rafmacalaba/datause-encoder-data huggingface dataset:
Importantly, both CIF and COD are net concepts. They do not represent gross purchases of domestic assets by foreign residents, or gross purchases of foreign assets by domestic residents. However, in keeping with common usage, we shall refer to them somewhat loosely as "gross in‡ows" and "gross out‡ows", respectively.

Like Broner et al (2013), we scale capital ‡ows by trend GDP.<sup>10</sup> We use trend rather than actual GDP to prevent the short-term cross-country comovement of GDP found in the data from distorting the estimates of the common factors and common components of capital ‡ows.

To study the covariates of the global and group factors, below we use a set of variables commonly employed to capture world real and ...nancial conditions. Speci...cally: (i) global risk, as measured by the CBOE Volatility Index (VIX) and similar measures; (ii) the global short-term interest rate, given by the FED e¤ective federal funds rate minus the U.S. GDP in‡ation rate; (iii) global growth, as measured by the real GDP growth rate of the U.S. (although we experiment also with G7 and world GDP growth); (iv) the real e¤ective exchange rate of the U.S. dollar; and (v) an index of real commodity prices –speci...cally, the UNCTAD index for metals and minerals, de‡ated by the U.S. GDP de‡ator.

Likewise, to assess the covariates of the factor loadings, we use a set of variables capturing countries’structural and policy framework. These include: (i) ...nancial openness, as measured by the Chinn-Ito index; (ii) trade openness, given by total exports plus imports as a percentage of GDP, and expressed in log terms; (iii) ...nancial depth, measured by domestic credit to the private sector as a percentage of GDP; and (iv) the exchange rate regime, summarized by the index of de facto exchange rate arrangements of Ghosh, Ostry and Qureshi (2015). Table A2 in the appendix gives the details on the data sources.

Figure 1 shows the time path of aggregate ‡ows, for the full country sample as well as each of the three country groups, expressed as percent of the respective group’s trend GDP. The full-sample ‡ows reveal two facts. First, in‡ows and out‡ows exhibit a high degree of comovement. Second, both in‡ows and out‡ows display pronounced cycles. Inspection of the ...gures corresponding the three country groups shows that the comovement between in‡ows and out‡ows is particularly noticeable among advanced countries. Also, the exact timing of the capital ‡ow cycles varies across country groups. Advanced-country ‡ows show an upward trend starting in the mid-1990s. They peak at around 25 percent of trend GDP just before the global crisis of 2007-2008, and collapse thereafter. In turn, emerging-market ‡ows exhibit wide swings, with sharp drops at the time of the 1981 debt crisis and the East Asia crisis of the late 1990s. They subsequently peak in 2006, and fall sharply afterwards. Finally,

> 10Trend GDP is calculated applying the Hodrick-Prescott ...lter, using a parameter of 100, to the series of nominal GDP in U.S. dollars.

9
**OPTIMIZING THE SIZE OF PUBLIC ROAD CONTRACTS**<sup>**‡**</sup>

April 2012 Atsushi Iimi<sup>♣ †¶</sup> Radia Benamghar<sup>§</sup> Key words: Public procurement; rural roads, auction theory; competition; market entry. JEL classification: D44, H54, H57, D82.

‡ This paper is partially based on our early working paper “Efficiency in public procurement in rural road projects of Nepal” World Bank Policy Research Working Paper No.5736.

♣ Corresponding author.

† Office of the Chief Economist, Sustainable Development Network. The World Bank.

¶ I express my special thanks to Department of Local Infrastructure and Agricultural Roads (DOLIDAR), District Development Committees (DDCs), Department of Hydrology and Meteorology, Ministry of Environment, and the Nepal Office of United Nations for their kind collaboration in collecting relevant data for this study. I am also grateful to many seminar participants from the DORIDAR, RAIDP, Department of Road (DOR), DDCs, District Technical Offices (DTOs), Office of Auditor General (OAG) and DFID for their insightful comments.

§ South Asia Sustainable Development Transport. The World Bank.
With our dataset we are able to control for a number of important characteristics of individuals which theory suggests should be associated with remitting behavior. These include demographic characteristics of the migrant (age and sex), differences in household demographics and the presence of family abroad (marital status, household size in the destination country, whether or not the spouse, children, and parents of the interviewee are abroad), income and employment status, years of education, the length of time spent abroad, and some qualitative indicator of intent to return home, and legal status.<sup>5</sup> We use all available data in these regressions. While we in total have data on 12,785 migrants, some of these are from surveys only at the extensive margin, and others only from surveys at the intensive margin. In order to account for differences in sample size across destination countries, we reweight the data using weights post-stratified by education level and country of birth. We therefore require education data to be nonmissing in these regressions. This gives a sample size of 8,953 usable observations at the extensive margin, 3,838 usable observations at the intensive margin, and 5,171 usable observations on total remittances (including zeros for those not remitting). The regressions in Table 3 then dummy out missing covariates in order not to reduce the sample further. For robustness we have also re-run Table 3 just using the 5,171 observations that have information both on whether individuals remit and how much they remit, obtaining very similar results.<sup>6</sup> Table 3 presents the results of these regressions. All regressions contain dataset fixed effects. For each remittance measure the first column shows results excluding the individual characteristics, while the second adds these individual characteristics. This allows us to see whether differences in remittance behavior remain across source countries after controlling for destination dataset fixed effects, and whether these differences persist after also controlling for observable characteristics of these migrants. The coefficients on country of birth indicate whether migrants born in a given country remit differently from those born in Ghana. An F-test tests the null hypothesis of joint

> 5 We dummy out missing covariates, which largely occur because not all covariates are available in all datasets (see Table 1).

> 6 Results not shown for space reasons, but available upon request.

13
We examine the bivariate relationship between demand and key firm and owner characteristics in more detail in Figure 5. This figure allows us to see how demand varies with different characteristics at different prices. We see the demand curves look similar for men and women, and that demand is higher for those who are wealthier, more risk-seeking, with higher sales, higher expected returns to training, and younger owners. We also test (not reported) whether the slopes vary by characteristic, and find that younger owners and those who have higher expected returns from training have more inelastic demands (flatter slopes).

Table 6 looks further at the correlates of willingness to actually pay. Column (1) repeats the last column of Table 5 in terms of the BDM WTP. Column (2) examines the correlates of actually paying for training in the BDM exercise, conditional on drawing a positive price to pay, and what that price was. We see that only education is significant, with more educated individuals being more likely to pay for training. Column (3) shows this continues to hold when using LASSO to reduce the number of coefficients. Columns (4) and (5) look at who reneges on payment after having a WTP greater than the price, and finds those with more education are less likely to renege. Finally, Columns (6) and (7) present the correlates for paying for the course in the TIOLI sample. Price offered is negatively correlated with paying, as we saw before. We see demand is higher for wealthier owners, and those with larger firms (more employees), as with the BDM. We find that those who think their practices compare well with other firms have lower demand. In contrast to the BDM, we find a marginally positive impact of age on demand (but note that the magnitude is small, and that the TIOLI sample is younger to begin with than the BDM sample).

# **4.3 How Does Pricing Affect Training Attendance?**

We have seen that charging higher prices affects how many people demand business training, and which entrepreneurs demand training. We now turn to measuring whether a higher price affects training attendance. There is a mechanical effect of lower demand on attendance — fewer people buying the course means fewer attending. There are two other channels of interest here: (i) a selection effect, whereby those with a higher willingness-topay are also those entrepreneurs who are more likely to attend training; and (ii) a potential causal effect of higher prices on attendance, through a psychological or sunk cost effect, where paying for training causes individuals to value it more and exert more effort in the learning process. We use our experiments to examine the evidence for both effects.

20
Table A1: Correlation between juvenile catch and measures of fishing productivity

||Dependen|t variable:|asinh(juv|enile catch)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|DistToCoast|0.0050|||0.0026|
||(0.0017)|||(0.0015)|
|TonsPerSet||0.0075<br>(0.0007)||0.0069<br>(0.0009)|
|TonsPerArea|||0.0138|0.0027|
||||(0.0036)|(0.0032)|
|Intercept|0.6375|0.3826|0.6975|0.3169|
||(0.0660)|(0.0524)|(0.0515)|(0.0556)|

All regressions have 34,272 observations. Dependent variable is inverse hyperbolic sine of millions of juveniles caught in a potential closure-treatment bin. Standard errors clustered at level of twoweek-of-sample by two-degree grid cell.

# **2 Treatment coefficients from estimating Equation 1 and variants of Equation 1**

Figure A2 displays the treatment coefficients from estimating Equation 1. The reader may interpret this figure in percentage terms by applying the transformation _exp_ ( _βst_ ) _−_ 1. My results exhibit the same pattern if I drop zero values and use a logarithmic transformation on the dependent variable instead of an inverse hyperbolic sine transformation (Figure A3). They also hold if I replace the dependent variable in Equation 1 with a binary indicator for positive juvenile catch (Figure A4) or if I replace treatment fraction with a binary indicator for positive treatment fraction (Figure A5).

My results are also robust to replacing the outcome variable with the inverse hyperbolic sine of tons of juveniles caught (Figure A6). Juvenile catch in tons is the number of individuals in each length interval times the weight of an individual in each length interval, summed over length intervals less than 12 cm.<sup>36</sup> When I convert these treatment coefficients into changes in levels and account for the reallocation in tons (of juveniles and adults) caught

> 36For a given set, tons of juveniles caught equals<sup>�[11</sup> _l_ =[3<sup>_._5</sup> _,_<sup>_,_</sup> 3<sup>12)</sup> _._ 5)<sup>_wlNl_,where</sup><sup>_wl_istheweightofanindividualin</sup> length interval _l_ and _Nl_ is the number of individuals in length interval _l_ that the set caught.

4
alter prices on a day-to-day basis.<sup>8</sup> Therefore, in all three tables of descriptive statistics, an observation is at the aggregated level.

There is a significant amount of variation in total catch, market prices, and cooperative prices (table 1). Comparing the second and third panels of table 1, the log of cooperative price and log of total catch show a marked difference between the exclusive and nonexclusive cooperatives, despite the fact that average market prices are similar. The log of cooperative price and the log of catch also have a higher coefficient of variation for the exclusive cooperative.

The final rows of each panel show the range of variation for the “Recruit Effect” variable and the “ONI X Recruit Effect” interaction. The majority of observations have a value of 0 for the “Recruit Effect” variable, but approximately 800 observations exhibit a positive recruitment effect, and more than 500 observations exhibit a negative effect (figure 2). This variation permits a test of the model’s third implication regarding population growth rates.

To understand the relationship between market prices in La Paz and market prices external to that fishing area, we use the daily transaction data to estimate a regression of La Paz prices on La Nueva Viga prices, separately for each species for which there are data from both sources. Of the 55,841 daily transactions across Abreojos, Sargento, and Pichilingue, the species that match across the logbooks and La Nueva Viga account for only 4,208 transactions. Moreover, some of these transactions have no information on La Paz market prices. In table 2, we run OLS regressions of the log La Paz price on the log La Nueva Viga price by species common name.

The results paint a mixed picture. For five of the nine common names, there is a positive and statistically significant relationship between La Nueva Viga prices and La Paz prices. For these, the R-squared ranges from 0.016 to 0.877, depending on the species. Two of the nine common names have coefficients that are not statistically distinguishable from zero. The remaining two common names have negative and statistically significant relationships. Moreover, these relationships are not simply an artifact of La Nueva Viga sourcing particular species from La Paz. Of the four species that are sometimes sourced from B.C.S. in the data ( _almeja_ , _calamar_ , _ostion_ , and _pulpo_ ), three have a positive coefficient while one has a negative and significant coefficient. Of the species not sourced from B.C.S., two have a positive coefficient, with _cazon_ having especially high representation in the logbook data. While this exercise is starkly limited by data challenges, it suggests that the prices for some species are not just locally determined in La Paz.

> 8. Based on daily transaction data, we do sometimes observe multiple prices paid for a given species on a particular day. It is difficult to know whether this is measurement error or variation in price due to differences in time of day (e.g., a higher price for a more inconvenient time).

1
obtains the same rewards as a more efficient firm, then over time the efficient firms will be less inclined to expend effort to reduce costs, because they can obtain the same rewards by adopting the inefficient businesses’ strategy of capitalization.

Finally, thus far, we have concentrated on the case where firm 2 behaves inefficiently by engaging in excessive investment. Our other definition of capitalization is simply where the firm reports a higher level of capital intensity without actually making inefficient decisions. In this case, firm 2 induces no extra inefficiency but simply protects its revenue by hiding its inefficiency through its reporting policy.

In their paper, Burns and Davies provide a number of indicators of capitalization, and at the 2000 distribution price control review, Offer made adjustments to the cost base of the businesses to reflect the capitalization that had occurred, and began work to develop better investment and quality incentives – the Information and Incentives Project (IIP).

# **V Incentive regulation and joint products**

The problem of distortion of inputs can be extended to consider the problem that output provision can be distorted if two outputs that depend on the same costs are subjected to separate regulatory regimes. Where the incentives are balanced, the operator naturally wishes to minimize the total costs of providing both outputs. However, where only one output is subjected to incentive based regulation, it will seek to minimize the costs associated with that output, even if that means the total cost of joint production is not minimized.

The experience of NGC in England and Wales is an interesting case study of the balance of incentives across services. At the privatization of the electricity industry in 1990, National Grid (NGC) was created to own and operate the highvoltage transmission system. In any electricity system, there are certain costs that depend on how well the transmission system is constructed and operated – such as the additional cost of “constrained on” generation (plant required to operate because of transmission constraints) and “ancillary services” (a range of services with no 20
# **3. EVIDENCE OF TENURE CHOICE**

# **Data and Methodology**

The data used in the study and presented in Table 1 are collected from the LSMS and HBS, nationally representative surveys developed to evaluate and track national living standards with the assistance of the World Bank and other donors.<sup>18</sup> The HBS includes more questions on household budgets than the LSMS; however, both surveys include information for creating a socioeconomic profile of households. Both have an advantage for housing sector analysis in that they focus on households, not physical housing stock units, so that questions about residuals arising from census data are of minor relevance.<sup>19</sup> The study team has analyzed the survey datasets available for five of the six studied countries for the most recent available year. For Lithuania, a specific household survey was performed in 2002 in cooperation with the World Bank with detailed housing sector questions, which qualifies it as a comparator.<sup>20</sup>

**Table 1. Household Survey Data Descriptors**

|**Country**|**Year**|**Survey**|**Households**|**Households**<br>**in urban**<br>**apartments**|**Income proxy**<br>**used**|
|---|---|---|---|---|---|
|Armenia|2001|Integrated Living Standards<br>Survey|4,037|1,738|Expenditure|
|Serbia|2002|Living Standards<br>Measurement Survey|6,386|1,063|Consumption|
|Romania|2002|Household Budget Survey|32,285|11,053|Consumption|
|Russia|2003|NOBUS|44,493|25,223|Expenditure|
|Poland|2000|Household Budget Survey|31,847|20,297|Expenditure|
|Lithuania|2002|Lithuania Household Survey|3,000|n/a|Expenditure|

In this study, the focus was on the responses of 59,374 households living in urban apartments in five countries of the ECA region. Almost 50 percent of all households live in urban apartments. Special attention has been paid to ensuring comparability of definitions. For each country, the quintiles were calculated on the basis of expenditure or consumption data. For Armenia, Poland, Russia, and Lithuania, per capita expenditure was used as an income proxy while for Serbia and Romania, per capita consumption was used. The age of the household was coded in four distinct

> 18  The surveys used here are described in greater detail in the associated poverty assessments, available at http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20204084 ~menuPK:443282~pagePK:148956~piPK:216618~theSitePK:430367,00.html (see Armenia 2004; Serbia 2003; Romania 2003; and Poland 2004). Information about the NOBUS in Russia is available at http://www.worldbank.org.ru/ECA/Russia.nsf/0/385AA5BB7DB68980C3256E53004B6F14. The Lithuanian survey is described in Lithuanian Free Market Institute (2002).

> 19  For example, second homes and vacancies.

> 20 The study was performed by the Lithuanian Free Market Institute within the scope of the National Housing Strategy project: http://www.am.lt/EN/VI/rubric.php3?rubric_id=134.

13
ownership), and community characteristics (fraction of population working in agriculture, access to schools and roads, and so on<sup>8</sup> ). μ is the unobserved component for household i.

M is an indicator for having a migrant or receiving remittances. To account for potential endogeneity, in the two-stage least squares regressions, 1993 and 1997 migration networks, defined as the percentage of households in the village with migrants, will be used as instruments for M in 2007. Only 1993 network will be used as instrument for M in the 2000 equation.

As female migration may have a differential impact on the outcomes, we include ―female_it‖—a dummy variable which denotes households with only female migrants, the omitted category being those households with at least some male migrants. Alternatively, this variable can refer to the female share among migrants, for each household with migrants. The coefficients of interest related to the impacts of migration are beta and gamma. Gamma, in particular, captures the differential impact of female migrants.

For robustness check, we will present estimations of probit model in addition to linear probability for binary variables and also estimate a non-linear relationship in the form of IV probit in estimating the probability of household head being employed, which is a binary outcome. We will discuss the robustness of the results with respect to various control variables.

# **6. Results**

This section first presents the estimated impacts on labor supply and child outcomes, and how the impacts vary with the gender of the migrant. The results for migration and remittances are interpreted together throughout this section.<sup>9</sup> This is because the results for migration and remittances are qualitatively very similar. Also, households receiving international remittances identified in the data are a strict subset of households with international migrants, therefore constituting a smaller number of observations. Subsequently, a discussion of robustness of the base results follows. The main results are

> 8 Other proxies for community characteristics that have been controlled for in checking for robustness in the estimations are village home ownership rate, access to clean water, existence of a bank in the village, existence of a slum area in the village, and electricity coverage. 9 The full set of remittance results, though not all presented, is available from the authors upon request.

14
**<u>Table 1.7.2: Omnibus Balance test using survey sample and variables.</u>**

||<br>T0 vs T1|<br>T0 vs T2|T1 vs T2|
|---|---|---|---|
|Married or in a partnership (dummy)|0.056|-0.012|-0.068|
||(0.162)<br>|(0.179)<br>|(0.188)<br>|
||[0.732]|[0.946]|[0.719]|
|Educational level of complete primary<br>l d|0.317|0.437|0.108|
|or ower (ummy)||||
||(0.218)|(0.256)*|(0.270)|
||[0.145]|[0.087]|[0.690]|
|Age|-0.010|-0.025|-0.015|
||(0.010)|(0.015)*|(0.015)|
||[0.356]|[0.095]|[0.308]|
|Has children (dummy)|0.816|0.273|-0.474|
||(0.307)***|(0.230)|(0.292)|
||[0.008]|[0.234]|[0.105]|
|Household size|-0.073|-0.019|0.058|
||(0.056)|(0.048)|(0.053)|
||[0.193]|[0.692]|[0.271]|
|Identifies with indigenous group<br>d|-0.119|-0.025|0.076|
|(ummy)||||
||(0.244)|(0.235)|(0.261)|
||[0.627]|[0.916]|[0.771]|
|Speaks indigenous language (dummy)|-1.162|-0.238|0.921|
||(0.560)**|(0.574)|(0.563)|
||[0.038]|[0.678]|[0.102]|
|Yucatán (dummy)|0.764|0.525|-0.257|
||(0.917)|(0.874)|(0.873)|
||[0.405]|[0.548]|[0.768]|
|Constant|0.076|0.593|0.440|
||(0.539)|(0.628)|(0.543)|
||[0.887]|[0.344]|[0.418]|
|Wald Chi2|23.5597|4.6559|9.6796|
|p-value|0.0027|0.7936|0.2882|
|Pseudo R2|0.0546|0.0155|0.0416|
|_N_|937|1,008|993|
|Specification|Logit|Logit|Logit|
|Base|T0|T0|T1|

_* p<0.1; ** p<0.05; *** p<0.01. P-values in squared brackets. Robust standard errors clustered by localities in parenthesis. Coefficients are log odds._ 14