rafmacalaba/datause-encoder-data huggingface dataset:
# **2. The ISI narrative**

The collapse of ISI and the ensuing “lost-decade” of the 1980s led to early critical reviews of inward-looking import substitution strategies in LA, opening the grounds for the drastic policy reshuffling of the Washington Consensus.<sup>10</sup> However, a stream of literature, including Bértola and Ocampo (2013) and many ECLAC-related contributions, have recently revisited LA’s ISI experience and argued that, with the right policy tweaking, import substitution in LA could have eventually led to a successful outward-oriented industrialization, much like in Asian countries. The economic problems LA faced at the end of the 1970s are viewed as not inherent to the ISI model but rather as the consequence of factors exogenous to it (bad fiscal policies at home, shocks from abroad). And the rise of inflation is attributed to factors outside ISI, such as the rapid depreciations caused by the debt crisis and the falling terms of trade.

This narrative, therefore, advocates the return to the ISI experience as a source of inspiration, especially to tame the excessive faith in _laissez faire_ and to promote a more proactive government, one that is engaged in more vertical (industry, sector, or cluster-oriented) policies. As we will show in subsequent sections, however, this narrative also can be misleading due to its failure to adequately identify the sustainability conditions and macro/demand implication of the inward-looking ISI model as applied in the LA region.

# **3. Some structural aspects**

At least three additional streams of research deserve mention because they complement or qualify the above perspectives by focusing on structural features that are germane to LA’s growth history. A first stream, inspired by the work of McMillan and Rodrik (2011), highlights structural change, defined by the movement of resources (particularly labor) across sectors. It highlights the observed contrast between the rise in productivity growth during the ISI years (which reflected the massive movement of labor from the lower productivity agricultural sector to the higher productivity industrial sector), with the subsequent stagnation and decline in aggregate Gauging quality requires therefore appropriate proxies, whether at the macro level, as in the case of Hausmann et al (2007), or at the micro level, based on enterprise and market surveys or granular price data. Thus, as recently shown by Bernard et al. (2020) for enterprises in advanced economies, differentiating products based on the product mix and prices, as well as on the skills used in production, uncovers quality differences that are associated with very different growth performances across firms. Along similar lines, Comin et al. (2020) find that demand shifts toward expenditure-elastic sectors (rather than technical change) explain a large share of the labor market polarization in advanced economies.

10 See Baer (1972) for an early assessment of the impact of ISI policies on the LA region and Irwin (2020) for a comprehensive review of the evolution of the academic discussion on this topic.

9
|S/N|**Table A3 cont**<br>Data dimension|**inued**<br>Data category|Representative indicators|Categorical disaggregation|Coverage element 1 scoring guidelines*|
|---|---|---|---|---|---|
|8|Social Statistics|Crime and Justice Statistics|(8.1) Homicide rate or count;<br>(8.2) Crime rate or count;|(8.1) Sex of victim; age of<br>victim; sex of perpetrator; age<br>of perpetrator;<br>victim/perpetrator<br>relationship.<br>(8.2) Crime type; age of victim;<br>sex of victim; sex of<br>perpetrator; age of perpetrator;<br>victim/perpetrator relationship.|To receive a full point:Must have all three indicators with one<br>disaggregation each or two indicators with two disaggregations<br>each. (8.2) must be disaggregated by crime type.<br>To receive half point: Must have one indicator with two<br>disaggregations or two indicators with one disaggregation each.|
||||(8.3) Persons in prison or<br>incarceration rate.|(8.3) Sentencing status; age;<br>sex.||
|9||Poverty Statistics|(9.1) Poverty rate;|(9.1, 9.2) None|To receive full point:Must have two indicators.|
||||(9.2) Distribution of income by<br>||To receive half point:Must have one indicator.|
||||deciles or Gini coefficient.|||
|10|Economic statistics|National Accounts|(10.1) GDP (production<br>approach) or gross value<br>added;<br>(10.2) GDP (expenditure<br>approach).|(10.1) Industrial classification;<br>(10.2) Major expenditure<br>categories.|To receive full point: Must have all indicators disaggregated<br>as follows: (10.1) by industrial classification and (10.2) by major<br>expenditure categories. Any data in the most recent 5 years<br>(2013 onward) must be presented on at least quarterly basis to<br>receive a full point.<br>To receive half point: Must have at least one indicator with<br>one disaggregation. Data can be presented on a monthly,<br>quarterly,or annual basis.|
|11<br>||Labor Statistics<br>|(11.1) Employment rate;<br>(11.2) Employment<br>distribution;<br>(11.3) Unemployment rate.<br>|(11.1) Sex; age;<br>(11.2) Industry; Occupation<br>type; Sex;<br>(11.3) Sex; age.<br>|To receive full point: Must have: (11.1) by sex, or (11.2) by<br>sex and one other disaggregation, and (11.3) by sex.<br>To receive half point:Must have at one indicator with one<br>disaggregation.<br> **Table A3 continued**|

37
# **Annex IID: Stamp Duty Rates for States and Union Territories FY81-FY2003**

|**Year**||**Tripura**||**Utta**|**r Prades**|**h**||**West**|**Bengal**|**U**|**nion Te**|**rritory o**|**f Delhi**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Basic**|**SC**|**Total**|**Basic**|**SC**|**Total**|**Basic**|**SC**|**Addl. SC**|**Total**<br>**B**|**asic**|**SC**|**Total**|
|**1980-81**|5|0|5||2|2||0|2|2|3|5|8|
|**1981-82**|5|0|5||2|2||0|2|2|3|5|8|
|**1982-83**|5|0|5||2|2||0|2|2|3|5|8|
|**1983-84**|5|0|5||2|2||0|2|2|3|5|8|
|**1984-85**|5|0|5||2|2||0|2|2|3|5|8|
|**1985-86**|5|0|5||2|2||0|2|2|3|5|8|
|**1986-87**|5|0|5||2|2||0|2|2|3|5|8|
|**1987-88**|5|0|5||2|2||0|2|2|3|5|8|
|**1988-89**|5|0|5|12.5|2|14.5|16|3.2|2|21.2|3|5|8|
|**1989-90**|5|0|5|12.5|2|14.5|16|3.2|2|21.2|3|5|8|
|**1990-91**|5|0|5|12.5|2|14.5|16|3.2|2|21.2|3|5|8|
|**1991-92**|5|0|5|12.5|2|14.5|16|3.2|2|21.2|3|5|8|
|**1992-93**|5|0|5|12.5|2|14.5|16|3.2|2|21.2|3|5|8|
|**1993-94**|5|0|5|12.5|2|14.5|16|3.2|2|21.2|3|5|8|
|**1994-95**|5|0|5|12.5|2|14.5|10|2|2|14|3|5|8|
|**1995-96**|5|0|5|12.5|2|14.5|10|2|2|14|3|5|8|
|**1996-97**|5|0|5|12.5|2|14.5|5|1|2|8|3|5|8|
|**1997-98**|5|0|5|12.5|2|14.5|5|1|2|8|3|5|8|
|**1998-99**|5|0|5|8|2|10|5|1|2|8|3|5|8|
|**1999-2000**|5|0|5|8|2|10|5|1|2|8|3|5|8|
|**2000-01**|5|0|5|8|2|10|5|1|2|8|3|5|8|
|**2001-02**|5|0|5|8|2|10|5|1|2|8|8|5|13|
|**2002-03**|5|0|5|8|2|10|5|1|2|8|8|5|13|
|**2003-04**|5|0|5|8|2|10|5|1|2|8|5|3|8|

69
**Figure 1: Relationship between the elasticity of enrollment with respect to the distance to the nearest primary school and mean distance to the nearest primary school**

<!-- Start of picture text --> Elasticity of enrollment with respect to Marginal effect of distance to<br>distance to primary school primary school on enrollment<br>0.1 0.005<br>0.000<br>0.0 -0.005<br>-0.010<br>-0.1 -0.015<br>-0.020<br>-0.2 -0.025<br>-0.030<br>-0.3 -0.035<br>0.0 2.0 4.0 6.0 8.0 10.0 0.0 2.0 4.0 6.0 8.0 10.0<br>Average distance to school Average distance to school<br>Elasticity<br>Marginal effect<br><!-- End of picture text --> _Source_ : Author’s calculations from DHS data.

How should one interpret these results? With distance being statistically significantly related to enrollment, can one infer that distance is “important”? Figure 2 illustrates a simulated scenario based on the parameter estimates in Table 1. The horizontal axis is the actual enrollment rate, the vertical axis is the simulated enrollment rate if the distance to all schools was set to zero (that is, corresponding to a school in every cluster). The left panel shows the result of setting only the distance to the nearest primary school to zero, the right panel shows the result of setting distances to both the nearest primary and secondary school to zero.<sup>4</sup> The graph illustrates the maximum impact on enrollment that one might expect from a policy that focused exclusively on putting a school in every village.

Clearly, distance to the nearest school is only weakly associated with enrollment: most points lie very close to the 45-degree line. There are only three countries in which a primary school in every cluster is associated with more than a 4 percentage point increase in enrollment: Mali, Chad and Central African Republic (8.6, 6.8 and 4.4 percentage points respectively). This is a small increment given both the overall shortfall from universal enrollment in these countries, as well as the number of new schools that would be implied by this simulation. In Mali, only about 35 percent of the surveyed clusters had a primary schools—this means that over doubling the number of clusters with a school would only be associated with an enrollment increase of 8.6 percentage points.

> 4 The simulation involves comparing the average predicted enrollment with each observation at actual values to the average predicted enrollment with each observation at actual values except for distance which is set to zero.

9
New Zealand FTA, ASEAN–China FTA, ASEAN–Japan Comprehensive Economic Partnership, AKFTA, and ASEAN–India FTA. In this study, we define the following 15 countries as “FTA member countries”: Australia, Brunei, Cambodia, China, India, Indonesia, Japan, Korea, Lao PDR, Malaysia, Myanmar, New Zealand, the Philippines, Singapore, and Vietnam. Except for Korea, with which Thailand concluded an FTA in 2010, all these countries have been FTA partner countries for Thailand since at least the beginning of our sample period of 2007. Other countries are defined as “FTA non-member countries.” One empirical issue that needs attention when examining the effects of FTA use on import prices is that FTA use and import prices are simultaneously determined. In addition, as shown in <u>Proposition</u> <u>2, the selection of FTA use is not random. Therefore, our identification strategy is as follows. First, by</u> taking advantage of the nature of our transaction-level panel data, we conduct a difference-indifferences (DID) analysis on the effects of FTA use on import prices. To do so, in addition to all FTA non-member countries, we include only one FTA member country, Korea, as an exporting country. As mentioned, the FTA with Korea was the only one to enter into force during our sample period (i.e., in 2010). Therefore, during 2007–2009, the sample firms could not use an FTA scheme, but some were able to do so during 2010–2011.

Second, another advantage of focusing on the AKFTA is that firms at the time were unable to accurately predict when the FTA would enter into force. The ASEAN countries and Korea began FTA negotiations in 2003. However, there was serious political turmoil in Thailand in 2006. As a result, 20
Scheve and Slaughter (2001) and Mayda and Rodrik (2003), which use survey evidence to evaluate determinants of individual preferences. They find that factor endowments guide trade preferences. But their focus is on the relationship between individual characteristics and protectionism and not on the conditions for trade barriers to be considered acceptable by a majority. Loss aversion may play a very important role in people’s perceptions, as the argument for free trade relies on the same weight being given to gains and losses.

In order to test to what extent loss aversion carries over to peoples perception of trade policy, we conducted a simple survey. The following three questions were asked with each person answering only one of the questions:<sup>4</sup>

1. A manufacturing industry faces competition from imports.  Its losses are expected to be $20 million this year and firms are expected to reduce salaries by 10%.  If a tariff is placed on imports of competing goods then losses will be avoided and salaries will be maintained.  The tariff will cause the price of the good, which everyone consumes, to rise from $30 to $40. Do you support the tariff?

N= 102 Yes: 60 percent No: 40 percent 2. A manufacturing industry faces competition from imports. If a tariff is placed on imports of competing goods then profits will increase by $20 million and firms in the industry will increase salaries by 10%. The tariff will cause the price of the good, which everyone consumes, to rise from $30 to $40. Do you support the tariff?

N= 100 Yes: 37 percent No: 63 percent 3. A manufacturing industry faces competition from imports, which are subject to a tariff. Removal of the tariff will cause profits to decline by $20 million and

> 3 We would like to thank Sendhil Mullainathan for suggesting us to conduct a survey to motivate the paper.

> 4 The survey was given to 303 people visiting the Washington Mall in June 2004.

6
32 PROPORTION_FASTER. For instance, in Turkey, South Africa, and Pakistan, less than 30 percent of the firms have growth rates that exceed the estimate of the maximum growth rate that can be financed by relying only on internal and short-term financing. In contrast, in Japan, Korea, Singapore, and Thailand, PROPORTION_FASTER is greater than 50 percent. Put differently, in these countries, more than half the firms require long-term financing to finance their growth according to the “percentage of sales” financial planning model.

To analyze whether financial development spurs firm growth, DM run the following cross-country regression regressions PROPORTION_FASTER = a + bFD(i,t) + cCV(i,t) + u(i,t) where _FD_ is a variety of measures of financial development, _CV_ is a set of control variables, and u is the error term. To measure financial development, DM use (a) the ratio of market capitalization to GDP (Market Capitalization/GDP), (b) Turnover, which equals the total value of shares traded divided by market capitalization, and (c) Bank Assets/GDP, which equals the ratio of domestic assets of deposit banks divided by GDP. Thus, DM include all domestic assets of deposit banks, not just credit to the private sector. As control variables, DM experiment with different combinations of control variables, including economic growth, inflation, the average market to book value of firms in the economy, government subsidies to firms in the economy, the net fixed assets divided by total assets of firms in the economy, the level real per capita GDP, the law and order tradition of the economy.

As summarized in Table 7, DM (1998) find that both banking system development and stock market liquidity are positively associated with the excess growth of firms. Thus, in countries with high Turnover and high Bank Assets/GDP a larger proportion of firms is growing at a level that requires access to external sources of long-term capital, holding other things 4 Beck, Demirguc-Kunt, Levine, and Maksimovic (2001) confirm the findings using an extended sample.
# **1. Introduction**

“First and foremost it will help alleviate poverty directly by providing jobs for rural and outer island workers who often lack income-generating work. The earnings they send home will support families, help pay for education and health, and sometimes provide capital for those wanting to start a small business.” Winston Peters, New Zealand Minister of Foreign Affairs, at the approval of the RSE program, October 2006.<sup>1</sup> “A guest worker program is the most effective contribution we can make to improving the lives of the world’s working poor.” Dani Rodrik in a _New York Times_ op-Ed, June 1, 2007.

International migration is probably the most effective mechanism we know to rapidly increase the incomes of poor people (Clemens et al., 2008). However, it is also one of the most controversial, with migrant-receiving countries worried about the costs of assimilating workers and their families. Temporary or circular migration programs are seen as a way of overcoming such concerns and enabling poorer, less-skilled workers to benefit from the higher incomes to be earned abroad as part of a “triple-win”, whereby migrants, the sending country, and the receiving country all benefit. Such programs have been recommended as one of the most promising ways to enhance the development benefits of migration by a wide range of international organizations (UN, 2004; GCIM, 2005; World Bank 2006), national governments (House of Commons International Development Committee, 2004) and academics (Winters et al., 2003; Pritchett, 2006, Rodrik, 2007).

Almost all OECD countries have temporary worker migration programs, with seasonal workers the largest single category, totaling 576,000 workers in 2006 (OECD, 2008). However, such programs remain controversial. This is particularly true of programs geared to low-skilled migrants, such as seasonal migration programs, as witnessed by the policy debate in the United States over a new guest worker program and continued debate in Western Europe about the role of seasonal workers. Some critics of such programs raise concerns that workers will over-stay and/or compete down the wages of native poorer workers (e.g. Borjas, 2007), while others raise concerns about the possible exploitation of workers and whether workers can earn enough to make it worthwhile if the duration of work is short.<sup>2</sup> Lacking from this debate is credible evidence as to what the development impact is of international seasonal worker programs. The few existing studies are based on ex-post surveys of migrants, and lack credible counterfactuals of what would have happened to households in the

> 1 Quoted in “Seasonal work policy benefits Pacific says Peters”, _Islands Business_ , October 26, 2006. <u>http://www.islandsbusiness.com/news/index_dynamic/containerNameToReplace=MiddleMiddle/focusModuleID=1 30/focusContentID=6691/tableName=mediaRelease/overideSkinName=newsArticle-full.tpl</u> (accessed August 11, 2010).

> 2 These concerns are discussed in Ruhs (2006), Pritchett (2006) and OECD (2008) among others.

2
# **Appendix Summary of main articles reviewed in the paper**

||**Geographic**|**Time**|**Data**|**Methodology**|**Findings**|
|---|---|---|---|---|---|
||**coverage**|**coverage**||||
|**Expansion of pu**<br>|<br>**blic transit**<br>|<br>||||
|Duranton &<br>Turner (2011)|United States|1983-2003|Average annual daily<br>traffic, road network from<br>the US Highway<br>Performance and<br>Monitoring System; 1995<br>Nationwide Personal<br>Transportation Survey and<br>2001 National Household<br>Survey|Exploiting 3 IV (routes<br>of major expeditions of<br>exploration between<br>1835 and 1950, major<br>rail routes in 1898 and<br>proposed routes of<br>interstate highways in<br>preliminary plan) to<br>predict incidence of<br>roads on traffic|Traffic volume increased<br>as a result of the expansion<br>of highway capacity|
|Chen &<br>Whalley (2012)|Taipei|1995|Air pollution and transit<br>ridership,<br>hourly air quality data (from<br>Taiwanese EPA air quality<br>monitoring network)|Regression<br>discontinuity|The opening of the subway<br>line reduced carbon<br>monoxide (CO) emissions<br>by 5–15 percent|
|Goel & Gupta<br>(2017)|Delhi|2004-2006|Pollution and weather data<br>(hourly, daily and weekly)|Regression<br>discontinuity|The expansion of Delhi<br>Metro result in a 34<br>percent localized reduction<br>in CO in the short run.|
|Gendron-<br>Carrier et al.<br>(2018)|43 cities<br>across the<br>world|2000-2014|Latitude, longitude and date<br>of opening of 171 subway<br>systems consist of 8,889<br>stations, Aerosol Optical<br>Depth (AOD), recorded by<br>the Moderate Resolution<br>Imaging Spectroradiometer<br>(MODIS)|Event study|Particulate concentrations<br>dropped by 4 percent on<br>average following the<br>opening of a new subway<br>system.|

44
To further rule out concerns over sample selection produced by endogenous migration, we run econometric models of a binary migration variable that captures households that moved to another Guatemalan municipality after Agatha occurred against the shock measures and a subset of covariates.<sup>15</sup> The results from these models –and also from others that measure migration according to the location of both the household head and the spouse– do not provide evidence that Agatha pushed systematically more (or less) households to migrate away from the villages where they resided before the shock happened (Table 8- Panel C).

Another potential concern is the possibility of a systematic –as opposed to random– measurement error in the shock variable. More specifically, the empirical models may be picking up the cumulative and persistent effect of multiple rainfall shocks (floods and droughts) that occurred in the past if the location of those overlap closely with the geographic coordinates of Agatha’s path. To investigate this bias in measurement, we checked if rainfall variability– including the occurrence and frequency of extreme events– in the pre-Agatha period (19702009) differ systematically between “affected” and “less- and non-affected” weather stations. To account for the different probability distribution of precipitation between the two groups of stations, we compute the coefficient of variation. The calculation of this normalized measure of dispersion shows that the coefficient of variation is comparable between treated (1.17) and control stations (1.12). Further, the geographical location of the precipitation anomalies of three large events that hit Guatemala in the 2000s (Hurricane Stan 2005, Tropical Storm Barbara 2008 and Hurricane Mitch 2008) does not overlap with the path of Agatha.

Another possible source of measurement error is the misallocation of historical rainfall and precipitation anomalies to households across municipalities. As noted before, municipalities are matched to their closest weather station. However, due to the low density of stations in some parts of the country, a subset of municipalities is paired with stations that are too far away to accurately track their rainfall patterns. For nearly 4% of households, the corresponding weather stations lie more than 50 kilometers away. We performed a sensitivity analysis restricting the sample to the rest of the households (96%) and rerun the consumption and poverty models of equation 1. The negative effects of Agatha on consumption and the associated increase in poverty not only hold in this subsample but are also more precisely

> 15 It is worth noting that the _Encovi_ surveys only track domestic migration.

18