rafmacalaba/datause-encoder-data huggingface dataset:
# **Further Reading**

- Aghion, Philippe, Philippe Bacchetta, Romain Ranciere, and Kenneth Rogoff. 2009. ―Exchange Rate Volatility and Productivity Growth: The Role of Financial Development.‖ _Journal of Monetary Economics_ 56, 494–513.

- Aizenman, Joshua and Jaewoo Lee. 2007. ―International Reserves: Precautionary versus

- Mercantilist Views, Theory and Evidence.‖ _Open Economies Review_ 18 (2), pp. 191-214.

- ———. 2008 ―Financial versus Monetary Mercantilism—Long-run View of Large International Reserves Hoarding.‖ _The World Economy_ , 31 (5): 593-611.

- Aizenman, J., M. Chinn and Hiro Ito. 2010. ―Assessing the Emerging Global Financial Architecture: Measuring the Trilemma's Configurations over Time,‖ NBER Working Paper No. 14533.

- ______ and Nancy P. Marion. 2003. ―The High Demand for International Reserves in the Far

- East: What‘s Going On?‖ _Journal of the Japanese and International Economies_ 17: 370– 400.

- ______ and Nancy P. Marion. 2004. ―International Reserves Holdings with Sovereign Risk and Costly Tax Collection.‖ _Economic Journal_ 114: 569–91.

- _____, Y. Lee and Y. Rhee. 2007. "International reserves management and capital mobility in a volatile world,‖ _Journal of the Japanese and International Economies_ , 21 (1), pp. 1-15.

- _____ and Daniel Riera-Crichton. 2008. "Real exchange rate and international reserves in the era of growing financial and trade integration," _Review of Economics and Statistics_ , 90: 4, pp. 812-815.

- _________ and Y. Sun. 2009. ―The financial crisis and sizable international reserves depletion: From 'fear of floating' to the 'fear of losing international reserves'?,‖ NBER WP # 15308.

- Aizenman J. and M. Spiegel. 2010. ―Takeoffs‖ Review of Development Economics, 14, 2: 177196.

- Aizenman, J. 2011. ―Hoarding International reserves versus a Pigovian Tax-Cum-Subsidy

- Scheme: Reflections on the deleveraging crisis of 2008-9 and a Cost Benefit Analysis,‖ forthcoming _, Journal of Economic Dynamics and Control_ .

- _________ 2010. ―Financial Crisis and the Paradox of Underregulation and Overregulation‖ _Annual World Bank Conference on Development Economics_ 2010, Lessons from East Asia and the Global Financial Crisis.

- Aizenman J. and V. Sushko. 2011a. ―Capital Flow Types, External Financing Needs, and Industrial Growth: 99 countries, 1991-2007 NBER Working Paper No. 17228

- Aizenman J. and V. Sushko. 2011b. ―Capital flows: Catalyst or Hindrance to economic takeoffs?‖ NBER Working Paper No. 17258.

- Ben-Bassat, Avraham, and Daniel Gottlieb. 1992. ―Optimal International Reserves and Sovereign Risk.‖ _Journal of International Economics_ 33: 345–62.

Bird Graham and Ramkishen Rajan, 2003, ―Too Much of a Good Thing? The Adequacy of

- International Reserves in the Aftermath of Crises,‖ The World Economy, 26: 6, pp. 873– 891.

- Blanchard O. and F. Giavazzi. 2002. ―Current Account Deficits in the Euro Area. The End of the Feldstein Horioka Puzzle?‖ Brookings Papers on Economic Activity. Washington, 2002.

- Burnside, Craig, Martin Eichenbaum and Sergio Rebelo. 2001. ―Prospective deficits and the Asian Currency Crisis.‖ _Journal of Political Economy_ 109, 1155–98.

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Table 4, the first thing to point out is that the pattern of estimates matches the findings from the country level aggregate model: air services liberalization increases the odds of a direct flight between any two cities belonging to the signatory countries, and any implied differences between plurilateral and bilateral agreements are not statistically significant. The scale of the policy coefficient depends on the exact specification. But the final row of Table 4 illustrates that the quantitative implications are not so different. That row shows that raising ALI to 50 among ACAC cities would increase the odds of a flight between any two given cities by a factor of 1.2 to 1.3.

# _V.3     Simulating a Turkey-ACAC agreement_

The policy experiment we have in mind when designing the counterfactual exercises is the inclusion of Turkey in regional air services arrangements equivalent to those of the ACAC. Air passenger markets in the Middle East are changing rapidly. Turkey, which aspires to serve the region as a hub, has seen rapid growth in air passenger traffic, within the region and with the rest of the world. The policy environment, however, has not kept pace with these developments. Turkey is not a member of the ACAC. Instead, WTO measures suggest that Turkey’s bilateral passenger traffic arrangements with these countries are quite restrictive. We consider a scenario in which the ACAC and Turkey negotiate an agreement at the most liberal level of policy commitment available, ALI = 50. Because existing policy commitments are quite restrictive in most cases, this represents a dramatic change in the degree of air services liberalization between Turkey and the ACAC member states.

Table 5 reports a number of useful statistics along with counterfactual results for the intensive margin at both the country and city level. The second and third columns of the table report total 2010 traffic between each country and Turkey, as reported by the ICAO data. The fourth column (routes) indicates the number of city pairs for which the database records a flight between the specified country and Turkey. The fifth column reports the level of ALI reported in the WTO database for Turkey’s 21
shares in agriculture, while richer countries have lower agricultural employment shares and higher shares in manufacturing and market services. The share of men in the home sector (i.e., not in the labor force) is small and relatively stable across different levels of economic development.

Figure 1b shows different employment patterns for women. Similar to men, women’s employment shares in agriculture are high in poor countries and low in richer ones. However, in low- and middle-income countries, lower agricultural employment shares among women coincide with higher shares in the home sector, i.e., with low female labor force participation. In richer countries, female labor force participation is higher again, with most women working in the service sector.<sup>8</sup> Female employment shares in manufacturing remain consistently low across all levels of economic development.

These gender differences in sectoral employment suggest that higher female labor force participation might increase employment in services relative to manufacturing. Hence, changes in gender roles and rising female labor force participation over recent decades may have contributed to the premature deindustrialization and service-led growth observed in many developing and emerging countries (Rodrik, 2016; Fan et al., 2023; Huneeus and Rogerson, 2024). To further examine this possibility, Figures 1c and 1d present how changes in agricultural employment shares correlate with corresponding changes in manufacturing and services between the 1970s and 2010s, separately for men and women, and restricting the sample to low- and middle-income countries. For each country, we calculate changes in sectoral employment shares over time, conditional on labor force participation, using the first (1970-1990) and last (2000-2019) years of available data. Figure 1c shows that a 10 percentage-point (p.p.) decline in men’s agricultural employment share is, on average, associated with an increase of 4.4 p.p. in manufacturing and 5.7 p.p. in services. For women, the shift toward services is stronger with employment shares increasing by 2.4 p.p. in manufacturing and by 7.7 p.p. in services (Figure 1d).<sup>9</sup>

> 8Figure 1b confirms the U-shaped relationship between female labor force participation and economic development, which has been extensively studied in the literature (Goldin, 1995, 2024; Mammen and Paxson, 2000).

> 9Figure OA.1 in the Online Appendix shows that gender differences are more pronounced when including changes in labor force participation. For men, a 10 p.p. decline in the combined share of agricultural and home sector employment is, on average, associated with a 5 p.p. increase in manufacturing and services. For women, these numbers are 1.7 p.p. for manufacturing and 8.4 p.p. for services. Figure OA.2 in the Online Appendix additionally shows that the flattening of total manufacturing employment shares in Brazil and Mexico during the past decades is driven by women, while men’s employment shares in manufacturing continue to rise.

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indirectly by the intervention and, as a result, even the estimates of columns 2-4 in Table 3 could be downward biased.

We use two strategies to assess spillover effects beyond the geographical cluster. First, we use the GPS location of every household in the study to compute the number of other treated and control households within a given distance radius and run the following OLS regression equation: Where is an indicator for whether household _h_ is not treated but has at least one treated household within a distance radius of _d_ meters, and is the total number of surveyed households within distance radius _d_ of household _h_ .<sup>26</sup> According to Table 1, the average distance between any two households in a cluster is 194.2 meters (0.12 miles) and the median distance is roughly 100 meters. In contrast, the median distance between any two surveyed households in a village is roughly one kilometer (0.67 miles). We consequently use a radius of 75, 100 and 200 meters, which corresponds roughly to the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentile of the distance between any two households within a cluster. The rest of the terms are as defined in Equation (1), and as before, standard errors are clustered at the geographical cluster level. The coefficients of interest are and . This specification allows for a simple test of the extent to which female turnout among control households near treated households resembles female turnout among directly treated households.

Panel A of Table 4 reports the results from Equation (3). The treatment effects are robustly significant and range between 12 to 15 percent. The estimate of is also significant in columns 1 and 2, and is comparable to the direct treatment effect, suggesting that geographical spillovers are large. Indeed, the _t_ -test for is never rejected. The coefficient on the number of households within a given radius, which can be interpreted as household density, is significant in columns 2 and 3, suggesting that turnout is higher in more densely populated areas within a village, perhaps because polling stations tend to locate there. Panel B reports the results for the

> 26 It is clear that the dummies and cannot both take value 1, since households are either treated or not. Both dummies take value zero if the household was not treated ( ) and was located more than _d_ meters from the closest treated household.

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Costinot, Donaldson, and Komunjer (2012) proposed a measure of comparative advantage that uses a regression of bilateral exports on fixed effects to isolate the component of productivity not driven by bilateral trade costs (an importer-exporter fixed effect) or foreign demand (an importer-product fixed effect). The remaining exporter-product fixed effect identifies “regression-revealed comparative advantage.” While theoretically consistent, regression-revealed comparative advantage may miss certain opportunities for export success. For instance, for geopolitical reasons, the United States may increase demand for imports from Mexico as it pursues a policy of friend-shoring (on potential consequences of friend-shoring, see Goldberg and Reed 2023b). Ranking sectors within Mexico by regression-revealed comparative advantage could potentially down-rank sectors with exports concentrated in the US market, since US demand is ignored. But the Mexican government, particularly at a moment of increased US demand, might reasonably prefer to prioritize sectors that benefit from that demand. Another challenge is that regression-revealed comparative advantage can be undefined, both in small countries that export a product to only one other country; and in the case of services exports, which in developing countries are often reported as flows to the world rather than as bilateral trade flows, given challenges in quantifying them through surveys.

Any measure of productivity based on realized export values could reflect economic distortions instead of actual productivity in a competitive market. There are two types of distortions: distortions in other economies and in one’s own economy. Developing countries complain about subsidies for agricultural production in other countries (for recent estimates of these see World Bank (2023)). Low revealed comparative advantage in rice or cotton, for instance, could reflect not low productivity relative to other countries in a competitive market, but a country’s inability to compete with subsidized exports from the United States. Still, given that a country cannot immediately change the subsidy policies of other countries—the Doha round of negotiations covering agricultural subsidies has indefinitely stalled—this quality may be an advantage of revealed comparative advantage. The measure shows what sectors are competitive, holding fixed the policies of competitors.

Distortions in one’s own economy can lead to a situation in which low revealed comparative advantage could reflect market failures, but high revealed comparative advantage could reflect own-government subsidy programs with low benefits net of costs that nonetheless 11
networks? Urban Studies 37: 513‐532.

Gordon, P., A. Kumar, and H.W. Richardson. 1989. The influence of metropolitan spatial structure on commuting time, Journal of Urban Economics, 26: 138–151.

Gordon, P. and H.W. Richardson. 1996. Beyond polycentricity: The dispersed metropolis, Los Angeles 1970–1990, Journal of the American Planning Association 62: 289–295.

Gupta, S., S. Kalmanje, and K. M. Kockelman. 2006. Road Pricing Simulations: Traffic, Simulations and Welfare Impacts for Austin, Texas. Transportation Planning and Technology 29(1): 1–23.

Hayuth, Y., 1981. Containerization and the load centre concept. Economic Geography 57: 160–176.

Hayuth, Y., 1988, Rationalization and deconcentration of the US container port system, The Professional Geographer, 40 (3): 279‐288 Helpman, E., Pines, D., 1977. Land use and zoning in an urban economy: further results. American Economic Review 67: 982–986.

Henderson, J. Vernon. 1974. The sizes and types of cities. American Economic Review 64(4):640–656.

Henderson, J. Vernon. 2005. ”Urbanization and Growth.” in (eds) Philippe Aghion and Steven Durlauf, Handbook of Economic Growth, North Holland.

Henderson, J. Vernon. 2010. Cities and Development. Journal of Regional Science 50(1): 515‐540.

Henderson, J.V. and A. Kuncoro, 1996. The Dynamics of Jabotabek Development, Bulletin of Indonesian Economic Studies.

Henderson J, Kuncoro A, Turner M. 1995. Industrial development in cities. Journal of Political Economy, 103: 1067–1085.

Henderson J.V. and Mitra A. 1996. The New Urban Landscape: Developers and Edge Cities, Regional Science and Urban Economics, 26: 613‐643.

Henderson, V. and Y. Ono. 2008. Where do manufacturing firms locate their headquarters? Journal of Urban Economics, 63: 431‐450 Henderson, J.V., Slade, E., 1993. Development games in nonmonocentric cities. Journal of Urban Economics 34: 207–229.

Henderson, J.V., A. Storeygard and U. Deichmann. (2014). Is climate change driving urbanization in Africa? Unpublished draft.

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The joint life formula contains the reversion of the annuity to the second beneficiary (typically the spouse) at a lower level (60 percent), and the survivorship of two lives determining the annuity payment. If the principal beneficiary is noted with symbols as above, and the reversionary beneficiary is noted with the same symbols but with a ‘^’ mark and is aged _y_ at commencement of the annuity, then the formula is as set out in equation (2). Note that the probability term in the numerator would be set to 1 during the period where annuity payments are guaranteed.

Note that all annuities in Chile are quoted in _Unidades de Fomento_ (UF), a unit of account indexed to consumer prices and widely used in financial contracts. In this analysis all values are expressed in UF and should be interpreted accordingly when making comparisons with other countries.<sup>3</sup>

# **_3.2. Mortality Tables_**

Most empirical studies estimate MWRs with two mortality tables, one reflecting the mortality of the general population and the other reflecting the mortality of the smaller annuitant population. These are necessarily cohort tables, constructed either by incorporating existing projections of future mortality for each cohort, or by estimating future mortality improvements and applying them to period tables.<sup>4</sup> The difference between the estimated MWRs using the general and the annuitant population assumptions is frequently interpreted as the effect of adverse selection.

In the case of Chile, there was no mortality table for the population that is updated and reliable at the time of writing, but three tables have been constructed for the annuities market. The first of these tables, known as RV-85, is a period table that was developed when the annuity system started and there were few annuities in force. The table purports to represent the period experience of annuitant mortality at the time it was developed, but was partly constructed by making adjustments to mortality data from other countries. The RV-85 was developed for regulatory purposes, and served until recently as the basis for the determination of phased withdrawal (PW) payments and the calculation of technical reserves for annuity providers.

The second table, referred to as RV-98, is a period table based on more extensive Chilean annuitant mortality data collected between 1995 and 1997. The table represents an improvement over the RV-85, by including more information on the mortality of the Chilean annuitant population. However, while the male tables were mostly determined from the data, the female tables largely impute the observed rate of change between the

> 3 The Pension Law approved in 2004 has allowed other types of annuities, but all the MWRs presented in this report refer to annuities fixed in UFs, i.e., annuities indexed to prices. 4 See, e.g., Brown et al (2001), and James, Song and Vittas (2003).
Next, we turn to the estimated effects on trust in the state. Since respondents may assess the elected government differently than the bureaucracy, we elicit trust in these two components separately. We also implement a lab-in-field game to measure trust in the state as elicitations may not fully reflect “real” behavior. In this game, respondents allocate Rs. 200,000 to either a government or non-state charity fund to support Covid-19 relief efforts in Pakistan. We use the share of funds allocated to the government as a behavioral measure of trust in the state. Whatever measure we use, we estimate no increase in trust in the state. If anything, we find the state positive treatment has a small but negative effect on trust.

Following our pre-analysis plan, we also investigate whether the treatments had heterogeneous effects along a large number of dimensions to ensure that these average effects do not mask large responses by certain subgroups. We observe no significant heterogeneity by education, economic exposure to the Covid-19 pandemic, religiosity or belief in conspiracy theories on the Covid-19 pandemic. However, we document significant heterogeneity by partisanship (measured by past vote choice and present media consumption). Respondents who lean more favorably towards the ruling party lend more support to government policy after receiving the information treatments. This effect is particularly strong for the citizen cooperation treatment, where pro-ruling party respondents increase support for government directives on hand-washing, social distancing, and avoiding congregational prayers by 0.1 to 0.15 standard deviations. However, here is where the experimenter demand condition becomes relevant: we find that these heterogeneity patterns are not robust to accounting for potential demand effects when we compare the treatment groups to the experiment demand group.

Our results provide a caveat to findings from Acemoglu et al.’s (2019) closely related study which shows that informing citizens of reduced delays in state courts increases expected usage of, allocations in high-stakes lab-in-the-field games to, and trust in state courts. State positive information about a specific policy may increase trust in a _specific_ institution. However, our results suggest that this finding may not necessarily generalize to other policy domains or towards _generalized_ measures of trust in the state. This has important implications for our understanding of the limitations faced by governments engaging in attempts to foster trust in state institutions.

Our paper contributes to a body of contemporaneous work studying how different forms of communication shape public perceptions about Covid-19 (Allcott et al., 2020; Ajzenman et al., 2020; Banerjee et al., 2020; Barrios and Hochberg, 2020; Bursztyn et al., 2020; Grossman et al., 2020; Rafkin et al., 2020). These studies focus on how people update different types of beliefs when informed about Covid-19. Most related to our work is a study by Rafkin et al. (2020) which explores the effects of highlighting government inconsistency on trust in the 5
**Table A.19:** Effects on Village Taking Action on Public Goods: Index Components

||**Index**|Water|Garbage|Roads|Electricity|Kindergarten|Schools|Health|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|New village head|0.165<sup>_∗∗_</sup>|0.213|0.543<sup>_∗∗∗_</sup>|-0.010|0.123|0.135|0.142|0.126|
||(0.086)|(0.134)|(0.149)|(0.142)|(0.156)|(0.135)|(0.166)|(0.089)|
|Observations|443|443|443|443|443|443|443|443|
|Control mean|0.76|0.69|0.72|0.75|0.66|0.85|0.66|0.91|
|Robust p-value|0.046|0.115|0.000|0.819|0.523|0.275|0.495|0.118|
|<br>MSE-opt. bandwidth|24.9|23.7|14.8|24.9|22.2|19.7|21.1|26.2|
|Effective obs.|241|235|167|241|221|207|216|250|

_Notes_ : This table reports RD estimates on the take action index used in Table 7, column 4, and individual components of the index. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

**Table A.20:** Citizen Demographics, by Social Distance to Village Government

||Female|Age|Disability|TertiaryEduc.|Not Working|MonthlyIncome|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||**Pa**|**nel A: First-**|**Degree Connect**|**ion**||
|Sample Mean|0.388|38.9|0.022|0.241|0.184|2572|
|Std. dev.|[0.487]|[11.4]|[0.146]|[0.428]|[0.388]|[3504]|
|Observations|2656|2637|2655|2647|2651|2146|
|||**Pan**|**el B: Second**|**-Degree Conne**|**ction**||
|Sample Mean|0.466|35.7|0.022|0.191|0.257|2405|
|Std. dev.|[0.499]|[10.9]|[0.147]|[0.393]|[0.437]|[3293]|
|Observations|2216|2198|2213|2207|2208|1629|
|||**Panel C**|**: Third-Deg**|**ree or More Con**|**nection**||
|Sample Mean|0.532|32.3|0.028|0.158|0.304|2220|
|<br>Std. dev.|[0.499]|[10.5]|[0.165]|[0.365]|[0.460]|[3096]|
|Observations|4008|3981|4005|3996|3997|2761|

_Notes_ : This table reports summary statistics of citizen demographics by degree of connection to the village government. The dependent variable is: in column 1, a dummy equal to 1 for female citizens; in column 2, the age of citizens in years; in column 3, a dummy for whether the citizen reports having a disability; in column 4, a dummy for whether the citizen has a tertiary level education; in column 5, a dummy for whether the citizen reports not working; in column 6, the citizen’s average monthly income in the last 6 months (conditional on working) winsorized at the top 1% and reported in thousands of Indonesian rupiah.

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