rafmacalaba/datause-encoder-data huggingface dataset:
there would be substantial welfare gains. The creation of the single market in the European Union led to innovative analysis that required the use of multi-region models with imperfect competition to capture the competition aspects of the single market (Harrison, Rutherford and Tarr, 1996; Smith and Venables, 1988).<sup>9</sup> These studies considered trade facilitation benefits and linked the competitive aspects of the single market to the standards and product regulation issues. Tariff changes were ignored since there already was free trade within the European Union prior to the single market reforms. That is, the single market reforms were exclusively about deep integration.

The North American Free Trade Agreement (NAFTA) led to many numerical studies summarized in the Francois and Shiells (1994) volume. One of the more interesting was by Levy and van Wijnbergen (1995). They use their dynamic model to argue that dynamic incentive problems in adjustment policies for Mexican agriculture imply that adjustment policies should focus on increasing the value of the assets of poor farmers, not their incomes. Preferential arrangements of the European Union with its Mediterranean and Eastern neighbors led to several assessments of deep aspects of these agreements in Armington models, including Harrison, Rutherford and Tarr (1997a) for Turkey and Rutherford, Rutstrom and Tarr (1997; 2000) for Morocco and Tunisia. Maliszewska _et al_ . (2009) employed a multi-region model with imperfect competition to examine bilateral deep integration between the EU and five countries in the former Soviet Union: the Russian Federation, Armenia, Azerbaijan, Georgia and Ukraine. They estimated substantial welfare gains to the partner countries of the EU from deep integration, but acknowledge their estimates are upward biased since they are based on a comparative steady-state model. Harrison, Rutherford and Tarr (2002) estimated that Chile would lose from individual preferential arrangements with Southern neighbors unless it lowered its 11 percent uniform tariff in place at the time. However, due to substantial estimated terms-of-trade gains to Chile in partner markets, the collective impact of its “additive regionalism” or “competitive regionalism” strategy produced estimated welfare gains many multiples of unilateral tariff liberalization.<sup>10</sup> Rutherford and Tarr (2003) showed that simply making the Chilean model dynamic will not increase the estimated gains from these agreements if there are no endogenous productivity effects.

Balistreri, Tarr and Yonezawa (2015) examined deep integration in East and Southern Africa, while Balistreri _et al_ . (2018) extended the analysis to a poverty and income distribution

> 9Baldwin, Forslid and Haarland (2000) assessed the investment impacts of the single market.

> 10  Harrison, Rutherford, Tarr and Gurgel (2004) found comparable results for Brazil and Uruguay.

5
lower costs. On the other hand, remittance costs are higher in richer corridors and in corridors with a higher share of banks among providers. Bank and MTO costs are associated with similar factors. In particular, across both types of institutions costs are higher in corridors with a smaller number of migrants, higher levels of incomes, and a larger participation of banks. As before, competition lowers costs charged by banks and MTOs at large. On the other hand, in the case of Western Union, costs appear to be insensitive to competition, perhaps a symptom of this firm’s role as a leader in the remittances market.

This paper is a first exploration of corridor variation in the cost of remittances and, therefore, is subject to certain caveats. First, this is a pure cross-sectional analysis, and we can only make limited, if any, inference on causality. Second, our analysis is also limited in scope since it includes only data from formal providers of remittance services. According to some estimates, at least a third of remittances are sent through informal channels (Freund and Spatafora, 2008). Notwithstanding these limitations, we believe the paper offers some interesting evidence that we hope will stimulate further data collection efforts and analysis.

The rest of the paper is organized as follows. Section II describes the data on the cost of remittances. Section III explains the empirical approach. Section IV presents the results, and Section V concludes.

# **II. Data on the cost of remittances**

The data we use on the cost of remittances come from a recent survey of remittance service providers conducted by the Payment System Unit of the World Bank. The cost of remittances is made up by a fee component and by an exchange rate spread component. The original World Bank data cover 14 sending and 72 receiving countries. However, because spread information is missing for remittances sent from Russia and due to missing data for some explanatory variables, we focus on 119 corridors, including 13 sending countries and 60 receiving countries (see Table 1).<sup>8</sup> In most cases, data cover the costs from the main sending location/area for the corridor in question to the capital city or most populous city in the receiving market. Data were collected by

> 8 The full data is available at <u>www.remittanceprices.org. Data on exchange rate spreads is also missing for some</u> institutions in Germany, France, and Japan. These institutions are excluded from the calculations of the average remittances costs from those countries.

4
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49. Increasing tax-financed infrastructure investments is unlikely to close the infrastructure gap in the short-run, especially if growth remains weak. In many developing countries, the tax base is low, tax administration weak and increases in taxation can be highly distortionary. Foster and Briceño-Garmendia (2010) argue that ―each dollar raised spent by a Sub-Saharan African government has a marginal cost of public funds of almost 20 percent‖. Moreover, if infrastructure financing leads to a crowding out of private sector investment whether through ―excessive‖ levels of taxation or increases in interest rates, its impact on growth will be diminished. In addition, the fiscal space to invest in infrastructure has narrowed for some emerging market economies in the wake of the financial crisis. Still, some governments could make strides in closing the infrastructure gap by spending existing resources more efficiently. The World Bank (2011a) estimates that more than 11 percent of electricity and about 24-50 percent of water is unaccounted for in developing countries. In Africa, as much as US$17 billion out of an overall spending need of about US$93 billion could be met, if existing resources were used more effectively. Steps to enhance efficiency include safeguarding maintenance spending, improving the performance of utilities and other service providers, addressing deficiency in public expenditure frameworks, and modernizing administrative and regulatory frameworks (Foster and Briceño-Garmendia 2010).

50. The scope for increasing infrastructure financing from traditional donors seems limited under current economic circumstances. Infrastructure aid doubled between 2006 and 2009, as multilateral creditors scaled up their infrastructure financing to dampen the impact of the financial crisis on developing countries (see Figure 5). Multilateral creditors provided infrastructure financing of around US$20 billion in 2006 to 2008, which climbed to US$50 billion in 2009 as the World Bank Group provided record level support of US$26 billion financing for infrastructure.<sup>31</sup> But since the 2008-09 economic crisis affected several donor countries, it is not unreasonable to expect that infrastructure aid is likely to decline in the years to come. Using panel data from 1977 to 2007, Dang, Knack, and Rogers (2009) find that banking crises in donor countries are associated with a significant decline in aid flows by 20 to 25 percent, starting to increase again only about a decade after the start of the crisis. This decline went beyond any

> 31 The World Bank Group’s lending for infrastructure was even larger in 2010, but is projected to decline going forward.

23
**Figure 5: Inequality earnings (Gini) by age group and gender: 2000, 2007 and 2014. Restricted to workers with college.**

<!-- Start of picture text --> A. Males B. Females<br>23-30 31-40 41-50 51+ 23-30 31-40 41-50 51+<br>.4 .4<br>.3 .3<br>Gini .2 Gini .2<br>.1 .1<br>0 0<br>2000 2007 2014 2000 2007 2014 2000 2007 2014 2000 2007 2014 2000 2007 2014 2000 2007 2014 2000 2007 2014 2000 2007 2014<br><!-- End of picture text --> Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2014. Earnings are in constant Mexican Pesos 2014:1. Earnings refer to full-time workers (at least 30 weekly hours of work) with a valid wage. Includes workers with college education: salaried, self-employment and owners. Earnings below and above the 1 and 99 percentile (within gender and education group) are dropped. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the previous and post observation.

In order to analyze the roles of the degraded tertiary and skill-obsolescence hypotheses, we calculate earnings over time and by cohort. This calculation is important because it depicts how wages have changed over time for the same type of workers.<sup>8</sup> The degraded tertiary hypothesis should show that wages for recent cohorts declined more pronouncedly (due to increased competition for the same jobs and that the quality of those workers is inferior to that of previous graduates). The skill-obsolescence hypothesis shows that older cohorts experienced a more pronounced decline in their wages over time.

**Table 3: Mean Log Hourly Wage by Cohort. Males with college education.**

|<br>Cohort|<br>2000|<br>2002|<br>2004|Year<br>2006|<br>2008|<br>2010|<br>2012|<br>2014|2015|
|---|---|---|---|---|---|---|---|---|---|
|1950-1954|4.29|4.28|4.19|4.25|4.16|4.14|3.99|3.93|3.85|
|1955-1959|4.17|4.22|4.18|4.16|4.15|4.01|4.00|3.89|3.95|
|1960-1964|4.11|4.13|4.11|4.16|4.17|4.03|3.96|3.96|3.94|
|1965-1969|4.12|4.14|4.08|4.13|4.09|3.97|3.95|3.93|3.97|
|1970-1974|3.96|4.06|4.01|4.05|4.07|3.98|3.96|3.93|3.92|
|1975-1979|3.75|3.83|3.86|3.92|3.97|3.86|3.89|3.87|3.87|
|1980-1984||||3.74|3.75|3.72|3.77|3.77|3.74|

8 We cannot follow the same workers as the database employed is not a panel. However, we can use synthetic panels following the same age group over time. If the sampling is random and there is no change in labor force participation (or at least it is random) then wages come from the “same” type of workers.

8
ment assignment. In the analysis, we conduct tests to verify that treatment effects are not sensitive to Phase 1 treatment status—for example, by restricting the analysis to workers who were not in the Phase 1 sample. Second, we do not include a pure Control condition in Phase 2. Consequently, we cannot directly compare the Non-private condition with the Control condition. Since this comparison is not core to our predictions, our chosen design maximizes our ability to test our key hypothesis. In addition, we leverage our cross-randomized design to test for confounds below.

# **4.4 Auxiliary tests: Privacy, self-control and piece-rate variation**

We supplement our design with three additional sources of variation. We provide an overview here, and explain each test in more detail in Section 8. First, towards the end of the experiment, we undertake placebo tests to examine workers’ willingness to share information about their savings behavior with their network members. We again offer to send publicity SMS messages, but which advertise information about _past_ savings behavior under blocked accounts, where funds would already have been spent—obviating the scope for transfer requests against the savings. This enables us to examine the value of privacy on its own, when redistributive pressure is not a relevant force. A high willingness to share such information would suggest that privacy concerns are unlikely to drive low take-up of Non-private accounts. In addition, we supplement our analysis with several pieces of data collected through surveys, in order to better understand the mechanisms underlying our results.

Second, self-control problems in consumption could increase the value of blocked savings accounts, but this channel could not alone explain differences in Private and Non-private take-up. However, to more fully examine the relevance of self-control, we test a core prediction of time inconsistency models. Among workers who take up blocked accounts, we surprise them with the option to opt out of the direct deposit for the current paycycle only. We randomly vary whether the option is provided four days before the payday or on the day of the payday itself.

Third, at the end of the experiment, we partner with the factory to randomize piece rates. We use this to compute a labor supply elasticity, which we can use as an input into our estimates of the social tax rate implied by the results of our main experiment.

18
28 Table 13: Social and private benefit-cost ratios for a representative ATP2 extension firm in a given region (Cordobas)

|Value of<br>production|Direct cost of<br>production|Gross margin|Producers payments<br>for extension|Transfer to firm by<br>government|Private cost-benefit<br>ratio|Social cost-<br>benefit ratio|
|---|---|---|---|---|---|---|
|(1)|(2)|_(3)_|(4)|_(5)_|(6)|(7)|
|||(1)-(2)|||(3)/(2)+(4)|(3)/(2)+(4)+(5)|
|30,393,420|16,375,867|14,017,553|180,000|626,676|1.835|1.769|

Source: Servitec S.A. (1996)
East Africa (Kenya, Uganda, Tanzania), we use the length of the rainy season ( _Length_ ) instead of _Rainfall_ since total precipitations are less of a limiting factor but the timing of precipitation greatly matters (Sultan _et al._ , 2010). Again, _Length_ is used in interaction with regional dummies to account for a different impact of the variable in the two different agro-ecological zones ( _Guinean_ and _East_ ). We also include the square of _Length_ , expecting decreasing returns. Table 9 and Figure 4, in the Annex, provide summary statistics characterizing precipitations in these four agro-ecological zones.

# _3.2.4 Other controls_

To account for the macroeconomic environment, we include the exchange rate against the dollar, in which cotton is traded internationally. This allows controlling for the different impact of world cotton and input prices across countries, depending on their macroeconomic situation. The fluctuation of the dollar value of the CFAF, the currency of several WCA countries, which is pegged to the euro, is, for example, said to play a key role in the profitability of cotton production in the region. The exchange rate data is taken from the Penn World Tables (Heston _et al._ , 2011). It is expressed as national currency units per one thousand US dollars, averaged annually.

We also include dummy variables to control for the effect of conflicts, which can disrupt production (e.g. Kaminski _et al._ , 2011, on the implications of the recent Ivorian crisis for cotton production). The data are taken from the UCDP/PRIO Armed Conflict Dataset (2009).<sup>16</sup>

# **4. Results**

# **4.1 Baseline Model Results**

The results of our baseline equation are given in Tables 1, 2 and 3. In Table 1, the institutional vector ( _I_ ) includes only _Post Reform_ . Columns (1) to (3) report results of regressions on yields and columns (4) to (6) the results of regressions on production. For each dependant variable, we test the immediate impact of the institutional variables as well as their impact lagged by one and two periods ( _I_ lag and _I_ lag2). Results in Table 1 show that, _ceteris paribus_ , the level of productivity was higher in

> 16 Three binary dummy variables are included, each indicating whether at least one conflict of three types occurred during year _t_ in country _i_ . „Conflict Type 2‟ indicates an interstate armed conflict, „Conflict Type 3‟ an internal armed conflict opposing the government to one or more internal opposition group(s) and „Conflict Type 4‟ an internationalized internal armed conflict occurring between a government and one or more internal opposition group(s) with intervention from other states (UCDP/PRIO, 2009: codebook). The first type reported in the database, Type 1, is excluded as it refers to conflicts occurring between a state and a non-state group outside its own territory.

13
23 running the other way or some other type of endogeneity that cannot be captured by the model in question. We perform reverse causality tests for all the models estimated in this paper.

# **5. Data**

We use annual data on provider payment methods in place and health sector outcomes for the 28 ECA countries, from 1990 to 2004.<sup>25</sup> Our dataset has been constructed using a variety of sources; the description in this section begins with our independent variables of interest, hospital payment methods, and then continues for our other variables.

## _5.1 Hospital payment methods_

We have used information contained in a number of sources—mainly the World Health Organization’s _Health Systems in Transition_ (HiT) series, in addition to personal communications with World Bank staff and a variety of country reports—to classify a country’s predominant hospital payment method in a given year as ( _i_ ) historical budgets or block grants (the prevailing method under the communist Semashko system), ( _ii_ ) fee-for-service (FFS), or ( _iii_ ) patient-based payment (PBP). Our FFS dummy is defined as taking the value of one at time _t_ if hospitals in country _i_ are mostly reimbursed (i.e. derive the majority of their revenues) through variants of the “classic” FFS arrangement in which hospitals are paid per procedure; arrangements whereby hospitals are reimbursed mainly by per diem variants; or a mixture of both arrangements. Our _PBPit_ variable takes the value of one if the main

> 25 In the case of Bosnia-Herzegovina, the period between 1992 and 1996 has been excluded from the analysis due to the lack of data for some variables and the complete disorganization of the health system during the war period.
larger scale. The specific limitations, challenges, and benefits of each of the abovementioned measurement methods are addressed in detail below.

# **_A. Self-Reported Area Estimation_**

Household surveys, particularly in developing countries, have historically relied on subjective measurements of land area, and for good reason. The marginal cost of adding one or two questions to a survey that is already being administered to a household is trivial and the exercise can be completed in a matter of minutes, with no need to visit the plot. As a result, item non-response for this item is usually negligible in existing surveys. The minimal financial investment required by this method, however, does not come without challenges, particularly in terms of data quality.

Several factors influence the accuracy of subjective farmer self-reported estimates of area, including respondent characteristics, plot characteristics, and the land registration or titling system. Land in developing countries is often passed down from generation to generation or distributed by the community, and rarely are there property rights or documentation to inform farmers of their true land area. In many cases measurement units are not standardized and determining accurate conversion factors is a timeconsuming and often ill-fated exercise. The most worrying aspect of some of the measurement error associated with self-reporting is that it may be systematic, and associated with key variables of interest.

The literature has shown the accuracy of subjective estimates to be sensitive to respondent characteristics. More educated farmers might be more numerate and more at ease at quantifying their own land area, while absentee landlords, or respondents for whom farming is only a secondary activity, may be less aware of the characteristics of their plots. Using data from Uganda, Carletto et al. (2013) analyze the determinants of the difference between farmer self-reported plot area and GPS measurements, finding that the age of the household head has a significant and positive relationship with measurement bias.

The quality of data collected through farmer self-reporting is also significantly degraded by the natural inclination of respondents to round off numbers. Distributional analysis of GPS and self-reported areas of the 2010/2011 Malawi Integrated Household Survey shows clear evidence of heaping at whole numbers and common fractions, such as 0.5 acres (Figure 1). Carletto et al. (2013 and 2015) and Desiere and D ’ Haese (2015) find rounding to be a significant factor in the discrepancy between GPS area and farmer self-reported estimates. Plot characteristics, such as boundary delineation and the existence of property rights, are known to significantly influence the measurement bias (Carletto et al. 2013, 2015). Plot slope or crop-type may also play a role in the ability of a farmer to estimate plot size. De Groote and Traoré (2005) assessed the accuracy of a method in which farmer self-report is elicited during a visit to the plot, 6
disaggregated the households in two groups: households receiving labor income exclusively from unskilled workers and household receiving labor income from skilled workers. Remittances are then received only by the first group. Other sources of income (from capital and other transfers) are disaggregated proportionally between the two groups. Since in reality, most households receive labor income from both types of workers, the described set up represents an extreme segmentation of income sources across household types and the results derived from it should be considered illustrative and should provide a useful contrast to those shown in the previous sections. Reflecting our findings from the analysis of wage determinants, we also segment the labor markets across agriculture and non-agriculture activities.

_Table 9 Macroeconomic results: revenue-neutral decrease in payroll taxes with 2 types of households_

||Remittance<br>effect, percent<br>change from<br>initial<br>equilibrium|Tax effect,<br>percent change<br>from remittance<br>shock|Total effect,<br>percent change<br>from initial<br>equilibrium|
|---|---|---|---|
|Remittances to unskilled headed hh|10.00|0.00|10.00|
|Remittances to skilled headed hh|0.00|0.00|0.00|
|_Total income_||||
|Unskilled headed household|4.52|0.97|5.53|
|Skilled headed household|-0.14|1.65|1.65|
|_Household labor income_||||
|Unskilled headed household|2.69|2.45|5.21|
|Skilled headed household|0.10|2.83|2.94|
|CPI|0.46|0.14|0.61|
|Real exchange rate|0.64|0.18|0.82|
|Real GDP|-0.24|0.19|-0.04|
|Private consumption|1.44|1.27|2.74|
|Investment|-0.08|0.17|0.09|
|Exports|-2.29|-1.19|-3.45|
|Imports|0.77|0.66|1.43|
|Government savings|0.00|0.00|0.00|
|Unskilled labor supply|-2.19|0.86|-1.35|
|Skilled labor supply|0.34|0.25|0.59|
|Unskilled wage|5.16|1.76|7.01|
|Skilled wage|-0.24|2.72|2.48|
|_Memo_||||
|Absolute change in unskilled labor supply|-11,048|4,258|-6,790|
|Absolute change in skilled labor supply|1,848|1,353|3,200|

The macroeconomic results of a revenue-neutral decrease in payroll taxes under the setup described in the previous paragraph are shown in Table 9. The main point here is that remittances increase non labor incomes for households with unskilled worker heads and as a 22