rafmacalaba/datause-encoder-data huggingface dataset:
numbers and sizes of cities.<sup>5</sup> The rest of the paper is organized as follows. Section 2 presents the decentralized model and solves its steady-state equilibrium. Section 3 does the same for the problem of a benevolent social planner. Section 4 presets a numerical example that allows us to compare the predictions of the two problems. Finally, Section 4 concludes the paper.

# 2 A Spatial Equilibrium Model of Rural-Urban Transformation

## 2.1 Setup

Our model is based on Cuberes (2009). The economy is closed and populated by a large number  of agents who work and live in one of two possible locations: an urban one ( ) and a rural one ().

## 2.2 The Decentralized Problem

### 2.2.1 Households

The maximization problem of a representative agent is contingent to the location where she lives.<sup>6</sup> Location U There are <sup></sup> agents living in location  at period . Utility in location  is given by  (<sup></sup> <sup>,Φ(</sup> <sup>)),where</sup> <sup>≡</sup> <sup></sup> <u></u> denotes consumption of the private good in per capita terms in location  . The function Φ() is increasing and it reflects the existence of an originated from network effects, knowledge spillovers, information sharing, companionship, safety, among others. The function  is increasing and concave in the two inputs.

An agent in location  has two sources of income: her wage earnings and the returns to her investment in the only asset in the economy, namely physical capital in location  . Let’s denote  ̇ the investment in asset . The agent  ’s budget constraint is then: where <sup>isthe wage rate in andisthe returnon the private asset .7</sup> The intertemporal problem of a representative agent is given by:

> 5 Richardson (1987) presents a cost-benefit analysis of the urbanization process in four different countries and proposes some policies to adress different policy issues.

> 6 For simplicity, the model assumes that households live and work in the same location.

> 7 We assume that the investment in the physical good must be positive. This assumption is clarified in the firm’s problem. In the agent’s optimization problem we further assume that this non-negativity constraint on investment is not binding, that is household invest a positive amount in every period.

5
# **1. INTRODUCTION**

Global trade has experienced significant expansion buoyed by technological progress and trade-supporting policy shifts. IMF data highlight that, by the year 2000, trade value had increased 60 times since 1960, reaching US$13 trillion. During this period, exports increased 61 times while imports rose 58 times.<sup>4</sup> Since then, global trade has seen curve-shifting expansion. There is an apparent correlation between the accelerated growth in trade and economic growth. According to the World Bank World Development Indicator data (WDI), world exports of goods and services increased from US$8 trillion in 2002 to over US$31 trillion in 2022 and as shown in Figure 1 below, the growth in exports was accompanied by global gross domestic product (GDP) growth. The patterns of trends in global trade tend to be similar for both advanced economies (AEs) and Emerging Markets and Developing Economies (EMDEs), albeit higher for the former. International trade has enabled EMDEs to integrate with the global economy, which has mirrored their growth. Figure 1 shows that EMDEs have increased their participation in global trade significantly since the 1960s as well as their share of global trade (from 19 percent in 1990 to 41 percent in 2021). Between the late 1990s and 2022, manufacturing exports from EMDEs increased eight-fold — from approximately US$ 629 billion in 1995 to US$ 5.5 trillion in 2022. Countries that trade more tend to achieve higher income, as illustrated in Figures A2, A3, and A4 in the Appendix.

**Figure 1. The relationship between trade and growth and the share of EMDEs trade**

<!-- Start of picture text --> a) Trade vs GDP (1970-2021) b) Share of EMDEs trade (1970-2021)<br>120 100%<br>100<br>80%<br>80<br>60%<br>60<br>40%<br>40<br>20%<br>20<br>0 0%<br>Exports GS (current US$) Imports GS (current US$) Emerging and Developing Economies<br>Trade GS (current US$) GDP (current US$) Advanced Economies<br>Trillions<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018 2021 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text -->

**Source:** Authors’ calculation based on data from the World Bank Group and the IMF. **Note:** GS in Panel (a) refers to “ _Good and Services_ ”.

There are specific examples of developing countries that have benefited from trade-supported industrialization. For example, the East Asian Tigers realized significant export-led growth during the period 1960s-1990s. China has been leading EMDE growth in the post-1990s and India is increasingly playing an important role recently. Several studies discover a pattern of faster growth for countries that have embraced the trade liberalization reform ( <mark>Falvey, Foster, and Greenaway</mark> 2012; Frankel and Romer 1999; Salinas and Aksoy 2006; Thirlwall 2000; Wacziarg and Welch 2008). EMDEs that are actively

> 4 According to the World Bank World Development Indicators, trade (% GDP) has doubled between 1970 and 2000, reaching 57% in 2021.

2
# Appendix A: Questionnaires

The first table below summarizes the structure of the questionnaires. Subsequent tables reproduce the English text used in the Facebook questionnaire for each substantive module, as well as notes on the comparability of the two questionnaires for that module. Both surveys were delivered entirely or almost entirely in Arabic, and while the Arabic text of the questionnaires is usually similar, it is rarely exactly the same. Substantive or systematic differences are noted in the module summaries that follow.

|**PPCS**|**Facebook survey**|
|---|---|
|Household questionnaire covering sociodemographic<br>details, housing, assistance and coping strategies,<br>consumption, household income, and mobility and<br>access to services||
||Q1. Agreement toparticipate|
|S1. Identification information: name, contact<br>information, marital status, sex, age|Q2-16. Sociodemographic questions: age,<br>region/governorate, gender, education, refugee<br>status, marital status, household size, relationship to<br>head of household,rooms in dwelling,work status|
|S2. Evaluation of services||
|S3. Conflict exposure – Gaza only|Q17-23. Conflict exposure(all respondents)|
|S4. Food Insecurity Experience Scale module|Q24-25. One of two food security modules (randomly<br>assigned): Food Insecurity Experience Scale or<br>Reduced CopingStrategies Index|
|S5. Aggression|Q26. Aggression|
|S6. Satisfaction||
|S7. General health questionnaire<br>WHO-5 depression module<br>Additional questions about mental health,<br>cognition, and behavior|Q27-28. General health questionnaire<br>Q29. WHO-5 depression module<br>[Information about Gaza Community Mental Health<br>Program]|
|S8. Traumatic events and PTSD||
|S9. COVID exposure||
|S10. Prosocial behavior|Q30. Prosocial behavior|
||Q31. COVID exposure|
||Q32-35. Weighting questions|

## **<mark>Personal exposure to conflict events</mark>**

|_English text of Facebookquestionnaire_||_Notes on comparability_|
|---|---|---|
|17<br>Have you been injured due to Israeli airstrikes or clashes<br>with Israeli securityforces in May2021?|Yes / No|Questions 18-23 were differently<br>ordered in the PPCS.|
|Has a household member of yours died due to Israeli|Yes / No||
|airstrikes or because of clashes with Israeli security forces||In the Arabic text of the PPCS, the|
|in May2021?||detailed phrasing about Israeli|
|Was any other household member injured in Israeli|Yes / No|airstrikes or clashes with Israeli|
|airstrikes or clashes with Israeli security forces in May||security forces in May 2021 is|
|2021?||replaced byaphrase approximately|

17
**Table 2: Multivariate correlates of changes in sales and profits and bankruptcy**

||Total|sales|Pro|fits|Bank|ruptcy|
|---|---|---|---|---|---|---|
||(OL|S)|(O|LS)|(Probit, mar|ginal effects)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Size_ _(dummy - omitted:_<br>_Small)_|||||||
|Medium (20-99|5.23|5.28|5.88|**6.04**|0.020|0.025|
|workers)|(3.73)|(3.72)|(3.68)|(3.65)|(0.028)|(0.027)|
|Large (at least 100|**10.4**|**9.37**|**11.0**|**10.0**|**0.074**|**0.064**|
|workers)|(4.94)|(5.03)|(4.54)|(4.51)|(0.042)|(0.041)|
|_Phnom Penh (dummy)_|**24.1**<br>(3.94)|**24.7**<br>(3.78)|**18.9**<br>(4.29)|**19.6**<br>(4.22)|-0.020<br>(0.036)|-0.021<br>(0.035)|
|_Sector (dummy -_<br>_omitted: Other_<br>_manufacturing)_|||||||
|Garments|-5.43|-5.68|5.98|5.81|-0.011|-0.012|
||(9.67)|(9.64)|(8.88)|(8.77)|(0.030)|(0.026)|
|Trade|-1.95|-2.92|-3.33|-3.84|0.009|0.007|
||(7.41)|(7.29)|(7.00)|(6.95)|(0.035)|(0.031)|
|Tourism|1.59|1.96|-4.35|-3.35|**-0.052**|**-0.047**|
||(7.54)|(7.37)|(7.23)|(7.12)|(0.022)|(0.020)|
|Other|-2.62|-3.56|-1.97|-2.63|-0.023|-0.027|
||(6.89)|(6.79)|(6.58)|(6.55)|(0.022)|(0.019)|
|_Labor productivity_<br>_(dummy - omitted: Low)_|||||||
|Medium|4.22|3.65|5.39|4.90|**-0.056**|**-0.055**|
||(3.43)|(3.42)|(3.56)|(3.56)|(0.019)|(0.018)|
|High|**10.5**|**9.86**|**12.4**|**11.5**|**-0.033**|**-0.030**|
||(3.64)|(3.66)|(3.53)|(3.57)|(0.017)|(0.016)|
|_Exporter (dummy)_|-5.89|-5.39|**-22.2**|**-21.0**|0.030|0.032|
||(8.26)|(8.26)|(7.34)|(7.36)|(0.032)|(0.031)|
|_Use of finance (percent -_<br>_omitted: Retained_<br>|||||||
|_earnings)_|||||||
|Formal institutions||**12.1**||**10.5**||-0.030|
|||(5.81)||(6.05)||(0.046)|
|Trade credit||-3.65||4.79||0.054|
|(suppliers/customers)||(7.38)||(7.45)||(0.037)|
|Informal||-1.47||-0.57||**-0.045**|
|||(3.75)||(3.49)||(0.023)|
|_Constant_|**-55.3**<br>(8.10)|**-55.2**<br>(7.91)|**-50.3**<br>(8.03)|**-51.2**<br>(7.93)|||
|Number of observations|231|231|231|231|453|453|
|(pseudo)R<sup>~~2~~</sup>|0.21|0.22|0.21|0.22|0.15|0.18|

Note: Huber/White/sandwich standard errors are reported in parentheses and coefficients which are significant at 10% are reported in bold.

Larger firms (above 100 workers) and more productive firms were more likely to weather the crisis, both in terms of changes in sales and changes in profits. On the other hand, smaller firms, those outside Phnom 7
# <u>https://doi.org/10.1016/j.foodpol.2017.02.006</u>

WFP (World Food Programme) (2008) Food Consumption Analysis: Calculating and Use of the Food Consumption Score in Food Security Analysis, Rome: WFP Vulnerability Analysis and Mapping Branch.

Woodridge, J. M. (2010). _Econometric analysis of cross section and panel data_ . London, England: MIT press.

Zeweld, W., Huylenbroeck, G. Van, Hidgot, A., Chandrakanth, M. G., & Speelman, S. (2015). Adoption of Small-Scale Irrigation and Its Livelihood Impacts in Northern Ethiopia. _Irrigation and Drainage_ , _64_ (5), 655–668. https://doi.org/10.1002/ird.1938 35
# **APPENDIX 1**

# **NAFTA BUSINESS TAX PROVISIONS BY COUNTRY: AN OVERVIEW**

This appendix this appendix provides an overview of business taxation in each of three NAFTA member countries: Canada, Mexico, and the United States. The business taxation means taxes that may affect business activities, particularly the real capital investment. The major business taxes include capital taxes, and transaction taxes on business inputs. The capital taxes in our context include the corporate income tax, personal income taxes on investment income34, and the property tax on immovable properties. The description presented in this appendix is based on the publication of International Bureau of Fiscal Documentation, the 1999 Worldwide Corporate Tax Guide published by Ernst & Young, and recent issues of Tax Note International.

Table Al summarizes the main features of each country's corporate tax system.

# **CANADA**

Corporations resident in Canada are taxed on their worldwide income from all sources including income from business or property and net taxable capital gains.

# **The Capital Tax Provisions**

_The corporate income tax rate._ The corporate income tax is levied at both federal and provincial level. The general federal CIT rate is 29 percent including the 4 percent surtax; however, manufacturing industries, pay a lower rate of 22 percent. The provincial CIT is not deductible for federal CIT purposes and the rates range from 8.9 percent to 17 percent. Some provinces also impose lower rates on manufacturing sector. The combined CIT rate, based on the industrial structure among provinces, is about 43 percent for the services sector and 35 percent for the manufacturing sector. However, Canadian governments at both Federal and provincial level are phasing in, or considering, significant tax reduction. As a result, the combined CIT rate 35 in Ontario by year 2005 will be only 30 percent for all industries _The tax depreciation rule._ The tax depreciation is based on the declining balance and varies by capital asset classified for the tax depreciation purpose. The average depreciation rate for buildings is 5 percent for manufacturing and 6 percent for services; the rate for machinery is 38 percent and 31 percent respectively<sup>3 6</sup> .

_Capital Taxes._ There are two types of capital taxes in Canada. At the federal level, a large corporate tax is imposed at a rate of 0.225 percent on paid-up capital in excess of $10 million. This tax, however, is creditable against the corporate surtax. At the provincial level, five provinces including British Columbia, Alberta, Manitoba, Ontario

> _34_ As illustrated by the effective tax rate analysis, taxes on any personal investment income could affect the cost of capital investment through financing.

> _35_ Refer to Canadian Federal Mini-Budget 2000 and Ontario-Budget 2000.

> _36_ These rates are our estimates based on the Canadian capital structure by industry.

15
satisfactory option for governments in the region. The question becomes to identify the realistic components of a successful infrastructure strategy that cuts down on the time needed to achieve the necessary improvements. The next section addresses this issue.

# **5. The Policy Challenges**

For a start, let us think of the potential gains to be had from speeding up improvements in infrastructure access. Taking again the example of water, and raising the income elasticity to 0.2 (that is above the current regional best performer) would allow the 9 best performing countries to connect all decile 1 households in less than 21 years. An even higher elasticity of 0.5 would allow all countries, except El Salvador, to complete that task in 16 years or less.

The elasticity used in our reduced form exercise is in fact the outcome of a model combining amount invested and efficiency of such investments on the supply side, with amount demanded as a result of price and quality options available on the demand side. However, the conventional wisdom has generally taken investment amounts as the main, if not the only, policy lever. Unfortunately, experience shows that overall investment levels are unlikely to adjust significantly in the short run.

As discussed in World Bank (2017), governments in the region face tight fiscal constraints. In the last decade, the combination of the commodity price bonanza, very low international interest rates, real exchange rate appreciations, and rising consumer credit, allowed major commodity exporters to rely on terms-of-trade windfalls to expand domestic demand, in particular through fiscal spending. As the recent, sharp and likely durable deterioration of their terms of trade brought down dramatically the purchasing power of their incomes, these countries (which include virtually all the major countries in South America) are now forced to reduce domestic spending.

The current level of total public investment is 3.4 percent. On average, about a third of this public investment goes to infrastructure. As for public-private partnerships (PPPs), the best figures available, from the World Bank PPI database, puts them at about 40 percent of Latin America’s infrastructure investments. However, an often-disregarded fact is that they depend heavily on government support. New figures that have been gathered starting in 2015 show that about a third of the recorded investments actually come from public sources, and about half are 17
**Discovery matrix** (Annex 4). Under visual inspection, the matrix is consistent with expert knowledge of countries, including developing countries targeted by World Bank assistance.

The data for European countries also provide some illustration of the differences between countries. Table 10 below illustrates the case of Germany, France, Italy, Netherlands, Spain, and the United Kingdom.

All countries are relatively close to their latent structure as measured by the indicators of distance and overlap. However, Germany clearly stands out by being closer and having a very small extensive margin of products to be revealed. Conversely, France and the Netherlands have a rather large extensive margin. At least in the case of France, this observation and the fact that its indicators are about the same as Spain cannot but be associated to the current debate in France about the competitiveness and the future of its industry (Giraud & Weil, 2013).

**Table 10. Latent comparative data for selected EU countries**

|countries|DEU<br>|ESP<br>|FRA<br>ITA|GBR|NLD|
|---|---|---|---|---|---|
|Theil actual|3.33|<br>3.42|<br>3.47<br>3.51|<br>3.35|<br>3.59|
|KL Distance|0.02|<br>0.12|<br>0.12<br>0.09|<br>0.07|<br>0.11|
|overlap|0.93|<br>0.80|<br>0.83<br>0.85|<br>0.86|<br>0.80|
|Type 1#|16|<br>25|<br>14<br>22|<br>18|<br>19|
|Type 2#|3|<br>15|<br>14<br>9|<br>9|<br>16|
|Type 3#|2|<br>3|<br>7<br>4|<br>0|<br>4|
|Type 4#|40|<br>18|<br>26<br>26|<br>34|<br>22|

**Fig 9. Distance between actual and real vs. WEF Global Competitiveness Indicator.**

<!-- Start of picture text --> 0 .5 1 1.5 2<br>KL Distance<br>6<br>5<br>4<br>WEF CGI<br>3<br>2<br><!-- End of picture text --> 30
Table 1. Oaxaca-Blinder decomposition of the change in FLFP between 2002 and 2018

||Overall<br>(1)|Explained<br>(2)|Unexplained<br>(3)|
|---|---|---|---|
|Year 2018|0.32<br>[0.0217]***|||
|Year 2005|0.264<br>[0.0256]***|||
|Difference|0.0569<br>[0.00706]***|||
|Total effect||0.153<br>[0.0247]***|-0.0964<br>[0.0252]***|
|Individual characteristics||0.0257|0.401|
|||[0.00235]***|[0.0323]***|
|Household characteristics||0.0139<br>[0.00155]***|-0.118<br>[0.00713]***|
|Economic situation||0.00624<br>[0.00211]***|0.00943<br>[0.00352]***|
|Local labor market variables||0.104<br>[0.0255]***|-0.0757<br>[0.0288]***|
|Geography||0.00341<br>[0.00200]*|0.0199<br>[0.00932]**|
|Constant|||-0.332<br>[0.0306]***|
|Observations|2,909,416|2,909,416|2,909,416|

Sources: 2002 and 2018 National Census of Guatemala (INE).

Notes: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. Women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves and who live in municipalities appearing in both censuses (331 municipalities). Individual characteristics include age and its squared, dummies of educational level and an indicator variable of belonging to an indigenous group. Household characteristics include a dummy of being married, number of children ages 0 to 3, ages 4 to 6 and ages 7 to 12, number of household members 13 years old or older, presence of grandmother, household headship. Economic situation includes an indicator of whether the spouse works, an indicators of income stability, and a household & WASH index. Local labor market variables include the share of male employment by economic sector. Geography factors include an indicator of urban area and department fixed effects. Table A1 in the Appendix provides details on variables’ definitions.

27
and Williams (2000) also find a negative and significant relation between job satisfaction and the reference income predicted with a wage equation.

Measures of relative deprivation have been found by several authors to be negatively associated with happiness or life satisfaction. Verme (2010) and Silber and Verme (2012) using the Consortium of Household Panels for European Socio-Economic Research (CHER) find that deprivation indices constructed with predicted income values are consistently and negatively correlated with income satisfaction. Oshio et al. (2011) find the Yitzhaki measure of relative deprivation to be negatively associated with happiness for China and Korea.

The choice of the _reference group_ is an important determinant of the outcome of the relative income-happiness relation. Most researchers use ‘alter’ comparisons, comparisons with other groups in society based on pre-defined characteristics. In Ferrer-i-Carbonell (2005) the reference group contains all the individuals with a similar education level, inside the same age bracket, and living in the same region. Akay and Martinsson (2011) used several types of reference groups to test the relative income hypothesis in Northern Ethiopia but found no significance of relative income as opposed to absolute income which is always found to be significant. Persky and Tam (1990) assume that all individuals living in the same region are part of the same reference group. McBride (2001) includes in the reference group of each individual all people in USA who are in the age range of 5 years younger and 5 years older than the individual concerned. Van de Stadt et al. (1985) define the reference group according to education level, age, and employment status. It was not possible to detect unequivocally certain reference groups as being better than others in determining a significant relation between happiness and relative income. All we can say is that most scholars use individual characteristics, either directly in the happiness equation or indirectly with the estimation of predicted income.

A number of studies find evidence that both the internal (ego) comparisons and external (alter) comparison matter. Knight et al. (2009) find this result for rural China where relative income seems to be more important that absolute income in explaining happiness while both past and expected incomes in the future are also found to be relevant for present happiness. Verme (2010) using a set of European panel surveys concludes that absolute income is more important than relative income in explaining happiness but also that relative income carries consistently a negative and significant sign in equations where the dependent variable is satisfaction with income.

22