rafmacalaba/datause-encoder-data huggingface dataset:
|rried to miners.|(7)<br>(8)<br>to a miner<br>Sales<br>Agriculture|0.027<br>-0.069***|(0.028)<br>(0.024)|-0.164***<br>0.073|(0.050)<br>(0.080)|4,628<br>4,628|0.191<br>0.319|11.74<br>2.943<br>0.000623<br>0.0864|gion fxed efects, regional<br>|
|---|---|---|---|---|---|---|---|---|---|
|for those ma|(6)<br>Married <br>Service|-0.029|(0.020)|0.080|(0.067)|4,628|0.133|2.425<br>0.120|year and re<br>0.05, * p<0.1|
|nd|||||||||for<br>p<|
|km opened a|(5)<br>Working|-0.075**|(0.035)|0.185**|(0.086)|4,628|0.172|7.951<br>0.00485|ions control <br> p<0.01, **|
|mine within 20|(4)<br>ng<br>Agriculture|-0.023|(0.018)|0.101|(0.094)|291,395|0.347|1.719<br>0.190|heses. All regress<br>gious beliefs. ***|
|before any|(3)<br>mine openi<br>Sales|-0.008|(0.013)|0.022|(0.039)|291,395|0.165|0.554<br>0.457|in parent<br>n, and reli|
|ed|e||||||||vel <br>tio|
|or those marri|(2)<br>Married befor<br>Service|0.029***|(0.009)|-0.035***|(0.012)|291,395|0.094|20.25<br>6.83e-06|DHS cluster le<br> years of educa|
|s of mining f|(1)<br>Working|0.003|(0.015)|0.033|(0.110)|291,395|0.201|0.0742<br>0.785|ered at the <br>an area, age,|
|: Efect||||||||=0|rs clust<br> an urb|
|.12||||||||ive|rro<br> in|
|Table A|LES|0 km)||(20 km)||ions|d|ctive-inact|tandard e<br>ds, living|
||AB|(2||ve||vat|are|: a<br>e|t s<br>ren|
||VARI|active||inacti||Obser|R-squ|F test<br>p valu|Robus<br>time t|

39
||(1)<br>trade|(2)<br>trade|(3)<br>trade|
|---|---|---|---|
|RTA|0.10<sup>_∗∗∗_</sup>|0.07<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|
||(11.97)|(6.82)|(5.43)|
|RTA_×_ SAR|0.10<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|
||(6.00)|(7.21)|(6.26)|
|RTA_×_ SAR-intra|0.20<sup>_∗∗∗_</sup>|0.20<sup>_∗∗∗_</sup>|0.20<sup>_∗∗∗_</sup>|
||(5.70)|(5.76)|(5.75)|
|EU||0.11<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|
|||(8.90)|(6.69)|
|EU-ROW|||-0.02|
||||(-1.11)|
|_N_|933660|933660|933660|

**Note:** the variable RTA measures the world average effect of trade agreements. The SAR effect is measured in addition to the average, and SAR-intra is in addition to the SAR effect. The EU and EU-ROW effects are in addition to the world average RTA effect. _t_ statistics in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

Table 3: South Asia manufacturing results controlling for EU 10
Table 5: Country-centric regression in levels

|||(1)<br>GDPD|(2)<br>GDPD|(3)<br>GDPD|(4)<br>GDPD|
|---|---|---|---|---|---|
|log|index dagexp fnx|-1.823<sup>_∗∗_</sup><br>(0.819)||||
|log|index dagexp intx|3.477<sup>_∗∗_</sup><br>(1.432)||||
|log|index exp rexpx|-2.463<sup>_∗_</sup><br>(1.292)||||
|log|index davax fnx||-1.788<sup>_∗∗_</sup><br>(0.797)|||
|log|index davax intx||3.168<sup>_∗∗_</sup><br>(1.431)|||
|log|index vax rexpx||-2.410<sup>_∗_</sup><br>(1.281)|||
|log|index gvce broadx|||-0.877<sup>_∗_</sup><br>(0.502)||
|log|index gvcx||||-1.191<sup>_∗∗_</sup><br>(0.511)|
|N||833|830|833|833|
|R2||0.281|0.281|0.271|0.274|
|Ti|me windows|5|5|5|5|
|Co|untries|177|177|177|177|
|Ti|me FE|YES|YES|YES|YES|
|Co|untry FE|YES|YES|YES|YES|

Standard errors in parentheses Note: observations with non-overlapping 5-year windows. The GVC indices used in this table are computed from the exporter side. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01 21
12 institutions and development in two ways. First, they demonstrate a previously unidentified relationship, between citizens‟ ability to act collectively and development outcomes, for which theory provides a causal explanation. Second, they demonstrate that other measures of collective action are more strongly associated with development outcomes than competitive elections. The results of this “horse race” are less likely to be the spurious outcome of omitted variable bias to the extent that omitted variables have a similar influence on both variables, though this assumption is evidently not testable.

# **The uneven impact of elections on development**

Though competitive elections could be viewed simply as a vehicle for reducing individuals‟ costs of influencing politician behavior, they are more accurately seen as lowering the costs to the general public of acting collectively to remove poorly performing incumbents. State-sponsored competitive elections solve the coordination problem of when and how citizens should act collectively, while subsidizing the machinery of collective action (elections themselves). The evidence presented in this section points to generally favorable policy effects of competitive elections, though among poor countries these effects disappear, and competitive elections appear to have no association with private investment.

Tables 1 and 2 identify the association between competitive elections and various development-relevant outcomes, holding constant income per capita (real, purchasing power parity-adjusted), and exogenous country features that might influence the public policy or the ability of citizens to organize collectively, independent of the presence of competitive elections: the land area of the country, its total population, and the fraction of the population that is rural or under the age of 15. Five development-related outcomes are considered: gross secondary and primary school enrollment from _World Development Indicators_ , capturing whether government policies towards education, including school
it in other ways. This can be done through household surveys that extend beyond the manufacturing sector. We also believe that satellite data on economic activity would be an especially powerful tool in this context.

# **References**

- Ahluwalia, M. (2000). “Economic Performance of States in the Post Reforms Period”. _Economic and Political Weekly_ , 35(19):1637-1648

- Banerjee, A., E. Duflo and N. Qian. (2012). “On the Road: Access to Transportation Infrastructure and Economic. Growth in China”. NBER Working Paper No. 17897.

- Baum-Snow, N. (2007). “Did Highways Cause Suburbanization?”. _Quarterly Journal of Economics_ 122(2): 775–805.

- Baum-Snow, N., L. Brandt, V. Henderson, M. Turner and Q. Zhang. (2012). “Roads, Railroads and Decentralization of Chinese Cities”.  Working Paper.

- Besley, T. and R. Burgess. (2004). “Can Labor Regulation Hinder Economic Performance? Evidence from India?”. _The Quarterly Journal of Economics_ , 119(1): 91-134.

- Bhagat, R. (2011). “Emerging Pattern of Urbanisation in India”. _Economic and Political Weekly_ , 46(34): 10-12.

- Brown, D., M. Fay, J. Felkner, S. Lall and H. Wang. (2008). “The Death of Distance? Economic Implications of Infrastructure Improvement in Russia”.  EIB Papers, 13(2): 126-147.

- Chandra, A. and E. Thompson, E. (2000). “Does Public Infrastructure Affect Economic Activity?  Evidence from the Rural Interstate Highway System”. _Regional Science and Urban Economics_ , 30(4): 457–490.

- Datta, S. (2011). “The Impact of Improved Highways on Indian Firms”. _Journal of Development Economics_ , 99(1): 46–57.

- Desmet, K., E. Ghani, S. O’Connell, and E. Rossi-Hansberg (2012), “The Spatial Development of India”.  World Bank Policy Research Paper 6060.

- Dinkelman, T. (2011). “The Effects of Rural Electrification on Employment: New Evidence from South Africa”. _American Economic Review_ , 101(7): 3078–3108.

- Donaldson, D. (2010). “Railroads and the Raj: The Economic Impact of Transportation Infrastructure”. NBER Working Paper No. 16487.

- Donaldson, D. and R. Hornbeck. (2012). “Railroads and American Economic Growth: New Data and Theory”. Working Paper.

21
or the long-term trend in the location the households now resides? After data cleaning to remove tracked and non-agricultural households, we are left with 3,250 household observations across three survey waves.

The LSMS-ISA data from Niger includes two waves, the first from 2011 and the second from 2014 (NIS, 2014; NIS, 2016). The sample is representative at the national and urban/rural-level. Data cleaning and removal of non-agricultural households gives us 3,913 household observations across two survey waves.

In Nigeria, we use the data from the 2010/11, 2012/13, and 2015/16 rounds of the General Household Survey - Panel, which is representative at the national and urban/rural-level (NBS, 2012; NBS, 2014; NBS, 2019). Data cleaning and removal of non-agricultural households yields 8,384 household observations across three survey waves.

In Tanzania, the data stem from the 2008/09, 2010/11, and 2012/13 rounds of the Tanzania National Panel Survey (TZNPS) (TNBS, 2011; TNBS, 2012; TNBS, 2015). Similar to Malawi, the LSMS-ISA from Tanzania sought to track households the split and moved locations. As a result of this, the sample size expands with each wave. Focusing on rural, crop producing households that do not move, we have 5,669 household observations across three survey waves.

In Uganda, we use the data from the 2009/10, 2010/11, and 2011/12 rounds of the Uganda National Panel Survey (UNPS) (UBOS, 2014a; UBOS, 2014b; UBOS, 2016). As with the other LSMS-ISA data, the Uganda sample was designed to be representative at the national-, urban/ruraland regional-level. We include 5,250 household observations after cleaning and removing nonagricultural households.

For the analysis, we combine data from the six countries and all waves to generate a single cross-country panel data set containing 33,738 household observations. For estimation, we include two measures of agricultural production: yield (kg/ha) of the primary cereal crop and the value (2010 USD/ha) of all seasonal crop production on the farm. As covariates in our regressions we include a set of inputs: labor, fertilizer, seed, pesticide, herbicide, and irrigation. Exact definitions of output and input variables are in Table 3. All cleaning code for each of the 17 data sets is available on Github.

# **2.3 Data Integration**

Methods of data integration are often overlooked in the process of merging spatial data, in particular weather data, with household surveys. Publicly available data sets obfuscate the exact GPS coordinates of unit-records to ensure privacy. If underlying data sets are fairly smooth and areas of interest are small relative to the resolution of spatial data, then the effect of integration method could be negligible. However, this is not known. As the spatial resolution of remote sensing data improves, these obfuscation methods are likely to matter more. Therefore, we include this as a parameter in our analysis, as well as the choice of spatial feature representation or abstraction.

11
# **Appendix Table 1: Sample Means**

||Ke<br>|nya<br>|M<br>|alawi<br>||Rwanda<br>||
|---|---|---|---|---|---|---|---|
||Maize|Coffee|Maize|Tobacco|Maize|Sorghum|Coffee|
|Long recall since harvest (months)|5.15|6.68|8.99|9.22|4.92|5.67|6.31|
|_Household characteristics_|5.99|5.01|4.68|5.24|5.06|5.01|5.52|
|Household size|1.29|2.24|2.33|3.65|1.84|2.29|2.46|
|Landholdings (acres)|0.17|0.15|0.28|0.22|0.13|0.16|0.15|
|Non-farm enterprise (percent)|46.67|54.12|43.82|40.66|44.04|43.74|48.10|
|Age of household head|0.24|0.27|0.25|0.10|0.30|0.29|0.26|
|Female headed (percent)|6.12|6.60|4.02|4.85|2.87|2.92|2.66|
|Years of education of head|5.15|6.68|8.99|9.22|4.92|5.67|6.31|
|Number of households|956|286|4,435|620|633|1,018|362|

Note: See text for description of the samples and datasets. Sample sizes vary in regressions depending on sub-samples for different agricultural outcome variables.

25
generational Mobility in Education? Evidence from Jordan. World Bank Economic Review, 32(3):633655.

- Azam, M. (2016). Intergenerational Educational Persistence among Daughters: Evidence from India. The B.E. Journal of Economic Analysis & Policy, 16(4):116.

- Azam, M. and Bhatt, V. (2015). Like Father, Like Son? Intergenerational Educational Mobility in India. Demography, 52(6):19291959.

- Azomahou, T. T. and Yitbarek, E. (2021). Intergenerational Mobility in Education: Is Africa Dierent? Contemporary Economic Policy, Forthcoming.

- Bank, W. (2011). Perspectives on poverty in India: Stylized facts from survey data. The World Bank.

- Becker, G., Kominers, S. D., Murphy, K., and Spenkuch, J. (2015). A Theory of Intergenerational Mobility. MPRA Paper 66334, University Library of Munich, Germany.

- Becker, G. and Tomes, N. (1986). Human Capital and the Rise and Fall of Families. Journal of Labor Economics, 4(3):139.

- Becker, G. S., Kominers, S. D., Murphy, K. M., and Spenkuch, J. L. (2018). A Theory of Intergenerational Mobility. Journal of Political Economy, 126(S1):725.

- Bhalotra, S. and Rawlings, S. (2013). Gradients of the Intergenerational Transmission of Health in Developing Countries. The Review of Economics and Statistics, 95(2):660672.

- Bingley, P. and Cappellari, L. (2019). Correlation of Brothers' Earnings and Intergenerational Transmission. The Review of Economics and Statistics, 101(2):370383.

- Bjorklund, A. and Jantti, M. (2020). Intergenerational mobility, intergenerational eects, sibling correlations, and equality of opportunity: A comparison of four approaches. Research in Social Stratication and Mobility, 70:100455.

- Bjorklund, A., Lindahl, L., and Lindquist, M. J. (2010). What More Than Parental Income, Education and Occupation? An Exploration of What Swedish Siblings Get from Their Parents. The B.E. Journal of Economic Analysis & Policy, 10(1):140.

- Bjorklund, A. and Salvanes, K. G. (2011). Education and Family Background: Mechanisms and Policies. In Hanushek, E., Machin, S., and Woessmann, L., editors, Handbook of the Economics of Education, volume 3, pages 201247. Elsevier.

- Black, S. E. and Devereux, P. J. (2011). Recent Developments in Intergenerational Mobility.

26
In Section 3 of the paper, we discuss the idea that auctioning the right to retaliate can have beneficial effects and boost the rate of implementation of WTO decisions. It is in this context that we refer specifically to the Mexican proposal, which is, for all practical purposes, based on the same reasoning. It is true that Mexico did not specifically refer to auction theory when submitting its proposal. In fact, Mexico did not even use the term “auctioning”, preferring to use the term “tradable rights” instead without any further detailed discussion. In this section, which draws heavily on Bagwell, Mavroidis and Staiger (2003), we will examine to what extent auction theory can lend useful support to the Mexican proposal. Finally, in Section 4 we briefly summarize our conclusions.

# **2. Implementation of WTO Obligations**

In a nutshell, the DSU makes it clear that WTO Members cannot unilaterally define inconsistencies with the WTO contract. Such definitions are the exclusive privilege of the WTO adjudicating bodies (Art. 23.2 DSU). To this effect, a WTO Member that believes that practices by another Member violate the WTO contract can request bilateral consultations (Art. 4DSU), which, if not fruitful, could lead to a procedure before a WTO panel (Art. 6 DSU). All Panel findings can be appealed (Art. 17 DSU). At the end of the adjudicating process, WTO Members will be granted an implementation period (in WTO parlance, the reasonable period of time, RPT) in case immediate compliance is not in the cards (Art. 21 DSU). In case there is disagreement as to the sufficiency of corrective actions taken during this period, a so-called compliance panel (Art. 21.5 DSU)—and, eventually, the Appellate Body (AB)—will be requested to pronounce on the issue. If a panel (or AB) decision has not been implemented during the appropriate implementation period, a WTO Member has the right to request countermeasures (Art. 22 DSU), which it can impose until implementation has occurred.

6
_Z_ = φ ( _Z_ , _C_ ) = _f_ ( _C_ ) (2) Note that _f_ (' _C_ ) ≥ 0 for all _C_ (under assumption iii).

As a special case, suppose that the social effect on personal subjective poverty only emerges when the mean consumption of the society in which one lives is above some critical * * * level, _C_ ; i.e., φ ( _y_ , _C_ ) = φ ( _y_ ) for _C_ ≤ _C_ and φ ( _y_ , _C_ ) is strictly increasing in _C_ for _C_ > _C_ . The interpretation is that in very poor countries levels of living are so low generally that there is little scope for personal feelings of relative deprivation.12 Then it is evident that the aggregate ' relationship between the SSPL and mean consumption will have the property that _f_ ( _C_ ) = 0 for * * * _C_ ≤ _C_ . The value of _Z_ = _f_ ( _C_ ) is the lower bound to national poverty lines.

The rest of this paper studies the relationship between national poverty lines and mean consumption, to see whether there is evidence of a social effect and (if so) whether there is an identifiable (positive) lower bound to the poverty line. This matters for both a relative measure of global poverty—the lower bound to the poverty line in the world is then taken to be the lower bound to any set of relative poverty lines—as well (of course) to an absolute poverty measure. We begin by presenting our new cross-country data set on poverty lines in developing countries.

# **3. National poverty lines across developing countries**

The set of national poverty lines collected by RDV covered 33 countries (both developed and developing) and drew on specialized, country-specific, mostly academic, studies of poverty spanning 1980-90. Clearly, this data set is now rather old.<sup>13</sup> Since RDV, there has been considerable expansion in research and analysis on poverty in developing countries through the World Bank’s country-level _Poverty Assessments_ (PA’s), which have now been done for many developing countries (though there were very few PA’s available in 1990). These are core reports within the Bank’s program of analytic work at country level; each report describes the extent of poverty and its causes in that country. The Bank’s PA

> 12 Ravallion and Lokshin (2007) provide survey-based evidence for one of the poorest countries, Malawi, indicating that relative deprivation is not a welfare concern to the bulk of the population, though they do find that it matters to better off groups in urban areas. For a theoretical analysis in a model combining risk sharing with positional goods see Ravallion (2008a).

> 13 The only prior update was done by Ravallion (1998), who tested the effect of including a number of new observations for Africa (which was clearly under-represented in the Ravallion et al., 1991, data set).  The main result of the earlier study by RDV on the relationship between poverty lines and mean consumption was found to be quite robust to including these extra observations.

6
Table 6 Export Participation and Market Density Share of Income

||Model 1|Model 2|Model 3|
|---|---|---|---|
|IV – Linear Model||||
|Coefcient|0.29|0.14|0.14|
|Standard error<br>|(0_._05)<sup>_∗∗∗_</sup>|(0_._06)<sup>_∗∗_</sup>|(0_._06)<sup>_∗∗_</sup>|
|_R_<sup>2</sup>|0.14|0.14|0.14|
|IV – Tobit||||
|Marginal efect|0_._35|0_._29|0_._29|
|Standard error|(0_._057)<sup>_∗∗∗_</sup>|(0_._049)<sup>_∗∗∗_</sup>|(0_._049)<sup>_∗∗∗_</sup>|
|Observations|6606|6606|6606|
|Export Price Index|No|Yes|Yes|
|Food Markets|No|No|Yes|
|Household characteristics|Yes|Yes|Yes|
|District characteristics|No|Yes|Yes|

Note: Export participation is measured with the share of income generated by export crops (coffee, tea, cotton, pineapples, passion fruit). The first panel shows results from 2SLS regressions of export participation on export market density. The second panel shows MLE results from a Tobit specification with endogenous variables (using the control function approach). Standard errors (within parenthesis) are cluster-corrected. Based on the Uganda National Household Survey 1999/2000.

36
and were offered the opportunity to test for Chlamydia through urine sample collection. During the same session they also participated in a game that consisted in choosing between a fixed amount of money and a certain number of condom packs. At the end of the session participants received contact details of HIV counselling and testing centres in their town and were given a voucher that would entitle them to free HIV testing at one of these centers. Through the ID number on the voucher we know who took the HIV test, but we do not know the results.<sup>14</sup> A second health camp was set up two weeks after the first, where participants were informed of the results of the Chlamydia test and —if they tested positive—they were prescribed treatment. Online Appendix D contains further details on the health camps.

In our analysis we will use the following outcomes collected at health camps: (i) whether participants attended the health camp and took the Chlamydia test; (ii) whether they tested positive for Chlamydia; (iii) whether they redeemed the voucher to get tested for HIV; and (iv) whether they chose condoms over money when given the choice.

Attendance to health camps was relatively high: 77 percent of the study participants attended, and on average this share was the same in treatment and control locations. Appendix Table A8 shows how baseline characteristics and baseline values of our outcomes correlate with the decision to participate in the health camp. People currently attending school and living outside the family were less likely to attend, possibly due to conflict with school schedules. Treatment status is uncorrelated with the decision to attend, as are baseline values of our outcome indexes.

# Expectations

Among the variables we collected through our survey, it is worth detailing how we elicited expectations regarding community attitudes, because these variables play an important role in our test for social effects. For the main attitudinal outcomes we elicited two types of responses. The first was the individual’s own position, for example: “If you had HIV and you had a boyfriend/girlfriend, would you reveal your status to him/her?”. This type of variable is used as dependent variable in our analysis.

The second category relates to the position of community members, for example: “If you picked 20 people of your age from your community who had a partner, how many would reveal their status to their partner if they had HIV?”. From this type of question we construct the share of community members who would choose a certain action (or support a certain statement) and we employ this variable as the ‘prior’ about the average choice in the peer population (ri0 in the model, Y<sup>�</sup> ilc0 in regression (12)). Due to constraints on the length of the questionnaire, we elicited these priors for some but not all of our outcomes of interest. For this reason, we will be able to estimate specifications (10) and (11) for all outcomes, and specification (12) for a subset of them.

# Randomization check

> 14 NACA (2015) estimates that in 2012 the South-West region had an adult HIV prevalence rate of 2.8 percent.

19
> Higher domestic inSteres rate (relative to those abroad) lead to less business borrowing and less lending from abroad. A ten percentage point increase in interest rates is associated with a reduction in business borrowing in the amount of 1.2% of GNPM<sup>0</sup> .

# 4.3 Pulling and pushing sectors

# The independence of foreign and government surpluses

Turning to the data on intersectoral flows, we examined the correlation between the surpluses of the various sectors and discovered that, although these are linked by an adding-up constraint, the degree of bivariate correlation varies widely. One striking fact is the apparent lack of correlation between foreign and government surpluses. (Equations 1 and 2). Though least squares estimates imply a correlation, instrumental variables correction in equation 2 shows this to be spurious. Thus, if the foreign surplus is thought of as exogenous to the business and household sectors, we can interpret this as a lack of response of the government surplus to changes in the availability of foreign funds. Such changes must thus be absorbed by the business and household sectors4 1.

_G_ - -0.271 - 0.286 F (1) (0.6) (2.7)

- RSQ-0.060 # of obs 118 (full sample) OLS

G - -2.208 0.359 F (2) (2.4) (1.3)

- RSQ--0.24 # of obs 118 (full sample) 2SLS

40 It should be noted that this result is not in line with the theory that higher interest rates lead to more domestic intermediation.

41 In all of the regression equations, the -bsolute value of the t-statistic is shown in parentheses below each regression coefficient.

39