rafmacalaba/datause-encoder-data huggingface dataset:
compensate for these psychic costs would be as inappropriate as transfers to render people indifferent among occupations even though strong preferences may exist.” However, I would like to argue that there are both empirical and behavioral reasons to believe that these psychic costs should not necessarily be taken as given, but rather that preferences may change with migration itself, making decision-making location-inconsistent. Gibson et al. (2019) show that migrating does not change some core aspects of the decision-making problem, such as risk and time preferences, or the decision-making efficiency in households. However, other aspects of preference for place may change. That is, I do not think we can simply rely on revealed-preference to observe that some people choose not to migrate, and therefore conclude that it would not be beneficial for them to do so given the information they have, liquidity constraints they face, the costs of moving, and their risk preferences.

Empirical evidence for the view that there are people who choose not to migrate, who would benefit from doing so, comes from studies of cases of forced internal movement in more developed countries.<sup>6</sup> For example, Deryugina et al. (2018) note that Hurricane Katrina displaced over onequarter of New Orleans households, providing a shock to their mobility. Comparing these households to matched households in similar cities, they find that hurricane victims actually have higher incomes than control households within a couple of years, with these gains highest for those who left and never returned. Sarvimäki et al. (2020) study the long-term impact of forced migration in Finland after World War II. They estimate large positive long-run effects of displacement on earnings of men working in agriculture prior to displacement, compared to nearby neighbors who were not displaced. The benefit of moving may be particularly high for those who are young. For example, Nakamura et al. (2021) study the consequences of a volcanic eruption that affected houses in a fishing community on an island off the coast of Iceland, with the whole island being evacuated for several months, and about one-third of the houses destroyed by lava. Those whose houses were destroyed were much more likely to move, and the authors find massive benefits to their children, with an estimated net present value on life-time earnings of $440,000. Chyn (2018) finds that children in Chicago who were forced to migrate because of demolition of public housing

> 6 This is definitely not to suggest that forced migration does not entail all sorts of costs to people, or is always beneficial, just that there are examples where people who would not have moved if not for this push appear to have benefited from doing so. See Becker and Ferrara (2019) for a recent survey that covers many other aspects of forced migration.

17
<mark>stories. Specific policy measures can only be identified by in-depth sector-specific analysis. An initial minimum list of core ingredients would include elements such as (a) setting up channels for privatepublic sector communication and partnerships, (b) facilitating mechanisms for private sector selforganization if necessary in industries or sectors in which producers are located, (c) developing institutions within the public sector to improve the provision of public goods.</mark>

# **XI. References**

Aghion, P. and Howitt, P. (1992) “A Model of Growth through Creative Destruction”. _Econometrica_ 60(2): 323-351.

Arellano, M. and Bond, S. (1991) “Some tests for specification for panel data: Monte Carlo evidence and an application to employment equations” _Review of Economic Studies_ 58(2): 277-97.

Arrow, K. (1962). “The Economic Implications of Learning by Doing.” _Review of Economic Studies_ 29(3):155-173.

Balassa, B. (1986). “Comparative Advantage in Manufactured Goods: A Reapraisal.” _The Review of Economics and Statistics_ 68(2):315-19.

Barro, R. J. and Lee, J. (1993) "International Comparisons of Educational Attainment", NBER Working Paper No. 4349.

Barro, R. J. and Lee J. (2000) "International Data on Educational Attainment: Updates and Implications", CID Working Paper No. 42, April.

Bernard, A. B., Eaton J., Jensen J.B. and Kortum S. (2003) “Plants and Productivity in International Trade,” American Economic Review 93, , 1268-1290.

Caballero R. and Jaffe A. (1993). “How High are the Giant’s Shoulders: An Empirical Assessment of Knowledge Spillovers and Creative Destruction in a Model of Economic Growth.” NBER Macroeconomics Annual 1993 O. Blanchard and S. Fischer (eds.) Cambridge, MA: pp.15-74.

Calderon C., Chong, A. and Loayza, N. (2002) “Determinants of Current Account Deficits in Developing Countries” Contribution to Macroeconomics, Volume 2, Issue 1, Article 2.

Caselli, F., Esquivel, G. and Lefort, F. (1996) “Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics”, Journal of Economic Growth, 1(3), 363-389.

De Ferranti D., Perry G., Lederman D. and Maloney W. (2002). “From Natural Resources to the Knowledge Economy” World Bank, Washington DC.

Diamond, D. (1989). “Reputations and Acquisition in Debt Markets” Journal of Political Economy 97: 828-62.

Evenson, R. and Westephal, L. (1995) “Technological Change and Technology Strategy,” Chapter 37 in Jere Behrman and T. N. Srinivasan, eds., Handbook of Development Economics, Vol. 3A, Amsterdam, North Holland, pp. 2209-2229.

Grossman, G. and Helpman, E. (1989). “Product Development and International Trade”. The Journal of Political Economy 97(6): 1261-1283.

Hausmann R., Hwang J. and Rodrik D., (2006) “What you Export Matters” _Journal of Economic Growth_ (forthcoming). Center for International Development Working Paper No. 123 and KSG Faculty Research Working Paper Series RWP05-063 and NBER Working Paper 11905, Dec. 2005.

40
_Annex 2 Table 3: Food prices (ln) and program saturation in nationally representative data; Model with cutoff at saturation of 75 percent_

||Share of food<br>expenditures|Saturation|Saturation*Above<br>cutoff|Above cutoff|N|Saturation +<br>Saturation*Above<br>cutoff|
|---|---|---|---|---|---|---|
|Rice, raw regular<br>milled<br>Other raw rice and|0.191|0.095 ***<br>(0.012)|-0.489 ***<br>(0.147)|0.440 ***<br>(0.127)|8370|-0.394 ***<br>(0.146)|
|grains|0.074|-0.193 **<br>(0.096)|-1.071<br>(1.103)|1.003<br>(0.963)|8555|-1.264<br>(1.093)|
|Roots and tubers|0.011|0.243 ***<br>(0.048)|1.315 **<br>(0.579)|-1.005 **<br>(0.500)|8487|1.558 ***<br>(0.574)|
|Eggs (fresh)|0.018|0.097 ***<br>(0.016)|0.220<br>(0.185)|-0.139<br>(0.160)|8551|0.317 *<br>(0.183)|
|Eggs (processed)|0.001|0.092 ***<br>(0.025)|-0.134<br>(0.509)|0.229<br>(0.440)|6449|-0.042<br>(0.508)|
|Fish (fresh)|0.058|-0.014|0.194|-0.116|8530|0.180|
|||(0.013)|(0.158)|(0.137)||(0.157)|
|Fish (processed)|0.023|-0.080 **|0.939 *|-0.773 *|8479|0.859 *|
|||(0.036)|(0.486)|(0.418)||(0.481)|
|Meats (fresh)|0.035|0.023 **|0.070|-0.047|8510|0.093|
|||(0.011)|(0.132)|(0.114)||(0.131)|
|Meats (processed)|0.011|1.476 ***|-6.996|5.103|8242|-5.520|
|||(0.321)|(4.295)|(3.667)||(4.268)|
|Fruits (fresh)|0.021|-0.154 ***<br>(0.030)|1.281 ***<br>(0.349)|-1.099 ***<br>(0.304)|8512|1.127 ***<br>(0.346)|
|Vegetables (fresh)|0.046|0.064 ***<br>(0.016)|0.154<br>(0.184)|-0.058<br>(0.161)|8561|0.217<br>(0.183)|
|Rice and grains<br>processed|0.021|0.148 ***|-1.073 **|0.848 *|8555|-0.925 *|
|||(0.047)|(0.534)|(0.466)||(0.530)|
|Coffee, cocoa, tea|0.025|0.126 ***|-0.381|0.306|8559|-0.256|
|||(0.040)|(0.466)|(0.406)||(0.462)|
|Sugar|0.016|-0.489 ***|0.993 ***|-0.750 ***|8529|0.505 *|
|||(0.025)|(0.289)|(0.252)||(0.286)|
|Milk Products|0.007|0.055|0.309|-0.250|8117|0.364|
|||(0.056)|(0.743)|(0.643)||(0.737)|

note: *** p<0.01, ** p<0.05, * p<.1. Standard errors in parenthesis. Each row corresponds to a separate regression. Observations are at the village-food item-year level and pooled across the years 2009, 2012, and 2015. All models include year and village fixed effects. Prices are calculated as unit values, see text for details.

Source: 2009, 2012, and 2015 Family Income and Expenditure Surveys and Department of Social Welfare Administrative Data on _Pantawid_ Program Enrollment 74
# **5. Conclusion**

Argentine youth are currently peaking in numbers, and this creates challenges but also opportunities for the country. It is immensely important to ensure young Argentines a smooth transition from childhood to adulthood to benefit from the large cohorts entering the working age. This requires further attention to and investment in youth to support them by increasing protective factors and reducing the negative factors affecting their general well-being and future outcomes. Based on a household survey covering youth in four urban areas in Argentina findings suggested that: On one hand important protective factors exist, and positive outcomes are observed for many youth in the sample. For example, a loving and supportive family and a social network to interact with and rely on benefit many youth. Also, most youth attend school and thus accumulate human capital, increasing their value in the labor market.

On the other hand, a significant share of Argentine youth are exposed to risk factors that jeopardize a positive outcome later in life, or which have already resulted in a negative outcome. For example, lack of parents in the household as support and role models or general lack of social integration, are serious risk factors for youth. Likewise, early (unsafe) sexual initiation, early marriage, and teenage pregnancy are disturbing issues affecting the well-being of youth. For out of school youth, unemployment and idleness are risk factors, because they may reduce self-esteem by the feeling of marginalization or lead to skill degradation.

A cluster analysis assigned youth to six clusters based on characteristics, and the clusters were organized in three risk levels:

1. High risk (24.9 percent): Risk factors and lack of protective factors are strongly connected and interactive, because besides the presence of risk factors, youth in this group also lack protective factors. These youths are predominantly older and low educated married females with children. Also among risk factors characterizing youth in this group are that they do not attend school, and live without their parents in areas not perceived to be safe.

2. Medium risk (21.5 percent): Some youth in this group are already in need of immediate assistance, but unless careful attention is paid, these and others may fall into the high risk group. While this group is relatively well educated and socially integrated via sports and cultural associations, they are also victims of crime and domestic violence, they start having sex and work early, and do not trust the community.

3. Low risk (53.6 percent): The consequences of living in a protected environment are clear: Youth in this group are to a large extent protected against becoming victims to crime or violence, they have their sexual debut late, and when they start having sex, they protect themselves, 22
Figure 4. Around 3.4 percent of the population residing in Mexico in 2015 migrated to another state during 2015-2020.

_Internal migrant population (five-year period) by sex, 2020_ <!-- Start of picture text --> 3,936,960<br>1,974,716 1,962,244<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

# 4.2. Patterns of internal migration

**Internal immigration rates in 2020 display a heterogeneous pattern across states, ranging from 1.1 to 11.4 percent.** The states of Quintana Roo (11.4 percent), Baja California Sur (9.5 percent), Querétaro (8.9 percent), Baja California (7.3 percent), Yucatán (5.6 percent), and Chihuahua (5.6 percent) account for the highest proportion of internal migrants relative to the total state population in 2020 (immigration rate). The lowest immigration rates are registered in Guerrero (1.9 percent) and Chiapas (1.1 percent), as observed in Figure 5. Gender gaps in immigration rates are especially relevant in Baja California Sur (10.1 percent for men and 8.8 percent of women) and Sonora (2.7 percent for men and 2.3 percent for women).

Figure 5. Quintana Roo, Baja California Sur, Querétaro, Colima, and Yucatán are the states that account for the highest immigration rates _Percentage of internal migrants by hosting state (immigration rates), 2020_ <!-- Start of picture text --> Men Women All<br>11.4<br>12.0<br>9.5<br>10.0<br>8.9<br>8.0 7.3<br>5.6<br>6.0<br>4.0<br>1.9 1.9<br>2. 7<br>2.0 1.1<br>0.0<br>Hosting states<br>Percentage<br>QROO BCS QRO BC YUC CHH HGO NL MOR AGC CAM NAY COL TLA TAM COA SLP PUE MEX CDMX DGO JAL OAX SON SIN VER ZAC MICH TAB GTO GRO CHS<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

10
|**Depend**|**Ta**<br>**ent Variab**<br>|**ble 7: Lan**<br>**le: Prefe**<br>|**d Regress**<br>**r Informa**<br>|**ions**<br>**l to Form**<br>|**al Financ**<br>|**e**<br>|
|---|---|---|---|---|---|---|
||All<br>Enterprises|Small<br>Enterprises|All<br>Enterprises|Small<br>Enterprises|All<br>Enterprises|Small<br>Enterprises|
|**Corruption**<br>**Regressions**|#1|#2|#3|#4|#5|#6|
||-0.002|-0.03|-0.002|-0.031|-0.002|-0.03|
|Size|[3.54]**|[4.28]**|[3.56]**|[4.29]**|[3.53]**|[4.27]**|
||-0.369|-0.326|-0.361|-0.314|-0.362|-0.318|
|Legal Status|[2.97]**|[2.43]*|[2.88]**|[2.31]*|[2.88]**|[2.30]*|
||0.061|0.068|||||
|Landreg_proc|[2.08]*|[1.95]+|||||
||||0.097|0.13|||
|Landreg_cost|||[2.03]*|[2.72]**|||
||||||0.004|0.004|
|Landreg_time|||||[3.38]**|[3.09]**|
|Country FE|Y|Y|Y|Y|Y|Y|
|IndustryFE|Y|Y|Y|Y|Y|Y|
|Observations|3568|2250|3568|2250|3568|2250|

Robust z statistics in brackets + significant at 10%; * significant at 5%; ** significant at 1% 28
in 1990-94 to 31.4 percent of GDP in 2010-14) while intra-regional trade remained almost unchanged at about 30% of GDP over the past two decades.

# **3 Trade and Growth: A Detailed Econometric Analysis.**

This section estimates the long-term growth effects of the different dimensions of international trade integration. The regression analysis is conducted on (unbalanced) panel data of non-overlapping 5- year period averages for 174 countries (of which 45 countries are located in Sub-Saharan Africa) from 1970 to 2014. This section not only evaluates the impact of international trade integration on economic growth, but it also examines the effects of international trade on the sources of growth; namely, growth of capital per worker and total factor productivity (TFP) growth.

## **_3.1 International trade integration and economic growth_**

**Table 2** reports the GMM-IV system estimation of growth per worker on the different dimensions of trade integration; namely, trade volume, export product concentration, export market concentration, and the dependence on natural resource exports. Column [1] reports the effects of trade, and market and product concentration while controlling for the initial level of GDP per worker. This specification will be referred to as the baseline specification for the remainder of this section. Column [2] adds the ratio of natural resources exports to GDP to the baseline specification. Column [3] adds growth determinants like education (as measured by the secondary enrollment rate) and institutional quality (proxied by the ICRG Political Risk Index). Finally, column [4] adds financial development (credit to the private sector as % of GDP), government consumption (as % of GDP) and growth volatility to the specification in column [3]. The initial level of real output per worker, expressed in logs and included in all the specifications estimated in Table 2, has a negative and significant coefficient. This is evidence of conditional convergence in labor productivity.

Trade openness, as measured by real exports and imports as a percentage of GDP, has a positive and significant effect on growth per worker. The coefficient estimates of trade openness in Table 2 range from 0.016 to 0.034. After accounting for the other dimensions of trade integration in the growth regression equation (say, export product diversification, export market diversification, and natural resource dependence), the coefficient estimated of trade openness is still positive and significant; however, the coefficient becomes smaller. Economically speaking, if trade volume in Sub-Saharan Africa doubles, 11
frastructure. This evidence is perhaps suggestive of the effects of mobile phones for developing countries in leapfrogging the digital barriers associated with the costly expansion of fixed line telephone networks.

While this paper does not present evidence on the potential mechanisms, it is noteworthy to emphasize that the literature is replete with evidence on the socioeconomic impact of access to mobile phones and mobile broadband Internet that are generally consistent with the findings of this paper. For instance, Aker (2010) shows that expansion in mobile phone access reduces price dispersion in agricultural markets in developing countries by reducing search costs. Several pieces of evidence also suggest that mobile phone access has improved financial inclusion through mobile money with positive impacts on household welfare (Aker and Mbiti, 2010; Suri and Jack, 2016; Suri, 2017; Batista et al., 2018). For instance, Bahia et al. (2020), using data from Nigeria show that access to mobile broadband Internet is associated with high household welfare via increasing labor market participation, particularly for women.<sup>3</sup> Further, there is evidence that access to digital infrastructure like broadband Internet exerts positive impacts on labor market outcomes (Akerman et al., 2015; Hjort and Poulsen, 2019; Zuo, 2020), and firm performance (DeStefano et al., 2018). There is also evidence of mobile phones as technologies of change via political mobilization (Manacorda and Tesei, 2020), encouraging migration (Adema et al., 2021), reducing corruption (Andersen et al., 2011; Gonzalez, 2021) and confidence in governments Guriev et al. (2021). These prior evidence highlight the numerous pathways through which the impact of mobile connectivity on economic growth, as shown in this paper, arises. Thus, this paper offers an important contribution to the literature by documenting the extent to which the various microeconomic impacts of mobile connectivity translate into aggregate economic outcomes.

This paper is closest with Czernich et al. (2011) and Goldbeck and Lindlacher (2021) who analyze the impact of broadband Internet on economic growth in the OECD and Sub-Saharan Africa respectively. Czernich et al. (2011) for instance using cross-country panel data for OECD countries between 1996 and 2007 found that a 1 pp increase in broadband penetration is associated with a 0.09 – 0.15 pp increase in economic growth. The paper however differs from Czernich et al. (2011) by analyzing the impact of a broader spectrum of digital infrastructure (voice communication (2G) and mobile broadband (3G and 4G)) using granular and recent data from about 120 countries. Goldbeck and Lindlacher (2021) also exploit the staggered arrival of the submarine fiber-optic cables to Africa and estimate that access to internet connectivity via the fiber cables is associated with a 2 pp increase in local economic growth. The estimates from Goldbeck and Lindlacher (2021) are similar in order of magnitude to the findings in this paper

> 3Beuermann et al. (2012), also finds similar evidence in Peru

4
# **Do Improved Biomass Cookstoves Reduce PM2.5 Concentrations? If So, for Whom? Empirical Evidence from Rural Ethiopia**

## **1. Introduction**

Approximately 40% of the world, or almost 3 billion people, rely on solid fuels, such as coal, wood and charcoal for cooking (WHO, undated; IEA, 2017). This proportion is much higher (68% to over 90% of the population) in Sub-Saharan Africa and is expected to increase through 2030 (IEA 2017, Rehfuess et al., 2006; Smith et al., 2004). In Ethiopia, which is the focus of this paper, the percentage is about 90% (Beyene et al, 2015a), which is typical for low-income countries (WHO, undated). Particularly because of the human respiratory effects of burning solid fuels in homes, but also because of environmental effects (Masera et al., 2015), shifting households from biomass to commercial energy, such as gas and electricity, is a key goal. Such commercial fuels are used for cooking by about three-fifths of the world (Smith et al., 2013, IEA, 2012), but as of 2010, about 1.3 billion people did not have access to electricity, mainly in rural areas of developing countries (MacCarty et al., 2008; Jeuland and Pattanayak, 2012), and many more find commercial fuels too expensive or irregularly supplied to use for cooking and heating.

Reliance on biomass fuels is likely to continue for the foreseeable future in Sub-Saharan Africa and elsewhere in low and lower-middle income countries (IEA 2017). Cookstoves burning gas and electricity produce little or no household air pollution (HAP), but at present such technologies are very rarely used in rural areas of Ethiopia due to their high costs and insufficient access to electricity. Relatively simple improved cookstove (ICS) technologies that use less biomass and may require only minor changes in household cooking habits are therefore potentially important intermediate technologies (Jeuland and Pattanayak, 2012) if they are truly appropriate for users (Mobarak et al., 2012).

In this paper, we analyze the Mirt (“best” in Amharic language) ICS, which is promoted in Ethiopia, and used to make injera, which is a type of pancake or crepe made from teff, which is one of the main staple grains grown in Ethiopia. In areas without refrigeration, injera is generally baked at least twice per week. That cooking does not occur every day makes injera a rather unique product. It is also very energy intensive per cooking event and overall; injera baking is the end-use for approximately 50% of all primary energy consumed in the country (Bizzarri, 2010; Tesfay 2014).

Though not nearly as flexible or portable as the traditional technology, which is the threestone tripod, the Mirt stove is more efficient than the traditional stove and has been estimated to use 2