rafmacalaba/datause-encoder-data huggingface dataset:
that service.<sup>11</sup> This allows us to restrict our modeling of satisfaction to the sample of households who are users of health/education facilities that can be matched with corresponding facility level data from the survey. This assumption it necessarily rules out any “reputational” effect of service quality on the satisfaction of non-users as well. However, it is reasonable given our objective, which is to identify the informational content in the satisfaction reported by the _users_ of a service.<sup>12</sup> Notably, this assumption also has a key implication for how we test the robustness of our results to selection bias, as described later in this section.

Following equation (3) above, our reduced form discrete choice models posit that expectations play a role in defining a benchmark to measure satisfaction with the facility. The estimated equations examine how the binary variable of satisfaction ( _S_ ) with public health and education facilities varies with objective quality of the facility and the governance environment within which the facilities operate, once the expectations of quality are taken into account.

_Satisfaction with public health facilities_ Given the assumption that the actual dimensions of quality of a facility are known to only those who have used that facility, the model is estimated for households who have reported using a _public health facility for which data is available from the facility survey_ (a sample of 1,786). The dependent variable _S_ takes the value 1 if the household is satisfied with a particular health facility and 0 otherwise. To capture possible differences in expectations of households

> 11 An experience good is a product or service where product characteristics (such as quality) are difficult to observe in advance, but can be ascertained upon consumption. The concept is originally due to Nelson (1970).

> 12 Moreover, even if reputational factors affect the satisfaction reported by non-users as well, the underlying model explaining their satisfaction levels is likely to be quite different from that of users, which would support the case for dropping non-users from our analysis.

14
Table 3.2: TURKEY, Representativity of sampling frame, complete case and sample with replacement

||Sampli<br>|ng frame<sup>(a)</sup>|Comp<br>|lete case<sup>(b)</sup>|With r<br>|eplacement<sup>(c)</sup>|
|---|---|---|---|---|---|---|
||# Obs|Perc over total|# Obs|Perc over total|# Obs|Perc over total|
|||**a) by Ind**|**ustry**||||
|Food andBev.|564|21.3|195|22.7|400|22.2|
|Textiles andApparel|738|27.9|176|20.5|435|24.1|
|Chemicals|352|13.3|104|12.1|214|11.9|
|Non-metallicmineralproducts|166|6.3|54|6.3|127|7.0|
|Metalproducts (ex. M&E)|284|10.7|107|12.5|209|11.6|
|Machinery andEquipment|256|9.7|106|12.3|194|10.8|
|Electrical machinery|134|5.1|53|6.2|100|5.5|
|Transport equipment|152|5.7|64|7.5|125|6.9|
|Total|2,646|100|859|100.0|1,804|100.0|
|||**b) by s**<br>|**ize**<br>||||
|Small|788|29.8|226|26.3|531|29.4|
|Medium|810|30.6|264|30.7|557|30.9|
|Large|1048|39.6|369|43.0|716|39.7|
|Total|2,646|100.0|859|100.0|1,804|100.0|

Notes: Same as Table 3.1.

Table 3.3: SOUTH AFRICA, Representativity of sampling frame, complete case and sample with replacement

||Sampl<br>|ingframe<sup>(a)</sup><br>|Comp<br>|lete case<sup>(b)</sup><br>|With<br>|replacement<sup>(c)</sup><br>|
|---|---|---|---|---|---|---|
||# Obs|Perc over total|# Obs|Perc over total|# Obs|Perc over total|
|||**a) b**<br>|**y Indust**<br>|**ry**<br>|||
|Food & beverages|189|10.9|131|10.7|159|10.7|
|Texts & apparel|180|10.4|107|8.7|143|9.6|
|Chemicals rubber &plastics|285|16.4|187|15.2|241|16.2|
|Paper, edition &publishing|159|9.2|120|9.8|137|9.2|
|Machinery& equipment|561|32.3|435|35.4|497|33.4|
|Wood & furniture|147|8.5|102|8.3|131|8.8|
|Non-metallicproducts|66|3.8|43|3.5|49|3.3|
|Other|150|8.6|104|8.5|129|8.7|
|Total|1,737|100|1,229|100|1,486|100|
|||**b) **|**by size**||||
|Small|189|11|104|8.5|139|9.4|
|Medium|743|43.4|534|43.5|647|43.7|
|Large|780|45.6|590|48|696|47|
|Total|1,712|100|1,228|100|1,482|100|

Notes: Same as Table 3.1.

15
The industry characteristic we use stems from the methodology used by Rajan and Zingales (1998). We would expect industries that naturally have low entry barriers to be most affected by regulations on entry. We therefore need to know what entry would look like if there were few artificial or infrastructural barriers to entry – not just bureaucratic barriers but also other potential barriers like rigid labor regulation or poor access to financing. Under the assumption that these barriers are low in the United States (for instance, entry costs in the U.S. are 0.5 percent of per capita GNP relative to an average of 20 percent of per capita GNP in our sample of European countries), we would expect the rate of entry in an industry in the United States to be a good proxy for the “natural” propensity for entry in that industry – reflecting technological barriers in that industry like economies of scale or incumbent organizational efficiencies obtained from experience. Of course, there is a degree of heroism in assuming that entry in the United States does not suffer from artificial barriers (or even in assuming that there is a clear distinction between natural and artificial barriers). Nevertheless, all that is important for us is that the rank ordering of entry in the United States corresponds to the rank ordering of natural barriers across industries, and this rank ordering carries over to other countries.

As a measure of industry share, we use the Amadeus database to construct the fraction of the industry’s sales in total sales of firms in the country. We refer to this variable as Industry Share. We use the average of this variable for the years 1998-1999. We calculate this countryindustry level variable for 2-digit NACE industries using data in Amadeus. These industry shares in total sales are expected to capture a potential convergence effect.

In the basic regression then, EntCost is our country characteristic and EntryUS is the industry characteristic indicating whether the industry has “naturally high entry”. If as hypothesized, bureaucratic entry requirements do have effect, they should particularly impede entry in industries that are naturally prone to entry (or seen another way, entry into an industry 17
- Kijima, Yoko (forthcoming). On the possibility of Rice Green Revolution in rainfed areas in Uganda: Impact evaluation of a management training program and guidebook distribution. In K. Otsuka and D.F. Larson (eds.), In Pursuit of an African Green Revolution: Views from Rice and Mize Farmers’ Fields. Tokyo: Springer

- Kilic, Talip, Amparo Palacios- López and Markus Goldstein (forthcoming). Caught in a productivity trap: a distributional perspective on gender differences in Malawian agriculture. World Development.

- Kurukulasuriya Pradeep and Robert Mendelsohn (2007). Crop selection: adapting to climate change in Africa. Policy Research Working Paper 4307. Washington: World Bank.

- Larson, Donald F. and Frank Plessmann (2009). Do farmers choose to be inefficient? Evidence from Bicol. Journal of Development Economics 90(1): 24-32.

- Larson, Donald F. Keijiro Ostuka, Tomoya Matsumoto and Talip Kilic (2014). Should African rural development strategies depend on smallholder farms? An exploration of the inverse-productivity hypothesis. Agricultural Economics 45(3): 355-367.

- Larson, Donald F., Jock R. Anderson, and Panos Varangis (2004). Policies on managing risk in agricultural markets. The World Bank Research Observer 19 (2): 199-230.

- Lipton, Michael, and Merle Lipton (1993) Creating rural livelihoods: Some lessons for South Africa from experience elsewhere. World Development 21 (9): 1515-1548.

- Lowder, Sarah K., Jakob Skoet, and Saumya Singh 2014). What do we really know about the number and distribution of farms and family farms in the world? Background paper for The State of Food and Agriculture. Rome: Food and Agriculture Organization.

- Maddison, David (2007). The perception of and adaptation to climate change in Africa. Policy Research Working Paper 4308. Washington: World Bank.

- Minten, Bart, Bethlehem Koru and David Stifel (2013). The last mile(s) in modern input distribution: pricing, profitability, and adoption. Agricultural Economics 44(6): 629–646.

- Morduch, Jonathan (1995). Income smoothing and consumption smoothing. Journal of Economic Perspectectives 9 (3): 103–114.

- Morduch, Jonathan (2005). Consumption smoothing across space: Testing theories of risk sharing in the ICRISAT study region of South India. In Dercon, Stefan (ed.) Insurance against Poverty. Oxford: Oxford University Press.

- Morrison, Barrie M. (1980) Rural household livelihood strategies in a Sri Lankan village. The Journal of Development Studies 16(4): 443-462.

- Mundlak Yair, Rita Butzer and Donald F. Larson (2012). Heterogeneous technology and panel data: The case of the agricultural production function. Journal of Development Economics 99 (1):139-149.

Muraoka, Rie, Tomoya Matsumoto, Songqing Jin, and Keijiro Otsuka (forthcoming). On the possibility of maize green revolution in highlands of Kenya: an assessment of emerging intensive farming systems. In K. Otsuka and D.F. Larson (eds.), In Pursuit of an African Green Revolution: Views from Rice and Mize Farmers’ Fields. Tokyo: Springer.

Myint, Hla. (1971). Economic Theory and the Underdeveloped Countries. New York: Oxford University Press.

13
Figure 4. Real exports and real imports in 2030 w.r.t 2019 following reshoring(%) <!-- Start of picture text --> 140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>-20%<br>-40%<br>China Rest of Western Europe & Latin Middle Rest of South Asia Sub- USA World<br>East Asia Europe Central America East & High Saharan<br>& Pacific Asia and North Income Africa<br>Caribbean Africa Countries<br>Baseline without Covid Covid L-shape recovery Reshoring all<br>Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports<br><!-- End of picture text --> Source: ENVISAGE simulations.

# Rethinking GVC restructuring

The economic impact of COVID-19 has fueled an already heated debate about the benefits and costs of global supply chains, yet reshoring would have a negative impact in most regions. The pandemic has highlighted the role of GVCs as channels for the faster spread of shocks, resulting in higher risks and vulnerabilities to external disruptions. With this assumption in mind, countries might be tempted to restructure their GVCs. A possible scenario is a reshoring of production by the leading economies and China, which would have a noticeably negative impact in most regions, particularly in developing regions, leaving them more vulnerable. Global real income would decrease by 1.5 percent, compared with the COVID L-shape recovery. China sees its real income falling by 2.6 percent. The regions that are more negatively affected are the Middle East and North Africa, with a drop in real income of 5.1 percent, followed by East Asia and Pacific with 3.8 percent, and Europe and Central Asia with 3.5 percent.

The best policy response to reshoring by leading countries is to make developing economies more GVCfriendly rather than responding in kind. To counter this sharp negative impact on regions that are highly dependent and integrated in the GVC, countries may be tempted to respond in kind by pursuing their own reshoring policies. Or they can follow a more GVC-friendly liberalization approach. As our analysis indicates, having all economies reshore would compound the damage done by leading economies reshoring their production. In fact, global real income would drop by 2.2 percent, versus 1.5 percent if only leading economies reshore. China would suffer a decrease of 4.2 percent in real income, a further loss of 1.6 percentage points compared to the previous scenario. The most negatively affected regions 12
Figure 3 splits the sample by rural workers who have completed secondary education and shows that the sharp increase in non-farm casual workers and employment in the construction sector is driven by workers lacking secondary education. Rural workers who have completed secondary or higher education are more likely to be employed as salaried workers in the service sector.

## **2.4.2 Rural Consumption**

The top left panel of Figure 4 shows the evolution of rural households’ monthly per capita consumption between 1999 and 2011. In real terms (1993 values), rural monthly per capita consumption displayed an average annual growth of 2.7 percent – from INR 324 to INR 445. The latter two figures in the top panel splits households by whether they have any a) secondary educated individual and b) non-farm worker. The middle panel splits household consumption by whether there is at least one household member participating in non-farm self-employed, casual or salaried work. The bottom panel splits rural households by whether they have some worker employed in either the manufacturing or service sector.

Figure 4, we see that rural households with some non-farm worker had 8-24 percent higher average monthly per capita consumption. This is driven by almost entirely by the – presence of salaried workers household with at least one salaried worker had per capita expenditures between 33 and 50 percent higher than households without any salaried worker. A large divergence in household per capita consumption is also observed for households with at least one secondary educated worker: monthly per capita consumption for these households is twice the per capita monthly consumption for households without any secondary worker. Finally, households with at least 1 worker employed in the service sector has 25 percent higher monthly consumption levels across this entire period.

# **3 Results**

We begin by discussing the predictors of rural non-farm employment using the historical NSS data. We subsequently study whether rural non-farm employment affects household wellbeing. Next, we explore barriers to informal micro-enterprise performance, which comprises a main source of rural non-farm employment. We conclude the empirical analysis by studying the relationship between individual characteristics and rural non-farm employment using the more recent CP data from 2015.

10
Table 3: Regression results from baseline specification with EU‐wide input‐output coefficients

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐TFP|Ackerberg_et al_.‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.229||||||
||(0.109)||||||
|Entryt‐2||‐0.025|||‐0.063|‐0.042|
|||(0.873)|||(0.659)|(0.762)|
|Conductt‐2|||‐0.139*||‐0.146*|‐0.167**|
||||(0.058)||(0.052)|(0.027)|
|FDI Overallt‐2||||‐0.090||‐0.174***|
|||||(0.125)||(0.000)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4966253|5413942|5046786|5818544|4966253|4965903|
|R2A|0.982|0.981|0.982|0.985|0.982|0.982|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.130|0.135|0.129|0.135|0.130|0.130|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

31
hypothesized as follows:

- _Hypothesis 1 (Extortion to Fear):_ Deportees who were extorted while migrating are more likely to avoid a range of situations out of fear in their origin country compared to deportees who were not victims of such abuse.

- _Hypothesis 1a (Extortion to Economic Hardship):_ Deportees who were extorted while migrating are more likely to experience economic hardship in their origin country than those who were not extorted.

- _Hypothesis 2 (Fear to Engagement):_ Deportees avoiding activities in their origin country because of fear are less likely to be politically engaged compared to those who are not avoiding activities out of fear.

- _Hypothesis 2a (Economic Hardship to Engagement):_ Deportees who experience economic hardship in their origin country are more likely to be politically engaged than deportees who do not experience economic hardship.

If fear and economic hardship both mediate the relationship between extortion and political engagement, a final hypothesis should concern which mechanism plays a larger role. If the emotional consequences of extortion are larger than the financial ones, extortion should be correlated with decreased rather than increased levels of political engagement. However, we have no prior reason to believe that one mechanism is more or less important than the other. Thus, any conclusions about the overall relationship between extortion and political engagement is exploratory.

# **4 Research Design: Data and Methods**

## **4.1 Deportee Survey**

We employ data from an original survey of recent migrants deported from from the United States and returned to Guatemala (“deportees” or “returnees”). The project was approved by Duke University’s IRB in protocol 2020-0075. Beginning in October 2019, we partnered with RTI International and Te Conecta, a Guatemalan NGO, to implement a face-to-face survey of newly arrived deportees at the Air Force airport in Guatemala City. This airport is the main arrival point for deportees sent to Guatemala and typically receives 3-5 planes of deportees 4 or 5 days per week. The first stage of the survey was implemented upon arrival and was conducted from October 2019 and March 2020. After that, COVID-19 made in-person data collection impossible. To initially recruit respondents for a survey, our survey 10
average, a girl in the control group expects to earn $4,101 MX, as opposed to an average estimated wage of $4,827 MX for a woman with high school degree between 30 and 40 years old, according to ENOE. The average expected wage for the average woman is $3,154 MX.

Comparing the mean expected wages with observed values might not be particularly meaningful in the presence of highly skewed expected income distributions. In order to better understand the extent of the misperceptions, we discretize the baseline answer applying the thresholds used in the follow-up data source - i.e. 12th grade _ENLACE de Contexto_ . Figures 2 plots the distribution of expected wages (self) at the baseline against the observed distribution in the 2009 ENOE separately for boys and girls. The largest difference between perceived and observed earnings realizations are concentrated in the first two bins of the distribution; the fraction of both boys and girls who think that they will earn less than $4,000 MX is much larger than the actual fraction of high school graduates earning 4,000 or less in the second trimester of 2009. For earnings higher than $7,000 MX we do not observe significant differences between the distribution of the expected earnings (self) and the current ones. The extent of this misperception becomes even more evident when we consider the distribution of the expected earnings of an average person between 30 and 40 and compare it with ENOE (Fig. AI).

Both for boys and girls the income expectations upon completing university are on average higher than the wages observed for a university graduate between 30 and 40 years old, as measured in the ENOE data. This is true irrespective of whether or not we consider the income expectation for oneself or for the average person.

In summary, the average perception on the wages that each of them can earn upon completing upper secondary is aligned with the average wage observed in the labor market. However, there is a large fraction of boys and girls who tend to underestimate the earnings of a high school graduate in the labor market. According the to the baseline survey, 79% of the respondents mentioned family members as one of the three main sources of information about the benefits of studying, followed by television (70%) and internet (48%). In order to provide _prima facie_ evidence on the link between expected earnings and school performance, Table 4 shows the correlation between baseline expectations about future earnings, expressed in logarithms, and follow-up outcomes for the individuals in control schools. The correlation between the expected log earning upon completing either upper secondary or tertiary education and the probability of being identified in the 2012 ENLACE 12th grade is small and not statistically significant, regardless of whether or not we consider the expectations for oneself or the average person. We do find evidence of a significant correlation between the expected earnings and the results in math and Spanish. The correlation is particularly strong and 12
# **Table B1.b. Schedule for Fund Manager Charges in the PPM System. For fund fees** **<u>credited individual accounts from 2008</u>**

|PPM assets under|Free fee, interest bearing|Free fee, equities|Discount,|
|---|---|---|---|
|management, millions of<br>kronor|instruments||in percent|
|0 – 1 000|0.10|0.15|65|
|1 000 – 5 000|0.10|0.15|75|
|5 000 – 10 000|0.10|0.15|85|
|over 10 000|0.10|0.15|90|

Source. PPM

# **<u>Table B2. EU Public Pension Expenditure Projections</u>**

|_Year_|_2004_|_2030_|_2050_|
|---|---|---|---|
|Country||||
|Belgium|10.4|14.7|15.5|
|Denmark|9.5|12.8|12.8|
|Germany|11.4|12.3|13.1|
|Spain|8.6|11.8|15.7|
|France|12.8|14.3|14.8|
|Ireland|4.7|7.9|11.1|
|Italy|14.2|15.0|14.7|
|Netherlands|7.7|10.7|11.2|
|Austria|13.4|14.0|12.2|
|Portugal|11.1|16.0|20.8|
|Finland|10.7|14.0|13.7|
|Sweden|10.6|11.1|11.2|
|UK|6.6|7.9|8.6|

Source. The impact of ageing on public expenditure. Report prepared by the Economic Policy Committee and the European Commission (DG ECFIN). Special Report no. 1/2006.

65
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