rafmacalaba/datause-encoder-data huggingface dataset:
We find a positive effect of migration on consumption levels and on the distance from the poverty line. We show that such effect is likely attributable to a physical mobility effect per se that goes beyond the effect associated with moving out of agriculture or moving out of a rural area. In fact most of the migrants do not move to an urban area from a rural location, and the large majority does not change sector of employment at destination. Why then do we not observe more people moving? Our analysis captures the benefits of migrating and does not account for the costs associated with it. Consumption might be a partial measure of the global net welfare benefit of migration. There might be financial constraints, physical mobility fears due to distance to destinations, psychological effect in the form of alienation from the place where one was born and raised, factors that might reduce the overall benefit or simply make some people hold back from such a choice.

The rest of the paper is structured as follows. Section 2 describes the sources of data used in the analysis and presents descriptive evidence on internal migration and consumption. Section 3 introduces our empirical methodology and identification strategy. Section 4 discusses our main findings and heterogeneous effects, and section 5 offers the concluding remarks.

# 2 Internal Migration in Ghana

## 2.1 Data

Data used in the analysis are from the 2012/13 Ghana Living Standards Survey (also known as GLSS-6), a nationally representative survey carried out by the Ghana Statistical Office at 7-year intervals. The survey was administered between October 2012 and October 2013 and collects a rich set of information on demographics, health, education, and living conditions, including income, expenditure, savings, credit, and transfers for about 16,700 households. The migration module has information about whether each household member, aged 7 or older, was born in the village (or town) where he was interviewed, and respondents who, at the time of the survey, lived in the place where they were born, are asked whether they ever lived (or moved) away for a year or longer.<sup>5</sup> The survey also has information, for those who were not born in the village/town where they lived at the time of the survey and for those who were born there yet moved away for a period of at least one year, about the time they moved from the place of birth (or returned to the current place of residence), the place they moved from, the labor market status before moving (education,

> 5 In the analysis we use module 5A, albeit GLSS6 also has a module (module 11) on income transfers which include remittances. Our choice is driven by the observation that section 11 restricts the collection of information about earlier migrants (over past 5 years) who used to work while away from home. It does not have any information about all migrants who, albeit away from home, reported to not have worked during their absence. This reduces the small sample size by over half (from 877 observations to 356).

5
Dupas, Pascaline, and Jonathan Robinson (2010), “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya,” _IPC Working Paper Series Number 111_ (September).

Dupas, Pascaline, and Jonathan Robinson (2011), “Why Don’t the Poor Save More? Evidence from Savings Experiments in Kenya”, mimeo.

Fafchamps, Marcel, David McKenzie, Simon Quinn, and Christopher Woodruff (2011), “When is capital enough to get female microenterprises growing? Evidence from a randomized experiment in Ghana”, mimeo.

Goldberg, Jessica (2010), “The Lesser of Two Evils: The Roles of Self-Interest and Impatience in Consumption Decisions,” University of Michigan, _mimeo_ .

Hoff, Karla and Arijit Sen (2006), “The Kin System as a Poverty Trap?” in _Poverty Traps_ Bowles, Samuel, Steven Durlauf and Karla Hoff (eds), Princeton University Press, pp. 95-115.

Jakiela, Pamela and Owen Ozier (2011), “Does Africa need a rotten kin theorem? Experimental evidence from village economies,” Washington University in St. Louis, _mimeo_ .

Kaboski, Joseph and Robert Townsend (forthcoming), “A Structural Evaluation of a LargeScale Quasi-Experimental Microfinance Initiative,” _Econometrica_ .

Karlan, Dean, Margaret McConnell, Sendhil Mullainathan and Jonathan Zinman (2010), “Getting to top of mind: How reminders increase saving”, mimeo.

Karlan, Dean and Jonathan Zinman (2010), "Expanding Microenterprise Credit Access: Using Randomized Supply Decisions to Estimate the Impacts in Manila," Yale University, _mimeo_ (January).

Kast, Felipe, Stephan Meier and Dina Pomeranz (2010), “Under-Savers Anonymous”, mimeo.

Kling, Jeffrey R, Jeffrey B Liebman, and Lawrence F Katz (2007), “Experimental Analysis of Neighborhood Effects,” _Econometrica_ 75:83-119.

Lacker, Jeffrey and John Weinberg (1989), “Optimal Contracts under Costly State Falsification” _Journal of Political Economy_ 97 (6): 1345-1363.

Ligon, Ethan, Jonathan P. Thomas, and Tim Worrall (2002), “Informal Insurance Arrangements with Limited Commitment: Theory and Evidence from Village Economies,” _Review of Economic Studies_ 69 (1):209-244.

Maranz, David (2001), _African Friends and Money Matters: Observations from Africa_ , SIL International (Observations in Ethnography, Vol. 37).

Morduch, Jonathan (1999), "The Microfinance Promise," _Journal of Economic Literature_ . Vol. 37 (4), December, pp. 1569-1614.

27
# **1. Introduction**

Like in many developing countries, interest rate spreads and margins have been high in Uganda over the past ten years. In 2004, net interest margins, banks’ net interest revenue as a ratio to total earning assets, were 13.4% in Uganda, compared to 8.3% in the average SubSaharan African country, 7.5% in the average low-income country and higher than in neighboring Kenya and Tanzania (Table 1). At the same time, the average interest rate spread – the difference between ex-ante contracted lending and deposit interest rates, hit 20%. During the past decade, however, the structure of Uganda’s banking system has been undergoing rapid and fundamental changes. Most importantly, the largest, government-owned bank was successfully privatized to a foreign bank in 2002, and the share of foreign-owned banks has increased from 62.4% to 86.7% in the deposit market and 60.7% to 81.9% in the loan market from 1999-2005. At the same time there was an increase in bank concentration in the deposit, but not in the loan market, mostly due to the privatization and foreign bank entry. What effect did these structural changes have on interest rate spreads and margins? What role do bank characteristics, market structure and macroeconomic factors, such as inflation and exchange rate policies, play in the variation of interest rate spreads and margins across banks and over time? Interest rate spreads and margins are often used as proxy variables for intermediation efficiency. Whereas in the perfect textbook world of no market frictions or transaction costs, deposit and lending rates are equal, intermediation costs and information asymmetries resulting in agency costs drive a spread between the interest rate paid to savers and the interest rate charged to borrowers, with negative repercussions for financial intermediation.<sup>1</sup> Additional to the contractual and informational framework and the macroeconomic environment, the market

> 1 Cross-country comparisons show a negative correlation between the level of financial development – as measured by private sector lending to GDP – and interest rate spreads and margins.

1
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Note: Relative bias is defined as the estimate minus the actual census value, divided by the actual census value, times 100. Bias is topcoded at 100 for the sake of presentation.
||_Budget Honesty Act_<br>_1998_)|_Federal Organic_<br>_Budget Act 2009_|||
|---|---|---|---|---|
|**Integrated with**<br>**the annual**<br>**budget process?**|Yes<br>High integration|Yes<br>High integration|Yes<br>High Integration|Yes<br>High Integration|
|**What is the**<br>**timeframe**<br>**covered?**|Four years<br>(Budget + three<br>outyears)|Four years<br>(t+1 to t+4)|Three years<br>Annual Reference<br>Level Update<br>(ARLU) is Budget<br>+ two years<br>Budget papers<br>report new<br>initiatives<br>separately|Four years + 1<br>(Annual budget<br>within 4 year<br>expenditure limits<br>set out in CA +<br>extrapolation year)|
|**Are forward**<br>**years on a no-**<br>**policy change**<br>**basis? How**<br>**defined?**|Yes<br>Forward years<br>exclude new policy,<br>but include<br>forecasts of<br>economic and non-<br>economic<br>parameters, which<br>are the basis for<br>costing existing<br>policies|No<br>Forward years<br>expenditure ceilings<br>have two<br>components: 75%<br>fixed nominal and<br>25% variable|Yes<br>ARLU ‘cost of<br>existing non-<br>statutory programs’<br>Forward years<br>exclude new policy,<br>but include<br>forecasts of<br>economic and non-<br>economic<br>parameters, which<br>are the basis for<br>costing existing<br>policies|Yes<br>Forward years<br>expenditure ceilings<br>are ‘real’|
|**How are**<br>**forward years**<br>**reported?**|Ministerial<br>portfolios and<br>programs|Headings (sectors)<br>and chapters<br>(ministries)|Departments and<br>programs|Chapters<br>(ministries) and<br>articles (programs)|
|**Are estimates**<br>**determined by**<br>**the Finance**<br>**Ministry?**|Yes<br>Finance approval<br>process for ministry<br>updates|Yes<br>BMF (Austrian<br>Ministry of<br>Finance) prepares<br>estimates in<br>consultation with<br>ministries|Yes<br>TBS (Treasury<br>Board Secretariat)<br>determines ARLU<br>in consultation with<br>departments|Yes<br>CA sets budget<br>totals<br>Finance manages a<br>‘trade-off’ system<br>within the totals|
|**How often are**<br>**forward year**<br>**projections**<br>**updated?**|Three times a year:<br>- Budget<br>- Midyear outlook<br>- ERC<br>consideration|Nominal ‘heading’<br>expenditure ceilings<br>for four years:<br>revised only as<br>necessary and<br>require<br>parliamentary<br>approval|Annual<br>Also, in-year<br>update of Economic<br>and Fiscal<br>Projections (Oct-<br>Nov)|Four times a year:<br>Most important are<br>Spring and Budget<br>Day<br>Independent Central<br>Policy Bureau<br>provides economic<br>parameters|
|**Does the**<br>**framework**<br>**distinguish**<br>**existing and**<br>**new programs?**|Yes<br>Budget papers<br>disclose all new<br>policy decisions<br>(referred to as<br>‘measures’, which<br>are specific<br>initiatives that|No|Yes<br>ARLU adjusts<br>existing spending;<br>Separate new<br>initiatives process;<br>Budget papers<br>report new|Yes<br>Annual Budget<br>process identifies<br>new spending<br>initiatives which<br>can only be funded<br>from changes to<br>existingspend|

14
project monitoring and information gathering to be done “offsite”. Thus, we foresee that this variable will have a negative impact on the share of local to total foreign claims.

We measure “economic” proximity between the home and the host country by including a measure of bilateral trade between these economies. This information comes from the IMF _Direction of Trade Statistics_ .<sup>12</sup> Finally, we measure home-host country “cultural” and “institutional” distance by including, respectively, a dummy that equals one if countries share a common language with the lender country and a dummy that equals one if countries have a common legal origin (which will then result in similar institutions). Data on common language and legal origin come from Glick and Rose (2002) and the CIA _World Factbook_ .

Government intervention in a country’s financial sector via regulations and/or taxation might affect the share of local to total foreign claims countries receive in different ways, depending on the kind of intervention. For example, controls on cross-border activity will negatively affect the level of cross-border claims and, therefore, will increase the share of local claims. Similarly, restriction on on-shore foreign currency lending, might also lead to a rise in cross-border claims to the degree that banks prefer to extend claims in foreign currencies. On the other hand, limits on “banking freedom”, in particular restrictions on bank activities and controls that affect foreign entry and foreign ownership of banks, will have the opposite effect.

In our estimations, we control for regulations affecting the volume of cross-border and local claims, respectively. First, we include a dummy that equals one if the country has controls on cross-border financial credits. Second, we control for restrictions on on-shore foreign currency lending by including a dummy for the countries where such restrictions are in place. Data for both variables come from the IMF _Annual Report on Exchange Arrangements and_

> 12 Ideally, we would have liked to control as well for the share of non-financial FDI from the home to the host country, however, this information was missing for many of the countries in our sample.

16
**Appendix C: Additional Simulation Results**

# **Table C.1. Real macro indicators for base and non‐base simulations (% annual growth 2018‐2030)**

|Item|2017|base|ginv‐<br>tdir|ginv‐<br>dbor|ginv‐<br>fbor|pwe|remit|
|---|---|---|---|---|---|---|---|
|Absorption|105.6|4.98|5.11|4.75|5.27|5.21|5.16|
|Consumption, private|80.8|4.96|4.94|4.71|5.14|5.26|5.19|
|Investment|19.2|4.96|5.78|4.78|5.82|4.98|5.01|
|Investment, private|14.4|4.96|4.95|3.44|5.01|4.99|5.02|
|Investment, government|4.8|4.96|7.89|7.89|7.89|4.96|4.96|
|Consumption, government|11.7|5.09|5.09|5.09|5.09|5.09|5.09|
|Exports|19.8|4.97|5.14|4.70|4.91|5.32|4.70|
|Imports|25.3|4.97|5.10|4.76|5.46|5.85|5.38|
|GDP factor cost|92.5|4.98|5.12|4.74|5.14|5.05|5.00|
|Real exchange rate (index)|1.00|0.01|0.02|0.00|‐0.16|‐0.52|‐0.20|
|Unemployment rate (%)|5.36|4.33|4.26|4.55|4.24|4.19|4.30|

Note: Except for unemployment, the 2017 column shows levels as GDP shares (%), while the simulation columns show annual growth rates 2018‐2030. For unemployment, the 2017 and simulation columns show the rates in 2017 and 2030, respectively.

Source: Authors’ calculations based on simulation results.

**Table C.2. Macro indicatores in 2017 and for simulations in 2030 (% of nominal GDP)**

|Item|2017|base|ginv‐<br>tdir|ginv‐<br>dbor|ginv‐<br>fbor|pwe|remit|
|---|---|---|---|---|---|---|---|
|Absorption|105.6|105.6|105.5|105.8|107.0|105.0|107.3|
|Consumption, private|80.8|80.8|79.2|80.9|80.7|81.0|82.6|
|Investment|19.2|19.2|20.9|19.4|20.9|18.6|19.2|
|Investment, private|14.4|14.4|14.1|12.3|14.2|14.0|14.4|
|Investment, government|4.8|4.8|6.7|7.1|6.7|4.6|4.8|
|Consumption, government|11.7|11.7|11.5|11.9|11.5|11.4|11.7|
|Exports|19.8|19.8|19.9|19.7|18.8|20.7|18.6|
|Imports|25.3|25.4|25.4|25.4|25.8|25.8|25.9|
|GDP factor cost|92.5|92.5|92.5|92.5|92.4|92.6|92.4|
|Foreign savings|4.9|4.9|4.8|5.0|6.3|4.4|4.7|
|Government savings|1.8|1.8|3.8|1.6|2.1|1.8|1.8|
|Domestic non‐gov savings|6.4|6.4|6.3|6.4|6.4|6.4|6.6|
|Foreign government debt|31.0|33.9|33.3|34.9|49.2|30.9|32.9|
|Foreign private debt|5.4|6.9|6.8|7.1|6.6|6.3|6.7|
|Domestic government debt|17.9|26.0|25.5|47.7|25.4|25.1|25.8|

Source: Authors’ calculations based on simulation results.

‐62‐
<!-- Start of picture text --> from Eurostat may be picking up some amount of re-exports or processing trade. I cannot be sure<br>as to the exact extent of this issue, but I come back to it below in the context of implementing<br>robustness checks for the empirical model.<br>4 Empirical Model and Estimation Results<br>The theoretical model suggests that international harmonization of EU standards in the textiles,<br>clothing, and footwear sectors should be positively associated with partner country export vari-<br>ety, provided that any additional compliance costs imposed by harmonized standards are not too<br>high.'* To test this hypothesis empirically, I use EUSDB to calculate the number of internationally<br>harmonized standards (750,;) as a percentage of the total number of EU standards (stds,;) for each<br>sector-year. I use this measure ae as a proxy for the degree of international harmonization. To<br>take account of the overall cost burden imposed by EU standards, I include In (stds,,) as a separate<br>regressor.<br>In addition to these factors, the theoretical model and past empirical work suggest that bigger<br>markets (proxied by exporter GDP), more developed exporter technology (proxied by per capita<br>GDP), and lower variable trade costs (tariffs T,;) should also impact product variety positively.'><br>I use the estimating equation (15) to test these predictions, taking advantage of the availability of<br>panel data to include fixed effects that control for unobserved heterogeneity in the exporter, sector,<br>and year dimensions.'® These fixed effects absorb influences such as EU GDP (which varies by<br>year only), sectoral expenditure shares (on the assumption that they are reasonably stable over<br>time), exporter resource endowments, and the type of international transport costs usually proxied<br>by great circle distance. To take account of the possible influence of quota arrangements under<br>the Agreement on Textiles and Clothing (ATC) and associated bilateral agreements between the<br>~ l4Due to lack of data, I do not consider intersectoral linkages or the possibility of cumulation of standards as goods<br>pass from one processing stage to another.<br>'ST source GDP data (aggregate and per capita) in constant 2000 US dollars from the World Development Indicators.<br>Simple average tariff rates (effectively applied) come from the UNCTAD-TRAINS database accessed via WITS.<br>'6T do not convert the isos; /stds.¢ term to logarithms, since it is already a percentage. (,/100 is therefore the<br>elasticity of export variety with respect to international harmonization.<br>17<br><!-- End of picture text -->
represented 61 percent of the total, providing a further indication of the development of the sector. The level of per capita premiums amounted to USD 156 in 2001. This compared with USD 12 in India, 10 in Sri Lanka and 113 in Mexico.

Table 1: l[nsurance Premiums, 2001

|percentofGDP|Total|Non-Life|Life|%Life|
|---|---|---|---|---|
|Mauritius|4.06|1.59|2.47|61|
|Chile|4.23|1.30|2.93|69|
|Cyprus|4.46|2.00|2.46|_55_|
|India|2.71|0.56|2.15|79|
|HongKong|6.34|1.21|5.13|81|
|Lebanon|2.70|2.22|0.48|18|
|Malaysia|5.18|1.80|3.38|65|
|Malta|4.26|2.28|1.99|47|
|Mexico|1.81|0.95|0.86|48|
|Morocco|2.82|2.01|0.81|29|
|Singapore|4.58|1.17|3.40|74|
|SouthAfrica<br>|17.97<br>|2.78<br>|15.19<br>|85<br>|
|SriLanka|1.20|0.67|0.53|44|

Source: FSC and Sigma.

Life business has been favored by generous tax incentives and has also benefited from the high level of development of pension funds on the one hand and housing finance on the other (Vittas 2003). In recent years, life insurance business and the total assets of insurance companies, which are linked to life policies, have grown faster or, at least, as fast as GDP (Table 2). The share of life premiums in total insurance premiums has been rising steadily.

The breadth of tax incentives cannot be exaggerated. Life premiums are deductible from income tax up to MUR 80,000 per family per year (about 80 percent of per capita income), while investment income is treated favorably and policy payouts are free from tax. In addition, both pension saving and housing loans enjoy considerable tax advantages. Pension contributions are exempt from tax without any ceiling, while interest on housing loans is deductible up to MUR 250,000 per family per year. Interest relief is available on all secured loans used for primary housing or education purposes, even those secured by life insurance policies - thus, providing a further incentive for purchasing life insurance to middle and high income people.

Table 2: Evolution of lInsurance lBusiness, 1997-2001

||1997|1998<br>(perce|1999<br>ntofGD|2000<br>P)|2001|
|---|---|---|---|---|---|
|Total Assets|16.0|16.7|17.9|17.8|18.2|
|LifePremiums|2.15|2.16|2.21|2.34|2.47|
|GeneralInsurancePremiums|2.07|1.81|1.68|1.66|1.59|
|TotalGrossPremiums|4.22|3.97|3.89|4.00|4.06|
|ShareofLife Business(%)|51<br>Source:FS|54<br>C|57|58|61|

2
technology and yields remains. Most agricultural companies in these countries are home grown, although they may rely on investment and technology transfer from abroad with several now publicly traded in European stock exchanges. Some Western European companies have also invested directly in large-scale farming in the region. For example, Black Earth, a Swedish company, farms more than 300,000 ha in Russia.

Ways of acquiring land vary depending on institutional arrangements. In Russia land is commonly leased but sometimes owned, and in Ukraine, where private land sales are not allowed, all land is leased, usually for 5 years although some operators try to lock in lease contracts at favorable terms for much longer. All over the region, land rents relative to land of comparable quality in other parts of the world are very low. Competitive markets for land rental have yet to emerge as imperfections in financial markets as well as those for inputs and output often make owner-cultivation difficult. Land owners’ weak bargaining power reduces rental rates and few of the potential benefits from large-scale cultivation are transmitted to them.

# **(c) Southeast Asia**

The perennial crop sector in Southeast Asia illustrates the plantation model of large-scale farming. Malaysia and Indonesia produce nearly 90 percent of the world’s palm oil, production of which has expanded rapidly in response to growing global demand for edible oils and strong government support. In Indonesia, planted area more than doubled from about 2.9 million ha in 1997 to 6.3 million ha in 2007. In contrast to annual crops, oil palm is highly labor intensive and the industry is estimated to have created an estimated 1.7 to 3 million jobs. Smallholders participate usually in association with plantations.

Given the processing requirements, large-scale production close to the processing unit, often complemented by outgrower schemes, is the norm, with the sourcing area for a typical palm oil mill averaging around 10,000 ha. In many cases, companies have integrated operational units horizontally to form some very large firms. Eight of the world’s 25 largest agricultural production–based companies identified in the 2009 _World Investment Report_ have major interests in oil palm (UNCTAD 2009). There has also been a strong trend toward consolidation in the industry through mergers and by vertical integration with refining oil and manufacturing of palm oil and palm kernel oil products. Several large oil palm companies now control plantations of 200,000-600,000 ha of oil palm.

The fact that more than half of the expansion of oil palm was at the expense of natural forests has been a source of major concern (Koh and Wilcove 2008). Policies aiming to foster development of the industry by providing land and timber at well below opportunity cost have been linked to deforestation of large areas. Concerns abound about oil palm expansion as a contributor to loss of biodiversity, greenhouse gas 7