rafmacalaba/datause-encoder-data huggingface dataset:
Results using **W1** (contiguity definition of neighborhood) and **W2** (distance definition) are, in general, very similar to those reported above. The main difference is for the period 1970-1975 for **W1** , for which Moran's I is statistically significant at conventional levels. Therefore, on a contiguity definition of neighborhood, there is no evidence of clustering for half of the sub-periods.

# **5. Cross-Country Growth Spillovers and their Spatial Heterogeneity**

# _5.1. Model specification_

While the above results are suggestive, we cannot be certain whether the observed patterns of clustering are attributable to genuine spillover effects—or their absence in the case of SSA—or the existence of cross-country variations in policies, institutions and physical geography, or even the existence of shared transitional dynamics. For instance, even in a world of complete autarky, the Solow (1956) model predicts that neighboring countries with similar policies and institutions will exhibit similar growth rates if they start-off with similar initial levels of income per capita. In this section, we test whether any cross-country correlation of growth rates remains after controlling for observable determinants of growth, as well as for unobservable time-invariant determinants, using data for 1970-2000.<sup>25</sup> This allows us to examine whether there is evidence of spatial heterogeneity in the strength of cross-country spillover effects which might be related to varying levels of integration.

> 25 The estimator we use is Elhorst's (2003) maximum likelihood (ML) estimator for a panel data model with FEs and a spatially lagged dependent variable.

18
# Appendix B. Additional Figures and Tables

# Figure A1. Historical decompositions of global inflation

## A. Global oil price shocks

<!-- Start of picture text --> Percentage point<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## B. Global supply shocks

<!-- Start of picture text --> Percentage point<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## C. Global demand shocks

## D. Global interest rate shocks

<!-- Start of picture text --> Percentage point Percentage point<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>-0.2 -0.2<br>-0.4 -0.4<br>-0.6 -0.6<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Note: The structural shocks are estimated with the global FAVAR model. Grey-shaded areas indicate global recessions (1975, 1982, 1991, 2009, and 2020) and red-shaded areas indicate periods of oil price spikes (1974, 1978-80, 1990, 2008). Orange-shaded areas indicate periods of oil price plunges (1985-86, 1990-91, 1997-98, 2001, 2007-09, 2014-16, and 2020).

45
that there may be a very small proportion of owners of large firms that fall into this category. In all, including or excluding owners as part of the informal self-employed sector does not change the aggregate dynamics of this group in either country, since the bulk (around 85%) correspond to individual independent workers, with the other 15% corresponding to owners of firms.

The remainder of the sample is divided into two non-employment groups identical to those in the advanced country literature: those out of the labor force (O), and the unemployed (U). These are defined as in the mainstream literature.

# III. Methodology

Recent analysis on worker flows has put special emphasis on correcting for time aggregation (Shimer, 2007 Elsby et al., 2007 Fujita and Ramey (2006 and 2007) and Petrongolo and Pissarides (2007). Since the data on worker flows is tabulated at discrete quarterly or monthly intervals, average transition probabilities may provide misleading picture of worker mobility since transitions that are reversed within two surveys are missing. When there are only two states of employment, the transition rates can be obtained analytically from the monthly worker flows as in Shimer (2007). However, with more than two states no analytical solution is available and the transition rates have to be simulated. We instead follow an earlier Bayesian procedure (see Geweke 1986) that allows simultaneously estimating the whole transition rate matrix for a set of employment statuses.<sup>13</sup> We briefly discuss the procedure here.

We assume that the observed discrete-time mobility process is generated by a continuous-time homogeneous Markov process _Xt_ defined over a discrete state-space _E = {_ 1 _,....K}_ where _K_ is the number of possible states (job sectors) a worker could be found in.<sup>14</sup> With observations on worker states at regular periodicity we construct a discrete time transition matrix _P(t,t+n)_ where _pij_ ( _t_ , _t_ + _n_ ) = Pr( _X_ ( _t_ + _n_ ) = _j_ | _X_ ( _t_ ) = _i_ for _t_ = 2,1,0,..., and _n_ = 2,1,0,...

> 13 This method has also been employed in Fougère and Kamionka (2003),

> 14 For early work in this tradition, see Flinn and Heckman 1982 a, b and 1983).

10
34 First, it is generally preferable to have a stable path of consumption [see Calvo (1986)]. Secondly, the excessive purchases of capital goods which take piace during the expansionary phase (in anticipation of a failure of the exchange rate policy) lead to an inefficient allocation of investment. Thirdly, the difficulty in correcting the overvaluation and redressing the excessive real wages exacerbates the recessionary phase and leads to an unnecessary loss of output.

In the case of programs which turn out to be temporary (this is the majority) there are additional considerations. In these cases the failure can also be reflected in a destabilization of the inflationary process, as was clearly the case in the aftermath of the Austral and Cruzado plans. The loss of credibility which results from failure to stabilize requires much more recessionary policies in future stabilizations and corresponding losses of output.

This raises two questions: Is the stabilization cycle unavoidable in practice? If not, what can be done (and what was done) to mitigate its effect? As for the first question, the evidence from the stabilizations of Brazil 196467 and Mexico 1988-89 show that the excessive expansion can be avoided. One possible explanation for the Mexican case is that the fiscal adjustment was carried out long before 1988. In fact, the fiscal deficit was cut very drastically as early as 1983 and the budget even showed an operational surplus in 1987. This may indicate that it is advisable to start with the fiscal adjustment before the stabilization of the exchange rate in order to enhance credibility in the program. It must be recognized, however, that this may not be feasible politically (in the case of Mexico this step-wise procedure was
# **IV Empirical Strategy: Intent to Treat**

We estimate an Intent to Treat (ITT) model by comparing RAMV and non-RAMV miand thus the individual for the PEP. To isolate se- grants exploit eligibility requirement lection into the RAMV, we control for a broad set of pre-migration and pre-program covariates, including some that potentially drove selection into the RAMV. We estimate the following model: (1) _Yijkt_ = _α_ 0 + _α_ 11[ _RAMVijk_ = 1] + _θ_<sup>_′_</sup> _Xijk × ζt_ + _φjk_ + _δk_ + _ζt_ + _εijkt_ where _Yijkt_ is the outcome of interest for household _i_ located in city _j_ of state _k_ in year _t_ , 1[ _RAMVijk_ = 1] is an indicator variable for migrants who were registered in the RAMV census, _Xijk_ is a vector of household controls that we interact with wave fixed effects ( _ζt_ ), which captures the migrant’s economic and social characteristics before migration and the program roll-out. Appendix Table A.5 reports the descriptive statistics for the control variables. We control for the time of residence in Colombia to attenuate the assimilation bias as well as for city and state fixed effects ( _φjk_ , _δk_ and wave fixed effect _ζt_ ). We also leverage the time variation across both waves on the outcomes of interest. Even though we do not have data on pre-Covid-19 baseline conditions for the outcomes, we exploit the variation of the outcome variables waves RAMV through by interacting eligibility with the wave fixed effect. We report our main results with and without the interaction. Standard errors are clustered at the family level.

The outcomes of interests focus on four dimensions that measure the resilience of forced migrants to cope with the pandemic: (i) capacity to adhere to NPIs, (ii) access to medical services when required, (iii) protection and detection against COVID-19, and (iv) resilience to the economic impacts of the pandemic (see Appendix A for the description of each index). The ITT provides the causal effect of eligibility to the PEP under the assumption of _conditional unconfoundedness_ and conditional on our ability to isolate selection into the PEP by sampling RAMV migrants with and without the PEP.

# **V Results**

We find that PEP strengthened the ability of eligible RAMV migrants to lessen the health and economic impacts of the pandemic shock. Results of the ITT estimates are reported in Table 1.

3
42 their respective shares in EU-extemal imports (see Appendix Table 8). Bulgaria and Slovenia recorded little, if any growth at all-their shares in EU-external imports of both office equipment and parts significantly contracted over 1993-98.

Table 16: Office machinery equipment EU-based networks, Czech Republic, Estonia, Poland, Hungary and total CEEC-10 (in million of US dollars and percent)

||CzechR|epublic|Esto|nia|Hung|ary|Pola|nd|CEE|C-10|
|---|---|---|---|---|---|---|---|---|---|---|
|Commodity & Parts|1993|1998|1993|1998|1993|1998|1993|1998|1993|1998|
|Exports of final products|18.6|5.2|0.0|0.2|1.4|2.9|<br>0.8|1.1|23.6|<br>10.6|
|<br>Exports of parts|27.9|116.3|0.1|63.9|10.0|432.2|<br>3.6|17.9|50.7|<br>687.4|
|<br>Importsof parts|129.0|226.1|1.3|26.8|80.4|734.9|68.2|191.4|347.5|1,343.5|
|Exports of final products<br>minus imports of parts<br>Imports of final products|-110.4<br>31.9|-220.9<br>19.4|-1.3<br>0.4|-26.6<br>3.9|-78.9<br>25.7|-732.0<br>29.0|-67.4<br>21.6|-190.3<br>63.3|-323.9<br>105.6|-1,332.9<br> <br>149.2|
|Memo:|||||||||||
|Share in EU-oriented<br>exports of manufactures (%)|0.9|0.9|0.1|5.2|0.3|3.0|<br>0.1|0.1|0.3|<br>1.1|
|<br>Share in imports of<br>manufactures from the<br>E U**(%)**<br>Overall Balance|2.2<br>-114.4|1.2<br>-84.4|0.8<br>-1.6|1.3<br>33.4|1.6<br>-94.6|4.9<br>-328.8|<br>0.9<br>-85.4|1.0<br>-235.7|1.3<br>-378.8|<br>1.7<br>-794.7|

Source: Computations based on EU data in UN COMTRADE Statistics.

CEEC-4 import significantly more parts from the EU than they export final products to EU markets. The latter are negligible. Imports of parts in terms of value rose from US$279 million in 1993 to US$750 million in 1998. An almost nine-fold increase in the value of exports of parts combined with a contraction in exports of final products suggests the expansion in intraproduct specialization.

But there are significant differences in emerging specialization among CEEC within office machinery equipment' network. Bulgaria, Czech Republic and Slovenia have a "double" comparative advantage in typewriter parts (on the import side) and typewriters (on the export side), which suggests their involvement in assembling typewriters (7511) for EU clients.22 So does Hungary. Its "double" comparative advantage in both aggregate exports and imports of parts for calculating/accounting machines (SITC. 7599) implies integration into intermediate stages of a supply chain. The share of Hungary in EU external imports of SITC. 7599 grew from 1.9 percent in 1997 to 2.7 percent in 1998 and in EU external exports from 2.6 percent to 4.4 percent respectively. Other CEEC, except Latvia and Slovakia, have comparative advantage in assembly of office machinery, but they do not seem to be involved in global networks of production and distribution. Their shares in EU external imports and exports remain negligible.

> 22 Their share in EU external imports of typewriters (SITC.75 11) fell from 0.5 percent (Bulgaria) and 9 percent (Czech Republic) in 1997 to 0.4 percent (Bulgaria) and 3.6 percent (Czech Republic) in 1998 (Appendix Table 7).
**<u>Table 2:</u> Benchmark model**

||[1]|
|---|---|
|Log real GDP per capita|2.255***<br>[0.559]|
|log real cost|-0.210***<br>[0.077]|
|Log lines per capita|2.155***<br>[0.266]|
|Log computers per capita|2.292***<br>[0.132]|
|Constant|-12.727**<br>[5.242]|
|Method of estimation|GLS|
|Number Observations<br>R<sup>2</sup>|955<br>0.91|

<u>Notes:</u> The dependent variable is the log of Internet users per capita. Robust standard errors in brackets. * , **, and *** indicate significance at the 10, 5, and 1 percent levels respectively. The regression uses countries with GDP per capita growth in the interval [-4.9 , 9.2 percent] and controls for country fixed effects.

# **5.1. The diffusion model**

Specification (2) has often been criticized because it does not account for the process of diffusion in Internet adoption. Following Estache and al. (2002), we include the lag of the number of Internet users (in logs) as a right-hand-side variable: Equation (3) is the reduced form of a Gompertz model of technology diffusion with a constant speed of adjustment. In such a model, the change in the number of users (from the current period to the next one) is expressed as a fraction—the speed of adjustment— of the gap between the number of users at the equilibrium and the number of current users. Hence, the number of new users who adopt a certain good or service in a given period depends on both the number of existing and potential users—which is itself determined by demand-side variables (income, costs, etc.), and other variables describing the demand or supply conditions or the technological infrastructure in each country _i_ (see Stoneman, 1983; Kiiski and Pohjola, 2002; and Estache _et al._ 2002 for more details).

The coefficient _β5_ measures the importance of network externalities in the diffusion of Internet. In the absence of diffusion, _β5_ is not significant. When _β5_ is positive and smaller than 1, the diffusion model is accepted. Thus the number of users in the current period explains (in a percentage given by _β5_ ) the number of Internet users the subsequent year.

13
- ?20 -

Turkey taxed capital goods imports at a high rate and the 198 study recommended that, since this placed Turkish entrepreneurs at a disadvantage, this tariff be eliminated.

GHANA: In 1986 a both the Fund and the Bank found the tariff structure in Ghana to be appropriate and recommended that the use of QRs be reduced. They noted that an overvalued exchange rate offset some of the protection provided by the tariff structure. A temporary across-the-board import surcharge was recommended to mop up scarcity premia due to quotas.

INDONESIA: A Bank report recommended eliminating QRs and gradually reducing tariffs while providing adjustment assistance to affected industries. Both the level and variance in ERPs were to be lowered over time. Tariffs on intermediate and final goods were to be lowered to encourage price competition and to reduce domestic costs. A subsequent report commented on tariff reforms undertaken by the government which reduced the maximum tariff (for all except 19 product groups) from 225% to 60%, reduced the number of tariff levels and the dispersion of rates. However, simultaneously, new QRs had been introduced. The report argued that the development of upstream industries through protection would raise costs for downstream industries and lower their effective protection. Future development should be along lines that would not require protection or subsidies. This required eliminating QRs and lowering tariffs.

In 1987, the Bank noted that recent government ations had reduced QRs and increased the relative importance of the need for tariff rationalization. Tariffs had been increased on certain of the intermediate and final goods which were freed from QRs. Other inputs had had tariff reductions. It was recommended that attention should now be paid to reducing tariffs on the outputs. The report emphasized the need for a tariff structure that restricted the range of tariffs to a narrow band.
Table 6: Trade, Legal Origins and Protection of Property Rights, Baseline Results Dep. Var.: log of average trade flow

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|IV|OLS|IV|
|Different Legal Origin|-0.25|-0.26|-0.29|-0.25|
||(-5.96)|(-5.76)|(-6.76)|(-5.38)|
|Protection of Property<br>|0.31|0.36|0.30|0.18|
|Rights|(21.43)|(6.34)|(19.71)|(3.40)|
|average weighted tariff|||-0.02|-0.02|
||||(-8.64)|(-8.52)|
|log distance|-1.20|-1.20|-1.15|-1.14|
||(-48.76)|(-48.47)|(-45.06)|(-44.00)|
|log product of GDP|0.98|0.97|0.99|1.02|
||(105.61)|(49.36)|(102.26)|(59.68)|
|log product of land|-0.10|-0.09|-0.10|-0.13|
|area|(-12.20)|(-6.45)|(-12.08)|(-9.76)|
|no. of landlocked|-0.54|-0.55|-0.55|-0.53|
||(-15.70)|(-15.35)|(-15.17)|(-14.15)|
|no. of islands|-0.18|-0.20|-0.18|-0.12|
||(-4.15)|(-3.98)|(-4.15)|(-2.42)|
|common border|1.04|1.05|0.96|0.95|
||(8.38)|(8.36)|(7.59)|(7.46)|
|common language|0.41|0.38|0.52|0.59|
||(7.72)|(6.26)|(9.94)|(9.78)|
|currency union|1.31|1.32|1.06|1.09|
||(5.46)|(5.47)|(2.93)|(3.15)|
|Observations|29772|29772|19536|19536|
|R^2|0.72|0.72|0.75|0.75|
|First stage R^2||0.42||0.42|
|% increase in trade<br>due to 1s.d. institution|57%|69%|55%|30%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant and year dummies are not reported. IV estimators use the sum of absolute latitude as an instrument for the sum of protection of property rights for both countries in a trading pair. The sum of protection of property rights ranges from 0 to 8 with mean 4.962 and standard deviation 1.47. The percent increase in trade due to 1s.d. institution (property rights) is calculated as follows: exp(coef*sd)-1.

40
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<mark>Source: Data provided by citrus RSE</mark> Table 7 presents similar data from an apple orchard that employed a mix of RSE workers, New Zealand contract labor, casual labor, and backpackers. Again the RSE workers are more productive in their second and third years than in their first, with the productivity premium similar in magnitude (for the change in the mean) to that in the citrus orchard. The RSE workers are 50-60 percent more productive than backpackers and New Zealand casual labor, and 11-18 percent more productive than other New Zealand contract workers, even those returning for multiple seasons.

**<mark>Table 7: Weekly earnings of RSE and local apple workers at one orchard during 2011 season</mark>**

||Number of<br>Workers|Mean Weekly<br>Earnings<br>per worker<br>in 2011|Productivity<br>premium over<br>1styear workers|Median<br>Earnings<br>per worker<br>in 2010/11|Productivity<br>premium over<br>1styear workers|
|---|---|---|---|---|---|
|_RSE workers_||||||
|Firstyear|46|679||641||
|Secondyear|25|733|8.0|773|20.6|
|Thirdyear|12|771|13.5|774|20.7|
|_New Zealand workers_||||||
|Firstyear|32|608||622||
|Secondyear|15|621|2.1|617|-0.8|
|Thirdyear|7|657|8.1|637|2.4|
|Backpackers|21|511||519||
|New Zealand casual labor|36|475||469||

It is unclear how much of the higher productivity of return RSE workers represents gains to experience versus selection effects of which workers farmers invite back for another season. Worker productivity and earnings are greatly affected by weather factors which determine how good the crop is from one year to the next. As such, even if workers are more productive in their second or third season, they may earn less than they did in their first year if drought or other weather factors reduce the amount of fruit available to pick. We therefore use microdata on workers in an apple orchard to explore this issue. We compare the earnings in 2010 of workers 19
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17