rafmacalaba/datause-encoder-data huggingface dataset:
We report the results for the impacts of total exports, as well as imports of intermediate and raw products, since these trade variables seemed the most affected by exposure to US automation (see Table 9). The results are consistent with those of Table 9, as the coefficients are not statistically significant.

One hypothesis to explain why exposure to US automation did not affect Mexico’s labor markets is that given that the share of wage employment in the working age population is rather small and exhibits a high variance across regions, the sample size may not be large enough to detect any impacts of exposure to US automation. In fact, a simple analysis of power (see Appendix) suggests that with the current sample size, the minimum detectable effect of exposure to US robots is rather high when compared to the impacts of local automation discussed below. To detect an impact comparable to the one we find for exposure to local automation, we would need to increase the sample size considerably (by about 1,000 local labor markets). In addition, the absence of labor market impacts could also be explained by the fact that the trade impacts of US automation on trade, while statistically significant, are rather small in magnitude. Given that exposure to US automation increased on average by 0.5 robots per worker, the estimates in Table 5 indicate that export growth per worker declined by around 3.3 percent. This differential growth is not substantially large considering that, during this period, exports per worker to the US grew 110 percent for the average LLM. For benchmark purposes, imports from China to Mexico per worker increased 15.4 times between 1998 and 2013, which reduced the manufacturing employment to working age population by only 0.19 percentage points during this period (Blyde et al. 2016).

However, the lack of significant impacts on manufacturing employment at the aggregate level hides heterogeneous impacts across areas. Table 13 shows the labor market impacts on areas that had different shares of jobs susceptible to being replaced by robots. It is expected that among areas experiencing similar levels of exposure to US automation, those with a higher replaceability index would experience larger employment declines. In fact, as seen in column 2, the OLS results show that exposure to US automation had a disproportionate 22
# **Table 3: Impact of Family Background on Parents' Years of Schooling**

||**(primary **|**schooling as the th**<br>|**reshold for Gran**<br>|**dparent’s ed**<br>|**ucation)**<br>|||
|---|---|---|---|---|---|---|---|
|||**(1)**<br>**Grandparents'**<br>**education**|**(2)**<br>**Grandparen**|**(3)**<br>**ts education an**|**(4)**<br>**d Occupation**|**(5)**<br>**Test of e**|**(6)**<br>**quality**|
|**Estimates**|||**Non-farm, low**|**Farm, high**|**Non-farm, high**|||
|||**Fullsample**|**edu**|<br>**edu**|<br>**edu**|||
|||||||**Z score**|**Z score**|
|**_Mothers_**||||||||
||OLS|**1.03**<br>12.05***|**0.58**<br>3.66***|**0.99**<br>10.48***|**1.48**<br>6.69***|-2.22*|-2.03*|
|**Matching**|A-I<br>matching||**0.67**<br>4.07***|**0.98**<br>9.14***|**1.63**<br>6.85***|-1.57|-2.48**|
|**estimator**|BC-RM||**0.78**<br>4.33***|**1.01**<br>8.99***|**1.47**<br>6.67***|-1.08|-1.85*|
|**Propensity**<br>**score**<br>**weighted**<br>|NIPW||**0.62**|**1.00**|**1.51**|-2.02*|-2.11**|
|**estimator**|||(0.39,0.89)|(0.87,1.15)|(1.05,1.88)|||
|**_Fathers_**||||||||
||OLS|**0.76**<br>10.64***|**0.42**<br>3.31***|**0.78**<br>10.04***|**0.80**<br>4.15***|-2.46**|-0.09|
|**Matching**|A-I<br>matching||**0.44**<br>3.35***|**0.91**<br>10.09***|**0.77**<br>3.44***|-2.94***|0.58|
|**estimator**|BC-RM||**0.55**<br>3.57***|**0.89**<br>9.73***|**0.73**<br>3.83***|-1.89*|0.75|
|**Propensity**<br>**score**<br>**weighted**<br>|NIPW||**0.41**|**0.78**|**0.81**|-2.41**|-0.11|
|**estimator**|||(0.22,0.62)|(0.66,0.92)|(0.51,1.24)|||

## **_Notes_**

- 1) Dependent variable=parents'  years of schooling;

- 2) For the column (1), the interest variable=grandparents' education dummy, dummy =1 if at least one of grandparents more than primary schooling,   0=otherwise;

- 3) For all columns, the control variables are parents'  age, parents' age squared, grandmother's age, grandmother's age squared, grandfather's age, grandfather's age squared;

- 4) For the columns (2) - (4), the interest variable is grandparents' education and occupation status dummy.  The dummy equals 1 if household belongs to 'treatment' group,   and 0 otherwise; the base group is (farm, low edu)

- 5) A-I matching=Abadie and Imbens (2002)

- 6) BC-RM = Biased corrected radius matching;

- 7) NIPW = normalized inverse probability weighted propensity score estimator of Hirano and Imbens (2001);

- 8) The computation of NIPW uses STATA program written by Millimet and Tchernis (2013);

- 9) Estimates and t statistics are shown. For NIPW, 90% confidence interval is given in parentheses, which is boostrapped using 250 replications;

- 10) "high edu" stands for more than primary schooling, "low edu" stands for primary schooling or less;

- 11) ***, **, and * Denotes statistical significance at 1%, 5%, and 10% level.
**<mark>-</mark>** **<u><mark>Table 3: Hypothesis 1 Household Sample Selection</mark></u>**

|(1)<br>VARIABLES<br>Vaccinated|
|---|
|Phone Survey Sample (Dummy)<br>0.00795<br>(0.00523)|
|Constant<br>0.272***<br>(0.00296)|
|Observations<br>33,432|
|R-squared<br>0.000|
|Weighted Vax Rate: ESS Sample<br>29.5|
|Weighted Vax Rate: Phone Sample<br>30.5|
|Note: Bivariate OLS regression of vaccination status on a dummy for living in a<br>household sampled for the phone survey. The dummy identifies respondents in a<br>general population sample from the Ethiopia Socioeconomic Survey (ESS 5) that live<br>in households that are also included in the phone surveys (vs. the entire general<br>population sample). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1|

# **Respondent selection**

Since the phone surveys’ main respondent was purposively selected, we hypothesize that vaccine uptake estimates from the sample of main respondents is biased upwards. We predicted that randomly selecting the household member to be interviewed or collecting (proxy-reported) data on the vaccination status of all household members would lead to lower estimated uptake.

Our results confirm this prediction (Table 4). In all three countries in which we have data on both the main and a randomly selected respondent, estimated uptake is significantly lower among randomly selected respondents compared to purposively selected main respondents. Effect sizes range between five percentage points in Malawi to 13 percentage points in Burkina Faso (Table 4, Panel A). Collecting proxy-reported information for all household members reduces estimated uptake even further, ranging from 13 percentage points in Malawi to 18 percentage points in Uganda (Panel B).

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# **6. Annex 1. Methodology to identify and select the studies about aspiration interventions**

## **Identification of relevant studies**

We started to identify the relevant literature (from economics) in June 2018 for an earlier literature review (see Fruttero, Muller, and Calvo-González 2024). The review is about the influence and determinants of aspirations, rather than a direct review of policy interventions, but does discuss various studies evaluating interventions given that they provide one type of evidence that fostered aspirations are linked to better outcomes. Through Google Scholar, we looked for studies that cited pioneer studies (Appadurai 2004; Ray 2006) and early theoretical papers (Dalton, Ghosal, and Mani 2016) and empirical papers (Nguyen 2008; Beaman et al. 2012; Chiapa, Garrido, and Prina 2012; Wydick, Glewwe, and Rutledge 2013, 2017; Macours and Vakis 2014; Bernard et al. 2022, which a widely circulated earlier version was from 2014; Janzen et al. 2017; Favara 2017; Favara and Sanchez 2017). We also looked at papers cited in previous literature reviews (World Bank 2015; Lybbert and Wydick 2018). From there, we gathered new papers that came to our attention through working paper series (IZA, NBER, World Bank) and Twitter. Some new papers were evidently about aspiration interventions as highlighted in their titles and abstracts, but we found many others by searching in their text keywords such as “aspir”, “hope”, “expectations” (the latter two terms are sometimes meant by aspirations; see Fruttero, Muller, and Calvo-González 2024 for a discussion), given that many educational or employment interventions are not primarily about raising aspirations but do evaluate the impacts of the interventions on them as part of a broad range of outcomes (for example: Chong et al. 2016; Avitabile and de Hoyos 2018; Bonilla, Bottan, and Ham 2019; Johnson et al. 2020; Agüero et al. 2021; Bettinger et al. 2021; Islam et al. 2022; Huillery et al. 2022; Seager et al. 2022; Adhvaryu, Kala, and Nyshadham 2023) (in a way, aspirations have become one common outcome measure in development interventions, although there are many different ways to measure them). Finally, we have frequently looked for the latest versions of papers (updated working papers or published articles) on the authors’ websites, and sometimes came across other papers of an author on the topic that way.

34
If the Turkish port of Trabson were to be used for the transshipments of Annenian trade goods instead of the Georgian port of Poti, the ground share of container transport costs would decrease by one-fourth. General cargo would probably continue to flow through Poti, since Trabson has no rail link. It is more economical to use rail cars than trucks for general cargo. Currently, about 5,000 containers per year pass through Poti on their way to/from Armenia. At a transshipment cost of $1,400 for a 20 foot container and a $1,800 cost for a 40 foot container, the savings would range from US$1.8 to 2.0 million a year.

If the Turkish port Mersin on the Mediterranean coast were used instead of Poti., it will help to eliminate transshipments from/to Mediterranean ports en route to North America, West Europe, and Asia7. Estimates indicate that this might result in as much as a 65 percent transport savings for general cargo8. However, the inability of Turkish railways to handle the massive transit of general cargo raises doubts about the viability of this scheme.

Ground transportation costs to/from Iran would go down by at least one-third if the road via Nakhichevan rather than Megri is used. This would result in savings ranging from US$3.6 to 4.8 million for Iranian imports to Armenia and from US$0.4 to 0.8 million for Armenian exports to Iran.

Total transportation savings for Armenia would amount to US$6.4-8.4 million. The 1999 BOP shows a US$63.6 million debit in freight services and a US$30.3 million deficit. Hence, the savings would decrease the debit by 10-13 percent and the deficit by 21-28 percent.

The effect on Russian transit to Armenia via Georgia would be only minor, since the majority of Russian exports are energy (natural gas and nuclear fuel), which do not use sea or rail routes. The transit of Armenian exports to Russia currently sent via Azerbaijani railway through Georgian intermediate addresses would become easier and quicker to arrange if the intermediary is eliminated. Substantial cost savings would arise only if Armenia were to enter railway tariff agreements with Azerbaijan.

# Azerbaijan

Opening the road between Azerbaijan and Turkey via Arnenia would reduce the transport cost between the two countries by ten percent. Trade is currently conducted via Georgia or Iran. Not all trade flows should be included in the calculations of potential transport savings. Twenty-nine percent of trade goods are delivered by sea and railway via Georgia rather than by road, since it is mainly general cargo, for which road transportation is not economical. Also, it is unrealistic to assume that transit through

> 7 The shallow-water port **Poti** cannot accept large ocean-going vessels necessitating transshipments in smnaller, so-called feeder, ships from/to Mediterranean ports. Container ships are smaller and can enter Poti. They do not require transshipments in the Mediterranean. Therefore, the above estimates refer to general cargo rather than containers.

> s Hurwitz' estimate quoted in _Transport Sector Review_ (1995).

30
# WPS3795

We also review much of the extant research on SME credit availability through the lens of this more complete framework, yielding several conclusions. First, the findings argue against drawing simplistic conclusions from the extant research. For example, the finding that large financial institutions have a comparative advantage in transactions lending technologies and comparative disadvantage in relationship lending does not necessarily imply that large institutions are disadvantaged in providing credit to informationally opaque SMEs. On the contrary, some of the transactions lending technologies used by large institutions are well-suited for funding opaque SMEs. Similarly, there is no simple answer to the policy question of whether a sizeable presence of small institutions is needed for overall SME credit availability. A limited amount of research using U.S. data suggests little concern on this issue, but other research suggests that small institution presence may be more important in other nations because differences in financial structures that may limit the use of some lending technologies.

Second, the results make a strong case for taking into account the presence of foreign- and stateowned institutions, as well the presence of large and small institutions and conventional measures of financial institution competition, particularly when analyzing developing nations. All of these elements of financial institution structure may affect SME credit availability through comparative advantages in the different lending technologies. In particular, a greater presence of foreign-owned institutions and a lesser presence of state-owned institutions is likely to be associated with significantly higher SME credit availability in developing nations because foreign-owned institutions appear to have advantages in some of the lending technologies, and state-owned institutions appear to be generally disadvantaged.

Third, our investigation strongly suggests lending infrastructures have important effects on SME credit availability. “Better” lending infrastructures may significantly improve SME credit availability through facilitating the use of the various lending technologies. As examples, better accounting standards may help spur the use of financial statement lending, better commercial laws on security interests may facilitate the use of asset-based lending and fixed-asset lending, and greater sharing of information may help facilitate the use of small business credit scoring, although other parts of the lending infrastructure must also be in place for these technologies to be legally and profitably employed. Similarly, “worse” lending infrastructures may appreciably reduce SME credit availability. This may occur if a restrictive regulatory environment constrains the financial institution structure, preventing some types of financial institutions from capitalizing on their 22
To empirically address the question of the trade effects of deep agreements, we exploit a new database on the content of PTAs (Hofmann et al., 2017). Since the early 1990s, a large number of trade agreements have entered into force. Focusing on the PTAs still in force in 2015, the number of preferential arrangements increased from 20 in 1990 to 279 at the end of 2015. The content of PTAs too has changed. Newer agreements are “deeper” in the sense that they generally expand the set of policy areas covered by older agreements. Specifically, older PTAs focused on fewer than 10 policy areas, mostly commitments on tariffs on industrial and agricultural goods and other border measures such as export taxes. As agreements become deeper, they increasingly extend their reach first to areas such as trade remedies (i.e. countervailing measures, antidumping duties) and subsidies and then to a broader set of behind the border measures such as intellectual property rights and standards.

To assess the impact of deep agreements on members’ and non-members’ trade, we augment a standard gravity model, which is widely used in the literature to assess the effects of PTAs on trade flows (see Head and Mayer, 2014; Limão, 2016). We include a variable of depth of agreements between PTA members, and a variable that captures the depth of the agreements of a trading partner with other countries. Using information from the content of PTAs database, we construct different measures of depth based on the policy areas regulated by the agreements and their legal enforceability. As standard in the literature, we include importer and exporter-year fixed effects to control for country-year specific shocks and for the multilateral resistance terms, and we introduce country-pair fixed effects to partially address endogeneity concerns (Baier and Bergstrand, 2007).<sup>6</sup> We also include measures of bilateral tariffs and preference margins (Kee et al. 2008, 2009; Fugazza and Nicita, 2013) to isolate the impact of changes in depth from changes in tariffs. Finally, we include additional controls, such as dummies to identify shallow PTAs, PTAs that are no longer in force, and the presence of other international agreements that may affect bilateral trade. Our sample covers 96 countries, including all major economies, for the period 2002-2014.<sup>7</sup> During this period, the share of country pairs with PTAs increased from 9 to 29 percent, average tariffs were cut by half, while depth (measured as the count of provisions included in the PTA) increased by a factor of three.

> 6 See Piermartini and Yotov (2016) for a useful guide on estimating trade policy effects with structural gravity models.

> 7 The country coverage is determined by the availability of comprehensive tariff data for the entire period.

3
The second component of Azteca’s expected profits at the municipality level is the cost of operating a branch, �<sup>m</sup> . As equation (15) shows, there are two variables that determine �<sup>m</sup> , which are population size and whether the municipality has an Elektra store. Let IE be an indicator variable that equals 1 if m has an Elektra store and 0 otherwise. To account that having an Elektra store substantially facilitates Azteca’s operations (Conger (2003)), �<sup>m</sup> consists of a fixed cost that differs for municipalities with an Elektra store. Population size enters Azteca’s cost function through a linear spline approximation to reflect potential economies of scale of having a branch in large enough municipalities, where IP is an indicator variable that equals 1 if m’s population size exceeds a threshold �4 estimated in the model.

Therefore, the expected profits of opening an Azteca branch in municipality m are given by: As this model focuses on the short-term effects of the entry of Banco Azteca, Azteca is the only credit supplier in the model that decides where to locate its branches. The location of the other credit suppliers is considered exogenous. A potential concern is the response of pawn shops to the entrance of Banco Azteca, not only in location of their branches but also in pricing of their loans. Since there is no reliable information at the national level on pawn shops’ pricing or location decisions before 2005, the reaction of pawn shops to the entrance of Azteca is unknown. The model assumes that in the short-run, pawn shops did not change their behavior once Azteca entered the market.

Additionally, the model abstracts from other decisions that credit suppliers make such as the interest rate to charge or the collateral to require. Data on these decisions began to be collected by the Mexican authorities after 2005. Hence, in the model these decisions are fixed to the choices observed from the 2005 data, which are assumed to be the optimal decisions that credit suppliers made in the past under the assumption that these decisions did not change by the entrance of Azteca.

20
a result that is broadly consistent with Barro (2000) and Barro (2008), Keane and Prasad (2002), and Ostry, Berg, and Tsangarides (2014). Our work in this regard is most closely related to Desai and Kharas (2017) who emphasize the importance of the middle class in poverty reduction. While these authors use historical data since 1870 to explore the relationship between the middle class and poverty reduction, we focus on a more recent period that is characterized by growing global integration and use counterfactual simulations to quantify the role of the middle class in sustained poverty reduction.

Third, our study contributes to a literature on the effects of trade policy on poverty (Autor, Dorn, and Hanson, 2016; Topalova, 2010; Harrison, 2007; P. K. Goldberg and Pavcnik, 2004; Winters, McCulloch, and McKay, 2004). We introduce to this literature a novel measure of integrated international market size, which in our model predicts sustained poverty reduction. This measure complements and extends the data sets of Sachs and Warner (1995) and Wacziarg and Welch (2008), which identify the specific years at which economies liberalize. By our treaty-based measure of liberalization, in which a country has access to some international market once it signs a trade agreement, many countries appear open in years when these other datasets consider them closed. Even though many countries are closed according to our measure in 1981, almost none are closed today, given almost complete membership in the WTO by UN member states. Despite this, there is large variation in the relative size of the integrated international market in many regions, driven for instance by China’s entry into the WTO in 2001, which lowered the average income of WTO member states by approximately 18 percent.

Finally, while models of development with firm-level increasing returns assume supply-side constraints, such as capital market imperfections, to explain why countries remain poor (Banerjee and Duflo, 2005), our framework does not rely on such assumptions. Our demand-side framework implies that the small size of the market itself may explain why countries remain poor. Support for the view that demand-side constraints may be binding comes from the empirical literature on the growth of small and medium-sized enterprises (Woodruff, 2018). While a decade of research on supply-side interventions, for instance microfinance (Banerjee, Karlan, and Zinman, 2015) and business training (McKenzie and Woodruff, 2014), has found mostly disappointing effects, several studies find that that expansion into export markets allows firms to increase their scale and adopt new technologies (Lileeva and Trefler, 2010; Bustos, 2011; Aw, Roberts, and Xu, 2011), and that more generally, boosts to demand may be effective in promoting productivity growth (Ferraz, Finan, and Szerman, 2015; Atkin, Khandelwal, and Osman, 2017; Alfaro-Urena, Manelici, and Vasquez, 2020).

7