rafmacalaba/datause-encoder-data huggingface dataset:
The model highlights the way that both factors influence the relationship between village political economy and migration risk, and mechanisms through which a household’s potential loss of land may influence the decision to participate in the migrant labor market. Within the household, the migration ability of individuals will vary with gender, age and educational attainment of different household members. In our empirical strategy below, we examine how future village‐level reallocation decisions influence individual migration decisions, while controlling for household and village level factors that may influence exposure to risk. Our model suggests that households recognize that lineage group differences, demographic change and proximity to an election, among other unobserved factors, influence a household’s perception of the risk of reallocation. In our identification strategy below, we exploit these observable differences to identify the effect of a future reallocation on current migration decisions.

# **3.0 Empirical Strategy**

## **3.1 Data Source and Descriptive Statistics**

For our primary analysis, we make use household and village surveys conducted in fifty‐five villages of four provinces (Anhui, Henan, Jiangsu and Shanxi) from August to October 2004 in collaboration with the Research Center for Rural Economy (RCRE) at the Ministry of Agriculture. This survey, supplemental to the annual RCRE survey, enumerated all 3,999 households in the 2003 wave of the RCRE panel for these four provinces, allowing us to match villages and households from the 2004 supplemental survey with a historical panel of villages and households that RCRE surveyed annually from 1986 to 2003.<sup>10</sup> As

> 10Both authors on this paper worked extensively on the funding, and with RCRE, on the design and implementation of the supplemental survey (Loren Brandt was also a key participant in this effort). A detailed discussion of a larger nine‐province sample from the RCRE panel dataset (translated from Chinese as the “National Fixed Point Survey”), including discussions of survey protocol, sampling, attrition, and comparisons with other data sources from rural China, can be found in the data appendix of Benjamin, Brandt and Giles (2005). Several useful resources, including original documentation from RCRE on the survey and sampling (and translations), are available at: sites.google.com/site/decrgjohngiles/datasets. Also available at the link are drafts of the supplemental household surveys conducted in four provinces (2004 Rural Household Social Network, Labor Allocation and Land Use Survey)

_Land Tenure Insecurity and Migration, Page 12_
# **Global Distortions to Agricultural Markets: New Indicators of Trade and Welfare Impacts, 1955 to 2007**

Despite reforms over the past quarter-century, world agricultural markets remain highly distorted, and international trade in farm products has grown much slower than trade in non-farm goods.<sup>1</sup> Traditional indicators such as the nominal rate of tariff protection from import competition understate the degree of distortion if there are other border taxes or subsidies or quantitative restrictions, and even more so if there are also domestic producer or consumer taxes or subsidies on farm products. The OECD’s producer and consumer support estimates (PSEs and CSEs) based on domestic to border price comparisons for high-income countries, and the World Bank’s new comparable estimates of nominal rates of assistance and consumer tax equivalents (NRAs and CTEs) for both high-income and developing countries, provide better indicators (OECD 2008; Anderson and Associates 2009).<sup>2</sup> Those estimates can be used in national and global computable general equilibrium models to provide an indication of the true trade and welfare effects of such distortionary policies. However, such models typically are calibrated only for a recent year, and so are incapable of providing estimates of trends over time; and they are not yet available for many smaller and poorer economies.

> 1 Based on their sample of 75 countries comprising more than 90 percent of the world’s agriculture, Anderson and Associates (2009) estimate that the share of global production of agricultural goods that is exported has increased from 11 percent in the 1960s and 1970s to just 16 percent in 1990-2004, a far smaller increase than for non-farm goods during that period of rapid globalization. When intra-EU trade is excluded, agriculture’s share of global production exported was just 8 percent in 2004, compared with 31 percent for other primary products and 25 percent for all other goods, according to the GTAP Version 7 database (www.gtap.org).

> 2 The main difference between the PSE/CSE and NRA/CTE concepts is that the former are expressed as a percentage of the distorted price whereas the latter are a percentage of the undistorted price (and the CSE has the opposite sign to the CTE). The NRA and CTE values are identical if the only government interventions are at a country’s border (such as a tariff on imports). In the case of agriculture, however, typically there are domestic production or consumption taxes or subsidies also in place, so the NRA often differs from the CTE.
-16 -

# Table 4

Countries That Are the Object of US Antidumping and Countervailing Cases and of "301" Cases; Comparison (antidumping and countervailing duty cases, 1980-1988; "301" cases, July 1975 - July 1991)

|_Cownry or group of countres_|_Cases against this county or gro_<br>_of total against allcountrie_|_up as a_X<br>_s_|_Percentage of 1989 US merchandieimports_<br>_that originate in this country orgroup_|
|---|---|---|---|
||_AntiduaipLg and coutrvailing_<br>_dut cams_|_"301" cas_||
|AN Counties|100|100|100|
|Deveped Countries|58|61|63|
|Develpng Countries|37|38|36|
|Eastem European Countries|5|1|0.5|
|European Community|40|34|18|
|Brazil|7|6|1.8|
|South Africa|2.6|0|0.3|
|Korea|4.7|9|4.2|
|Mexico|4.5|0|5.7|
|Taiwan, China|3.7|6|5.1|
|Hong Kong|0.1|0|2.1|
|Singapore|0.|0|1.9|
|Canada|5|8|19|
|Japan|6|14|20|
|Argentina|1.4|a|0.3|

aoure Tabulated from Office of the US Trade Representative, "Secton 301 Table<sup>of Cases," Washington, DC, USTR,</sup> August 15, 1991, photocopied.
# 5 Conclusion

This paper attempts to peer inside the black box of deliberative democracy. We use a unique dataset of transcripts of gram sabhas (village meetings) in South India to learn about the process of deliberation. These meetings are a part of the system of village government, held at regular intervals, and are empowered by the Indian constitution to make important decisions for the village. We ...nd that powerful groups, such as large landowners exert an unduly large in‡uence on the deliberative process, as their preferences are more likely to be mentioned and dominate the deliberations by taking up more time. This e¤ect is a true dominance e¤ect as it occurs in the villagers’ discourse, and does not re‡ect preferential treatment from o¢ cials who attend the meeting. Our results also show that the needs of disadvantaged castes are also re‡ected in the deliberative process, but this occurs because these needs are mentioned by o¢ cials. We also ...nd these e¤ects are in‡uenced by village heterogeneity; high literacy tempers the extent to which gram sabhas are dominated by landlords. Landlord domination is also reduced when the Block Development O¢ cer - an important local o¢ cial - attends the meetings. On the other hand, in villages where the presidency is reserved for lower castes, the discourse tends to be even more dominated by landowners suggesting that political reservations may produce weak leaders.

Thus, in this paper we examine the innards of the deliberative process by conducting an examination of the discourse of deliberation within gram sabhas in rural India. These meetings are among the most widespread deliberative spaces in regular and routine use within a system of government in human history. By matching proceedings within transcripts of gram sabhas with the preferences of villagers we are able to see whose voices are heard, whose priorities are mentioned, and how institutions a¤ect deliberative dominance by elites.

20
**Table 14 Employment**<sup>**ProtectionLegislationStrictnessfor Croatia and Central**</sup> **and East European EU**<sup>**accessioncountries (CEEEUAC)**</sup>

|Contr|Reular|EPLstrict<br>Temporary|ness<br>Collective<br>umma|ryindex|
|---|---|---|---|---|
|uy|g<br>employment<br> <br>|<br>employment<br>|<br><br>dismissals<br>||
|Croatia<br>|2.8<br>|3.9<br>|5.0<br>|3.6<br>21|
|CzechRepublic|2.8<br>|0.5<br>|4.3<br>|.<br>26|
|Estonia|3.1<br>|1.4<br>|4.1<br>|.<br>|
|Hungary|2.1<br>|0.6<br>|3.4<br>|1.7<br>|
|Poland<br>|2.2<br>|1.0<br>|3.9<br>|2.0<br>24|
|SlovakRepublic|2.6<br>|1.4<br>|4.4<br>|.<br>|
|Slovenia|3.4|2.4|4.8|3.5|
|CEEEUACaverage|2.7<br>|1.2<br>|4.1<br>|2.4<br>|
|EUaverage|2.4<br>|2.1<br>|3.2<br>|2.4<br>|
|OECDaverage|2.0|1.7|2.9|2.0|
|CEEEUACminimum|2.1<br>|0.5<br>|3.4<br>|1.7<br>|
|EUminimum|0.8<br>|0.3<br>|2.1<br>|0.9<br>|
|OECDminimum|0.2|0.3|0.4|0.7|

Sources: OECD (1999),<sup>WorldBankestimatesforEstoniaandSlovenia,Biondicetal.</sup> (2002) for Croatia.

The overall high level of<sup>EPL in Croatiareflectregulatoryconstraintsin three</sup> basic areas: high procedural<sup>andmonetarycosts associated with individualdismissals,</sup> restrictions on temporary<sup>employment,and finallyhigh costsof collectivedismissals.</sup> In all three cases regulations<sup>inCroatiaaremorestringent thaninvastmajorityof</sup> transition economies, EU<sup>andOECD.However,collective dismissalsare byfar the</sup> most difficult and costly<sup>inCroatia acrossthewholeOECD.Thepertinentindex</sup> reaches the maximum<sup>valueof 5 in Croatia,comparedwith 3.4inHungaryand 2.1in</sup> Ireland. This means that<sup>thecostsof collectivedismissals-whichcanmeanas few</sup> as five workers - can in<sup>mancases beprohibitiveinCroatia.Below wepresent the</sup> most important legal provisions<sup>contributing to highdismissal costsin Croatia.</sup> _Individual dismissals_<sup>arecostlydue tothelongadvancednoticeperiodand</sup> high severance pay. In<sup>addition, expectedcostsareincreasedby highprobability of</sup> job readmission and generous monetary compensation ordered by courts. Specifically, advanced notice of dismissal<sup>forworkerswith20ormoreyearsof</sup> service is 6 months in<sup>Croatia,comparedwith3 monthsin Hungary or Polandand2</sup> _-43-_
|CEMAC - Transport-Transit Facilitation|Pierre Xavier Bonneau|
|---|---|
|Second Programmatic Environment Development PolicyLoan|Ernesto Sanchez-Triana|
|Second Programmatic Environment Development PolicyLoan|Ernesto Sanchez-Triana|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|West Africa Regional Transport and Transit Facilitation Project|Fabio Galli|
|GEF BiosafetyProject|Eija Pehu|
|GEF BiosafetyProject|Eija Pehu|
|GEF BiosafetyProject|Eija Pehu|
|GEF BiosafetyProject|Eija Pehu|
|GEF BiosafetyProject|Eija Pehu|
|GEF BiosafetyProject|Eija Pehu|
|GEF BiosafetyProject|Eija Pehu|
|GEF BiosafetyProject|Eija Pehu|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Strategic Roads Infrastructure Project|Mitsuyoshi Asada|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
|Sustainable Management of Mineral Resources Project|Christopher Gilbert Sheldon|
**Table 1: Empirical results from baseline models of inflation (July 1998 – September 2020)**

|**Variables**||**Cereals**|**Food**|**Non-**<br>**food**|**CPI**|
|---|---|---|---|---|---|
||**_Adjustment e_**<br> <br>|**_ffects_**||||
|EC-monetary<br>sector|[(m1−p) −1.04λ+ 0.29pρem<br>+ 6.46]t−1<br>|0.027**<br>(0.03)|0.020***<br>(0.01)|0.002<br>(0.58)|0.015***<br>(0.01)|
|EC-cereals|<br><br> <br>[(eps+ wρwlp) −0.78ceρp<br>|0.029**|0.026**||0.020**|
|(grains)|<br>−4.22]t−1<br>|(0.04)|(0.01)||(0.03)|
|EC-non-food|<br><br> <br>[(eps+ wmwlp) −0.77lfp+ 4.02]t−1<br>|||0.038***<br>(0.00)|0.025<br>(0.26)|
||<br>**_Domestic fa_**<br>|**_ctors_**||||
|Cereal output|ceρwpt−1<br>|-0.136***|-0.060**||-0.039**|
|gap||(0.00)|(0.03)||(0.02)|
|Money growth|∆(m1−p)t−12<br>|0.008||0.047***|0.026|
||<br>|(0.86)||(0.00)|(0.17)|
|Money growth|<br>∆(m1−p)t−3<br>||0.130***<br>(0.00)|||
|Cereal inflation|<br>∆ceρpt−9<br>|0.062<br>(0.14)||||
|Cereal inflation|∆ceρpt−8<br>||||0.029<br>(0.17)|
|Food inflation|∆fpt−3<br>||0.101***<br>(0.00)|||
|Food inflation|∆fpt−6<br>||||0.038**<br>(0.04)|
|Non-food|∆lfpt−12|||0.177***|0.073|
|inflation||||(0.00)|(0.18)|
||**_External fa_**<br>|**_ctors_**||||
|World grain|∆wρwlpt−4<br>|0.092**||||
|price||(0.04)||||
|World grain|∆wρwlpt−3<br>||0.075**||0.046*|
|price|||(0.02)||(0.06)|
|Exchange rate|∆pρemt−11|0.006|0.008**|0.002*|0.006***|
|premium||(0.19)|(0.01)|(0.06)|(0.01)|
|**_Dummies_**||Meher|Meher||Meher|
|**_included_**||cerealpeaks|foodpeaks||foodpeaks|
|||cerealdeeps|fooddrops||cpi_jul2000|
|||cerealjumps|cpi_jul2000<br>cpisep2000||cpi_sep2000|
|Constant||0.012***|_<br>0.012***|0.001|0.006*|
|||(0.00)|(0.00)|(0.53)|(0.09)|
|Observations||259|259|259|259|
|R-squared||0.572|0.860|0.148|0.810|
|<br>DW||2.10|1.94|1.93|2.00|

Robust p-values in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

_Note_ : Estimates are corrected for autocorrelation except for non-food CPI equation where no autocorrelation was detected.

_Dummies_ : Meher = main harvest season = 1 for September-February; foodpeaks = Food price peaks = 1 for 1999:1; 2000:7; 2008:5-9; 2011:3-10; fooddrops = Food price drops: 2000:9-12; 2008:10-12; cerealjumps = Cereal price jumps = 1 for 1999:2, 6-9; 2001:1; 2005:6; 2008:3, 5-7; 2011: 3-6; 2012:3; 2017:7-11; cerealdeeps = Cereal price deeps = 1 for 1997:12; 1998:11,12; 1999:1; 2003:12; 2008:11, 12; cpi_jul2000 = 1 for 2000:7; cpi_sep2000 = 1 for 2000:9.

18
# **3.3. Sensitivity Analysis of Educational Mobility**

As discussed in the introduction, when one finds that the intergenerational linkages in economic status are numerically and statistically significant in multivariate regression analysis, as we find above in Table 2, the estimated persistence can still be driven primarily (or solely) by unobserved heterogeneity such as transmission of genetic endowments from parents to children (both ability and preference transmission). If the observed intergenerational persistence is due to genetic correlations rather than economic environment, the scope for policy interventions to improve economic mobility in a society would be more limited.

In this section, we use a bivariate Probit model to explore the question whether a small amount of selection on unobservables (such as genetic endowment) can explain away the estimated partial correlations in education of parents and children in Table 2. For the bivariate probit analysis, we use the most complete specification as in column (7) of table 2.<sup>24</sup> The binary indicators of educational attainment are defined as follows. For parents, the educational attainment dummy equals 1 if at least one of the parents has more than primary schooling and zero otherwise, for both 1988 and 2002. For children, the education dummy in 1988 is defined same as that for parents. But in 2002, we use middle school (9 years of schooling) as the relevant threshold for the children, as average schooling has increased substantially over the 14 years for children, and primary schooling is no longer meaningful as an indicator of educational “attainment” (the average years of schooling is 9 years for both sons and daughters in 2002)

> 24 As discussed by AET (2005), biprobit may face significant convergence problems with large number of dummies as is the case in our application, because we use county fixed effects. Since the model fails to converge with county fixed effects, we follow Emran and Shilpi (2011) and use an index of county fixed effects derived from univariate probit.

17
4 at home. Recent efforts to test the altruistic vs. self-interest hypothesis with respect to remittances at the micro-economic level include Agarwal and Horowitz (2002), Foster and Rosenzweig (2001) and Ilahi and Jafarey (1999).

At the same time, other researchers have used aggregate data to analyze the macroeconomic factors that affect the behavior of remitters. These researchers have suggested that macroeconomic factors – like interest rates, exchange rates and political instability – all have an impact on the level of international remittances received by countries (Higgins et al, 2004, El-Sakka and McNabb, 1999; Glytsos, 1997; and Faini, 1994). These analysts typically find that interest and exchange rates need to be competitive and that countries need to be politically stable in order encourage the flow of remittances to labor-sending countries.

The analysis pursued in this paper attempts to combine the microeconomic and macroeconomic approaches to remittance determination by doing two things. First, the analysis adds a new (and important) variable into the modeling effort: poverty. Because of the lack of poverty data for many developing countries, no past study has attempted to analyze how the level of poverty in a labor-sending country affects the level of international remittances received. On the one hand, since remittances are largely individual transfers, made from a migrant to his family back home, it might be argued that the poverty of the household is a more important determinant of remittances than the poverty of the country. However, since the poverty data used in this paper is based on the results of nationally-representative household surveys in particular labor-sending countries; such data represents a useful means for identifying how country-level poverty affects the level of international remittances received by countries. If, for example,
candidates that had the required skills in higher‐skilled occupations such as managers and service workers. We do not see such a difference for lower‐skilled occupations such as elementary occupations, craftsmen, and machine operators. These findings are also consistent with findings reported in Figures 13‐15 that a lack of skilled workers is a distinguishing factor of labor markets in the Northern and Eastern provinces. One issue that Figure 14 does speak to is the gender difference in the skills shortage.

**Figure 16: Firms state inadequately educated workforce as a major or severe constraint**

a. Firms state inadequately educated workforce as a major or severe constraint b. Problems encountered by firms during hiring: Applicants lacked required skills (% of firms) <!-- Start of picture text --> 59% 100% 100%<br>84%<br>80% 79% 78% 76%<br>67%<br>59%<br>25%<br>22%<br>13%<br>4% 5% 6% 6% 8%<br>Crafts, operators, Clerical, service, sales Managers, professionals,<br>elementary technicians<br>Northern & Eastern Western Other provinces<br>Southern North‐Western Uva Sabaragamuwa North‐Central Central Western Eastern Northern<br><!-- End of picture text --> In sum, evidence from a skills survey and an employer’s survey suggests that skills are a greater constraint to productivity in the North and East than elsewhere in the country. However, we need to take these results with caution since the correlation between self‐reported and objective reading skills is fairly low and more evidence is necessary to understand this relationship better.

# 4.1.3 Access to Formal Finance

Another factor that could explain lower employment rates in the Northern and Eastern provinces is difficulty in accessing formal finance. The low levels of self‐employment observed in the service and industrial sectors in these provinces could stem from higher indebtedness. Table 7 summarizes the nature of debts taken by households in different parts of Sri Lanka. The three sections of this table describe the percentage of households that report debt, the share of total debt from different sources, and debt as a share of annual household consumption.

The first panel of Table 6 suggests that most of the debt taken by households tends to be from informal sources such as pawnshops and retail shops, although there are no significant differences between the different regions. About two out of three of all Sri Lankan households have loans from some source. Among indebted households, pawning of assets and debt from retail shops represent more than half of all debts of indebted households in the Northern and Eastern provinces. These are also the largest sources 20