rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Reduced Form Estimates for Final Goods

|VARIABLES|(1)|(2)|_M _<sup>_k_</sup><br>_j,t_<br>(3)|(4)|(5)|
|---|---|---|---|---|---|
|ln(_distj_)|-1.593*<br>(0.818)|-2.206**<br>(0.873)|-2.873***<br>(0.863)|-5.415***<br>(1.060)|-4.599***<br>(1.130)|
|_HOMEj_|2.220***|2.203***|1.688***|2.425***|1.739***|
||(0.0639)|(0.0583)|(0.484)|(0.602)|(0.602)|
|_V sh_<sup>_k_</sup><br>_t_||0.658***<br>(0.207)|2.304***<br>(0.310)|-10.50***<br>(1.950)|-11.21***<br>(1.855)|
|ln(_WV _<sup>_k_</sup>)||0.392***|0.823***|1.112***|0.975***|
|||(0.0626)|(0.101)|(0.343)|(0.365)|
|(ln(_WV _<sup>_k_</sup>))<sup>2</sup>||0.0134***|0.0121|-0.138***|-0.156***|
|||(0.00470)|(0.0280)|(0.0502)|(0.0520)|
|_Ctnr_<sup>_k_</sup><br>_t_||-1.060***|-1.935***|-0.171|1.165|
|||(0.138)|(0.237)|(1.960)|(2.012)|
|_V sh_<sup>_k_</sup><br>_t _<sup>_× HOMEj_</sup>|||-1.679***<br>(0.455)|-2.193***<br>(0.507)|-1.538***<br>(0.498)|
|ln(_WV _<sup>_k_</sup>)_× HOMEj_|||-0.674***<br>(0.133)|-0.718***<br>(0.113)|-0.617***<br>(0.114)|
|(ln(_WV _<sup>_k_</sup>))<sup>2</sup> _× HOMEj_|||-0.0144|-0.0380***|-0.0260**|
||||(0.0283)|(0.0109)|(0.0115)|
|_Ctnr_<sup>_k_</sup><br>_t _<sup>_× HOMEj_</sup>|||1.190***|1.019***|0.722***|
||||(0.345)|(0.221)|(0.216)|
|(ln(_distj_))<sup>2</sup>|0.0136|0.0535|0.0956|0.151**|0.0964|
||(0.0576)|(0.0609)|(0.0602)|(0.0671)|(0.0741)|
|_IHST_( <sup>�</sup><br>_X_<sup>_k_</sup><br>_jt_<sup>)</sup>|0.679***|0.641***|0.643***|0.647***|0.651***|
|_,_|(0.0308)|(0.0248)|(0.0249)|(0.0259)|(0.0263)|
|Observations|1,075,452|1,075,452|1,075,452|1,075,452|1,070,496|
|Year FE|YES|YES|YES|YES|YES|
|Flexible distance controls|NO|NO|NO|YES|YES|
|U.S. trade policy controls|NO|NO|NO|NO|YES|
|Pseudo R2|0.494|0.512|0.518|0.521|0.524|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Estimates over the sample of HS6 products with values of the upstreamness index _≤_ 1 _._ 3. The LHS variable in all models is _M_<sup>_k_in year</sup><sup>_t_.All models are</sup> _j,t_<sup>, the value of product</sup><sup>_k_imports to PR from origin</sup><sup>_j_</sup> estimated using the PPML estimator on PR’s import data pooled across years, HS6 digit products and places of origin, with year fixed effects included in the estimation. IHST denotes the Inverse Hyperbolic Sine Transformation. Pharmaceutical products are excluded from the estimation.

25
amounting to 15 percent of contributions), workers below 36 years of age would tend to switch to the new system, whereas workers above that age would find attractive to stay in the reformed PAYG. The higher replacement ratios of younger workers are essentially due to the effect of interest compounding over a longer number of years. This interest accumulation effect outweighs the reduction in accrued rights for workers under 36 years of age, but is not sufficient for workers above that age.

Of course, these are rather conservative assumptions about pension fund returns. As shown in Figure 6, the rate of return-wage growth differential in the 1980s and 1990s was higher than 2 percent in a sample of countries with large funded systems, even in countries that imposed portfolio restrictions and/or followed very conservative portfolio strategies, such as Denmark and Switzerland. For the sake of illustration, assuming returns of 3 percent above wage growth, the equilibrium cut-off <!-- Start of picture text --> Figure 5: Replacem ent Ratios in the Old and New Systems for<br>Each Cohort. Reforms 2% above wage growth<br>(Gross Pension/ Gross W age, in% )<br>765 b _<br>70 5_ %<br>60% '=__________<br>40%<br>22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52<br>lMM onoPillar M ulti 8%<br>l _ _ Multi _ 8% (in 2003) B Multi 6%<br>Figure 6: Real W age G rowth and Real Returns on Pension Funds (1984-<br>1996)<br>8 - -- -- --- - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - -<br>6 - --- ---- -- - - ----- -- -- - - - -- - - -- - - -<br>4 - - -<br>-202--- - LLL: lLLr<br>-2-Ireland UKChile Netherlands Denrnark Switzerland,-<br>| Real Return of Pension Funds *Real W age Growth 3Difference<br>Source: EFRP (1996) and OECD (1998)<br><!-- End of picture text -->

10
**Figure 8: Government debt and housing supply (% of GDP)**

Data Source: Authors’ calculation based on WDI data When stepping up housing investment, governments can either tap domestic public borrowing or tap international capital markets. To further explore the correlation between domestic borrowing and housing investment, we focus first on the domestic credit market that can be leveraged for financing housing investment. As expected, figure 9 shows a positive relationship between domestic credit and housing supply (both as % share of GDP) across regions. However, the important point here is the variation in credit supply. The figure suggests that among all regions, domestic credit, as a percent of GDP is even lower than 40 percent in SSA compared with around 50 percent in EAP and as high as 80 percent in MNA and LAC.

18
- <u>Appendix Table 1. Estimates of differences over time in social programs, household level shocks and fertility by migrant status</u>

||<br>S|<br>ocialprogram|<br>s|<br>Ho|<br>usehold sho|<br>cks||<br>Fertility||
|---|---|---|---|---|---|---|---|---|---|
||Received<br>social<br>assistance =<br>1|Value social<br>assistance<br>received<br>(Quetzales)|Health<br>program<br>beneficiary<br>= 1|Illness shock<br>= 1|Natural<br>disaster<br>shock = 1|Security<br>(crime or<br>robbery)<br>shock = 1|Number of<br>children < 1<br>years old|Number of<br>children < 2<br>years old|Number of<br>children < 3<br>years old|
|_Difference-in-difference (DD) m_|_odel_|||||||||
|Migrant household = 1|-0.013|-23.302|0.030|0.022|0.022|0.006|-0.091***|-0.143***|-0.239***|
||(0.012)|(16.047)|(0.027)|(0.017)|(0.033)|(0.016)|(0.022)|(0.031)|(0.037)|
|Year 2012 = 1|-0.012|0.600|-0.158***|-0.013|-0.012|-0.008|-0.032|-0.035|-0.046|
||(0.011)|(18.959)|(0.034)|(0.016)|(0.039)|(0.015)|(0.020)|(0.031)|(0.037)|
|DD|-0.005|-0.344|-0.052|-0.010|-0.004|0.015|0.130***|0.192***|0.282***|
||(0.017)|(19.550)|(0.032)|(0.027)|(0.053)|(0.025)|(0.033)|(0.059)|(0.068)|
|Constant|0.058***|34.913**|0.279***|0.103***|0.602***|0.080***|0.202***|0.370***|0.554***|
||(0.011)|(16.066)|(0.032)|(0.015)|(0.036)|(0.013)|(0.018)|(0.027)|(0.032)|
|N|2,530|2,530|2,530|2,524|2,530|2,530|2,530|2,530|2,530|

Notes: Sample includes all households. Migrant household is defined as a household with at least one migrant in the U.S. in three of the four years 2005-08 for 2008 and three of the four years 2009-12 for 2012. Social assistance includes cash or in-kind transfers from government or non-governmental organizations. Health programs include child and maternal health programs. Shocks retrospectively measured for past five years in 2008 and past four years in 2012. Standard errors estimated allowing for clustering at the community level in parentheses. *** indicates significance at p<0.01, ** at p<0.05, and * at p<0.10.
to Agatha occurred in late May, right in the middle of the seeding period of maize and beans. There is anecdotal evidence that the flooding damaged maize crops in some of the affected areas but not so much in others. In regard to coffee and sugar cane, the excessive precipitation fell well outside their traditional harvesting season. Using data from _Faostat_ (FAO), we construct indices to track the annual production of these four crops in Guatemala during the period 20062012. While the production of sugar cane fell slightly after Agatha, consistent with our hypothesis, we do not observe a large drop in the annual yields of any of the four crops for the interval between the shock and the reference period covered by the _Encovi_ survey (shown by the dotted vertical lines in Figure 7), ruling out supply shocks.

Finally, there is a possibility that formal social protection policy also played a role in partly shielding the basic welfare of rural families from the adverse effects of the shock. In April, 2008, the Government of Guatemala established and began implementing a standard Conditional Cash Transfer (CCT) program, _Mi Familia Progresa_ (currently known as _Mi Bono Seguro_ ). The program transfers money to families living in poverty and extreme poverty that have children ages 0 to 15 years and/or pregnant women (or nursing mothers). A particular design feature of the program is that most of its beneficiaries are rural households.<sup>18</sup> At the time of Agatha, the CCT program benefited nearly 800,000 families, many of them located in parts of the country severely stricken by the floods. While it is possible that the cash transfers contributed to smooth basic consumption, the weak monitoring and enforcement of the school attendance conditionality –as reported by program managers– did not avoid that some children missed school. Yet, more analysis is necessary to sign the net effect from the interaction between the participation in the CCT program and the shock.

# **6. Conclusions**

This paper provides robust evidence that Agatha, the strongest tropical storm to ever strike Guatemala since rainfall records have been kept, led to a sizable deterioration of human welfare among affected households. On average, per capita consumption fell by 5.5% in households matched to weather stations that recorded precipitation anomalies of two or more standard deviations from the historical mean during the days of the shock. While negative impacts triggered by excessive rainfall have been documented in the literature, most of the studies show

> 18 There is also a fraction of program participant from marginal areas in the peripheries of urban centers.

21
productivity that raise competitiveness also raise factor prices and help offset the original increase in competitiveness.

# **Developments in Trade**

China and India have grown relatively rapidly in recent years, and, in both, the importance of trade has risen substantially relative to GDP. Although both of these large, low-income countries had very low export-to-GDP ratios around 1980, when the process of reform was beginning in China (Srinivasan 2004), since then both have increased their exports sharply, although India’s export growth has been much more modest than China’s (Figure 1).<sup>2</sup> From the mid-1990s, as the export processing arrangements were broadened beyond the initial special economic zones in China,<sup>3</sup> the share of exports in China’s GDP began to climb sharply. With the sharp devaluation of the official exchange rate in 1994, the share of exports in GDP rose, but then stabilized or declined in the mid-1990s. From 2001 to 2004, China’s export share rose dramatically, to around 40%, over two and a half times India’s export share. Even the upward revision to GDP of 17 percent in 2004 (see World Bank Office, Beijing 2006) leaves China’s export share at 31%, more than double India’s level.

China’s export growth has been accompanied by tremendous growth in product variety. While China was present in 9 percent of all manufacturing product categories in 1972, it was present in 70 percent of categories by 2001 (Schott 2007). The quality gap between China and the developed countries has been increasing with time in some industries suggesting that developed economies might be responding to competition from China and other low-wage countries by raising the sophistication of their exports or dropping the least-sophisticated varieties from their export bundle.

China’s export growth has been accompanied by tremendous growth in product variety. While China was present in 9 percent of all manufacturing product categories in 1972, it was present in 70 percent of categories by 2001 (Schott 2007). The quality gap between China and the developed countries has been

> 2 China’s trade volume is much larger than India’s, but the cost of switching trade flows away from China are much lower than those of India. This is the case because China supplies mostly modular production – i.e. components that can be added on or off with ease (Steinfeld 2002), while India supplies customized and some “mission critical” services to multination corporation – i.e. operations with extremely high switching costs.

> 3 The export processing arrangements included duty exemptions on imports used for the production of exports. These exemptions were offered to foreign invested enterprises that initially were located in special economic zones in the southern coastal regions of China, but were subsequently broadened to a wide range of enterprises (World Bank 1994) which typically did not receive the economically questionable and (now WTO-inconsistent) income-tax concessions traditionally available in the zones.

4
writing.

Figure S8 now depicts graphically the relationship between columns “Difference” and normalized “Difference*” in Table S6 and S7. Plotted are ISO codes for each nation, each previously given in those tables. Along the 45 degree diagonal in black are C graded countries, which receive no normalization. To the left of the (0,0) coordinate axis are countries with understated output in 2020 as reported in GDP, while to the right are instances of overstatement. From the skew of the figure, it is clear that instances of understatement are in general larger.

When an ISO code in red is above the diagonal, then normalization implies that GDP is either less understated or more overstated. Below the diagonal, GDP is either more understated or less overstated. For instance, the A-graded UK (ISO=“GBR”) appears at (-0.41,-1.81). Thus, on the basis of its grading, its GDP understates true output growth by -0.41%. However, if we normalized it to grade C, that understatement would be even larger, at -1.81%. Before normalization, the UK seems to have significantly less understatement than grade D Malta (MLT), with an original difference of -3.27%. But when normalized, Malta has a difference of -2.06%, much closer to the UK. This figure thus communicates how much conclusions about relative under- or overreporting are due to how countries are classified based on data grades.

Other countries where conclusions change markedly include A graded countries with overstatement, including Luxembourg (LUX), Ireland (IRL), Norway (NOR), Netherlands (NED), and Denmark (DNK), near the top of the figure.

# **References**

- **Bazzi, Samuel, Martin Fiszbein, and Mesay Gebresilasse** , ““Rugged individualism” and collective (In) action during the COVID-19 pandemic,” _Journal of Public Economics_ , 2021, _195_ , 104357.

- **Chen, Xi and William D. Nordhaus** , “Using Luminosity Data as a Proxy for Economic Statistics,” _Proceedings of the National Academy of Sciences_ , 2011, _108_ (21), 8589–8594.

- **and** , “VIIRS Nighttime Lights in the Estimation of Cross-Sectional and Time-Series

- GDP,” _Remote Sensing_ , 2019, _11_ (9), 1057.

- **Elvidge, Christopher D., Feng-Chi Hsu, Kimberly E. Baugh, and Tilottama Ghosh** , “National Trends in Satellite-Observed Lighting,” _Global Urban Monitoring and Assessment Through Earth Observation_ , 2014, _23_ , 97–118.

- **, Kimberly Baugh, Mikhail Zhizhin, Feng Chi Hsu, and Tilottama Ghosh** , “VIIRS Night-Time Lights,” _International Journal of Remote Sensing_ , 2017, _38_ (21), 5860–5879.

- **Geddes, Jeffrey A, Randall V Martin, Brian L Boys, and Aaron van Donkelaar** , “Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations,” _Environmental health perspectives_ , 2016, _124_ (3), 281–289.

S16
# **VI. Concluding Remarks**

Given the rising prevalence of gender reservations and quotas in developing country governments, our study seeks to better understand the role of gender in public goods provision in a specific setting. Using data from 144 village-level governments in Tamil Nadu, we investigate women’s reservations at the GP level and evaluate the role of presidents’ gender on local public goods provision. For some public goods, provision is similar among female and male presidents. However, we find very suggestive evidence that public goods provision by women presidents in women-reserved GPs is lower than that of their male counterparts. In particular, we find strong evidence that GPs with women-reserved president seats created fewer schools and roads than unreserved GPs during the period under study. We find weak evidence that women are providing fewer streetlights and household drinking water and toilet connections. These findings are correlated with strong evidence of women’s lack of interaction with higher-level government officials and women’s low knowledge of the GP.

These findings contribute to the literature on women’s reservations in India by highlighting that the GP reservation system may be falling short in providing knowledge of the GP to women who are new to political office and in providing a pathway for connections between women GP presidents and higher-level officials. To our knowledge, this is the first study to document a clear lack of communication between women GP presidents and higherlevel officials at the block and district levels and to link this lack of communication to demonstrably worse outcomes for the GP, particularly in terms of the provision of schools and roads.

This study highlights the need—in at least some parts of India—to improve women’s preparedness for the office of GP president and their connections while in office. Increased or 24
7 working on the plot.<sup>10</sup> Only labor input of adult household members (above 15 years old) is considered in the results reported below. They contribute almost all labor for crop production.<sup>11</sup> To obtain countrylevel estimates of the female labor share in crop production, the plot-level unit record data of each household member’s labor input are cleaned and compiled as follows. First, the individualdisaggregated data on labor inputs collected at the plot-level for various activity domains are compiled into a household member-level database. Second, outliers in individual labor aggregates across plots and activities (in hours or days, depending on the survey) are identified based on common sense parameters and are replaced by missing values. For each individual, the number of weeks for land preparation and planting, for weeding, fertilizing and other non-harvest activities, and for harvesting are capped at 13, 26, and 13 weeks, respectively. A typical week was taken to be no more than 7 days, and the number of hours in a typical work day was capped at 12 hours. Third, the top and bottom 1 percent of individual-level labor aggregate values, within each region, sex and age combination, are further tagged as outliers and replaced by missing values. Finally, following Scheffer (2002), the resulting missing values<sup>12</sup> were imputed using single regressionbased imputation.<sup>13</sup>

> 10 Appendix A provides a more detailed, cross-survey overview of the different approaches to the household agricultural labor data collection.

> 11 In each country, adult household members contributed at least 90 percent of all labor devoted to crops (including child and hired labor) and 97 percent of all adult labor. Inclusion of child labor or hired labor does not meaningfully affect the reported labor shares by gender.

> 12 In all countries, the percentage of missing values is less than 2.5 percent, except in Nigeria where it reaches 20 percent. This includes the outliers that have been converted to missing. Additional tests are undertaken to examine the robustness of results from Nigeria.

13 The linear Ordinary Least Squares regression underlying the predictions features the individual-level labor aggregate as the dependent variable, and a range of plot, individual laborer, household and respondent characteristics as independent variables. Plot characteristics include area, agro-ecological potential for maize, primary crop planted on the plot, gender of the manager; input use reflects the use of organic fertilizer, inorganic fertilizer, pesticide, soil quality, agricultural implements index; household characteristics include household size, dependency ratio, head of household’s age, maximum years of schooling in the household; and as worker characteristics their gender, age, age squared are included. The respondent characteristics included are the age, age squared, gender of the respondent and whether the respondent is the manager.
# Results

We start by presenting results to benchmark the three countries against the other countries in the Europe and Central Asia region. Figure 13 plots the Altham statistic based on the educational transition matrices for all countries in the sample in the LiTS survey of 2016. As discussed above, this statistic measures “distance” from a perfectly mobile society, implying that countries such as Russia, Ukraine and Italy with high values of the statistic are furthest away from perfect mobility whereas those such as Uzbekistan and Lithuania are at the other end of the spectrum with the weakest association between parental and child education. The high degree of intergenerational persistence for Italy in particular, is confirmed also in other studies (Hertz et al 2007). In this spectrum, the countries in the South Caucasus line up roughly in the middle with Armenia depicting the highest mobility and Georgia the lowest. It has to be noted that the actual difference in the value of the statistic is not that large, at least across the three countries.

Figure 13: Benchmarking Intergenerational Mobility in the South Caucasus with the rest of Europe & Central Asia Region <!-- Start of picture text --> UzbekistanLithuaniaMongoliaKosovoTurkeyEstoniaLatviaCyprusFYR MacedoniaArmeniaBosnia and HerzegovinaGreece TajikistanPolandSloveniaKyrgyzstanAzerbaijanSlovak RepublicAlbania CroatiaBulgariaSerbiaCzech RepublicGeorgiaMontenegroGermanyKazakhstanHungaryBelarusMoldovaRomania ItalyUkraineRussia<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source_ : Tiwari et al (2017) In addition to a snapshot for a given year, often it is of interest to find out how intergenerational mobility has changed over time. In particular, is it the case that for the younger generations the association between their attainment levels and those of their parents is weaker than what you might find for the older generations? As society develops economically, it is often expected that the role and the influence of one’s original circumstance plays an increasingly weaker role in what one can achieve. In fact, in many ways that origin-independence is itself a measure of how a society as progressed from being a traditional, stratified society in which status is locked in at birth towards one that is more meritocratic.

Figure 14 presents the mobility measures for five decadal age cohorts in Armenia, Azerbaijan and Georgia. Note that around the year of the transition, these cohorts would have been roughly 0-9 30