rafmacalaba/datause-encoder-data huggingface dataset:
- 57 -

Commander, S., The State and Agricultural Development in Egypt since 1973, London: Ithaca Press, 1987.

- average wages in agriculture (AVGWAG)

Ikramn, K. Egypt: Economic NManagement in a Period of Transition, Baltimore: Johns Hopkins University Press, 1980.

- domestic bank financing of the government deficit (DBFGDN)

- government expenditure (GOVEX)

# International Monetary Fund, International Financial Statistics, *arious issues.

- domestic credit claims on the private sector (PRVCRDN)

- discount rate, end of period (DISCR)

- consumer price index (CPI)

- GDP deflator (GDPDEF)

- London interbank interest rate (LIBOR)

- world wholesale price index (WWPI)

International Monetary Fund, "Arab Republic of Egypt - Recent Economic Developments," various issues.

- private investment (PRIVIN)

- government investment (GOVIN)

- government investment in infrastructure (GVIINFN)

- gross domestic product at factor prices (GDPN)

Mabro, R. and S. Radwan, The Industrialization of Egypt. 1939-73, London, 1976.

- gross domestic product at factor prices (GDPN)

Ministry of Planning, "Follow-up Report to the Five Year Plan," (in Arabic), various issues.

- private investment (PRIVIN)

- government investment (GOVIND

# Pick's, World Currency Yearbook, various issues.

- nominal parallel mnarket price of foreign exchange (EBM)

Shura Council, "Investrnent Policy during the Period 1959/60-1982/83," Arab Republic of Egypt, unpublished report, 1985 (in Arabic).
more effectively with China in the United States. On the other hand, the January 2005 removal of quotas in place under the Multi-Fiber Agreement has led to an increased Chinese presence in US apparel and textile consumption. Beyond changes in the realm of trade policy, an additional factor that would impact Latin American exports to the United States is the possibility of a steep appreciation of the renminbi.

How much would hemispheric free trade improve the competitiveness of Latin American exports to the United States? How will the elimination of apparel and textile import quotas in place under the Multi-Fiber Agreement (MFA) affect the region's exports of these products? How much would a revaluation of the Chinese currency, the renminbi, translate in increased LAC exports? In order to shed light on some of the above questions, in this paper we use detailed U.S. import data (6-digit HS level, disaggregated by partner country, over the 1990-2003 period) to measure the elasticity of substitution between Latin American and Chinese exports to the United States. We assume that the preferences for domestic and imported varieties of a representative consumer in the U.S. market, are represented by a utility function with constant elasticity of substitution. We then derive an expression for U.S. demand for imports from each country, which is a function of the price of imports from the given country, a sectoral price index, the elasticity of substitution, and U.S. income allocated to the consumption of the product in question.

In order to correctly estimate the elasticity of substitution in the demand equation, one needs to deal with the endogeneity bias that arises between the demand equation and the price level of the good. Therefore, we instrument the price of the final good with three sets of instruments: _i) transport costs, ii) import tariffs, and iii) wages, cost of inputs, and cost of capital_ . Given the level of desegregation of our data, we are able to estimate the elasticity of substitution per economic sector, which are in line with the results obtained by recent studies. We use the estimated elasticities to forecast how alternative policy scenarios that affect the relative price of Latin American and Chinese goods would change U.S. import patterns. The scenarios considered are: _i) a revaluation of the renminbi; ii) the elimination of tariffs on hemispheric exports to the United States as a result of a continent-wide free trade agreement; and iii) apparel and textile import quota elimination in the U.S. market._ 5
# **1. Introduction**

Recently, political risk has become a more multidimensional phenomenon from the outright takeovers of foreign-owned businesses to widespread indirect expropriations and breach of contract (BOC). Since 1990, the incidence of host governments’ contract breach with multinational corporations (MNCs) has been increasing, but with a large variations across countries and over time (Wellhausen, 2013). This trend also seems to be reflected in firms’ business perceptions in the business climate as evidenced by major firm-level surveys conducted by the World Bank (such as the Enterprise Surveys and the MIGA-EIU Political Risk Survey) that show that breach of contract has been the biggest concern for foreign investors.

An increase in BOC cases also reflects the reconsideration of the role of government in providing public services. Since the early 1980s, there has been a shift in the role of the public and private sectors in the provision of infrastructure (electricity, water and sanitation, telecommunication, road, railroad, port, and airport), which has increased public-private partnership (PPP) projects. The mainstreaming of PPP, however, has also accompanied the elevated tensions between host country and investors in resolving disputes on honoring contractual obligations. With an increasing number of private investments in infrastructure, it has become critical to understand risk management in countries where it is difficult for the parties to enforce contracts.

Despite an increase in BOC risk relative to outright expropriation risk, the triggers of breach of contract have not been separately studied in the literature. This paper fills in this gap by examining the key triggers of breach of contract.

Previous papers have discussed the wide variety of factors as potential triggers of breach of contract, which range from micro factors (i.e., contract design) to macro factors (such as external shocks (both economic and socio-political instabilities), political institutions, and level of development). Recent literature on FDI focuses more on macro factors, especially host country governing institutions, and claims the importance of democratic and more cohesive economic and political institutions.<sup>3</sup> Some scholars put a particular focus on the effects of natural resources, arguing that governments in natural resource dependent economies are less sensitive to reputation costs from reneging on contracts, leading to higher probability of expropriation and contract disputes (Jensen and Johnston, 2011). The phenomenon of the natural resource curse would also be cyclical (Frankel, 2012), implying that the host government has stronger incentives to breach contracts during a commodity boom.

> 3 MNCs are expected to enjoy more investment security either in democracies (Jensen 2008), in countries with strong property rights and a constrained executive (Li and Resnick, 2003; Li, 2009; Humphreys and Bates, 2005), or in countries with a stable government with lower political turnovers.

2
**UP household survey:** A comprehensive assessment at baseline (spring 2016) and a followup survey (summer 2018) were conducted to measure primary and secondary outcomes of interest for participating households in the treatment and control groups. This instrument was divided into two parts: Lady of the Household (LHH; approximately 2 hours) or primary women, and the Man Head of the Household (MHHH; approximately 45 mins). The woman in the household with the most knowledge and decision power completed the LHH survey. When she was not also the head of the household, the MHHH survey was administered to the man head. Where no lady of the household existed, the men household heads completed the LHH survey modules. They are referred in the analysis as primary woman and primary man, respectively.

**Saliva collection/Cortisol tests:** Saliva samples were collected to measure cortisol levels, a neurobiological marker of stress, at follow-up, using SaliCap tubes. A single saliva sample was collected at the end of each survey from both the primary woman and the primary man (if there was one). All samples were kept at 4 ° C for no longer than necessary before freezing them at or below -20 ° C. The samples were then shipped to Dresden Lab Services (Germany) where they were assayed for cortisol using a standard chemiluminescent immunoassay.

**Village and market surveys:** Additionally, two shorter instruments were used: One for village leaders to record basic village-level data such as infrastructure, violence, and other village shocks, and the other at the district market level to collect data on food prices for calculating consumption expenditures from quantities. Field supervisors visited the largest market in each of the four districts and collected sales prices for all food items found in the household consumption survey. These instruments were administered in parallel with the household surveys.

Primary Outcome Measures We generate primary outcome measures as follows:

1. **Consumption** : Total value of food for the last 7 days, and non-food expenditures in the past month, where non-food expenditures includes personal and household items, education and medical expenses, household repairs, social expenses (e.g. weddings, funerals and other ceremonies), and temptation goods. Non-food item values are estimated by household respondents. Food values use the district market price from the market survey where it exists, or the median market price from the surveys conducted in the remaining districts otherwise.

2. **Assets** : An index generated using principal component analysis of number and type of productive and household assets (excluding land/property), a proxy for wealth following Filmer & Pritchett (2001).

3. **Financial inclusion index** : A standardized index combining the following variables: (i) primary woman knows different places to save; (ii) anyone in the household has a 15
**Table 1B:  Average growth rate in real credit (%), 2004-2013**

|||**Average An**|**nual Growth**|**Rate (%) in**|**Average An**|**nual Growth**|**Rate (%) in**|
|---|---|---|---|---|---|---|---|
||Country|**R**|**eal HH Cred**|**it**|**R**|**eal NFC Cre**|**dit**|
|Country Name|<br>Code|||||||
|||2004-2008|2009-2013|Difference*|2004-2008|2009-2013|Difference*|
|Argentina|ARG|27.5|17.0|-10.5|-4.7|9.1|13.8|
|Australia|AUS|2.2|7.7|5.5|4.8|3.4|-1.4|
|Austria|AUT|1.8|1.6|-0.2|1.2|3.1|1.9|
|Belgium|BEL|3.6|5.1|1.4|3.3|3.4|0.1|
|Brazil|BRA|15.6|19.9|4.2|-0.2|12.8|13.0|
|Canada|CAN|4.4|6.9|2.5|-0.3|6.8|7.2|
|Chile|CHL|8.2|12.2|4.0|0.5|11.2|10.7|
|China|CHN|56.0|25.4|-30.5|8.8|19.4|10.6|
|Czech Republic|CZE|17.6|5.6|-11.9|2.1|4.9|2.9|
|Denmark|DNK|5.4|1.2|-4.2|7.8|-0.1|-7.9|
|Finland|FIN|7.1|5.2|-1.9|3.8|2.9|-0.9|
|France|FRA|4.8|4.7|-0.1|1.0|3.7|2.7|
|Germany|DEU|-5.0|1.2|6.2|-3.1|1.9|5.0|
|Greece|GRC|16.3|-2.4|-18.8|7.2|-4.8|-12.0|
|Hong Kong<br>SAR, China|HKG|1.4|5.3|4.0|10.8|9.3|-1.5|
|Hungary|HUN|15.1|-3.3|-18.4|6.1|2.2|-3.9|
|India|IND|-11.1|6.2|17.4|-0.5|11.8|12.4|
|Indonesia|IDN|12.4|15.3|2.9|6.5|14.2|7.7|
|Ireland|IRL|12.1|-1.4|-13.5|14.8|4.5|-10.3|
|Israel|ISR|2.4|7.3|4.8|3.8|3.3|-0.5|
|Italy|ITA|5.5|2.0|-3.5|3.0|0.6|-2.3|
|<br>Japan|JPN|-0.3|-4.0|-3.7|0.7|-5.0|-5.7|
|Korea, Rep.|KOR|3.5|8.9|5.4|4.6|7.6|3.0|
|Luxembourg|LUX|6.1|7.1|1.0|22.9|5.2|-17.7|
|<br>Malaysia|MYS|1.0|10.1|9.1|3.1|4.5|1.5|
|Mexico|MEX|9.9|7.9|-2.0|3.2|9.8|6.6|
|Netherlands|NLD|3.1|1.3|-1.8|-1.3|1.3|2.6|
|New Zealand|NZL|2.7|6.6|3.8|3.0|3.4|0.4|
|Norway|NOR|5.1|10.6|5.4|9.0|4.8|-4.2|
|Poland|POL|20.7|12.4|-8.3|5.0|10.7|5.7|
|Portugal<br>|PRT|3.2|-0.9|-4.1|2.0|2.2|0.2|
|Russian<br>Federation|RUS|53.0|14.4|-38.6|18.5|6.1|-12.4|
|Saudi Arabia|SAU|11.6|6.9|-4.7|27.3|1.0|-26.3|
|Singapore|SGP|3.6|11.5|7.8|6.5|6.8|0.3|
|<br>South Africa|ZAF|-|2.6|-|-|1.7|-|
|Spain|ESP|8.7|-2.5|-11.1|9.3|-2.5|-11.8|
|<br>Sweden|SWE|3.1|10.1|6.9|5.0|5.9|0.9|
|Switzerland|CHE|1.1|5.1|4.0|3.4|5.5|2.1|
|Thailand|THA|4.2|11.7|7.5|-0.5|4.5|5.0|
|Turkey|TUR|40.5|16.6|-23.9|17.8|14.3|-3.5|
|United Kingdom|GBR|0.3|4.1|3.9|1.1|2.8|1.7|
|United States|USA|4.5|-2.2|-6.7|5.1|-0.5|-5.6|

*: Difference is the percentage point change between the average annual growth rates of 2004-2008 and 2009-2013. Source: Household (HH) and Nonfinancial Corporation (NFC) credit measured in billion US$ at current prices is from BIS Statistics. It is converted to constant LCU using exchange rates from IMF International Financial Statistics and the consumer price index from BIS Statistics.

8
Tanser, F., Sharp, B. and le Sueur, D. (2003) Potential effect of climate change on malaria transmission in Africa. _The Lancet_ 362(9398): 1792-1798.

Tuñón, M. (2006) _Internal Labour Migration in China: Features and Responses_ . International Labour Organisation, Beijing.

UN (2007) _World Population Prospects: The 2006 Revision_ . United Nations Population Division, viewed 7 December 2008, <http://esa.un.org/unpp/> UNEP (2004) _Environmental Knowledge for Change_ . United Nations Environment Program and GRID-ARendal, viewed 8 December 2008, <http://maps.grida.no/go/graphic/nordic-countriesarctic-indigenous-population> UNFCCC (United Nations Framework Convention on Climate Change Secretariat) 2005. _Compendium on Methods and Tools to Evaluate Impacts of, and Vulnerability and Adaptation to, Climate Change_ . UNFCCC Secretariat with Stratus Consulting Inc. Bonn: http://unfccc.int/adaptation/methodologies_for/vulnerability_and_adaptation/items/2674.php UNHCR (2006) _The State of the World's Refugees: Human Displacement in the New Millennium_ . United Nations High Commissioner for Refugees. Oxford University Press. Oxford.

Unruh, J. (2004) Migration induced legal pluralism in land tenure. Implications for environmental change. In J. Unruh, M. Krol and N. Kliot (eds.), _Environmental Change and its Implications for Population Migration_ . Kluwer Academic Publishers, Dordecht, Boston, London.

Van der Geest, K., and de Jeu, R. (2008) Ghana. In M. Couldrey and M. Herson (eds.), _Climate Change and Displacement_ . Forced Migration Review, issue 31. Refugee Studies Centre, University of Oxford, UNEP: 16.

Van Ireland, E., Klaassen, M., Nierop, T., and van der Wusten, H. (1996) _Climate Change: SocioEconomic Impacts and Violent Conflict_ . Dutch National Research Programme on Global Air Pollution and Climate Change, Report No. 410 200 006, Wageningen.

Vertovec, S. (2004) Migrant transnationalism and modes of transformation. _International Migration Review_ 38(3): 970-1001.

Ware, H. (2007) Melanesian seasonal migration as a potential contribution to security. _Global Change, Peace and Security_ 19(3): 221-242.

Warner. K., Afifi, T., Dun, O., Stal, M. and Schmidl, S. (2008) _Human Security, Climate Change and Environmentally Induced Migration._ United Nations University, Institute for Environment and Human Security.

61
# **_Single-country estimation_**

The challenge we face is to estimate<sup>long-and short-run relationshipswithout being able</sup> to observe the long- and short-run<sup>componentsof the variables involved.Overthelast decadeor</sup> so, a booming cointegration<sup>literaturehas focusedon the estimationof long-runrelationships</sup> among **I(1)** variables (Johanssen<sup>**_1995,_**Phillipsand Hansen</sup><sup>**1990).**From thisliterature,two</sup> common misconceptions have been<sup>derived.Thefirst oneis that iong-runrelationshipsexist</sup> _only_ in the context of cointegration<sup>among integratedvariables.Thesecondone isthat standard</sup> methods of estimation and inference<sup>areincorrect.</sup>

**A** recent literature, represented<sup>in Pesaran andSmith</sup><sup>**_(1995),_**Pesaran</sup><sup>**(1997)**and Pesaran</sup> and Shin **(1999),** has argued against<sup>both misconceptions. These authors showthat simple</sup> modifications to standard methods<sup>can render consistentand efficient estimatesof the parameters</sup> in a long-run relationship between<sup>both integrated and stationary variablesand thatinferenceon</sup> these parameters can be conducted<sup>using standard tests.Furthermore,these methods avoid the</sup> need for pre-testing and order-of-<sup>integration conformabilitygiven that theyare valid whether the</sup> variables of interest are 1(0) or<sup>I(1).The main requirementsfor the validity of this methodology</sup> are that, first, there exist a long-run<sup>relationshipamong the variablesof interest and,second, the</sup> dynamic specification of<sup>themodel be augmentedsuch that theregressorsarestrictly exogenous</sup> and the resulting residual is serially<sup>uncorrelated. 9Pesaran and co-authorslabel this the</sup> "autoregressive distributed lag (ARDL)<sup>approach"to long-runmodeling.AppendixBpresents</sup> an illustration of the main assumptions<sup>and propertiesof the ARDL approach.</sup>

In order to comply with the requirements<sup>for standard estimationand inference, weembed</sup> the long-run portfolio equilibrium<sup>condition</sup><sup>**_(5)_**intoan</sup><sup>**ARDL(p,q)**model.In error-correction</sup> form, this can be written as<sup>follows:</sup>

**9** It is worth noting that the assumption<sup>of a unique long-runrelationship underlies implicitlythe various single-</sup> equation based estimators of<sup>long-runrelationships commonly found in the cointegration literature. Without such</sup> assumption, these estimators would<sup>at best identifysome linear combinationof all the long-runrelationships present</sup> in the data.

**8**
pliance with environmental regulations. Civic activism is proxied here with voter turnout, and income per capita is also used as an explanatory variable. Table 7 shows that voter turnout has a significant and negative effect on compliance. The interpretation of this result is in line with the suggestions of the earlier literature. But it is important to understand the potential reason for the negative sign is that the larger the civic activism, the larger is the pressure for environmental regulations to be enforced. This implies that when there is more social pressure for environmental issues, authorities tackle more effectively these issues and find more environmental irregularities. No significance is found on the effect of GDP per capita when included with voter turnout but it is positive and significant when included without voter turnout. Regarding the self-reported data, one finds that the effect of voter turnout is the opposite, being positive. Following the previous analysis, one could argue that this is in line with the previous result suggesting that more social pressure leads the firms to self-report themselves to be more in compliance. In self-reported data, GDP per capita is only significant and positive when included with voter turnout.

||(1)<br>Self mon.|(2)<br>Self mon.|(3)<br>Self mon.|(4)<br>Inspections|(5)<br>Inspections|(6)<br>Inspections|
|---|---|---|---|---|---|---|
|Voter turnout|0.299<sup>_∗∗∗_</sup>||0.299<sup>_∗∗∗_</sup>|-0.203<sup>_∗∗∗_</sup>||-0.203<sup>_∗∗∗_</sup>|
||(0.055)||(0.055)|(0.059)||(0.059)|
|GDP||47.271|161.248<sup>_∗∗∗_</sup>||77.232<sup>_∗∗∗_</sup>|2.469|
|||(44.509)|(49.098)||(28.071)|(35.454)|
|Air category|0.725<sup>_∗∗∗_</sup>|0.829<sup>_∗∗∗_</sup>|0.725<sup>_∗∗∗_</sup>|-0.560<sup>_∗∗∗_</sup>|-0.551<sup>_∗∗∗_</sup>|-0.560<sup>_∗∗∗_</sup>|
||(0.090)|(0.087)|(0.090)|(0.068)|(0.068)|(0.068)|
|Observations|5624|5624|5624|3638|3638|3638|
|Pseudo _R_<sup>2</sup>|0.179|0.165|0.179|0.120|0.117|0.120|

Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01 Table 7: Voter turnout and GDP

# **5.6 Past Inspections**

One final step in the analysis is to understand whether inspections have an impact on the compliance level with environmental regulations. Table 8 presents some interesting results. The number of inspections in the previous year has no significant effect on the compliance 18
# **4.2 Seasonal weather conditions**

Variables measuring seasonal rainfall and temperature levels can measure the impact of intra‐annual variation and their changes between years in order to test how income effects depend on the timing of weather shocks. Many studies measure temperature and rainfall levels at various points in the production year to reflect the seasonality of weather conditions and many income activities (Hsiang, 2010; Mendelsohn et al., 1994; Welch et al., 2010; Yang and Choi, 2007).

Based on the long‐term intra‐annual weather patterns (Figure A.3), this study differentiates between three seasons: January – April, which are drier and colder ( _S1_ ), May – August with wet months and the highest temperatures ( _S2_ ) and September – December with wet months and lower temperatures (S3). This division is also broadly in line with the growing cycle of some crops. For each of these periods in the last 12 months prior to the survey date, the mean of monthly rainfall and mean temperature level is calculated.

Seasonal conditions do not only vary between years but also zones. All survey years are affected by large intra‐annual variation in rainfall and temperature conditions (Figure A.4). In all three years the seasonal variation between zones and within zones is highest for temperature _S1_ and for rainfall in _S3_ (Figure 6). In 2010 dry season temperatures in _S1_ are higher than in the other years, while in 2012 and 2014 rainfall levels in _S3_ are larger than 2010. The rainfall outliers in _S3_ in the _Wet‐cold_ and _Wet‐hot_ zones capture the extensive rainfalls in November 2013 in the Central provinces.<sup>14</sup> Within 3 days some provinces experienced up to 400‐973 mm of rain (UN, 2013).

> 14 The weather variables are constructed so as to reflect the weather conditions in the 12 months prior to the survey. For example, for a household interviewed in March 2012, the first month indicates January 2012, while the 12<sup>th</sup> months indicate December 2011. Accordingly, the December values in the 2014 graph show the rainfall in December 2013.

13
in food and non-alcoholic beverages prices. The largest inflation gap between the bottom and top deciles occurred in 2015, where overall inflation averaged 7.7% but food inflation reached 10.9%. Prices for transportation good and services, increased only by 1.5%.

Table A.1 in the annex shows the contribution of each of the expenditure category to the overall inflation experienced by each of the income deciles for the three latest years with monthly price data available. In 2019, for instance, nearly 75% of the overall inflation borne by the first decile is explained exclusive by price pressures on food and housing expenditures. In contrast, the corresponding figure for the upper decile is 37%.

<!-- Start of picture text --> Figure 4: Contribution of expenditure groups to decile-specific inflation (2011-2020)<br>1st Decile 10th Decile<br>18 18<br>16 16<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>Food and non-alcoholic beverages Alcoholic beverages and tobacco Housing, water, electricity, gas etc.<br>Furnishings, household equipment etc. Transport Hotels, cafes and restaurants<br>Others<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK The incidence of inflation in the 2018 crisis shows the opposite pattern. Households from upper parts of the distribution experienced relatively higher inflation (17.4% for the top decile compared to 16% in the 1st decile) largely due to faster price increases in transportation (22%) than in food and non-alcoholic beverages (18%). In the period 2011-2020, increases in food prices explain 43% of the total inflation borne by poor families, a ratio that drops to 15% for the wealthiest households. In contrast, inflation of transportation expenditures comprise over a quarter (26%) of the total inflation recorded for the consumption bundles purchased by better-off households –as opposed to 6% for the 1st decile. These differences are also evident when the comparison is made between households from the Bottom 40 and those from the Top 60 (Figure A.2 in the Annex). Finally, 15