rafmacalaba/datause-encoder-data huggingface dataset:
# **Economic consequences of cabotage restrictions: The effect of the Jones Act on Puerto Rico**

Russell Hillberry Manuel I. Jimenez<sup>*</sup>

**Keywords:** Maritime Shipping, Cabotage, Jones Act, Puerto Rico

**JEL Codes:** F13, F14, L91, R13, R48

> *Department of Agricultural Economics, Purdue University, West Lafayette IN, 47907; e-mail: rhillber@purdue.edu & jimene19@purdue.edu. This research was financed by USDA NIFA grant #2020-67023-30963, USDA Hatch Projects IND-00134214 and Purdue University’s AGEC Graduate Program Networking, Relationship Building, and Recognition Grant. We thank the Puerto Rican Department of Treasury, the Department of Economic Development and Trade, the Planning Board and several local freight-forwarders for access to data and for their insights into Puerto Rico’s external freight market. We received useful feedback on earlier versions of the paper from Colin Grabow, David Hummels, Peter Morrow and Anson Soderbery, as well as audiences at the European Trade Study Group, the Midwest International Economics Group, the Global Trade Analysis Project conference, Empirical Investigations in International Trade, World Bank DECTI, the U.S. International Trade Commission, the Cato Institute, American University and the University of PR-Mayaguez. All errors are our own.
focusing on average effects is misleading when assessing climate-induced migration, since averages wash away the substantially heterogeneous responses of structurally different groups. Without thoroughly unpacking this heterogeneity puzzle, it is implausible to make statements _a priori_ on current patterns of climate mobility, let alone future projections. <mark>It follows that rigorously estimating heterogeneous treatment effects is an essential prerequisite for the optimal targeting of</mark> climate and development policies.

Guided by these reflections, we couple a causal machine learning approach based on a data-driven search for treatment effect heterogeneity (Athey & Imbens, 2016; Wager & Athey, 2018), tailored to a conceptual framework bridging the New Economics of Labor Migration (NELM) and the poverty traps literature, with longitudinal, multi-topic household survey data collected in Nigeria with support from World Bank's Living Standards Measurement Study (LSMS). When we refer to a ‘data-driven search for treatment effect heterogeneity’, we mean that we go beyond estimating average treatment effects. Instead, we calculate the impacts of weather shocks at the household level. These individual impacts are then aggregated into group-average treatment effects. Importantly, the groups are identified through data-driven sample splits based on specific household characteristics before the shock. Our non-parametric estimates of treatment effect heterogeneity suggest that some key variables —namely, pre-shock asset levels, _in situ_ adaptive capacity, and cumulative exposure to weather shocks—can alter not just the magnitude, but even the sign of the causal impact of growing season weather anomalies on the (im)mobility outcomes of Nigerian agricultural households. Overall, our analysis provides empirical evidence that suggests the presence of climate-induced immobility traps.

Among recent works related to this paper, <mark>Martinez Flores, Milusheva and Reichert</mark> (2021) leverage high-frequency data collected from the International Organization for Migration in 17 West and Central African countries over the period 2018-2019, and estimate that droughts occurring during the growing season reduce international migration. As they find these effects only in middle-income areas (and not in rich nor poor ones), they infer that liquidity constraints and income losses are the key channels. Mueller et al. (2020) use data from LSMS-supported longitudinal surveys conducted in Ethiopia, Malawi, Tanzania, and Uganda over the period 20092014 and find that climate variability and weather anomalies cause a reduction in temporary urban 3
P a g e <u>|</u> **28**

||**Gr**<br>|**eater Mumbai P**<br>|**opulation betwee**<br>|**n 1991 and 200**<br>|**1**<br>|||
|---|---|---|---|---|---|---|---|
|Popula|tion|1991|% of Total|2001|% of Total|Growth Rate||
||Formal|5,749,562|57.9%|5,438,958|45.6%|-0.55%||
||Slums|4,176,329|42.1%|6,475,440|54.3%|4.48%||
|Total||9,927,882|100.0%|11,916,399|100.0%|1.84%||
|House|hold Size|||||||
||Formal|4.54||4.5||||
||Slums|5.2||5||||
|Total||4.8||4.7||||
|Numbe|r of Househ<br>|olds<br>||||||
||Formal|1,266,423|61.2%|1,220,501|48.5%|-0.4%|(4,500)|
||Slums|803,140|38.8%|1,295,088|51.5%|4.9%|63,382|
|Total||2,068,309|100.1%|2,515,589|100.0%|2.0%|49,734|
||Hy|pothesis of Form|al Growth and N|ew Supply||||
||Pe|rcent of househo|lds who can affo|rd new formal h|ousing|25%|628,897|
||Up<br>am|grading of Hous<br>ongst these hou|ing and New hou<br>seholds|sehold formatio|n|3.5%|22,011|

# **Table 3: Mumbai - Changes in population living in slums and formal housing between 1991 and 2001**

Even abstracting from this perverse outcome, obliging private developers to provide housing for the poor will necessarily offer a limited supply of housing for slum dwellers. This is because the yearly flow of formal housing construction is so small in relation to the total housing stock. We illustrate with estimates from Mumbai where developer acceptance of TDRs has been relatively good.<sup>8</sup> In Greater Mumbai , we evaluate the flow of new formal housing at about 27,000 units a year or about 0.8% of the total housing stock (see Table 3). This new construction is the only source of supply for low cost developer incentive housing. Yet in Mumbai slums account for nearly 45 % of the total housing stock— which represents 56 times the annual flow of new formal housing. The flow of formal housing is dramatically disproportionate to the resources needed to replace the housing stock in slums with incentive based units. This can be seen using an example based on the current TDR program in Mumbai ( _<u>Table 4</u>_ ).

> 8 In cities like Bangalore these incentives have had less success because other regulations prevent developers from benefiting from additional  FAR. Developers will only agree to the incentive units if it is worth something to them.

28
- 36 -

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In the last ten years, all countries in the Sahel region have launched some kind of pilot social safety-net projects, and some have already reached the first scale-up phase (Beegle et al., 2018). Cash transfer programs constitute one of the main components of the social safety-net systems implemented in the Sahelian countries. These cash transfer programs have been shown to generate various improvements in household consumption levels, food security, human capital, productive activities and resilience (Akresh et al., 2016; Stoeffler et al., 2019, Kandpal et al., 2021; Premand & Stoeffler, 2020).

However, there is a heated policy debate on the best targeting method to use in a Sahel environment, typically opposing PMT and CBT (Schnitzer, 2019). Besides, there is also a policy disagreement on the well-being metric that should be used for targeting households, namely food security or per-capita consumption. This calls for a clarification of the concepts and objectives employed in the targeting policy discussion and related analyses.

# _3.2 Targeting methods and existing evidence_

Proxy means testing (PMT) and community-based targeting (CBT) are the two most common methods employed for selecting beneficiaries of social safety-net programs in the Sahel and in other developing countries (Monchuk, 2013).<sup>16</sup> In the Sahel as well as in low-income settings where the coverage of safety net interventions is small relative to needs, PMT and CBT methods are usually applied only after geographical targeting is conducted (focusing on the poorest areas of the country in principle).

The PMT method relies on predicting household income, consumption or poverty status from a limited set of observable household characteristics (Grosh & Baker, 1995; Stoeffler et al., 2015; Brown et al., 2018). In contexts in which the means-testing of benefits is not an administratively feasible option, PMT provides the advantage of relying on information that can be measured relatively quickly and transparently. While the implementation of PMT does not vary as much (relative to CBT), Brown et al. (2018) show that the different ways to develop PMT formulas can significantly affect targeting performance.

On the other hand, CBT relies on communities to identify beneficiaries (Conning & Kevane, 2002; Beaugé et al., 2018). CBT has the advantage of leveraging community knowledge and involvement for targeting, which has the potential to improve both accuracy and legitimacy. However, CBT in some contexts is plagued by elite capture (Pan & Christiaensen, 2012; Alatas et al., 2019; Basurto et al., 2020; Stoeffler et al., 2020). There is a wide range of ways in which CBT is implemented. Among others, the degree and type of guidance provided to communities on how they should conduct the process vary greatly. In the Sahel region in particular, the Household Economy Analysis (HEA) represents a widely used type of CBT,

> 16 For good descriptions of the various targeting methods employed, see Coady et al. (2004), Del Ninno & Mills (2015) and Devereux et al. (2017).

10
Figure A7: DVAR of Aggregate Exports (Single-industry Firms, 2000-2007) <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Measured DVAR (Aggregate Exp) 95 c.i. (upper bound)<br>95 c.i. (lower bound)<br>.75<br>.7<br>DVAR<br>.65<br>.6<br><!-- End of picture text -->
# **Table A9: Identifying the $1.90 poor based on covariates of poverty**

|||_India 2011_<br>|_/2012_<br>||||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.001***|-0.011***|0|-0.017***|-0.026***|0|
|Asset index|-0.012***|-0.059***|0|-0.145***|-0.191***|0|
|Literacy|-0.015***|-0.088***|0|-0.143***|-0.216***|0|
|Access to electricity|-0.017***|-0.077***|0|-0.243***|-0.302***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

# **Table A10: Identifying the $3.20 poor based on covariates of poverty** _India 2011/2012_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|0.000|-0.002***|0|-0.034***|-0.036***|0|
|Asset index|0.005***|0.006**|0.6341|-0.210***|-0.208***|0.6341|
|Literacy|0.003|-0.002|0.3235|-0.247***|-0.252***|0.3235|
|Access to electricity|0.014***|0.032***|0.0313|-0.317***|-0.299***|0.0313|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 52
<!-- Start of picture text -->
1.8%-<br>1.6%-<br>1.4%  -<br>1.2%  -<br>1.0%<br>0.8%<br>0.6%-  _<br>0.4/o  _=_<br>0.2%<br>0.2%  - ._ ............  ___ 1l<br>0  500  1000  1500  2000  2500  3000  3500  4000<br>(billions  of pesos)<br>- Domestic  (non-linear  + interaction)  -_-  Foreign (non-linear + interaction)<br><!-- End of picture text -->

Figure 6: Estimated share of lending to small businesses by bank size for foreign and domestic banks in Colombia at end of period.

Note: Estimated shares use coefficients from Table 4 and are calculated using the median values of continuous variables for banks of that type. Calculations are only performed for bank sizes between (approximately) the 5' and 95"t percentiles for banks of that type. Estimates are for the final period for each country.

<!-- Start of picture text --> 40%<br>35% -<br>30% -<br>25% -<br>20%<br>15%i.<br>100/% ~ ~ ~ '4 '<br>5%<br>0% -,,,,,, _<br>0 2000 4000 6000 8000 10000 12000 14000 16000<br>(millions of Sols)<br>-4--Domestic (non-linear + interaction) U Foreign (non-linear + interaction)<br><!-- End of picture text --> Figure 7: Estimated share of lending to small businesses by bank size for foreign and domestic banks in Peru at end of period.

Note: Estimated shares use coefficients from Table 4 and are calculated using the median values of continuous variables for banks of that type. Calculations are only performed for bank sizes between (approximately) the 5" and 95"' percentiles for banks of that type. Estimates are for the final period for each country.

33
**<u>Figure 2.6:  Rural Income Composition in Mexico, Selected Years 1992-2002</u>**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1992 1994 1996 1998 2000 2002<br>Year<br>farm_inc farm_lb self_consumption off_farm_labor Remmitances<br>private Public_transfer leasing oth_nomonetary nonfarm_enterprise<br>Percent<br><!-- End of picture text --> Source: Calculations based on the ENIGH surveys.

**Table 2.4:  Agricultural Income Shares by Source and Region, Rural Mexico 1992 and 2002**

||||**19**<br>|**92**<br>||||||**2002**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Cultiva<br>-tion|Farm<br>enter-<br>prise|Self –<br>consump<br>-tion|Agricul-<br>tural<br>labor|Farm<br>labor|**Total**<br>**Agricul**<br>**ture**|Cultiva<br>tion|Farm<br>Enter<br>prise|Self –<br>consump<br>tion|Agricul-<br>tural<br>Labor|Farm<br>Labor|**Total**<br>**Agricul**<br>**ture**|**Change**<br>**1991-2002**<br>**(%)**|
|**Mexico**|17.9|11.9|8.7|10.2|2.1|**50.8**|5.1|4.4|3.1|8.0|3.3|**23.8**|-53.1|
|**North**|21.1|14.4|4.3|16.6|2.8|**59.2**|2.0|3.5|1.8|5.1|7.1|**19.5**|-67.0|
|**Capital**|8.1|9.4|8.0|7.8|0.9|**34.1**|1.5|1.4|2.2|5.1|0.8|**11.0**|-67.8|
|**Gulf**|20.0|9.5|7.8|6.4|4.4|**48.1**|3.1|1.6|1.8|10.9|4.8|**22.2**|-53.9|
|**Pacific**|11.0|8.7|7.1|11.6|1.8|**40.3**|3.4|2.7|1.3|3.7|1.3|**12.4**|-69.2|
|**South**|21.2|18.1|15.4|10.3|1.6|**66.6**|9.2|9.1|6.5|11.0|5.4|**41.2**|-38.1|
|**Center-**<br>**North**|22.2|10.9|7.2|6.6|1.0|**47.8**|7.4|4.7|2.5|8.5|2.2|**25.4**|-47.0|
|**Center**|9.9|6.5|6.9|15.1|0.6|**38.9**|4.7|4.4|4.8|12.4|1.0|**27.3**|-29.8|
|Source: EN<br>Note: Local|IGH 2002.<br>ities with l|<br>ess than|2,500 inhabit|ants.||||||||||

14
Political openness includes the institutionilization of channels of communicav )n between citizens and officials, as opposed to personalized channels, which empower special interests and provide opportunities for corruption. Openness in this sense correlates with expanded options for specialized interest groups, mass media, and other non-governmental institutions that provide checks on public officials and enhance accountability. The information revolution created a new dimension to cross border information flows, expanding the options for "openness" beyond the press and the media, to personal computers, fax machines tape recorders, and videos (Annis 1990).

Transparent decision-making processes and the availability of information constitute important dimensions of openness. Governments can promote transparency even when the system is not open in the economic or political sense, although experience suggests that the high degree of governmental discretion in command economies may impede transparency. Transparent systems have clear procedures for public decision making; they publish budget information, including military expenditures, and minimize "caisses noires" and other hidden budget categories. Subsidies are clearly stipulated; appointing, promoting, and dismissing personnel done according to stated, objective criteria. Information availability can range from requirements such as the publishing of audited accounts that add confidence to capital markets, to the disclosure of procurement procedures and the release of complete national budgets.

Distinguishing these separate aspects of openness is necessary in order to relate "openness" to econo..iic performance, discussed below. Many aspects of openness can be traced to capacity problems, rather than to conscious decisions to restrict access. Funding for compiling economic statistics may be absent; auditors may not be available; those available may not be well trained. Mechanisms that impede information flows may not signal deliberate closure, yet these can affect performance. One kind of openness may be present and others missing: closed polities but extensive foreign investment and sophisticated market information systems characterize some of the Asian NICs. While committed to a degree of economic liberalization, some of these countries were not willing to allow wide public access to sensitive financial data. Nor were they willing to embrace greater tolerance for divergent political 22
# Table A4: Alternative robotization measure

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_log_(1 + <sup>_obs.robots_</sup><br>_hours_ <sup>)</sup>|0.1387**|0.3854***|-0.2467***|0.5679***|1.0943***|-0.5264***|
||(0.0669)|(0.1180)|(0.0646)|(0.1960)|(0.2843)|(0.1392)|
|First stage :|||||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||||0.0030***<br>(0.0003)|0.0030***<br>(0.0003)|0.0030***<br>(0.0003)|
|Observations|888,813|888,813|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6351|0.7739|0.3913|0.6313|0.7656|0.3890|
|Kleibergen-Paap rk Wald-F-stat||||115.912|115.912|115.912|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the baseline estimation sample and an alternative robotization measure (the observed stock of robots, ignoring depreciation). Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
# **Annex Table 3:  Weighted means and standard deviations of key variables (Men 15-59 excl students)**

||**RURA**|**L**|**URB**|**AN**|
|---|---|---|---|---|
|**Variable**|**Mean**<br>**St**|**d. Dev.**<br>|**Mean**<br>**St**|**d. Dev.**|
|In the labor force|0.97|0.16|0.97|0.18|
|Regular|0.09|0.29|0.39|0.49|
|Non-Farm Self-Emp|0.20|0.40|0.37|0.48|
|Self-emp Farmers|0.33|0.47|0.03|0.17|
|Casual|0.33|0.47|0.13|0.34|
|Not in LF|0.03|0.16|0.03|0.18|
|Age|34.11|11.57|34.54|11.26|
|Age2|1297.45|833.88|1319.91|822.92|
|Married|0.75|0.43|0.71|0.46|
|Household size|5.79|2.79|5.33|2.78|
|Below Primary|0.13|0.34|0.08|0.28|
|Primary Completed|0.16|0.37|0.14|0.35|
|Post-primary|0.39|0.49|0.65|0.48|
|Household head|0.59|0.49|0.59|0.49|
|Land Possessed|0.99|1.92|0.18|2.41|
|Region: North|0.11|0.32|0.16|0.37|
|Region: South|0.22|0.41|0.26|0.44|
|Region: East|0.14|0.34|0.11|0.31|
|Region: West|0.13|0.33|0.23|0.42|
|Region: NE|0.05|0.21|0.02|0.12|
|Muslim|0.11|0.31|0.15|0.36|
|Other religion|0.05|0.22|0.06|0.24|
|Dalit|0.21|0.41|0.15|0.36|
|Adivasi|0.11|0.31|0.03|0.16|
|SC*Primary Completed|0.04|0.18|0.03|0.17|
|SC*Post-primary|0.06|0.24|0.07|0.26|
|ST*Primary Completed|0.02|0.13|0.00|0.06|
|ST*Post-primary|0.02|0.15|0.01|0.12|
|Muslim*Primary Completed|0.02|0.14|0.03|0.16|
|Muslim*Post-primary|0.03|0.17|0.07|0.26|

21
1998; Dickson et al., 2000; Miguel and Kremer, 2004; Vermeersch and Kremer, 2005; Bobonis et al., 2006). Nevertheless, in experimental studies one observes only short-term impacts due to a limited period. Yet short-term positive impacts of health on education (such as school attendance) may not necessarily imply improvement of individuals’ final education. Indeed, since health is also positively correlated with individuals’ labor market outcomes such as wage rates (Thomas and Strauss, 1997; Thomas et al., 2006), labor supplies (Strauss and Thomas, 1998; Thirumurthy et al., 2008), self-employment profits (Singh et al., 1986; Strauss, 1986), and agricultural productivities (Audibert, 1986; Strauss, 1986), a youth may choose to join labor markets rather than to stay in school. So we could find short-term gains in employment and income but long-term losses in education. Indeed, Pitt et al. (2012) show that this scenario is more likely to happen on male youth since their comparative advantages in brawn-intensive work lead to a stronger pull to the labor market for male than for female when their health improves. Thus, even if health interventions from randomized experiments improve some educational outcomes instantly, such as class attendances and academic grades, their impacts on individuals’ final education remain uncertain.

To obtain a better understanding about the relationship between health and education, it is thus helpful to explore the _final_ educational effects of health-oriented government programs. Two such studies in the U.S. find some educational benefits of health programs. Bleakley (2007) finds that US hookworm eradication in the 1910s improved school enrollments of children between 8 and 16 years old, but showed a significant increase only in quality of education instead of quantity of education (years of schoolings) for those exposed cohorts in the long term. Quynh (2010) finds that the introduction of iodized salt in U.S. in 1924 improved boys’ schooling and raised men’s income. Our analysis follows this line of research by studying the educational effects of the rural drinking water program in China.

One of the most ambitious programs for improving water quality for poor people, this rural water program, starting from the early 1980s, had incurred a cost more than $8.8 billion U.S. dollars by 2002 (Meng et al. 2004) and had covered around 300 million people by 2008 (Center for Health Statistics and Information, Ministry of Health of the People’s Republic of China, 2009). This program aims to build up water plants and pipelines to provide rural residents with treated drinking water. A key component of the water treatment plants, besides improving 3
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