rafmacalaba/datause-encoder-data huggingface dataset:
The microsimulation model generates counterfactual individual labor and non-labor incomes for each MAMS scenario. This new individual income distribution is used to compute a counterfactual household income distribution. Then, we calculate several standard inequality and poverty indicators such as the Gini coefficient and the poverty headcount ratio.

# **4. MDG simulations and analysis of results**

This section presents the simulations and analyzes the results for both MAMS and the microsimulation model. The first simulation (base) addresses the question of whether Yemen will achieve the MDGs under current policies and trends.

# _Base scenario_

For the base scenario, which serves as a benchmark for comparisons, we impose the observed growth rates in real GDP at factor cost for the period 2005-2010, and an average growth of 5.2 percent starting from 2011, based on projections from the Fourth Five-Year Socio-Economic Development Plan for Poverty Reduction 2011-2015.<sup>12</sup> In addition, a decrease in the exploitation of the natural resource factor in the oil and gas extraction sector is also imposed to reflect the expected evolution of the oil and gas sector in Yemen.<sup>13</sup> Government consumption of (or demand for) education and non-education services is kept fixed as a share of GDP at the base year value. Transfers from government to households are also kept fixed as a share of GDP. Tax rates are fixed over time, while the amount spent on commodity subsidies (basically, refined oil products) decreases gradually between 2011 and 2015, according to official projections. The ratio between domestic government debt stock and GDP increases from 10 percent in 2004 to about 17 percent during 2011-2015; domestic borrowing is adjusted accordingly. The ratio of foreign debt to GDP increases from 30 percent in the base year to 33.6 percent in 2015,

> 12 The exogenous part of TFP growth is adjusted to generate such a growth path. GDP growth is endogenous for all non-base scenarios.

> 13 Specifically, our assumption is based on past and expected evolution of the oil and gas extraction sector real GDP; data was taken from official sources.

21
# **7. References**

Aedo, Cristian, and Ian Walker. 2012. _Skills for the 21_<sup>_st_</sup> _Century in Latin America and the Caribbean._ The World Bank: Washington, DC.

Ammermueller, Andreas, Anja Kuckulenz, and Thomas Zwick. 2009. “Aggregate Unemployment Decreases Individual Returns to Education”. _Economics of Education Review_ 28:217-226.

Angrist, Joshua. 1991. “Grouped-data Estimation and Testing in Simple Labor Supply Models”. _Journal of Econometrics_ 47:243-266.

Baltagi, Badi, and Uwe Blien. 1998. “The German Wage Curve: Evidence from the IAB Employment Sample”. _Economics Letters_ 61:135-142.

Brambilla, Irene, Rafael Dix-Carneiro, Daniel Lederman, and Guido Porto. 2011. “Skills, Exports and the Wages of Seven Million Latin American Workers.” _World Bank Economic Review_ 26(1):34-60.

Brambilla, Irene, Daniel Lederman, and Guido Porto. 2012. “Exports, Exports Destinations and Skills”. _American Economic Review_ 102(7):3406-3438.

Card, David. 1994. “Earnings, Schooling and Ability Revisited”. _NBER Working Paper_ No. 4832.

Deaton, Angus. 1985. “Panel Data from Times Series of Cross-Sections”. _Journal of Econometrics_ 30:109126.

Deaton, Angus. 1997. _The Analysis of Household Surveys: A Microeconometric Approach_ . World Bank and John Hopkins University Press.

Deaton, Angus, and Christina Paxson. 1994. “Saving, Growth, and Aging in Taiwan.” In David A. Wise, ed., _Studies in the Economics of Aging_ . University of Chicago Press for the National Bureau of Economic Research: Cambridge, MA.

Devereux, Paul. 2007a. “Improved Error-in-Variables Estimators for Grouped Data”. _Journal of Business & Economic Statistics_ 25(3):278-287.

Devereux, Paul. 2007b. “Small-Sample Bias in Synthetic Cohort Models of Labor Supply”. _Journal of Applied Econometrics_ 22:839-848.

Devereux, Paul. 2004. “Cyclical Quality Adjusment in the Labor Marker”. _Southern Economic Journal_ 70(3):600-615.

Dickerson, Andy, Francis Green, and Jorge Saba. 2001. “Trade Liberalization and Returns to Education: A Pseudo-Panel Approach”. _Studies in Economics_ 0114, Department of Economics, University of Kent.

Di Giovanni, Julian and Andrei Levchaenko. 2009. “Trade, Openness and Volatility”. _The Review of Economics and Statistics_ , 91(3): 558-585.

17
27 Then NFI+±CX1N is a measure cf net factrr payments from abroad accruing to the private sectc*r (mainly ',vorkers remittancesi. Finally eq. 13 deFines private saving.

The behaviour of the gc vernment sector is described very simply by a set of accounting and technical identities. As far as taxation Is concerned, we distinguish between trade (eq. 14) and income (eq. 16) taxes. Import duties are estimated endogenously in the model by applying the relevant duty rate to imports of consumption, investment, intermediate and other commodities. We also allow for the temporary levy on petroleum (eq. 15), which was introduced in 1986 and should be gradually phased out over the next few years. Information on the revenuF likely to be generated by this levy was provided by IMF source-. Finally the income tax rate is determined r esidually from total revenue data. Eq. 17 defines total tax revenue. Government expenditures are also fairly disaggregated. We distinguish between government investment (eq. 19), government current spending on goods and services (eq. 20), current transfers (eq. 18) and interest payments on both domestic (B in eq. 21) and foreign (XD in eq. 22) debt. We can therefore acccdnt in our simulatlons for debt dynamics. The accumulation of foreign debt is determined by the current account deficit and by real exchange rate variations which induce a capital gain or loss in the value of outstanding external debt (eq. 25). The change In domestic debt (A B ) is equal to that part of the budget deficit which cannot be financed aroad (eq. 26). Simply manipulations show that A B can be also expressed as the difference between private saving and pgivate investment. The last item in the consolidated government Central Bank budget identity is revenue from seignorage and the inflation tax (eq. 24).

Government revenue from seignorage and the inflation tax is determined by the private sector's choice of assets. Following the analysis in section 2, the demand for currency (eq. 27), for demand deposits (eq. 28) and for time deposits (eq. 29) is simply modelled as a tf,nction of income, inflation **(7r)** and the interest rate on time deposits (i **).** In eq. 30 monetary financing (MF) is defined as the change of narrowly defined monetary base, i.e. of currency and of demRpd deposits the latter multiplied by the required reserve ratio (rr **).** Finally conditions in the money market will help determine, through the banks balance sheets identity, the equilibrium in the credit market. The
any changes in landed prices will be reflected in the incomes of local fishers as well a boat owners. Indeed, there is evidence of the retrenchment of investments in fishing vessels. Thus, since the restrictions on exports to the EU was lifted, the number of registered craft declined from 13,800 in 2000 to 12,209 in 2002 (Fisheries Department, 2003).

The impact of the EU’s measures on fish traders is likely to have been limited. Whilst traded volumes of Nile perch may have initially declined, it has been shown that the income of traders is insensitive to the landed price of Nile perch (Visser, 2002; SMEC, 2002).

**<u>Table 10. Loss of sales during periods of restrictions on exports to EU:</u>**

|**Factory**||**Loss (US$)**<br>**(% loss in parenthese**<br>|**s)**<br>|
|---|---|---|---|
||**1997**|**1998**|**1999-2000**|
|A|0<br>(0.0%)|0<br>(0.0%)|0<br>(0.0%)|
|B|900,000<br>(30%)|640,000<br>(18%)|510,000<br>(12%)|
|C|260,000<br>(30%)|260,000<br>(10%)|390,000<br>(40%)|
|D|640,000<br>(50%)|960,000<br>(40%)|140,000<br>(70%)|
|E|-|-|-|
|F|0<br>(0.0%)|510,000<br>(40%)|380,000<br>(20%)|
|G|640,000<br>(100%)|770,000<br>(100%)|380,000<br>(30%)|
|H|-|-|-|
|N|260,000<br>(50%)|192,000<br>(30%)|260,000<br>(50%)|
|M|510,000<br>(100%)|n/a|n/a|
|O|-|-|n/a|
|P|510,000<br>(80%)|640,000<br>(80%)|890,000<br>(80%)|
|Q|-<br>-|-|n/a|
|R|510,000<br>(80%)|770,000<br>(90%)|900,000<br>(100%)|

_Source: Survey of fish processors, March 2003._ 52
# Urbanization and Property Rights<sup>_∗_</sup>

Yongyang Cai<sup>_†_</sup> , Harris Selod<sup>_‡_</sup> , and Jevgenijs Steinbuks<sup>_§_</sup>

**JEL classification** : O43, P14, R14

**Keywords** : agglomeration, economic development, informality, land property rights

> _∗_ we are grateful to Gilles Duranton, Remi Jedwab, Kenneth Judd, David Palmer, Gulnaz Sharafutdinova, Anthony Yezer, Yves Zenou and participants to the World Bank/George Washington University 2nd Urbanization and Poverty Reduction Research Conference for useful comments. The views expressed in this paper are those of the authors and do not necessarily reflect those of the world bank, its board of directors or the countries they represent.

> _†_ Yongyang Cai: Hoover Institution, Stanford University and Becker-Friedman Institute, University of Chicago. Email: yycai@stanford.edu.

> _‡_ Harris Selod: Energy and Environment Team, Development Research Group, The World Bank. Email: hselod@worldbank.org.

> _§_ Jevgenijs Steinbuks: Energy and Environment Team, Development Research Group, The World Bank. Email: jsteinbuks@worldbank.org.
using averages might correct the attenuation bias due to substantial noise in firm-level formality data (table 12).

# **VII. Conclusions**

We use firm-level, cross-county data from Investment Climate surveys in 49 countries to investigate the relationship between tax compliance and access to credit. Access to credit has long been recognized as an important engine of growth, while informality has increasingly attracted attention from academics and policymakers alike, especially because of its potential impact on firm performance.

In this work, we focus on a well-defined concept of informality: tax compliance. Our sample of firms thus includes registered businesses which decide the extent of their tax compliance. Although tax compliance captures only a part of a much wider concept of informality, we provide aggregate evidence that this informality measure correlates in the expected direction with per-capita income and other accepted measures of informality across countries.

We consistently find that more tax compliance is robustly and significantly associated with more access to credit, in OLS as well as in FE estimates. More importantly, we find that the link between credit and formality is stronger in high-formality countries. This suggests that firms’ balance sheets are relatively more informative for financial institutions in environments where signal extraction is a less noisy process. As widespread informality weakens, the perceived informational content of balance sheets, effectively a firm’s choice not to comply with taxes provides its competitors a further incentive to choose informality. From a policy perspective, this underscores the fact that policies directed at improving the functioning of capital markets are unlikely to be fully successful unless they are complemented by policies – such as increased enforcement and simplification of tax codes – aimed at decreasing the level of informality and improving transparency.

Although our results are robust to a large battery of checks, including controlling for an extensive list of correlates, using different measures of access to credit, and two-stage estimation, we cannot completely rule out that unobserved firm-specific effects might bias the estimated relationship between formality and credit access. The progressive availability of new rounds of surveys with a panel component will make it possible to address this issue in a fully satisfactory manner as well as it will allow estimating reduced forms to investigate the overall impact of informality on subsequent firm growth.

15
21 declined from around 50 per year to 20 in 1986. Some observers have attributed this decline to the depletion of research opportunities,<sup>but the bulk of the</sup> evidence suggests that this was primarily associated with<sup>the 1962 regulations.</sup> This is borne out not only by time series analysis of the<sup>U.S. but also bv cross-</sup> section analysis.

The consumer loss from lower drug innovations as well as the fact that even approved drugs are introduced with an important<sup>lrg in relation to</sup> other countries can be substantial. The counterpart benefits<sup>of the new drug</sup> regulations have apparently been small. For example, Peltzman (1975) has estimated that the cost of avoiding the introduction of<sup>ineffective drugs could</sup> be four times higher than the benefits that would accrue<sup>if more and faster</sup> introduction of drugs were allowed.

The question has also been looked at in an international perspective by comparing the U.S. with Europe, which has less stringent<sup>regulations. Perhaps</sup> because of this, between 1964 and 1975, Europe discontinued<sup>the marketing of five</sup> drugs. The benefits for the U.S. of not having introduced<sup>these drugs--because</sup> Europe learned before of the risks--has not been estimated,<sup>but there is</sup> agreement that these benefits probably have been small." An indicative and serious case, in terms of health consequences in<sup>the U.S., was Zomax, a</sup> prescription painkiller. After more than two years this product<sup>was withdrawn.</sup> from the U.S. market in early 1983 when reports showed<sup>that severe allergy</sup> reactions could occur. Later in 1985, a study by the<sup>FDA concluded that Zomax</sup>

> There has also been a case of a new drug--Oraflex--that was introduced into the U.S. market and later discontinued, when<sup>in</sup> Europe--where the drug had beer- introduced before--it was noticed that in some cases it had severe side effects.
**Table D.9:** Water and Sanitation

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Water and sanitation index**(SUR)||0.110<br>(0.044)<sup>_∗∗_</sup><br>[0.037]<sup>_∗∗_</sup>|0.066<br>(0.051)<br>[0.210]|0.154<br>(0.051)<sup>_∗∗∗_</sup><br>[0.025]<sup>_∗∗_</sup>|5053|
|(1) Water and sanitation HH index|0.000<br>(1.000)|0.068<br>(0.058)<br>[0.089]<sup>_∗_</sup>|_−_0.022<br>(0.068)<br>[0.362]|0.159<br>(0.063)<sup>_∗∗_</sup><br>[0.040]<sup>_∗∗_</sup>|5053|
|(2) Water and sanitation village index|_−_0.016<br>(1.003)|0.175<br>(0.087)<sup>_∗∗_</sup><br>[0.073]<sup>_∗_</sup>|0.132<br>(0.097)<br>[0.298]|0.218<br>(0.104)<sup>_∗∗_</sup><br>[0.040]<sup>_∗∗_</sup>|5053|
|(3) Satisfaction with village sanitation index|0.000<br>(1.000)|0.087<br>(0.043)<sup>_∗∗_</sup><br>[0.073]<sup>_∗_</sup>|0.087<br>(0.049)<sup>_∗_</sup><br>[0.298]|0.086<br>(0.050)<sup>_∗_</sup><br>[0.059]<sup>_∗_</sup>|5051|

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. The household index is comprised of water sources for drinking, for other uses, the existence and type of toilet facilities and the actions households take to make water safe to drink. The village index contains the existence and types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.10:** Economic Outcomes

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Economic outcomes index**(SUR)||0.053|0.036|0.070|5053|
|||(0.032)|(0.037)|(0.040)<sup>_∗_</sup>||
|||[0.138]|[0.262]|[0.119]||
|(1) Physical asset index|0.000|0.020|_−_0.023|0.063|5052|
||(1.000)|(0.048)|(0.053)|(0.059)||
|||[1.000]|[1.000]|[0.404]||
|(2) Agricultural asset index|0.000|0.120|0.174|0.065|5051|
||(1.000)|(0.044)<sup>_∗∗∗_</sup>|(0.057)<sup>_∗∗∗_</sup>|(0.050)||
|||[0.026]<sup>_∗∗_</sup>|[0.010]<sup>_∗∗∗_</sup>|[0.404]||
|(3) Dwelling materials index|0.000|0.053|0.020|0.087|5052|
||(1.000)|(0.053)|(0.060)|(0.058)||
|||[0.875]|[1.000]|[0.404]||
|(4) Total consumption expenditure|0.000|0.019|_−_0.027|0.066|5053|
||(1.000)|(0.045)|(0.050)|(0.055)||
|||[1.000]|[1.000]|[0.404]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A21
induce firms to choose labor-intensive technologies. However, because labor-employment regulations raise labor costs, formal firms in developing countries tend to be abnormally capital intensive. 5 Bureaucratic requirements (red tape and paper work) are also a significant cost of remaining formal. Alonzo (1991, pp. 48-49) reports that in the Philippines, "no matter how small the business, an owner needs an accountant and a lawyer to comply with all of the requirements." Chickering and Salahdine (1991, p. 191) write that in Egypt, "much of the country's entrepreneurial talent is consumed in circumventing the country's nightmare bureaucratic regulatory system." De Soto (1989) surveyed 37 formal firms operating in areas in which informal firms abound. He found that 40 percent of the administrative personnel working time is spent fulfilling the bureaucratic paper work imposed by the state.

# **The Costs of Informality**

Informal enterprises face two kinds of costs: First, penalties when the informal activity is detected, and second, the inability to take full advantage of governmentprovided goods. Penalties for informal activities are usually stiff; very often, detected firms have to surrender a considerable part of their output or physical capital stock. De Soto (1989) finds that informal entrepreneurs pay between 10 to 15 percent of their gross income in bribes to corrupt government officials, whereas formal entrepreneurs pay an average of only 1 percent of gross income in bribes (without counting bribes used to become formal). In order to avoid being caught, firms scale down the size of their informal operations. In the case of purely informal firms, the efforts to avoid detection

-7-
Belize, Dominica, Haiti, Saint Lucia, and Suriname. A data collection instrument adapted for the purpose of the exercise is readily available as a public resource.

The data provide insight not only into disaster risk preparedness, but also into the correlates of ability to cope with natural disasters, and the impact of the COVID-19 pandemic on household ability to cope with disasters. The analysis makes clear that the majority of households in the five countries is not well-prepared to cope with disasters. Moreover, there is a clear income gradient in the ability to cope with a disaster risk. One of the most important explanatory factors is that wealthier countries and households have better access to emergency supplies. Households with more highly educated heads, which are more likely to discuss measures to cope with disaster risks, are also better able to cope with disaster risks. Households with children and households residing in rural areas, in contrast, are less able to handle disasters. The COVID-19 pandemic had a negative impact on disaster risk preparedness, primarily due to households’ deteriorating financial circumstances. These findings have direct implications for policy, as discussed in the closing section.

This paper contributes to a broader literature on adaptation to climate change and household emergency preparedness (Strömberg, 2007). A literature review by Levac et al. (2012), discusses that “personal and contextual factors”, such as the sociodemographic characteristics we examine in this paper, determine preparedness. They further discuss that knowledge and resources, which we examine, are required for effective ability to cope with emergencies. Indeed, various country case studies find that socioeconomic status matters for preparedness. For instance, in Chile working age adults are best prepared to cope with disasters (Bronfman et al., 2019). Likewise, in the U.S. income and education matter for emergency preparedness (Nukpezah and Soujaa, 2018). Relevant for this paper is a study by Ferdinand et al. (2012) assessing ability to cope with disasters at the community level in the Caribbean Windward Islands. The study finds that while strong social ties help them cope with disasters, poorer communities find it harder to engage with third party institutions that can support coping.

This paper starts by providing a discussion of the data and methods in Section 2. Section 3 describes the socioeconomic context of the countries included in the study. Section 4 presents the findings and Section 5 discusses the policy implications of the analysis.

2
_Table A2: Robustness Alternative Selection Methods, Imports from Japan_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A: Auto**||Au|to|||Intermed|iate auto|||Final|auto||
|(Selection|||||||||||||
|intermediate<br>imports)|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|
|(Dummy share|||||||||||||
|2004-2010 > 15)|-0.529***|||-0.041|-0.369*|||0.030|-0.827**|||-0.211|
||(0.174)|||(0.223)|(0.193)|||(0.283)|(0.383)|||(0.444)|
|(Dummy share|||||||||||||
|2004-2010 > 10)||-0.645***||-0.394**||-0.534***||-0.434||-0.857***||-0.367|
|||(0.143)||(0.196)||(0.165)||(0.280)||(0.295)||(0.335)|
|(Dummy share|||||||||||||
|2004-2010 > 5)|||-0.520***|-0.359***|||-0.380***|-0.277*|||-0.825***|-0.608**|
||||(0.116)|(0.124)|||(0.144)|(0.157)|||(0.259)|(0.258)|
|Observations|5,538|5,538|5,538|5,538|3,825|3,825|3,825|3,825|1,713|1,713|1,713|1,713|
|R-squared|0.924|0.924|0.925|0.925|0.933|0.933|0.933|0.934|0.912|0.913|0.914|0.914|
|Treated|40|60|125|125|26|36|72|72|14|24|53|53|
|Total country-|||||||||||||
|products|405|405|405|405|285|285|285|285|120|120|120|120|
|**Panel B: Auto**||Au|to|||Intermed|iate auto|||Final|auto||
|(Selection final|log of|log of|log of|log of|log of|log of|log of|log of|log of|log of|log of|log of|
|exports)|imports|imports|imports|imports|imports|imports|imports|imports|imports|imports|imports|imports|
|(Dummy share|||||||||||||
|2004-2010 > 15)|-0.763***|||-0.289|-0.586***|||-0.239|-1.024**|||-0.166|
||(0.177)|||(0.212)|(0.188)|||(0.219)|(0.396)|||(0.488)|
|(Dummy share|||||||||||||
|2004-2010 > 10)||-0.824***||-0.469**||-0.653***||-0.460**||-1.090***||-0.527|
|||(0.150)||(0.188)||(0.159)||(0.190)||(0.315)||(0.387)|
|(Dummy share|||||||||||||
|<br>2004-2010 > 5)|||-0.586***|-0.294**|||-0.319**|-0.098|||-1.081***|-0.763**|
||||(0.128)|(0.127)|||(0.156)|(0.153)|||(0.292)|(0.299)|
|Observations|5,573|5,573|5,573|5,573|3,863|3,863|3,863|3,863|1,710|1,710|1,710|1,710|
|R-squared|0.913|0.913|0.913|0.914|0.931|0.931|0.930|0.931|0.897|0.899|0.899|0.900|
|Treated|65|93|158|158|38|57|98|98|27|36|60|60|
|Total country-|||||||||||||
|products|405|405|405|405|285|285|285|285|120|120|120|120|

24