inclusion or exclusion.

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Bloomberg, 2019; The Guardian, 2019; WEF, 2019). Others were somewhat more optimistic and argued that risks were manageable or that, at least, a possible recession would not be as severe (Powell, 2017; S&P, 2019; The Economist, 2019).

The COVID-19 pandemic that broke out in 2020 raised new concerns about the high levels of nonfinancial corporate debt. As lockdowns and border closures caused a plunge in global economic activity, the question of how firms manage their debt burden has become a central topic in economic discussions. High nonfinancial corporate debt could be the Achilles heel in the global economy that exacerbates the downturn and hampers economic recovery (The Economist, 2020; Lustig and Mariscal, 2020). In fact, some analysts have argued that corporate debt relief should be a central feature of emergency aid packages (Becker et al., 2020).

The aim of this paper is twofold. First, it presents some of the main stylized facts about the boom in nonfinancial corporate debt after the GFC, while discussing its drivers. Second, it summarizes the main risks and policy challenges associated with higher nonfinancial corporate debt. Whereas the paper shows a few new statistics, it is primarily a survey of the arguments presented in the growing literature on this topic. The main messages are summarized as follows.

First, the rise of nonfinancial corporate debt was concentrated in emerging economies. Between 2008 and 2018, this type of debt grew almost twice as fast as GDP in emerging economies, rising from 56 to 96 percent of GDP. In contrast, nonfinancial corporate debt grew at the same pace as GDP since 2008 in developed economies. An expansion in bond issuances was a main driver of the growth in nonfinancial corporate debt in emerging economies. When excluding bond securities, the ratio of nonfinancial corporate debt to GDP remained fairly stagnant across emerging economies (aside from China). According to the literature, international financial conditions since the GFC, namely accommodative monetary policy in developed economies, played a major role in the boom in nonfinancial corporate debt.

2
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**Table 6:** The effect of ’Job Posting’ related provisions on the bilateral migration stocks

|Dep var:|Bilateral|Stock of M|igrants|
|---|---|---|---|
||(1)|(2)|(3)|
|Movement natural persons|0.077***<br>(0.027)|||
|Specifc categories||0.092**<br>(0.045)||
|Movement of investors|||0.074**<br>(0.030)|
|GDP diference (ln)|-0.002|-0.001|-0.002|
||(0.008)|(0.008)|(0.008)|
|RTA|0.029|0.036*|0.036*|
||(0.021)|(0.020)|(0.021)|
|Schenghen|0.185***|0.186***|0.188***|
||(0.039)|(0.039)|(0.039)|
|Estimator|PPML|PPML|PPML|
|Country-Year Fixed Efects|Yes|Yes|Yes|
|Country-Pair Fixed Efects|Yes|Yes|Yes|
|Sample period|1990-2020|1990-2020|1990-2020|
|Observations|63,803|63,803|63,803|
|Pseudo R-squared|0.99|0.99|0.99|

Note: Dependent variable is the stock of immigrants in country _j_ originating from _i_ . Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
_Et_ exports _ref_<sup>_E_</sup> _t_ reference exports adjusted for changing export share _G t_ government consumption

- _It_ investment

- _K t_ capital stock

- _Lt_ labor demand

- _L_ 0 base-year labor supply

- _LSt_ labor supply at time _t Mt_ final imports _ref_

- <sup>_M_</sup> _t_ reference final imports adjusted for changing import share

- _MK t_ capital imports

- _MN t_ intermediate imports<sup>_Q_</sup> _t_ gross output

- _V t_ value added

- _Rk t_ marginal net revenue product of capital _X t_ aggregate supply

# **_Values_**

- _Bt_ foreign borrowings or capital inflows

_DEBTt_ outstanding foreign debt at time _t_ _EFLOWSt_ net exogenous flows from abroad (excluding grants and new borrowings) _FGRSt_ foreign grants (interest free)

- _SG t_ government savings

_GTRSt_ government transfers to households _Jt_ total investment expenditures, including adjustment cost θ (<sup>_x_</sup> _t_ ) adjustment cost function 35
Policy Research Working Paper 5194

# **Abstract**

The fact that developing countries do not have carbon emission caps under the Kyoto Protocol has led to the current interest in high-income countries in border taxes on the “virtual” carbon content of imports. The authors use Global Trade Analysis Project data and input-output analysis to estimate the flows of virtual carbon implicit in domestic production technologies and the pattern of international trade. The results present striking evidence on the wide variation in the carbon-intensiveness of trade across countries, with major developing countries being large net exporters of virtual carbon. The analysis suggests that tax rates of $50 per ton of virtual carbon could lead to very substantial effective tariff rates on the exports of the most carbon-intensive developing nations.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2010: Development in a Changing Climate_ —is a product of the Development Economics Vice Presidency. The views expressed in this paper are those of the authors and do not reflect the views of the World Bank or its affiliated organizations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at g.atkinson@lse.ac.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
simplification; steps that can be taken both in tax laws and tax administration which would move a tax administration towards simplification, and hence on a path of lower tax corruption.

Section 2 gives information on the measurement of the tax corruption variable, as well as the indicators of tax complexity. Section 3 focuses on regression analyses and experiments. Section 4 presents some policy implications of the empirical results and includes suggestions on how to simplify taxes. Section 5 concludes.

# **2. Tax Simplification and Tax Corruption: Data Issues**

# **_2.1 Measuring Tax Simplicity_**

As the intuitive analysis tells us, a simpler tax system creates fewer chances for rent seeking and lowers the opportunity for corruption in the tax system. The question arises, how does one define “tax simplicity”, particularly in a way that would allow comparisons on an international level and across a time period? The only viable option available is to use the Doing Business reports produced by the World Bank Group. The Doing Business reports measure the ease of doing business as reflected in 10 indicators, including one on complying with the tax system: Paying Taxes. 2 sub-indicators of the Paying Taxes indicator are: Time to Comply and Number of Payments. The premise is that the lower the time taken to comply with the tax system and the fewer the number of payments, the easier it is for businesses to comply with their tax paying obligations. Based on the definitions of the sub-indicators and the methodology of collecting data around them, it appears that for the purposes of this paper, the sub-indicators, Time to Comply (TAXTIME) and Number of Payments (TAXPAY), are the best suited measures of “tax simplicity”. It may be noted that these two variables are also used to measure the complexity of tax systems by Lawless (2013). That paper investigates the impacts of changing tax complexity on foreign direct investment flows. The definitions and methodologies as set out in the Doing Business reports are provided in Appendix 1 (Doing Business Paying Taxes, 2013).

The TAXTIME indicator measures the time it takes to prepare and file tax returns for the three major taxes that impact an average medium-sized business, and the time taken to make the payments of these taxes. The preparation time includes the time taken to collect all 5
intentions. . . Translating public concern for global warming into effective action requires real knowledge. General environmental concern or concern for the negative effects of air pollution appear not to motivate people to support programs designed to control global warming.” Stated intentions are of course very different from engaging in actual behaviors.

The work of O’Connor et al. 2002 uses a heuristic model approach to compare cognitive, economic, and partisan explanations for who supports reductions in climate emissions. The authors conclude that so called “cognitive” explanations of support for reducing greenhouse gas emissions are more powerful than economic or partisan heuristic ones. “People want to reduce emissions if they understand the causes of climate change, if they perceive substantial risks from climate change if average surface temperatures increase, and if they think climate change mitigation policies will not cost them their jobs.” The authors find that people who can both accurately identify causes of climate change and who expect negative future climate scenarios are the most likely to support both government and voluntary actions. “Economic circumstances and anxieties are not important predictors, but the belief that environmental protection efforts do not threaten jobs for people like the respondent, limit personal freedoms, and hurt the economy is a strong predictor” (ibid). From another angle, Stanford social psychologist Jon Krosnic has studied various aspects of the relationship between efficacy, concern and willingness to take action. In recent work on public perceptions of climate change, Krosnic et al. 2006 observe that people stopped paying attention to global climate change when they realize that there is no easy solution for it. Instead they note that many people judge as serious only those problems for which they think action can be taken.

# **EXPLANATIONS FOR LACK OF RESPONSE**

A wide range of scholars from multiple disciplinary backgrounds have addressed the question of why the public has largely failed to respond to climate change. The dominant disciplines to consider are psychology, social-psychology and sociology. Some scholars working on this topic approach non-response from highly theoretical orientations. Others conduct public opinion surveys take it at face value, but their work still offers insight into the dynamics of non-response.

14
the timing of the event, we assume that if a crop field gets flooded and the crop field is fully damaged (see Table D2 for fragility curve), the entire production within a grid cell is lost (either single, double, or tripped cropped), which is considered an upper bound.

By overlaying the hazard maps with the exposed assets and land-use and combining this with the vulnerability curves and reconstruction costs, we model the damages to the assets and agricultural fields. To estimate risk, we adopt the trapezoidal rule, which is common practice to estimate the risk given a discrete number of return periods.

# **3.3 Service accessibility analysis**

Apart from direct damages to assets, service disruptions can occur if the assets that provide essential services to dependent households are damaged and are out of service for a given amount of time. In this study, we link these service disruptions to the MDP constructed. As such, we consider households’ access to schools (for rural households having a child below 12),<sup>1</sup> health centers ( _i.e._ , family welfare centers), hospitals, energy (for those urban household having electricity<sup>2</sup> ), latrines (for those households using latrines) and water (for those households using tube wells). Moreover, we consider housing as an infrastructure service, with direct damages to housing considered a ‘loss of service’. We briefly describe the methodology below and a summary of the methodology in Table 2. Supporting information is included in Appendix E.

**Table 2** . Overview of the methodology to connect geolocated households to infrastructure services.

|Infrastructure type|Household connected|Method accessibility|
|---|---|---|
|Schools|Rural households with child<br>below 12|Connected via road network to<br>closest school.|
|Health centers|All households|Connected via road network to<br>closest school.|
|Hospitals|All households|Connected via road network to<br>closest school.|
|Energy (substations)|All urban households having an<br>energy connection|Connect to substation in closest<br>proximity.|
|Sanitation|All households using pit latrines|Assumed<br>co-located<br>with<br>household location|
|Water|All households using tube wells|Assumed<br>co-located<br>with<br>household location|

> 1 The database on educational facilities from LGED only includes educational facilities in rural areas. As a result, we limited ourselves to the accessibility of rural households with respect to schools.

> 2 The synthetic household survey does not distinguish the type of electricity access (e.g. grid or off-grid solution). We hereby assume that only households in urban settlements with electricity are connected to the main electricity grid, while rural settlement have alternative energy provision, such as solar systems through the Bangladesh Solar Home Systems (SHS) Program, the largest national off-grid electrification program in the world.

16
**Figure 1: The changes in the farm size-productivity relationship 1982, 1999, and 2008**

<!-- Start of picture text --> 1999<br>2008<br>1982<br>-4 -2 0 2 4 6<br>Log of land (acre)<br>10<br>9<br>8<br>Log of output per acre<br>7<br>6<br><!-- End of picture text --> Source: Based on results from locally weighted polynomial regressions of total money yield against farm size for 1982, 1999, and 2008. Note: Confidence intervals are at 95 percent level.

23
The dependent variable F refers to the fraction of households being tenants in prefectureSShFFDDFF PPoo PPF _i_ in yearPP hPPRRPPFFhPPMMRRPP _t_ between 1918 and 1921. ii,tt F F F is a dummy variable indicating the occurrence of famine in prefecture _i_ during the land survey ii,tt period. We allow both contemporaneous and lagged measures of famines since it might take time for the effects of famines to show up on land ownership. We control for prefecture and year fixed effects and cluster standard errors at the province level.

The first three columns of Table 10 show that the occurrence of a famine is associated with a higher fraction of tenant households by 1.7 percentage points in the following year, indicating that many peasants lost their land completely and had to work on rented land.

The significant increases in the number of landless peasants after famines likely played into the hands of the CCP. To see if this is true, we now assess the association between the share of landless peasants and the subsequent communist revolution: C (13) Here C is the logarithm of the number of fallen communist soldiers in the communist PPii = ββLLF RRMMFFP ii + γγXXii + TTkk + εεii revolution, PPii _Landless_ takes the form of the fraction of tenant households in 1918. Column (4) of Table 10 shows that the share of landless peasants in a prefecture is positively related to the number of local people who died for communism in subsequent decades. Consistent with the seminal study by Moore (1966), the CCP indeed gained support from landless peasants. We conclude that rural land inequality, exacerbated by the warlord-era famines, was one of the many factors that helped bring down the Republic of China, as it did to the Republic of Rome.

# **7. Conclusion**

A century ago, after millennia of imperial rule, the founding President of a new Republic died. This unexpected death was followed by a dozen years of warlord fragmentation and an increasing incidence of famines. This period also witnessed the spread of Communist ideology into China from Russia and the founding of the Chinese Communist Party. Are there connections between warlord-period famines and the Communist triumph in China? Using prefecture-level data, we find a sharp increase in the number of famines when China fell into 30
# **1. Introduction**

Social service delivery is weak across the developing world. While there is substantial heterogeneity across regions and countries, the picture of failing services is a familiar one. Problems such as systematically high levels of absenteeism among teachers, doctors and nurses (see Figures 1 and 2); persistent rates of drug stock outs –particularly in rural health clinics; rates of leakage in public funds reaching the frontline health or school facilities of 50% or more; and the use of safety net programs as a source of political patronage are some extremes manifestations of those failures.

**Figure 1: Provider Absence Rate in Health (%)**

<!-- Start of picture text --> Papua New Guinea 19<br>Peru 24<br>Honduras 27<br>Uganda 37<br>Chad 17<br>Cameroon 5.6<br>Indonesia 40<br>India (UP) 40<br>Bangladesh 35<br>0 10 20 30 40 50<br><!-- End of picture text --> SOURCE: ROGERS AND KOZIOL (2011) 2
<!-- Start of picture text -->
*@-+ Credit Bureau ~#- Phone Indicators<br>0.70,<br>0.65 +<br>g j<br>= 0.60<br>0.55 ><br>sas<br>0.50 =<br>South American Telecom<br>>=3 2 1* 0<br>Entities Reporting to Credit Bureau<br><!-- End of picture text -->

<!-- Start of picture text -->
- creeli Guneau = hans indicatarsi<br>0.70<br>A<br>0.65 a<br>oO2 A° ‘<br>= 0.60<br>0.55<br>:<br>0.50 e<br>South American Telecom<br>>=3 2 1* 0<br>Entities Reporting to Credit Bureau<br><!-- End of picture text -->
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# E. Transport Costs: The Potential Policy Dimension

Two key questions relating to the present analysis are: (i) what factors account for the adverse African transport costs; and (2) what types of corrective policy measures might be employed to deal with them. While the question needs further in depth investigation evidence suggests (see Box 6) that the anti-competitive cargo reservation policies adopted by most African governments have had an important adverse influence on freight costs.<sup>37</sup> Recognition of the true effects of these anticompetitive measures carries with it the policy prescription for corrective action -- deregulation. World Bank studies for other regions show that deregulation, and the promotion of measures to stimulate competition for shipping services may reduce freight rates in the liner trades by as much as 50 percent (see, for example, Bennathan et. al. 1989).

This report showed that international transport costs constitute a major barrier to African exports and more information is needed on these charges level and incidence -- particularly in the European Union and Japan. Customs officials in these markets should assist African countries and concerned international organizations by making existing information available and to improve procedures for the future collection, processing and tabulation of information on international transport costs.

A final point to note is that numerous investigations show that governments and traders generally have far more policy options for reducing transport costs than is generally recognized (UNCTAD 1975, Yeats 1985). These options cover such measures as cargo bulking to achieve transport economies of scale in transport, rationalization of shipping services and improved scheduling for liners, adoption of procedures to speed vessel turnaround, utilization of potentially lower cost tramp services where feasible, development or improvement of coastal feeder services, unitization and the adoption of new transport technologies, promotion of shippers associations, or port and 'An OECD (1992, p. 43) provides a description of these anti-competitive practices and the current situation regarding shipping in Africa in West and Central Africa. 'In 1992, West and Central African states showed no indication of liberalizing their protectionist shipping policies based largely on the unilateral interpretation of certain provisions of the UN Liner Code Convention. On the contrary there were various moves towards enacting existing, but not yet implemented restrictive policies. These attempts met with opposition by OECD member countries and their shipping lines which considered this as both protectionist and discriminatory. However, the operation of some 50 shipping lines offering regular services to West Africa from most ports of the world was not only hampered by protectionism. Civil unrest, economic depression, a sharp increase in criminal activities towards vessels together with poor port management and severe and often discriminatory customs regulations were factors shipping lines had to struggle with.
|P<br>Water<br>a|South<br>;<br>Africa|Upper<br>th<br>Middle<br>Income|SSA|Middle<br>East<br>and<br>Noth<br>.<br>Africa|South<br>;<br>Asia|East<br>..<br>Asia<br>.<br>Pacif|Latin<br>‘<br>America<br>&<br>ic.<br>Caribbean|Europe<br>&<br>;<br>Central <br>.<br>Asia|Low<br> Income|Middle<br> Income|High<br>a<br>Income <br>\<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Access<br>to<br>Improved<br>Water<br>Source:<br>setae<br>inact<br>ile aa<br>(% ofpopulation)|87|8||8s|2|7|90|87|65|89|99|80|
|Access<br>to<br>Improved<br>Sanitati<br>eeess<br>fo Proveg ues<br>(% ofpopulation)|67|8H|T|TT|a||TT|3%|41|2|100|64|
|‘S<br>rcial Perception<br>ofWater<br>Servi<br>es<br>a cnn<br>(1=worst 7=best)|57|50|42|«56|064||OT|46-|400|4|953|s|
|Households Using PipedorWellWateras MainSource of<br>DrinkingWater<br>(%ofhouseholds)|5<br>a|at|7<br>ia|ae|ct|3<br>:|75|a|7<br>i|iP|7|7<br>is|

Note: Data used isfrom the latest observation available between 1997 and 2602, Source: Esiache, Antonio and Ana Goicoechea (2005), “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, lufrastructure Network, Office ofthe Vice President, June 2005.