rafmacalaba/datause-encoder-data huggingface dataset:
is the school’s actual percent of white, Hispanic and non-poor students, calculated by averaging _D_ s for all students in _j_ .

- _ξ jgt_ is an unobserved (to us) characteristic of the school and grade.

We define a market as a (grade _g_ , year _t_ ) pair. Market size _Mgt_ is the number of students who are eligible to enroll in grade _g_ in year _t_ .

The household indirect utility function is: where _δ jgt_ and _μijgt_ are defined below in (2) and (3). As explained in Appendix D.1, (1) incorporates both household preference over school characteristics as well as the contribution of those characteristics to expected student achievement. Lack of achievement data at the student level prevents us from separately identifying those two aspects. The baseline utility component _δ jgt_ is equal to: where vector _β_ captures student preferences for school focus as well as focus impact on achievement, and vector _α_ captures household preferences over peer characteristics as well as the impact of these on expected achievement. Thus, the model captures the potential tension between enhancing productivity and attracting students. For example, an Arts curriculum may not enhance achievement, but parents may like it. Similarly, _ξ jgt_ captures elements such as teacher characteristics that may reflect a similar tension. The student-specific component of (1) is: The household may choose the outside good ( _j_ = 0) with normalized utility _Ui_ 0 _gt_ = _εi_ 0 _gt_ . The outside good may represent home schooling, dropping out of school, etc.

As in Nevo (2000, 2001), we decompose the demand shock as follows: _ξ jgt_ = _ξ j_ + _ξg_ + _ξt_ + ∆ _ξ jgt_ , where _ξ j_ captures school-specific elements such as culture and educational philosophy; we refer to _ξ j_ as “school quality.” Component _ξg_ captures grade-specific elements, while _ξt_ captures time-varying elements common to all schools and grades, such as city-wide income shocks. We normalize as follows: _E_ (∆ _ξ jgt_ ) = 0. Hence, _ξ j_ + _ξg_ + _ξt_ is the mean of _ξ jgt_ , and ∆ _ξ jgt_ is a deviation from it.

Among schools offering grade _g_ in year _t_ , the school choice set for child _i_ encompasses all public schools (as if there were open enrollment),<sup>13</sup> all charter schools,

> 13Data limitations motivate this “open enrollment” assumption, innocuous for the development of the model. Using GIS software we established the public schools assigned to children in each block group depending on their grade level. However, based on the resulting assignment and other sources (Filardo et al 2008, and phone conversations with DCPS staff), we concluded that the _actual_ assignment mechanism in D.C. during the sample period was based on residential location only to a limited extent. For instance, Filardo et al document that approximately half of the children enrolled in public schools attend an out-ofboundary shool. Moreover, the mechanism was seemingly not systematic across the District. Hence, we simplified by assuming public school open enrollment.

16
52

|Dependent variable|Log<br>exp/capita<br>(excl. health<br>expenditure)|Log (asset and<br>consumer<br>durable value)|Disaster<br>relief|Domestic<br>remittance<br>s|Remittance<br>s from<br>outside VN|Herfindahl<br>index|
|---|---|---|---|---|---|---|
||ML|OLS||Tobit||OLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
||parameter/|parameter/|parameter/|parameter/|parameter/|parameter/|
||t‐value|t‐value|t‐value|t‐value|t‐value|t‐value|
|1 if year 2004; 0 otherwise|0.33537***|0.7683***|‐5885***|729***|‐304|‐0.0395***|
||44.92|23.52|‐5.23|6.33|‐0.2|‐10.53|
|1 if year 2006; 0 otherwise|0.64451***|1.0031***|‐4341***|1050***|249|‐0.0246***|
||88.39|30.59|‐5.08|9.79|0.16|‐6.59|
|**Constant**|7.0465***|5.7400***|‐12589***|‐5697***|‐121334***|0.7821***|
||67.38|14.06|‐4.1|‐8.86|‐8.12|22.73|
|r2/ln L||0.3||||0.29|
|F||135.18|0.77|16.14|3.56|127.76|
|N|47,167|46985|46985|46985|46985|46916|

Pooled VHLSS data from 2002, 2004 and 2006. ML corrected for spatial correlation, cluster design and heteroskedasticity. OLS and Tobit estimators corrected for cluster design and heteroskedasiticy using Hubert‐White correction. Drought if 20% below annual median rainfall; excess rainfall if > 300 mm in 5 consecutive days at least once in a year; flooding recorded in Darthmouth Flood Observatory and within 2 m elevation difference from river or coast; hurricane force winds if wind 65 knots (118 km/hour) or more.

ln(hours) from 500k city are in effect the ln(1+ tenths of hours) from 500k city with tenths of hours being 6 minute periods
Pot i *y Ris 5Ai(CH WXO RKIMN IA,F Pi< 2860

# **Abstract**

The paper revscvi s rc.cint evidciic<sup>**O0n**trh.iilplactoI</sup> privatization. It focuses<sup>onl</sup> traditioial<sup>priv satitioll</sup> n1anIVst. cfforts invols iiig firimis In compe titive nitr;kcts.<sup>Irsiows</sup> that pris atizarion Inimprmo es fir-is' fi nan. al anid opcratinog performanicc yields positivc fiscal<sup>andmacrocconomic</sup> heniefits (pr-occeds are _saved_ rateirc-<sup>thanspent,transfcrs</sup> dcclinc, anid gos erniicits start **.11. .'0** r\aes froiil privarized firmis), anild improves overall<sup>wvelfare.The</sup> poptIlar viev that privatization alwa\s<sup>leadstolayoffsis</sup> tonfoL1nded. V/hleC hii`hl\ protecred<sup>firmshia\csccii</sup> s,ignifiant dcclinls in niet employment,<sup>competitivefirilis</sup> genierally cxpericiceci sight dcclines if an. Privatiziation's cffects on svealth aid micome distrihiitioii hiavc onl! re-rcently cn receiving<sup>thicattciitiollof</sup> an1d rcscarrClil ILIst gertitg dllLemdrs av. -T-lic paper Iigllights the coniditionis for<sup>sticcessfn l</sup> pri **v** riiatioll strong Political coimi mitmnet<sup>conihi ned</sup> with \s idcr phic ICinderstanlding of aid sLpport<sup>for-rIte</sup> proccss; creationl of competitive markets<sup>tIhroLogI</sup> removal Oit entr and cxit harriers, filjacial sec(tor refol-ilms that create conmiiircialkI oriented<sup>bankinig</sup> systclims cffcct\ve r egnlatorv framesviorks<sup>thatreilifol-cc</sup> lie henefits of private owiinership; transpareicy<sup>inthe</sup> privatization process; and nicastires<sup>romitigateadverse</sup> s(cial _nlld_ nviron-(meIjItal effects This paper-a pr'oCdL Lt **if** tl Pr-ls vate Pro\ siomi of PuIblic Sersc'ies Divisioiin,<sup>Pris atiSectoiAdvisoiiryScr\ icesD)pr</sup> crimentIs part of a la-gcr effort in t lie department<sup>toaialyze atcl</sup> n lissclniiate r ecenit Iindings in priv'ate<sup>sCct(oldeeloiipmentiCopies</sup> of the paper aLre avaiahlbe free<sup>froiinithe\WorldBank,II 81-1</sup> Strecet N\, \ashlinigton, I . 2043 **3.** Please contact Rosario .atrtoli lme, room 19-05 1 , telephone 2021-473-5703, fa\<sup>202-5 2-34',1 , emilml addrcssrhartolonime(iworldhanl-org.Policy</sup> Re scarch Working Paper;s are also posted<sup>onthe\VehathTrphie'eco.svorldh'ikC)lg'Ie</sup> authors mias he conitacted at skiker(V svoriirldhaik.org or nellisI(/ starposver.rnt.<sup>jlIon2002.(.(OpaLges)</sup> _I-/JL P'<l' Keseh Ww',k,,- 1'.7/,(,CS_ iced **by** the _Irsimidmiis_ Rksea,,rth Adisir _,_ taff _om,s{z,c 1/ ,, cxcbm7 tdc,, ab(a_ Porodurcedt I)v til. RCCItareLI Adivisor\v St7ff
# **Figure 4: Net Impact of the Domestic Carrier Measure and Net Exports in the Presence of Threshold Effects**

## **Net NX and SAA Impact**

<!-- Start of picture text --> 0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>Year<br>NX SAA<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997<br><!-- End of picture text --> 47
|**Table 5.1 continued:**<br>**Location**|**Pover**<br>**P0**|**ty Pr**<br>**P1**|**ofile,**<br>**P2**|**Pará, 200**<br>**Pop. Shar**|**0**<br>**e**<br>**Contrib. t**|**o**<br>**Mean income**|
|---|---|---|---|---|---|---|
|||||<br>**(Fk)**|<br>**Poverty**<br>**(Sk)**|**Spatially deflated**<br>**Sep/2002 values**<br>**(R$)**|
|Urban|37.6|17.5|11.6|69.2|59.1|275.0|
|urb.village/city|37.5|17.4|11.6|68.3|58.2|276.5|
|n-urb.village/city|46.3|22.3|15.3|0.8|0.8|147.7|
|Isolated urb.|50.0|22.1|13.5|0.0|0.0|116.7|
|Rural|58.4|33.8|24.7|30.8|40.9|136.4|
|Urban extension|43.9|22.3|15.7|0.2|0.2|201.8|
|Povoado|52.6|29.6|21.3|5.1|6.1|144.6|
|Núcleo|15.5|6.7|4.1|0.7|0.2|587.0|
|outros aglom.|0.0|0.0|0.0|0.0|0.0|0.0|
|excl. aglom.|60.8|35.4|26.0|24.9|34.4|122.1|
|**Work**|||||||
|**Signed Worksheet**Yes|26.7|8.6|3.9|40.5|31.2|283.6|
|No|40.1|16.1|8.7|59.5|68.8|226.5|
|**Econ. Active**<br>Yes<br>|||||||
|No|||||||
|**Working**<br>Yes|38.7|16.4|9.3|69.7|61.3|268.5|
|No|56.2|36.5|30.3|30.3|38.7|148.8|
|**Work Sector**<br>Primary|61.6|34.5|24.5|31.0|46.4|128.9|
|Mineral Ind.|14.9|5.7|3.2|1.0|0.4|442.4|
|Secondary|41.0|16.2|8.7|18.6|18.6|204.4|
|Services|31.0|11.8|6.2|33.9|25.5|335.0|
|Social|19.2|6.3|3.0|5.4|2.5|453.5|
|Public|20.0|7.2|3.6|6.0|2.9|430.2|
|Other|50.5|30.1|23.9|29.3|33.7|174.2|
|**Work Position**<br>Employee|34.7|13.1|6.8|48.9|41.3|249.7|
|Self-employed|45.4|21.2|12.7|43.1|47.6|214.3|
|Employer|7.7|2.7|1.4|2.9|0.5|1396.0|
|Unpaid|85.1|70.0|64.0|5.1|10.6|41.1|
|**_Education:_**|||||||
|**Read and Write**|||||||
|Yes|39.6|19.8|13.6|78.7|70.7|267.4|
|No|60.5|32.6|23.2|21.3|29.3|102.4|
|**Years of Schooling:**|||||||
|1 to 4 years|52.1|27.0|18.7|40.2|47.6|144.8|
|4 to 8 years|39.9|19.2|13.2|19.6|17.8|202.5|
|8 to 12 years|19.8|9.0|6.2|15.5|7.0|391.3|
|More than 12<br>_Source:_Author’s calculations based on Census|3.7<br>2000.|2.0|1.7|4.2|0.4|1222.8|

31
effectiveness, while increasing scope increases persistence but reduces cost effectiveness at commonly evaluated time horizons. To produce these results, we leverage a deep literature that has experimentally estimated the impacts of temporary unconditional cash transfers and multifaceted graduation programs. We pool across studies, and exploit variation in both intervention size and timing of estimates. We present theory that suggests these results are fully consistent with the presence of both scarcity and frictional poverty traps, or alternatively with decreasing returns to investment, in a standard buffer stock model. This challenges the conventional wisdom that the presence of poverty traps implies larger interventions are more cost effective.

Our analysis includes 38 estimates of impacts on household consumption from 17 randomized control trials (RCTs) of temporary unconditional cash transfers (UCT) and multifaceted graduation programs that include temporary unconditional transfers as a component (“Targeting the Ultra Poor”, or TUP) in 14 developing countries. We focus on household consumption for three main reasons. First, economic theory makes strong predictions on the persistence of UCT of varying sizes (Carroll & Kimball, 1996). Second, impacts of UCT on consumption are valuable for disciplining key parameters in economic models of interest (Kaplan & Violante, 2014; Auclert et al., 2018). Third, UCT and TUP programs are commonly motivated as targeting poverty reduction through increasing consumption (Banerjee et al., 2015; Bedoya et al., 2019). For each estimate, we collect impacts on annualized household consumption and standard errors, transfer size, and program cost in USD PPP.<sup>2</sup> To measure cost effectiveness, we focus our analysis on effects on consumption normalized either per dollar of transfer or program cost. We further leverage variation both within and across trials to estimate the effects of the size of transfers and the time since transfers occurred, and we compare estimates across UCT and TUP programs.

We begin by aggregating UCT estimates across contexts, and find the average UCT increased consumption by 0.35 per unit of transfer. We use tools from the metaänalysis

> 2We convert reported estimates, transfer size, and costs from each study in USD PPP indexed to 2010 dollars to enable comparison across studies, following Meager (2019).

4
Figure 3. NPCs and RPCs in CFA countries for major export crops <!-- Start of picture text -->
1.3.<br>_<br>0.9<br>0.7<br>0.5-<br>0.3<br>1970  1972  1974  1976  1978  1980  1982  1984  1986<br>NPC  -RPC<br><!-- End of picture text -->
are also significantly higher in these two countries than in Brazil and India. It should be noted however, that these data points do not indicate whether a higher level of government procurement of advanced technology or fiscal incentives for private R&D or both were necessary for encouraging private investments in R&D in Korea and China. They also do not specify the optimal balance between these policy instruments. Is there a magic formula that Brazil and India have yet to discover?

The role of foreign technology transfer through licensing underscores its importance for catch up in all four countries, but the role of foreign co-inventors does not. Does Korea’s low score suggest that foreign co-inventors are relatively unimportant or simply that China and Brazil are better at attracting foreign co-inventors?

Patent-related innovation indicators show that the BICs have graduated beyond the technological adaptation stage and, like Korea, are playing catch up. Consider, for example, the number of triadic patents, i.e., patents submitted to the European Patents Office, the US Patent and Trademark Office and the Japanese Patent Office in 2005. Korean patent records are firm evidence of its achievement in patenting. In 2005, even though it lagged the U.S. by a large margin (U.S. had 15,700 and Korea had 2,800), it had made phenomenal progress relative to 1985 when it had only 7 patents. Similarly in 2005, the figures for the BICs were impressive when compared to 104 for Singapore, 20 for Hong Kong and 4 for Chile.

The main policy insight which can be drawn from Table 4 is that each BIC and Korea had its own brand of distance shortening policies. Evidently, _in addition_ to and _prior_ to trade liberalization, in varying degrees Korea and the BICs leveraged a package of distance shortening policies to foster domestic innovation capabilities that prepared them better to compete in the global market when they opened up. The crux lay not in a single indicator or two or even three, but in a comprehensive package of support to innovation capabilities that could be measured by indicators such as those in Table 4. Among the policy instruments they used were targeted fiscal incentives for innovation R&D, FDI for technology transfer, and heavy targeted investments in human resources for S&T.

30
2 inequality using NA data and combine it with the empirical observation that withinnational income distributions tend to follow a log-normal pattern. Then the only additional piece of information needed is a Gini coefficient or some other summary inequality statistic describing national income distributions. They are published in various compendia of the Gini coefficients such as WIDER, the Deininger-Squire database etc.<sup>9</sup> Under the assumption of lognormal distribution of income, the inequality statistics allow us to retrieve an estimate of the variance of each national distribution. Once we know the variance and the mean, and given the assumption of log-normality, we can estimate the entire distribution, that is, each fractile’s income. It is then a relatively simple task to combine these national distributions into a single world-wide income distribution, particularly so if one uses an exactly decomposable measure of inequality like the Theil coefficient or the variance of logs. This was precisely the approach followed by many early and some recent studies of global inequality (Berry, Bourguignon and Morrison, 1983; Grosh and Nafziger 1986; Quah 1999; Shultze 1998; Chotikapanich et al. 1997). At times this approach can be refined by using a bit more information than what is contained in a Gini or Theil index. Sala-i-Martin and Bhalla have used quintiles of income distribution to get a better handle on national distributions and thus a more precise estimate of global distribution. All these methods can be considered _tatonnements_ , groping for the global distribution.

These methods are quite ingenious given their rather minimal information requirements. But they are also very “costly” because it is often the numerous assumptions, piled up one upon another, that drive the results rather than the data. A lot of assumptions are made simultaneously (e.g. that each country’s distribution is lognormal; that GDP per capita gives the correct mean income and that its under- or overestimation compared to household surveys is constant across poor and rich alike) and it is quasi impossible to tell the impact which each of the assumptions separately has on the results. Further, since even the minimal data requirements (national Ginis) are not satisfied annually, authors are led to make additional assumptions (for example, that national inequality does not change or changes in an assumed fashion), so that at the end

> 9 Available at <u>http://www.wider.unu.edu/wiid/wiid.htm</u> and http://econ.worldbank.org.

6
Rodrik found that ethno-linguistic fragmentation was not strongly linked to income inequality (r=0.13), but remained significant even after controlling for institutional quality. Hence the evidence suggests that ethnic cleavages matter in the ability to manage shocks even after the quality of institutions is controlled for. To test the robustness of this conclusion, he uses additional measure of social divisions, such as home language, the proportion of people who speak the country's official language at home, racial tension, lack of trust, and the Gini coefficient for land. The relation between these social conflict proxy measures and the growth differential between 1960-75 and 1975-89 was negative and significant. The correlations between the measures varied, thus each measure is capturing a different social characteristic. For example, he found that "no trust" is highly correlated with income inequality, but not with ethnic and linguistic measures.

Fedderke and Klitgaard (1998) found significant correlations between income inequality, degree of hierarchical steepness in society and degree of individualism (Hofstede cultural variables) and levels of per capital GDP, but not with growth. Knack and Keefer (1997) demonstrate that the Gini coefficient for income inequality is strongly associated with lower trust and civic co-operation. They also demonstrate that limits on executive power increase trust.

When income inequality is considered a proxy (albeit imperfect) of social cleavages and social exclusion, two findings are worth noting. First is the fact that measures of income inequality are quite stable, even during periods of rapid growth. A possible interpretation is that since the dominant social groups make policy and resource allocation decisions, these decisions reinforce their power and prosperity, and hence policy interventions do not result in major changes for those excluded. The second consistent finding is that countries with initial unequal income distributions experience slower growth. This finding is even stronger for initial unequal distribution of land in developing countries (Deininger and Squire, 1997). Birdsall and Sabot (1994) come to similar conclusions in comparing high inequality and low growth in Latin America and low inequality and high growth in East Asia, but they turn to political economy considerations to explain the different strategies in the two regions. In Latin America, the political elite has little incentive to invest in the well-being of the poor, but in East Asia the threat of communist insurgency led to government investment in the rural poor, in education and health and in rural infrastructure. Larrain and Vergara (1997), using data for 45 developed and developing countries, conclude that distributive inequalities give rise to social pressures and conflict, and these in turn generate greater instability in economic policies and lower investment. Rodrik (1997) also concludes on the basis of cross-country regressions that severe income 24
# TABLE 5

The demand for tasks and exports (shares of employment) IV-FE

|||exp|ort intens|ity||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|A) Skilled and Unskilled Labor||||||
|share highly-skilled|0.08***|0.09***|0.09***|0.09***|0.09***|
||(0.031)|(0.032)|(0.032)|(0.032)|(0.032)|
|share skilled|0.08**|0.09***|0.09***|0.09***|0.09***|
||(0.032)|(0.033)|(0.034)|(0.034)|(0.034)|
|B) Production and Non-Production Labor||||||
|share production|0.03**|0.01|0.01|0.01|0.01|
||(0.012)|(0.009)|(0.009)|(0.009)|(0.009)|
|C) Tasks||||||
|share managers|–0.01**|–0.01|–0.01|–0.009|–0.009|
||(0.006)|(0.005)|(0.005)|(0.005)|(0.005)|
|share engineers|0.09***|0.10***|0.10***|0.10***|0.09***|
||(0.031)|(0.031)|(0.032)|(0.032)|(0.032)|
|share services|–0.009|0.009|0.009|–0.009|–0.009|
||(0.008)|(0.007)|(0.007)|(0.007)|(0.007)|
|share blue-collar|–0.07**|–0.08**|–0.08**|–0.08**|–0.08**|
||(0.031)|(0.033)|(0.034)|(0.034)|(0.034)|
|share maintenance|–0.01**|–0.01*|–0.01*|–0.01*|–0.01*|
||(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|
|_N_|20544|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of log employment and employment shares on export intensity (exports/sales). The instruments are the weighted average of the real exchange rate of a firm export partners, _z_<sup>0</sup> , and its interaction with initial sales (in 2001). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

19
## **Debit card ownership among account holders**

Adults with a formal account by debit card use (as % of all adults) <!-- Start of picture text --> 100 Adults with<br>an account<br>80 DOES NOT<br>HAVE DEBIT CARD<br>60<br>40<br>HAS DEBIT CARD<br>20<br>0<br>MIDDLE EAST SUB-SAHARAN SOUTH LATIN AMERICA EUROPE & EAST ASIA HIGH-INCOME<br>& NORTH AFRICA AFRICA ASIA & CARIBBEAN CENTRAL ASIA & PACIFIC ECONOMIES<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012.

The use of debit or ATM cards, another vehicle for carrying out financial transactions, is far more common in high-income than in developing economies. In the Netherlands, for example, about 98 percent of adults report having a debit card. In South Asia no country has more than 10 percent of adults reporting that they have a debit card. Yet in a handful of developing economies—including Belarus, Brazil, the Islamic Republic of Iran, Lithuania, Mauritius, and Mongolia—more than 40 percent of adults report having a debit card.

An interesting question is what share of account holders have a debit card. South Asia again stands out, with only 22 percent of account holders having one (figure 1.17). Europe and Central Asia has the largest share among regions, with 81 percent of account holders reporting that they have a debit card. In high-income economies 69 percent of account holders have a debit card.

# PAYMENT SYSTEMS

Just as the most common methods that account holders use for making withdrawals and deposits differ between developing and high-income economies, so do the payment systems they use. As might be expected, checks and electronic payments are far more commonly used in high-income than in developing economies. Adults in high-income economies are nine times as likely to report having used a check to make a payment or to buy something in the past 12 months. In the developing world use of electronic payments—such as wire transfers or online payments—is rare. Only 5 percent of adults in developing economies report having used any type of electronic payment to make payments on bills or to buy things in the past year.

**23**

MEASURING FINANCIAL INCLUSION