rafmacalaba/datause-encoder-data huggingface dataset:
# 2011).<sup>29</sup>

The second strand of literature focuses more closely on the effects of infrastructure. Baum-Snow’s (2007) pioneering work finds that the construction of the interstate highway system was a major impetus behind the suburbanization of US cities. Duranton and Turner (2012) also find that more kilometers of interstate highways in US metropolitan areas in the early 1980s led to faster population growth over the next 20 years.<sup>30</sup> These results are confirmed by Holl and Viladecans-Marsal (2011) for Spain. For rural areas in the United States, Chandra and Thompson (2000) and Michaels (2008) find that new highways lead to a displacement of economic activity toward the counties served by these new highways and an increase in the demand for skills. For US metropolitan areas, Duranton, Morrow, and Turner (2014) find that highways lead to a greater specialization into ‘heavy sectors’, for which transportation costs are higher. That is, having more highways in a metropolitan area increases the weight of its exports but not its value.<sup>31</sup> This type of approach is also being applied to developing countries. In a remarkable piece of work, Donaldson (2014) documents the effects of the construction of India’s railroad network by its colonial power. He shows that railroads increased trade and reduced price differences across regions. Even more importantly, railroads increased real incomes and welfare. To minimize identification problems, he compares the network that was built with other networks that were considered but never developed. In related work on modern India, Ghani, Goswami, and Kerr (2012) show that a major upgrade of India’s main highways had positive effects on the productivity and entry of plants in districts close to improved highways. In a slightly different vein, Jedwab and Moradi (2011) document that railway lines built by the British colonial power in Ghana for the exploitation of inland mines had a large effect on 37
6 enviromnent of slower growth, although in a number of countries the impact was probably negligible, and in some it may have been positive.

Or more recently, Cassen (1994) summarizes the state of the art, Is seems likely that, in a poor country ... a growth rate that doubles the population in 20 or 30 years would hanm the rate of improvement in living standards. But neither iheory no econometrics has so far been able to demonstrate this relationship beyond doubt.

Table 1 summarizes the estmates of the impact of population growth from the literature 8 using cross national regressions . Population growth is not a robusdy significant correlate of per capita output growth. As with the simple correlation, the partial correlations (linar regression coefficients) after controlling for other determinants of economic growth (either factor accumulation) or other factors (e.g., trade orientation) are typically small (most estimates are between -.14 and -.3)9 and only a few of the results are statistically significant, depending on the variable definition, country sample, and time period.

_8 The_ simple bivariae correlaio are _not_ reviewed _here_ as _there_ is ample dission of those in _ocher_ sources. Winfrey, 1992, provides a summary of carelation estimates from various countries and periods. Of the 14 studies. with estimates for LDC samples for various periods only 4 show a statstically significant negative relationship (and those are from dLe more recent periods).

_9_ Smallness, like beauty may be in the eye of the beholder. If tt impact of poplation growth is .2 then a 1 percentage point decrease in populaton growth would lead to a .2 percentage point decrease in the per capita growth rate. About the largest conceivable reduction in population growth in developing counties is about 2 percentage points-which is roughly the avaage population growth difference between Sub-aa Africa of (2.8 percent) and the High income countries (.7 percent). This enormous (and implausible) reductioa in population growth would only accelerate per capita growth by .4 percentage points-an effect on growth which is dwarfed by the actual observed variation in per capita growth rates in developing counties. The standard deviation of long-run growth is about 2 percent so that at an esdmate of .2 a huge reduction in population growth would raise per capita growth by only about one fif of a standard deviation.
family and about 24 percent reporting obtaining credit from a financial institution or using a credit card. The share of formal borrowing increased slightly (by 2 percentage points between 2014 and 2017), while the share of informal borrowing declined by 3 percentage points, though these changes are not statistically significant. Informal borrowing is common in the developing economies of ECA, except in Armenia, Croatia, and Turkey, where most borrowing is from a financial institution or credit card (figure 12).

**Figure 12. Informal borrowing is prevalent in most developing economies in the region**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Formal (bank or credit card) Semi‐formal Family/friends Other<br>Percent of adults who reported borrowing<br><!-- End of picture text -->

## _Source_ : Global Findex database.

_Note_ : The height of the bars represents the percentage of adults aged 15 and above who indicated borrowing in any way in the previous 12 months.

One common purpose of borrowing is to buy land or a home, the largest financial investment many people make. In 2017, 27 percent of adults in high‐income economies reported having an outstanding housing loan from a bank or other type of financial institution. The share was less than 10 percent in developing economies. In the developing economies of ECA, the proportions were 13 percent in 2014 and 12 percent in 2017. In the euro area, the figures were 23 and 25 percent, respectively.

# _Financial Resilience_

The 2017 Global Findex survey provides information on financial resilience by asking respondents whether they would be able to come up with an amount equal to 1/20th of gross national income per capita in local currency within the next month. It also asks what their main source of funding would be.

The results show that adults in high‐income economies are far more likely (73 percent) to say that they could raise emergency funds than are adults in developing economies (50 percent) (figure 13). Most respondents in high‐income economies who report that they could come up with the emergency funds say that they would rely on savings, whereas most respondents in developing economies say that they would turn to family or friends or use money from working.

23
identification, the studies use micro panel data, higher frequency (monthly) data, or both. In our sample, the most studies use time series data in monthly frequency (940 estimates).

# [Figure 6 about here]

Typically, papers do not control for any confounding factors and estimate a bivariate relationship between the reference rate and a specific lending rate. However, some factors may indeed bias the passthrough coefficient (see Section 2). Therefore, we distinguish between literature that controls for confounding factors such as bank credit risk, bank competition, bank concentration, and the effect of the global financial crisis, and literature estimating only a bivariate relationship (see Figure 7). Studies control for credit risk and bank competition only rarely (59 and 71 estimates). This is rather surprising, considering that most studies are motivated by the Rousseas (1985) framework, which assigns a significant role to bank competition. Overall, 122 pass-through estimates control for a possible GFC effect using a crisis dummy. Somewhat less than half of these estimates consider the dummy also in an interaction with the reference rate (60).

# [Figure 7 about here]

Next, we review the quality of authors and the publication outlet in which the papers selected for our meta-analysis were published.

To measure the quality of authors, we use the individual author’s _h_ -index from the ResearchGate and Scopus databases. Because the _h_ -index of some co-authors is not available, we use the _h_ -index value for the co-author with the highest available _h_ -index. We construct three conditioning variables for the meta-regression based on the highest _h_ -index from among the co-authors. The first variable is constructed using the Scopus database. The second and third variables are constructed using the ResearchGate database, distinguishing between the overall _h_ -index and the _h_ -index that excludes selfcitations. Due to better data availability, we use the overall _h_ -index from the ResearchGate database in our baseline meta-regression. Figure 8 (left panel) shows the histogram of the highest _h_ -index from among the co-authors per study. The figure indicates that pass-through coefficients are most often published by authors with _h_ -index between 17.5 and 20.

11
Coto Millán, P., Baños Pino, J. and Rodríguez Álvarez, A. (2000) “Economic Efficiency in Spanish Ports: some Empirical Evidence”, _Maritime Policy and Management_ , 27 (2): 169174.

Cuesta, R.A. and Orea, L. (2002) “Mergers and Technical Efficiency in Spanish Saving Banks: a Stochastic Distance Function Approach”, _Journal of Banking & Finance_ , 26: 2231-2247.

Cullinane, K., Song, D.W. and Gray, R. (2002) “A Stochastic Frontier Model of the Efficiency of Major Container Terminals in Asia: assessing the Influence of Administrative and Ownership Structures”, _Transportation Research,_ Part A, 36: 743-762.

Díaz, J.J. (2003) “Descomposición de la productividad, la eficiencia y el cambio técnico a través de la función de costes cuadrática. Una aplicación a la operación de estiba en España”, tesis doctoral, Universidad de La Laguna.

Estache, A., González, M. and Trujillo, L. (2002) “Efficiency Gains from Port Reform and the Potential for Yardstick Competition: lessons from México”, _World Development_ , 30 (4): 545-560.

European Parliament (1993) _“Política de puertos marítimos de la comunidad”_ , Documento de trabajo, Dirección General de Estudios.

Färe, R. and Primont, D. (1995) _Multi-Output Production and Duality: Theory and Applications_ , Kluwer Academic Publishers, Norwell, Massachusetts.

González, M. (2004) “Eficiencia en la provisión de servicios de infraestructura portuaria: una aplicación al tráfico de contenedores en España”, tesis doctoral, Universidad de Las Palmas de Gran Canaria.

Goss, R. (1967) “Towards an Economic Appraisal of Port Investment”, _Journal of Transport Economics and Policy_ , 1 (3): 249-272.

Goss, R. (1990) “Economic Policies and Seaports: 2. The Diversity of Port Policies”, _Maritime Policy and Management_ , 17: 221-234.

Heaver, T.D. (1995) “The Implications of Increased Competition among Ports for Port Policy and Management”, _Maritime Policy and Management_ , 22 (2): 125-133.

Heggie, I. (1974) “Charging for Port Facilities”, _Journal of Transport Economic and Policy_ , 8 (1): 3-25.

Jansson, J.O. and Shneerson, D. (1982) _Port Economics_ , The MIT Press, Massachusetts, 1982.

Juhel, M. (1997) “Government Regulation of Port Activities: what Balance Between Public and Private Sector?”, _International Course on Privatization and Regulation of Transport Services_ , World Bank, Washington, D.C.

19
**Table 7. Impacts on Children's Schooling and Labor Force Participation**

||_Sc_<br>|_hool Attenda_<br>|_nce_<br>|_Labor_<br>|_force partici_<br>|_pation_<br>|
|---|---|---|---|---|---|---|
||_7 to 15_|_7 to 11_|_12 to 15_|_7 to 15_|_7 to 11_|_12 to 15_|
|**Measure of Shock**|(1)|(2)|(3)|(4)|(5)|(6)|
|||**Panel A: T**<br>|**otal**||||
|t * (rainfall z-score> 2)|-0.022*|-0.028**|-0.017|0.031*|0.020|0.047*|
||[0.012]|[0.013]|[0.017]|[0.018]|[0.016]|[0.027]|
|Observations|33,022|18,977|14,045|33,222|12,464|9,028|
|Baseline Mean|0.833|0.906|0.782|0.183|0.101|0.300|
|||**Panel B: U**|**rban**||||
|t * (rainfall z-score> 2)|0.006|0.002|0.010|-0.021|-0.023|-0.020|
||[0.018]|[0.017]|[0.031]|[0.022]|[0.019]|[0.038]|
|Observations|11,530|6,513|5,017|11,599|6,515|5,084|
|Baseline Mean|0.886|0.937|0.855|0.136|0.0644|0.233|
|||**Panel C: R**<br>|**ural**||||
|t * (rainfall z-score> 2)|-0.027*|-0.035**|-0.022|0.042*|0.030|0.061*|
||[0.015]|[0.015]|[0.021]|[0.023]|[0.023]|[0.033]|
|Observations|21,492|12,464|9,028|21,623|12,461|9,162|
|Baseline Mean|0.804|0.890|0.742|0.236|0.120|0.374|

_Notes_ : Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Unit of observation are the children surveyed in ENCOVI 2006 and 2011. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1. _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

30
separate question is asked on the number of automobiles that household members have available for their regular use. The final asset questions concern holdings of domestic livestock (pigs, chicken, cattle, goats and horses). We examine the impact of having household members emigrate to New Zealand on the value of durables, the number of cars, and the holdings of the main livestock; pigs, chickens and cattle.

Table 6 reports the estimated impact of having household members migrate to New Zealand on each of these outcomes. Again, impacts are estimated both without any control variables and with controls to deal with small sample differences between treatment and control groups. Although some outcomes are discrete, we continue to present estimates from linear instrumental variable regression models. We also estimated treatment effects for the discrete outcomes using the equivalent simultaneous equations probit models and found nearly identical marginal effects as those presented for the models with no covariates, but had difficulty getting the models with control variables to converge (a small number of covariates perfectly predict whether households are in the non-complier group).

We find that having household members migrate to New Zealand leads to the remaining members having fewer cars and livestock and being less likely to have a bank account or ATM card. When control variables are added, these effects persist for chickens and the financial access variables. The impacts are large, with the remaining family of emigrants having half as many chickens as non-migrant households and being 17% less likely to have a bank account and 31-34% less likely to have an ATM card. We also find negative, albeit insignificant, impacts on home ownership, the likelihood of having renovations, and the value of durable goods.

It is worth emphasizing that all of these results merely reflect changes in household level assets and/or financial access. These changes may be occurring for a number of reasons: i) households may have sold-off assets so the proceeds could used by the individuals moving to New Zealand; ii) the lower incomes caused by having these family members move to New Zealand may have caused a reduction in assets and financial access relative to unsuccessful stayer households; iii) the individual in the household who used a bank account may have been the person who migrated, or iv) the change in household composition (eg the moving away of working-age household members) reduces needs for particular assets (such as cars and computers) or financial access. Only 10 percent of migrant households in New Zealand with family members remaining in Tonga report selling livestock, vehicles or other assets before moving to New Zealand, suggesting that the first explanation is not the main channel.

20
# H Computational appendix

This section outlines the procedures we use to compute the Markov-perfect equilibrium steady state used in the quantitative section. We focus on policy functions that depend **differentiably** on current states ( _b_<sup>_n_</sup> , _d_ ). Using Proposition 2 to simplify the problem, we do not need to solve for the uniform transfer _Tt_<sup>_u_.This leaves</sup> three policy functions to solve for: _b_<sup>_n′_</sup> = Φ<sup>_b_</sup> ( _b_<sup>_n_</sup> , _d_ ), _d_<sup>_′_</sup> = Φ<sup>_d_</sup> ( _b_<sup>_n_</sup> , _d_ ), and _τ_ = Φ<sup>_τ_</sup> ( _b_<sup>_n_</sup> , _d_ ).

We solve a system of equations for the policy function and their derivatives with respect to each state. We adopt a local approximation method similar to Klein et al. (2008). We use a linear approximation and assume policy functions are linear functions of states around the steady state.<sup>60</sup> We solve for the three policy functions and their first-order derivatives using a system of nine equations: Local government’s Euler equation (8), central government’s GEEs (D.11) and (D.12), and their derivatives with respect to _b_<sup>_n_</sup> and _d_ where Φ<sup>_b_Φ</sup><sup>_b_with respect to</sup><sup>_d_, and functional derivatives are computed</sup> _d_<sup>is the derivative of policy function</sup> as follows, for example, where _∂_ Euler/ _∂b_<sup>_n_</sup> is approximated by for some tiny value ∆, and second derivatives are set to zero.

> 60We checked higher-order (2nd, 3rd) approximations in Mathematica and found that the estimated steady states and first-order derivatives are similar to what we get using a linear approximation.

39
the non‐compliers.<sup>3</sup> Our main survey approach was face‐to‐face interviewing using an extensive survey that asked about employment outcomes, took physical health measurements, asked about preferences, and played incentivized lab‐in‐the‐field games which we describe in detail later in the paper. Each principal applicant in this long survey was linked to a partner, defined as either a parent or sibling, who, according to the PAC rules, would not be eligible to move to New Zealand even if the principal applicant was a ballot winner. These partners were used for the household efficiency game described in Section 5.

We supplemented this long, in‐person, survey with a shorter telephone‐based survey designed to reach those who had on‐migrated from New Zealand or who were in less accessible parts of New Zealand. Since surveying often uncovers details for other people (through snowball effects) we also gave this short survey to some recently identified migrants in areas where the longer survey had already been fielded, as well as to additional households in Tonga.

Figure 1 details the sampling procedure by group. Altogether we have a sample of 282 principal applicants who received the long survey and played all the lab‐in‐the‐field games, comprising of 133 migrants (out of 307 in total), 6 non‐compliers (out of 60 in total), and 143 ballot losers (out of 4,329 in total). We have an additional 103 principal applicants who were given the short survey (61 migrants, 3 non‐compliers, and 39 ballot losers) for a total sample of 385 principal applicants. In the end, we were able to survey 64 percent of the principal applicants who had ever migrated to New Zealand (Figure 1). Since non‐ compliers were sampled at a lower rate, we use the administrative data to reweight the migrants and non‐complier samples to be representative of the ballot winners. We intentionally surveyed only enough ballot losers to optimize the power to detect impacts of migration and the vast majority of ballot losers we made contact with agreed to be interviewed.

Table 1 then provides some basic summary statistics for the ballot winners and ballot losers. Given the time that has passed since individuals applied for the ballot, we have few pre‐determined characteristics on which to check balance: age, gender, and place of birth. We see that the principal applicants are 41 on average, two‐thirds are male, and almost one‐quarter were born in the outer islands. Although the ballot losers are slightly older, a joint test of orthogonality cannot reject the null hypothesis that age, gender, and birthplace are unrelated to treatment status (p=0.306). This suggests that our treatment (ballot winner) and control (ballot loser) samples are comparable and we can use the random assignment

> 3 Villages in Tonga average about 90 dwellings and so door‐to‐door methods are a feasible way to locate ballot losers and non‐migrant winners.

6
serious problem for our geographically-scattered municipality sample. To test for robustness, we fit the model using two versions of equation (8) (with separate and composite municipality incomes) and three estimation techniques: robust regression, GLS (which incorporates nonuniform error variances across municipalities), and fixed effects estimation. We reproduce the full regression database in the Appendix.

Table 3 summarizes our estimation results.<sup>12</sup> Parameter estimates for the _pucca_ share, salinity and road network length are the products of the underlying cost function parameters (γ, δ1, δ2) and (1 + α1), where α1 is the elasticity of road quality demand w.r.t. unit maintenance cost. The upper bound of α1 is zero, but it might be less than -1. Our results for groundwater salinity in Table 3, which are uniformly positive and significant, imply that

[-1 < α1 < 0]. Since δ1 (the cost elasticity of salinity) is unambiguously positive, β2 (= (α1+1)δ1) is positive only if α1 is greater than -1.

# **5.3  Robust and GLS Results**

# **5.3.1  Salinity**

To clarify our interpretation of the implied cost elasticity of salinity, consider possible values of (α1+1), rounded to two decimal places within the admissible range: [.01 , .99]. The upper bound (.99) reflects a quality demand elasticity w.r.t. unit maintenance cost that is near zero. In that case, the estimated parameter β2 (= 0.99 δ1) is extremely close to the underlying unit maintenance cost elasticity of salinity. Since all four GLS and Robust estimates in Table 3 are effectively 1.86, the implication is that unit maintenance cost increases by _at least_ 1.86% for each 1% increase in groundwater salinity.

> 12 We have also tested for period effects using yearly dummy variables.  They are uniformly insignificant, so we have excluded them from the final regressions.

13
|**Speed**|2|20|3|5|1|1|
|---|---|---|---|---|---|---|
|**Connection with other**<br>**platforms (e.g. Treasury,**<br>**Excel, Bloomberg)**|1|10|12|7|1|1|
|**Integration among**<br>**modules**<br>**(front/middle/back**<br>**offices)**|4|15|5|6|1|1|
|**Straight-Through-**<br>**Processing (e.g.**<br>**settlement, repayment)**|7|11|9|2|1|2|
|**Customization (e.g. ability**<br>**to create new**<br>**instruments/transactions)**|5|14|7|2|2|2|
|**Maintenance and upgrade**<br>**cost**|8|12|7|2|1|2|
|**Access to historical**<br>**information**|9|18|2|1|1|1|
|**Does your current**<br>**hardware satisfactorily**<br>**support the DMS?**|28|0|0|0|3|1|

**6. Cost and Development Needs Associated with Debt Management Systems**

|||**Yes**|**No**|**No**<br>**Resp. **|
|---|---|---|---|---|
|**Doyou have the appropriate systems technical support?**||28|3|1|
|**Doyou thinkyour DMS needs updating? **||20|10|2|
|**Doyou have resources available to updateyour DMS?**||22|7|3|
|||**New**|**Upgrade**|**No**<br>**Resp. **|
|**Would you prefer a new DMS or an upgrade to the existing**<br>**system?**||6|23|3|
||**< $1m**|**$1 - $2m**|**> $2m**|**No Resp.**|
|**What are your expectations of the total costs to**<br>**update/implement a new DMS?**|19|2|6|5|
|**How much can your institution allocate toward**<br>**updating/implementing a new DMS?**|21|1|4|6|
||**< 1y**|**1y - 2y**|**> 2y**|**No Resp.**|
|**How long would you expect the implementation of a new**<br>**DMS to take to become fully operational?**|5|12|11|4|

32