rafmacalaba/datause-encoder-data huggingface dataset:
# _Project selection and the local distributions of income and influence_

Given the political process outlined above, village decisions on whether to apply for project _π1_ or _π2_ hinge on which case of Equation (5) applies, and then on whether or not _y_ * ≥ ~ _y_ . As shown in the proof to Proposition 1, under Case I, project π 2 is chosen unanimously. It is natural to interpret Case I as one in which village need for public good _gj_ is very high. Perhaps there is no school at all, or no roads through which to transport produce to nearby markets. If need for _gj_ is so great that even those without latrines (or refrigerators) prefer an increment in the level of the local public good than to get access to a unit of _x_ , then there is unanimous support for the local public good project.

Under Case II, the utility gain (to those individuals who do not yet own a unit of _x)_ from project π 1 - which provides an additional unit of good _x_ to each individual – is greater than the gain from project π 2 - which provides a given increment in the supply of the local public good, ∆ _g j_ .<sup>17</sup> But we know from the pre-social fund equilibrium that this is the case only for those individuals with _yi_ < ~ _y_ . Those with incomes _yi_ ≥ ~ _y_ already own a unit of _x_ , and always prefer π 2 . Project preferences therefore differ on ‘class’ lines, with the poorest agents in _j_ supporting project _π1_ , and the richest agents supporting _π2_ . In this case, since the expected pivotal voter is at percentile _p_ * = _G_ ( _y_ * ) , the decision hinges on the relationship between the economic threshold<sup>~</sup> _y_ and the political threshold, y*. If _y_ * ≥ ~ _y_ , _π2_ is chosen. _π1_ is chosen otherwise.

> 16 See Bénabou (2000) and Ferreira (2001) for earlier applications of the pivotal voter model, and its consistency with modified “median-voter type” results when preferences satisfy the single-crossing condition. This framework provides a simple alternative to the probabilistic voting model used, e.g., by Bardhan and Mookherjee (2005), which is better suited to modeling the election of a local government than to the selection of project types.

> 17 Case II corresponds to situations in which the endowment of the local public good _gj_ is not so low, and those without lumpy private goods (e.g. latrines) gain a greater welfare improvement from a unit of the latter than from the proposed expansion in schools, health clinics or roads.

13
12 creation), iii) large existing trade levels (reduces potential for trade diversion), iv) complementarity in demand (increases trade creation), and v) differences in economic structure based on differences in factor enc.owments or in income levels (increase potential for trade creation). The extent to which a preferential trading arrangement leaves scope for unilateral liberalization can also be important. Administrative costs and the political economy of protection may influence the choice between customs unions and free trade areas.

In economies in transition the case for preferential trading arrangements has distinguished between short and long terms issues (Tarr-Michalopoulos 1992a). The process of transition has its own arguments for regional arrangements. Preferential trade may help ease adjustment costs during transition and the survival of positive value-added industries. Benefits from temporary preferential arrangements during tranmition are related to shares of "East-dependent" industry (machinery) in pre-independence exports, likely terms-of-wrade losses from price adjustments, or speeds of adjustment in the partner countries.

Below the economic, administrative, and political economy arguments of deeper integration with the East and among the Balkics are discussed in more detail. Past trade patterns are too distorted to serve as basis for analysis. Trade policies in the Baltics and its trading partners are also changing rapidly making assessment difficult. Recent economic and trade data in the East is patchy, but it is used to the extent possible.

# 3.1 Integration with the FSU

# i) **Eisfing** trade flows

During central planning the Baltics were heavily dependent on trade with the FSU. This gives some weight to the trade maintenance argument. Lithuania and to some extent to Latvia with the highest shares of machinery in exports (Table 4) would have largest incentives to pursue preferential twade with the East. No information is available on the share of potentially viable industries in this trade.
for domestically produced goods and the demand for imports. Again, given the nature of the price indices, we estimate the model using differences in demands. Since the equilibrium condition is that the domestic supply equal the domestic demand of domestically produced goods, the measures of supply and demand must be in the same units. Because domestic supply St is deflated by real capital, we also deflate domestic demand Dt by real capital. Thus, the demand side of the model is given by<sup>8</sup> : where: Dt the domestic demand for domestically produced goods; Me the demand for imported goods; Yt real GDP; **_P=_** the price of imported goods; the b's, a's, j's, and y's are parameters to be estimated; and all other variables are defined as before.

# _3. THE DA TA_

The data used in the estimation are drawn from two sources. The export, 8 The steps in going from the expenditure function to the specific functional form for the demand for domestically produced goods were as follows. First, partially differentiate the expenditure function with respect to the domestic price Ptd and divide by YV. Express in difference form (Dt/Y,) - (Dt /Y,-l). Take (Dt-,/Yt-1 ) over to the right hand side with a coefficient equal to one. Finally, multiply both left and right hand sides by _(Yt/hK)._ A similar procedure was followed for the derivation of the demand for imports.

- 13 -
|development (published in<br>journals before 2005)|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|Recent articles published in<br>journals|0.356***|0.300***|0.333**|0.556|0.250***|0.500|0.368***|0.750|0.294***|0.463**|
|Number of published journal|||||||||||
|articles1|10.686*|1.353***|3.667**|5.000*|1.400***|5.100**|6.869***|19.267|3.786***|14.083|
|Working papers and other<br>unpublished research output<br>(mimeos) 1|0.491|0.118***|0.000***|0.400|0.300*|0.500|0.376**|0.667|0.333*|0.383**|
|Reports and documents from<br>international organization<br>such as the United Nations,<br>the World Bank, IADB,<br>ADB, governments or NGOs<br>etc. 1|0.528|0.176|0.167|0.200|0.300|1.000***|0.455|0.333|0.400|0.383|
|Blog posts1|0.019*|0.000|0.000|0.000|0.000|0.100|0.020**|0.133|0.067|0.000***|
|Online data sources, such as<br>for instance the World<br>Development Indicators,<br>World Bank microdata<br>catalogue, Penn World<br>Tables1|0.057|0.118|0.000|0.000|0.100|0.200|0.079|0.067|0.267|0.033|
|# countries|16|14|5|4|9|3|52|1|1|33|
|# courses|59|30|9|9|12|14|136|16|17|80|

*,**,*** indicate statistically different from non-top 20 U.S. economics departments at the 10, 5 and 1 percent level respectively.

1 Data only from syllabi, not from survey responses.

30
Table 5: Heterogenous effect by motivation

||Enlace (Y/N)|Math|Spanish|Enlace (Y/N)|Math|Spanish|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Motivated (M)|0.127**|0.345**|0.331**||||
||(0.054)|(0.134)|(0.141)||||
|Highly Motivated (HM)||||0.210***|0.530***|0.346*|
|||||(0.065)|(0.180)|(0.192)|
|T x Motivation|-0.019|0.045|-0.035|0.001|0.063|-0.058|
||(0.075)|(0.185)|(0.196)|(0.093)|(0.257)|(0.274)|
|Treatment (T)|0.013|0.141|0.025|-0.006|0.084|0.037|
||(0.052)|(0.133)|(0.141)|(0.083)|(0.237)|(0.253)|
|(RW p-value Treatment)|0.96|0.63|0.96|0.98|0.97|0.98|
|Observations|564|410|410|564|410|410|
|Romano|-Wolf p-values|(RW) ***|p_<_0.01, *|* p_<_0.05, * p_<_0.|1||

RW p-values for two groups of coefficients, columns (1), (2), (3) and columns (4), (5), (6).

to get a postgraduate degree. We classify these two groups as “motivated students” and “highly motivated students”, respectively.

Table 5 shows the results of a specification including, separately, our two definitions of motivation on EMS graduation and test scores. As expected, motivated and highly motivated students have a higher probability of graduating and better test scores at the end of EMS. However, the treatment effect of PROBEMS on graduation and test scores remains zero in these new specifications and the interaction between the treatment and motivation is not statistically significant. In other words, even among motivated and highly motivated students, the scholarship is not relevant for increasing their likelihood of finishing EMS or obtaining better grades. The results remain when we use the selfdeclared hours spent doing homework (from the _ENLACE de contexto_ ) as an alternative proxy for “motivated students”.

# **5.3 Academic** **_Readiness_**

We now consider the role of school readiness and whether this can explain the lack of impact. Academic _readiness_ is measured by the proficiency level at the end of lower sec20
detrimental to growth while inequality of efforts tends to help growth. Despite the intuitive appeal, other studies have since failed to reproduce these results when applied to data for other countries; see e.g. Ferreira _et al._ , 2014.

Remarkably, all of the above mentioned studies focus exclusively on growth of average incomes. This seems rather paradoxical. Measures of inequality summarize at any given point in time how incomes are distributed across the population. Yet when we investigate inequality’s relationship to income growth we appear only interested in how it might affect growth of the average income, not how it might affect growth rates at various parts of the distribution. One would think that we would specifically be interested in how individuals at different steps of the socio-economic ladder would fare in societies with different levels of inequality.

Indeed, the logical next step is to also disaggregate growth, and to verify whether income growths of the poor and the rich are affected differently by inequality; whether good (bad) inequality is good (bad) for both the poor and the rich. We explore this empirical question using data for the United States covering the period 1960 to 2010 (at 10 year intervals). Specifically, we regress total inequality as well as bottom and top inequality against growth at a wide range of percentiles of the income distribution. Our results are both conclusive and robust.

We find that high overall inequality only appears to hurt income growth of the poor. While inequality is also found to have a positive effect on growth, this positive effect is exclusively reserved for the top end of the income distribution. This means that the type of growth that inequality stimulates is the type that further advances inequality. It also offers an alternative explanation for why the relationship between average income growth and inequality is so fragile, as the negative and the positive effects from the different ends of the income distribution may cancel each other out.

When we distinguish between bottom and top inequality, by regressing disaggregated inequality on disaggregated growth, we find that it is mostly top inequality that is holding back growth at the bottom. One possible explanation for this observation is 3
nested, hierarchLcal data such as aro found ln educatLonal systems, the programme faLliLtating such modelling have not gone through the years of testing that **single** level programes have undergone.

A prefatory caveat ia needed to axplain why some analysts were carried out in thl study and not others. Numerous problems were encountered in usLng ML3, **the** multilovel regression programme. The size of the data **met** and the numbers of variables collected, thoagh in fact, qult. limLted, as dLicussed above, were at tLmes too great for the capacity of the programme. Certain combinatLons of varlables overran the workspace ceortaln deletLons of miLsing data seemingly occupLed more, not less workspace, frustrating the testing of potentLal models; and reading data Lnto the workspace was not always a straLghtforward qxercise. Nonetheless, lt was still thought preferable to continue usLng hL3, aven with its remaining 'bugs', for a picture is paLnted which is far closer to reality than any single level model can paint.i in single level regression models, there is a single residual term illustrating the varLance whlch has not been explained. In multi)evel models, residual terms at each level are specified. For example, ln the three-levol models descrtebd below, three rooldual terms reflect the differing effects of the explanatory varlables at the pupil, the class and the school levels, Zn other **words, instead** of beLng 'throw-away', error terms, the residual V in faLrness, many of these bugs' have **been** eliminated slnce the first writLng of this report, and in addLtLon, an extended memory versLon of ML3 ls up and running, facilitating **the** LnclusLon of **more** variables. However, **the** limitod number of schools Ln the study still constralns the number of school level varLiables which can be fit successfully ln any one model.

19
country analysis."<sup>6</sup> But it is also an obstacle to econometric estimation of the impact of liberalization on a cross-country basis, as knowledge of the timing is all but indispensable.

An alternative is to try to infer the timing of key aspects of liberalization from the statistical properties of the interest rate data themselves."<sup>7</sup> Two classic approaches to measuring the degree of external capital account liberalization by the looking at interest rate and other macroeconomic time series have been proposed by Edwards (1985) and Edwards and Khan (1985), where interest rate data is available, and by Haque and Montiel (1991) where interest rate data is not available. Each of these approaches assumes that the effective interest rate is a weighted average of that which would prevail in fully controlled and uncontrolled regimes respectively; the estimated weiglht then becomes an indicator variable representing the _degree to_ which the domestic money market is open and uncontrolled. They could be adapted to allow some tiime-variation in the indicator, and hence in principle to identify a liberalization date. While both approaches thus offer elegant solutions to the problem at hand, they have the important shortcoming that each assumes that uncovered interest parity UIP prevails in an uncontrolled market. The well-known fact, that UIP is empirically questionable even for countries without any form of foreign exchange control, mars the use of this as an identifying assumption. There are also difficulties with the specification of equilibrium in the controlled market. Further commentary on these approaches, together with some discussion of possible alternatives, is included as Appendix 1.

A simpler approach that does not rely on UIP depends instead on the assumption that the short-run dynamic behavior of interest rates changes with liberalization.

'5 Thus it is not surprising to find, for example, that a recent study's table presenting just 27 liberalization dates had to be accompanied by two-and-a-half pages of qualifying notes (Galbis, 1993).

> 16 As illustrated by several contributions to the present research (Cho, 1999; Montes-Negret and Landa, 1999; Wyplosz, 1999).

17 If an unregulated curb market exists alongside the formal market, and if interest rate data for the curb market exist, then differences between the rates can be used as a measure of the degree to which the controls bite. This approach has been used for domestic curb markets by Reisen and Yeches (1993), and extensively for off-shore "euro"-markets, cf. Wyplosz (1999).

11
In contrast, in the new systems:<sup>(1)Financial instruments(stocks,bonds and</sup> mortgages) held by the private<sup>pillar generallyearna positiverealrate of return(that is,</sup> above the rate of inflation) in<sup>thelong run. (2)Internationaldiversificationof investments</sup> is permitted, which further protects<sup>pensionfundsfromcountry-specificinflation;(3)In</sup> Chile private annuities acquired<sup>uponretirementareindexed,asareallmedium- and</sup> long-term financial transactions-a<sup>responsetoahistoryof highinflation.InMexico</sup> public pillar benefits are price-indexed<sup>andregulationscallfor annuitiesto beindexed--</sup> but-'it is not clear whether<sup>privateinsurance companieswillbeabletoprovidethis</sup> product and if they do, whether<sup>itwillbecredibleandcost-effective (see Jameset al</sup> 2001a, 2001b).

We abstract from inflation because<sup>both the rate of priceincreaseand the typeand</sup> speed of pension adjustment<sup>were not consistentacrosscountries and even within a given</sup> country they varied considerably<sup>overtime-butthese effectswerealwaysstrongand</sup> negative. If we had taken inflation<sup>into account,thereal value of expected benefitsin the</sup> old systems would have been<sup>farlower,theincrementinvalue fromthenew system</sup> would show up as much higher,<sup>and often theseeffectswere particularlybeneficialfor</sup> women.

CemJpaErISof of lffetme beimelto unmder<sup>mew</sup><sup>_Md_</sup> _lOd_ $y$uem$ Using the old system formulae<sup>togenerate the expectedpresent value(at age65)</sup> of lifetime pensions, we start<sup>with ourfirstquestion--whogainedorlost themostfrom</sup> the change in systems? To<sup>addressthisquestion wecalculatetheratioof postand pre-</sup> reform lifetime benefits and normalize<sup>accordingto the ratiofor male workersin the top</sup> educational group. Based on<sup>the discussion to this point,we wouldexpectthefollowing</sup> groups to be the biggest gainers:

- i) low earners (as proxied by<sup>loweducational category)ofbothgendersbut</sup> especially women, who are<sup>thelowestearnersineachcategory--duetothe</sup> targeted public pillar;

- 2) single men--because they<sup>nolongerhavetosubsidizethewidow'sbenefitthat</sup> was financed from the common<sup>pool in the old systems;</sup>

33
Table D3: Robustness of Firms’ Adjustment to GDP Decline in Firms’ Destinations using Alternative Shocks

||Panel A:|Firm size|Panel B: Firm|total real wages|
|---|---|---|---|---|
||Shock GDP growth<br>export destinations<br>fxing weights to<br>2004-2007|Shock GDP growth<br>export destinations<br>using weights<br>in t-1 and t-2|Shock GDP growth<br>export destinations<br>fxing weights to<br>2004-2007|Shock GDP growth<br>export destinations<br>using weights<br>in t-1 and t-2|
||(1)|(2)|(3)|(4)|
|GFC frm shock*2009|-0.0130***|-0.0164***|-0.0165***|-0.0206***|
||(0.0021)|(0.0024)|(0.0026)|(0.0030)|
|GFC frm shock*2010|-0.0138***|-0.0206***|-0.0173***|-0.0255***|
||(0.0025)|(0.0029)|(0.0028)|(0.0032)|
|GFC frm shock*2011|-0.0157***|-0.0243***|-0.0183***|-0.0271***|
||(0.0027)|(0.0031)|(0.0030)|(0.0035)|
|GFC frm shock*2012|-0.0144***|-0.0242***|-0.0192***|-0.0293***|
||(0.0030)|(0.0035)|(0.0032)|(0.0037)|
|GFC frm shock*2013|-0.0173***|-0.0267***|-0.0195***|-0.0297***|
||(0.0032)|(0.0037)|(0.0034)|(0.0039)|
|GFC frm shock*2014|-0.0175***|-0.0270***|-0.0207***|-0.0309***|
||(0.0035)|(0.0040)|(0.0036)|(0.0041)|
|GFC frm shock*2015|-0.0196***|-0.0284***|-0.0183***|-0.0281***|
||(0.0036)|(0.0041)|(0.0037)|(0.0042)|
|GFC frm shock*2016|-0.0130***|-0.0219***|-0.0171***|-0.0255***|
||(0.0038)|(0.0044)|(0.0039)|(0.0046)|
|GFC frm shock*2017|-0.0117***|-0.0211***|-0.0153***|-0.0234***|
||(0.0040)|(0.0046)|(0.0041)|(0.0048)|
|Observations|144,464|108,960|132,562|98,686|
|R-squared|0.5358|0.5366|0.4796|0.4807|
||Panel C:|Firm exit|Panel D: Firm shar|e of skilled workers|
||Shock GDP growth|Shock GDP growth|Shock GDP growth|Shock GDP growth|
||export destinations|export destinations|export destinations|export destinations|
||fxing weights to|using weights|fxing weights to|using weights|
||2004-2007|in t-1 and t-2|2004-2007|in t-1 and t-2|
||(5)|(6)|(7)|(8)|
|GFC frm shock*2009|0.0002|0.0003|0.0018***|0.0013***|
||(0.0005)|(0.0006)|(0.0003)|(0.0004)|
|GFC frm shock*2010|0.0018***|0.0020***|0.0016***|0.0012***|
||(0.0005)|(0.0005)|(0.0003)|(0.0004)|
|GFC frm shock*2011|0.0008|0.0013**|0.0018***|0.0016***|
||(0.0005)|(0.0006)|(0.0003)|(0.0004)|
|GFC frm shock*2012|0.0000|0.0005|0.0017***|0.0017***|
||(0.0005)|(0.0006)|(0.0004)|(0.0004)|
|GFC frm shock*2013|0.0007|0.0016***|0.0019***|0.0016***|
|GFC frm shock*2014|(0.0005)<br>0.0004|(0.0006)<br>0.0000|(0.0004)<br>0.0021***|(0.0004)<br>0.0017***|
||(0.0006)|(0.0006)|(0.0004)|(0.0004)|
|GFCfrmshock*2015|0.0007|0.0014**|0.0021***|0.0014***|
||(0.0006)|(0.0006)|(0.0004)|(0.0005)|
|GFC frm shock*2016|0.0018***|0.0014**|0.0022***|0.0016***|
||(0.0006)|(0.0007)|(0.0004)|(0.0005)|
|GFCfrmshock*2017|||00018***|00014**|
||||.<br>(0.0005)|.<br>(0.0006)|
|Observations|161,077|124,806|178,188|138,216|
|R-squared|0.0148|0.0159|0.4761|0.4757|
|2-digit industry X Year FE<br>State X Year FE|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|Yes<br>Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for a vector of firm characteristics as of 2007 described in Section _C._ . FE stands for 52
- Moya, A. 2005. Impacto de la Erradicación Forzosa y el Desarrollo Alternativo Sobre los Cultivos de Hoja de Coca. Masters thesis. Facultad de Economía. Universidad de Los Andes, Bogotá, Colombia.

- Relyea, R. 2005. “The Impact of Insecticides and Herbicides on Biodiversity and Productivity of Aquatic Communities.” _Ecological Society of America_ 15 (2): 618–27.

- Reyes, L. 2014. “Estimating the Causal Effect of Forced Eradication on Coca Cultivation in Colombian Municipalities.” _World Development_ 61: 70–84.

- Rosenbaum, P., and D. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” _Biometrika_ 70 (1): 41–50.

- Rozo, S. V. 2014. “On the Unintended Consequences of Anti-Drug Programs in Producing Countries.” Association for Public Policy Analysis and Management. http://www.appam.org/assets/1/7/On_the_Unintended_Effects_of_Spraying.pdf

- Stigler, G. J. 1970. “The Optimum Enforcement of Laws.” _Journal of Political Economy_ 78: 526–36.

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us to have a sufficient number of credible variables that can capture exogenous entrepreneurial ability, “A” in the theory of Section 2, and a data set that allows sufficient matching of firms similar on ability, some of which can be argued to be selected randomly into compliance and others into noncompliance.

Unfortunately, this does not seem to be a plausible exercise for the Factories Act. The two complementary data sets which allow us to quantify the magnitude of compliance versus noncompliance are collected for different purposes and under different protocols. This is useful in identifying noncompliance, because the issue of illegality is not broached in the NSSO survey. However, the two data sets do not have a sufficient number of varaibles in common which can be plausibly identified with exogenous entrepreneurial ability, and which when applied lead to a credible matching process.<sup>12</sup>

> 12 We have been more successful with this exercise for the Employees Provident Fund Act, 1952, because compliance and noncompliance can be identified in the same data set. These results are described in Chatterjee and Kanbur (2013).

16