rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Reform in Laqqed OB Indicator and Change in ES S0th-10th spread<br>a<br>iar<br>Cc<br>o<br>Eg<br>cal<br>Ww<br>= B,<br>I 7<br>5 om<br>en THR, 3<br>ca! ae UG BLE<br>a ARM2 4<br>Bt a ——yee HRVALB3_4 GEO_?3<br>co 3 4 AO 28<br>o 20 DB Import tq/OB_Import_tt4 60 a0<br>n==20<br>Labels are: ‘country code’ ‘years since OB reform’ ‘years between ES surveys’<br><!-- End of picture text -->
|**Table 2 Mexico: Antidumping Duty**|**Levels in Different Situations**<br>|
|---|---|
|Price comparison|Level of duty|
|Normal value = International price >|Duty = Dumping margin|
|Export price||
|Normal value > International price >|Duty = Difference between export price and|
|Export price|international price|
|Export price = International price <|No antidumping duty – injury (if observed) is|
|Normal value|from normal international competition|

**Source: WPS3684**

45
32 country. But what is the implication of having consumption cities for growth?

Comparing the returns to experience for the UT and UNT sectors might be one way to answer it. Indeed, if the returns to experience capture human accumulation at work (Lagakos et al., 2018; Jedwab et al., 2020a), and if there is more human capital accumulation at work in the UT sector, we should observe higher returns in UT than in UNT. However, we do not find that this is the case.<sup>49</sup> Yet, in some specifications we found significantly lower urban returns to experience in the aggregate in NRXGDP and DEINDU countries, which may be due to more general factors in consumption cities.<sup>50</sup> Another way to answer the question about the growth implications of consumption cities, especially large ones, would be to globally estimate separately the agglomeration economies (AEs) for the two sectors, UT and UNT. We cannot estimate AEs by sector using the I2D2 because we cannot identify cities in the database (only or the number of the cluster are Since this regions survey given). paper identifies production/consumption city-ness (PCC) for many urban areas in a large number of countries, we could obtain from the literature and compare the estimates of AEs for countries whose cities are mostly consumption cities and the AEs of countries whose cities are mostly production cities. One can draw on many such urban studies, which consider one at a time and for a of cities or typically country sample regress log wages log productivity on the log of population, population density or employment. Chauvin et al. (2017) estimated the AEs for China – a country which our analysis shows has mostly production cities – at more than 16%, for India and the United States – two countries that we show have a mix of production, consumption and neutral cities – at 8% and 5%, respectively, and for Brazil – a country with mostly neutral and consumption cities – at 3%. The fact that these estimated AEs decrease as consumption cities become more dominant in a country’s urban hierarchy suggests that urban economic growth might also weaken.

Other studies corroborate the potential negative growth implications of countries’ having an urban hierarchy dominated by consumption cities. Venables (2017) show

> 49If we estimate eq. (13) separately for UT and UNT and focus on the sample countries for the period 1990-2017, we find similar returns at around 2% (N = 59-67). This is also the case if we consider the whole world including developed economies for which the returns might be better estimated.

> 50 Human capital could be accumulated at work _passively_ or _actively_ . While Jedwab et al. (2020a) think of human capital accumulation at work as being a primarily passive process, Ma et al. (2021) show that the process can be active in developed economies. Note that our sample focuses on developing economies.
The structure of the paper is as follows. Section 2 describes the survey. Section 3 presents summary statistics. Section 4 describes the main characteristics of Venezuelan migrants, using natives in the same local area as benchmark. Section 5 analyzes the occupations of Venezuelans in Ecuador and in the home country. Section 6 collects our policy analysis and Section 7 concludes.

# **2 Data: The EPEC Survey**

The goal of the EPEC survey was to collect household-level information on the population of recent Venezuelan migrants to Ecuador along with the native population in the receiving communities. The survey focuses on Venezuela-born individuals that arrived in Ecuador after January 2016 and over-samples this population.<sup>2</sup> In June and July 2019, almost 1,900 households were interviewed (in person) and provided information on 6,425 individuals. The survey is representative of the population of recent Venezuelan migrants in Ecuador and of the native population residing in the areas where Venezuelan migrants are found. Besides the typical socio-demographic and labor market information of labor force surveys, the questionnaire also includes questions regarding pre-migration employment, the gateways of entry into the country, health, children’s schooling, access to internet and remittances, among other topics.<sup>3</sup> In the final sample (of 6,425 individuals), 10% of respondents resided in sectors with low density of Venezuelan migrants (below 5% of the sector’s population), 43% in medium-density sectors (with a density ranging between 5% and 15%), and 47% from high-density sectors (with a density of Venezuelan migrants above 15% of the sector’s population). The final sample contained 6,425 individuals. Among these, 1,715 were born in Venezuela and considered recent migrants.

> 2EPEC stands for _Encuesta a Personas en Movilidad Humana y en Comunidades Receptoras en Ecuador_ , which can be translated as Survey of Migrants and Receiving Communities in Ecuador.

> 3To be specific, Ecuador is administratively organized into 221 cantons. To implement the survey, the cantons were subdivided into _sectors_ classified according to the density of Venezuelans estimated using mobile phone data (described in detail in Olivieri et al. (2020)). About 190 sectors were randomly selected and, within those, households were also selected for the interview at random. By virtue of the sampling strategy, the survey is nationally representative of the target populations and the three strata of sectors defined by low, medium and high density of Venezuelan migrants.

4
Klasen, S. (2000): Does Gender Inequality Reduce Growth and Development? Evidence from Cross-Country Regressions. Collaborative Research Center 386, Discussion Paper 212.

Knowles, S., P.K., Lorgelly and P.D. Owen (2002): Are educational gender gaps a brake on economic development? Some cross-country empirical evidence. Oxford Economic Papers, 54 (1), pages 118-149.

̈Ozden, C., C.R. Parsons, M. Schiff and T. Walmsley (2011): Where on earth is everybody? The evolution of global bilateral migration 1960-2000? World Bank Economic Review, 25 (1), pages 12-56.

Parsons, C.R., R. Skeldon, T.L. Walmsley and L.A. Winters (2007): Quantifying international migration: a database of bilateral migrant stocks. Policy Research Working Paper no. 4165, World Bank, Washington, DC.

Ortega, F. and G. Peri (2012): The Effect of Income and Immigration Policies on International Migration. NBER Working Paper no. 18322 Santos Silva, J.M.C. and S. Tenreyro (2006): The log of gravity. Review of Economics and Statistics, 88 (4), pages 641–658.

# **8 Appendix**

## **8.1 Data sources**

The statistical appendix in DLM (2009, p. 317) describes the data sources for the 30 OECD countries in their sample. Table A1 below describes the various data sources used for the 46 45
**Table 5. Results from estimating** **_β_ -convergence models**

||**1985 arrival**||**1975 arrival**||
|---|---|---|---|---|
||**1990-2000**|**1980-1990**|**1980-2000**|**1990-2000**|
||**(1)**|**(2)**|**(3)**|**(4)**|
||**EDUCATION I**|**NDEX 1**|||
|**Predicted Log of OP in 1990**|-0.0291***<br>[0.00522]|||-0.0278***<br>[0.00726]|
|**Predicted Log of OP in 1980**||-0.0523***<br>[0.00442]|-0.0297***<br>[0.00166]||
|**Constant**|0.202***<br>[0.0341]|0.359***<br>[0.0281]|0.205***<br>[0.0105]|0.189***<br>[0.0481]|
|**Observations**|79|80|80|80|
|**R-squared**|0.554|0.834|0.906|0.343|
||**PRESTIGE I**|**NDEX**|||
|**Predicted Log of OP in 1990**|-0.0290***<br>[0.00377]|||-0.0183**<br>[0.00706]|
|**Predicted Log of OP in 1980**||-0.0341***<br>[0.00566]|-0.0177***<br>[0.00210]||
|**Constant**<br>|0.188***<br>[0.0236]|0.218***<br>[0.0353]|0.114***<br>[0.0132]|0.118***<br>[0.0447]|
|**Observations**<br>|79|80|80|80|
|**R-squared**|0.562|0.419|0.546|0.178|
||**EDUCATION I**|**NDEX 2**|||
|**Predicted Log of OP in 1990**|-0.0234***<br>[0.00492]|||-0.0198***<br>[0.00677]|
|**Predicted Log of OP in 1980**||-0.0382***<br>[0.00469]|-0.0214***<br>[0.00172]||
|**Constant**|0.0660***<br>[0.0133]|0.107***<br>[0.0125]|0.0603***<br>[0.00457]|0.0552***<br>[0.0184]|
|**Observations**|79|80|80|80|
|**R-squared**|0.414|0.647|0.773|0.213|
|Robust standard errors in brackets<br>*** p<0.01, ** p<0.05, * p<0.1|||||

33
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While we lack an appropriate counterfactual for examining how institutional fragmentation affects the participation decision, the comparison between migrants and local residents is informative. Given that migrants are far more mobile than local residents, we expect that differences with local residents in the responsiveness to incentives created by the labor tax wedge are likely to be driven by factors affecting portability.

_<u>Who Participates in Voluntary Social Insurance Programs</u>_ <u>? After examining participation of employed</u> workers, we expand the sample to include all working-age adults and then examine the correlates of decisions to participate in urban residents’ health insurance and pension programs, and collective medical insurance and pension schemes targeted to rural residents. Of particular interest is evidence of whether participation is associated with self-employment and the extent to which individuals use voluntary social insurance programs as a substitute for employer-based programs for which they may be ineligible.

# **4.2 The China Urban Labor Survey**

The analysis of individual participation in social insurance uses data from the 2005 and 2010 waves of the China Urban Labor Survey (CULS).<sup>36</sup> We make use of data from the five large cities that were surveyed in both years (and also in the 2001 wave), which are located in different regions of the country. Shanghai is in the Yangtze River Delta near the coast; Wuhan in Hubei Province in central China; Shenyang in Liaoning Province in the northeast; Fuzhou in Fujian Province in the southeast; and Xian in Shaanxi Province in the northwest. In each city, representative samples of local residents and migrants were independently selected in a two-stage procedure. Using previous year data (2004 and 2008) on the local resident population of each neighborhood, a fixed number of neighborhoods were selected in each city using probability proportionate to size (PPS) sampling. As the cities had limited information on the number of migrants living in each neighborhood, neighborhoods were first selected based on local resident populations, and weights are used to correct for differences in the relative sizes of migrant and local resident populations based on population estimates made by neighborhood office staff. Neighborhood staff then helped to construct an updated list of households, including unregistered migrants living in the neighborhood, to serve as a sampling frame. In each year, a fixed number of

> 36These are the second and third waves of the CULS, which is a repeated cross-sectional survey conducted under the direction of the Institute of Population and Labor Economics of the Chinese Academy of Social Sciences, in collaboration with international scholars and with the support of the World Bank.

18
Table 4 Treatment Effects: Learning

# **Panel A: transactions outside the workplace**

||||_Dependent _|_variable:_|||
|---|---|---|---|---|---|---|
||Deposits<br>(1)|Withdrawals<br>(2)|Send-money<br>(3)|Total<br>(4)|Excl. withdrawals<br>(5)|Balance<br>(6)|
|Bank or mobile payroll|0.058***<br>|0.260***<br>|0.292***<br>|0.353***<br>|0.199***<br>|440.763***<br>|
||(0.004)<br>|(0.006)<br>|(0.010)<br>|(0.008)<br>|(0.008)<br>|(47.377)<br>|
||[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|Bank or mobile account|0.049***|0.049***|0.021***|0.082***|0.072***|106.719**|
||(0.006)|(0.005)|(0.004)|(0.008)|(0.008)|(47.498)|
||[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.003]|
|Test: payroll = account|0.197|0.000|0.000|0.000|0.000|0.000|
|Observations|40,458|40,458|24,046|40,458|40,458|40,458|
|R-squared|0.018|0.113<br>**P**|0.181<br>**anel B: send mo**|0.150<br>**ney transactions**|0.068|0.011|
||||_Dependent _|_variable:_|||
||Agent-to-agent|Agent-to-person|Person-to-agent|Person-to-person|Indirect|Direct|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Mobile payroll|-0.194***|-0.061**|0.362***|0.343***|-0.277***|0.494***|
||(0.029)|(0.026)|(0.024)|(0.023)|(0.030)|(0.025)|
||[0.001]|[0.047]|[0.001]|[0.001]|[0.001]|[0.001]|
|Mobile account|-0.030|0.094***|0.038|0.094***|0.051|0.071***|
||(0.037)|(0.034)|(0.023)|(0.022)|(0.035)|(0.027)|
||[0.260]|[0.019]|[0.128]|[0.001]|[0.160]|[0.022]|
|Baseline controls|X|X|X|X|X|X|
|Test: payroll = account|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1,311|1,311|1,311|1,311|1,311|1,311|
|R-squared|0.094|0.068|0.250|0.256|0.153|0.353|
|Mean control [endline]|0.402|0.252|0.081|0.049|0.613|0.120|

Notes: The table reports treatment effects on learning. Estimates in panel A are based on administrative data. The dependent variable is a dummy equal to one if at least one transaction was recorded in a given month. The omitted group consists of observations prior to random assignment to treatment, for which all outcomes are mechanically equal to zero. Robust standard errors, in parentheses, are clustered at the account level. Estimates in panel B are based on endline survey data. The omitted group consists of respondents in the control who continued to be paid in cash and did not receive an account. Controls include stratification dummies, marital status, number of dependents, dummies for savings, formal savings, years of work experience, and years with current employer. Robust standard errors in parentheses, sharpened q-values in brackets. * _p <_ 0.1, ** _p <_ 0.05, *** _p <_ 0.01.
marked “bunching up” is evident just above $1.25 a day, which also points to continuing vulnerability to downward shocks.

As an aside, there is no sign in Figure 3 that the developing world’s trend rate of decline in poverty has been any greater since the MDGs were officially ratified at the _Millennium Summit_ of 2000. However, one should not make too much of that observation. For one thing, the pre-2000 trajectory may not the right counterfactual, in the absence of the MDGs. For another, it can be noted that Africa’s turning point in progress against poverty was around 2000, although attribution to the MDGs would clearly be a large and (potentially) contentious step.

# **An optimistic trajectory for the next 10 years**

The guiding principle for the proposed optimistic benchmark is that the recent success against extreme poverty is maintained. There are two ways one can extrapolate that success forward over time. The first uses a simple linear projection. This gives a poverty rate for the developing world as a whole in the year of writing (2012) of 19.0% (with a standard error of 0.6%), representing 1.1 billion people). For 2017 the projection is 13.8% (s.e.=0.8%) or 0.9 billion people, while for 2022 it is 8.6% (s.e.=1.0%), or 0.6 billion people.<sup>15</sup> The second method is to apply country-level growth projections to baseline poverty measures under assumed distributional changes, and then aggregate up to the global level. This is a more complicated calculation. But the two methods are in are in close accord, at least in the near term. Trajectories for poverty in 2015 by the second method are reported in the 2012 _Global Monitoring Report_ (GMR), drawing on the Bank’s country-specific growth projections. This method gives a poverty rate of 16.3% for 2015 (World Bank, 2012a).<sup>16</sup> This is very close to that implied by the first method, namely 15.9%. Table 1 includes the forecasts by region for 2015 from the GMR.

The proposed “optimistic trajectory” for 10 years ahead is the simple linear projection, implying a benchmark of 9% for 2022. There are a number of points to note about this trajectory.

> 15 The Bank’s current population projections in millions for the developing world, as defined in 2012, are 5884 in 2012, 6230 in 2017 and 6561 in 2022. Using the 1990 classification of developing countries (to be consistent with PovcalNet) the numbers are 6009, 6355 and 6687 respectively. This difference does not change the poverty counts reported in this paper at the stated number of significant figures.

> 16 These forecasts factor in historical gaps between growth rates in the survey means and growth rates in private consumption per capita in the national accounts. For most countries, about 90% of the national accounts growth rate is passed onto the survey means, but for India it was only about half, consistent with the larger gap between the two growth rates for India. In addition an allowance is made for rising inequality in both China and India. For further details see World Bank (2008).

7
# I. INTRODUCTION

Economists have been seeking to comprehend why some countries are rich and others poor for well over 200 years. A better understanding of the national policies associated with long-run growth would both contribute to our ability to explain cross country differences in per capita incomes and provide a basis for making policy recommendations that could lead to improvements in human welfare. Recently, economists have used cross country regressions to search for empirical linkages between long-run growth and indicators of national policies (e.g., Roger Kormendi and Philip Meguire, 1985; Robert J. Barro, 1991). The large cross country growth literature has identified various fiscal, monetary, trade, exchange rate, and financial policy indicators that are significantly correlated with long-run growth. Yet, Ross Levine and David Renelt (1992) show that many of these findings are fragile to small alterations in the conditioning information set. That is, small changes in the right-hand-side variables produce different conclusions regarding the relationship between individual policies and growth. This paper's purpose is to take stock of what the profession has learned from cross country regression studies of the linkages between long-run growth and indicators of monetary, fiscal, exchange rate, trade, and financial policies.

# 1. Why use cross-countrv regressions?

To gauge what we have learned - and may potentially learn - from cross country regressions, we should humbly face the daunting array of methodological, conceptual, and measurement problems that plague our ability to interpret cross-country growth regressions confidently (see Levine and Renelt (1991)). While the title of this paper is "Looking at the Facts ... ," the inherent problems associated with cross-country
# **Microbiology Analysis**

||Contro|l mean<br>**District**<br>**School**|Treatm<br>**level interv**<br>**component**|ent mean<br>**ention**<br>**sample**||
|---|---|---|---|---|---|
||N|Avg.|N|Avg.|Difference|
|**Prevalence of E.Coli in water**|85|0.329|74|0.514|-0.184*|
|**Log10 E. coli, MPN/100ml, Child**|86|0.584|74|0.463|0.121|
|**Log10 E. coli, MPN/100ml, Mother**|86|0.702|74|0.829|-0.127|
|**Log10 E. coli, MPN/100ml, Object**|82|0.595|72|0.621|-0.026|
|**Stool sample, Ascaris detected**|86|0.023|74|0.000|0.023|
|**Stool sample, Blastocystis detected**|86|0.105|74|0.108|-0.003|
|**Stool sample, Giardia detected**|86|0.105|74|0.041|0.064|
|**Parasite count in stools**|86|0.233|74|0.149|0.084|
|**Prevalence ofparasites in stools**<br>*p < 10%, ** < 5%, *** < 1%|86|0.186|74|0.122|0.064|

54
example, too restrictive pre-requirements are always questionable from the corruption perspective (Ware _et al._ , 2007).

Nonetheless, it is worth noting that a well-designed multidimensional auction might be conducive to excluding prospect firms and consortia having insufficient technical and financial capacity to implement the agreed contract. The main reason for the public system requiring the prequalification process is that prequalification is expected to help to examine the reliability of prospective bidders on the basis of their experience, human and physical resources, and financial positions. In addition, through the prequalification process the public agencies can assess the potential interest from qualified firms and save the amount of work and time in evaluating bids from unqualified contractors (ADB, 2006).

Significantly, in PPP infrastructure transactions many contracts have been renegotiated within about two years after their awards (Guasch, 2004). About 30 percent of concessions granted in the Latin American and Caribbean region during 1985–2000 underwent renegotiation. Why does renegotiation so often take place, regardless of careful preparations for PPP transactions during several years?

There are a number of reasons for this phenomenon. First, auction theory suggests that if bidders are asymmetry—i.e., either weak or strong—a weaker (fringe) bidder tends to bid more aggressively in the presence of a strong (incumbent) bidder (Maskin and Rileys, 2000). Their proposition is supported by the evidence in the road construction auctions (De Silva _et al_ ., 2002, 2003). This bidder asymmetry may partly explain such frequent renegotiations. This is the case when private operators are turned out too optimistic about the demand forecast for undertaken services. In the context of road concessions, fringe contractors may tend to overestimate the future traffic; however, lower-than expected traffic would easily make them go into bankruptcy.

Second, given the expected hold-up problem of auctioneers, bidders may have a strong incentive to submit unrealistic low bids, which is referred to as “low balling” (Ware _et al_ ., 2007). This strategic behavior must of necessity result in an increase in renegotiation.

Finally, the other alleged reason is a failure in designing the multidimensional evaluation framework to select the most efficient firm. In the PPP infrastructure context, 4