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Fuchs, A. and H. Wolff. 2011. “Drought and Retribution: Evidence from a large scale Rainfall Indexed Insurance Program in Mexico”, University of California Berkeley Working Paper Hazell, P., J. Anderson, N. Balzar, A. Hastrup Clemmensen, U. Hess and F. Rispoli. 2010. _Potential for scale and sustainability in weather index insurance for agriculture and rural livelihoods_ . International Fund for Agricultural Development and World Food Programme. Rome Healy, A. and N. Malhotra. 2009. “Myopic Voters and Natural Disaster Policy”, _American Political Science Review_ **103** (3): 357–406.

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Page **17** of **29**
**Table A4 – continued from** **<u>previous page</u>**

||Year|14-18|18-25|21-25|20-23|14-18|18-25|21-25|20-23|
|---|---|---|---|---|---|---|---|---|---|
|Guatemala|1994|93.5|64.5|53.5|63.5|88|104|61|51|
|Guatemala|2002|93.5|67.8|57.8|67.2|122|159|93|80|
|Haiti|1971|94.9|66.9|52.8|66.8|37|45|25|21|
|Haiti|1982|93.8|67.7|56.3|67.9|9|14|8|7|
|Haiti|2003|94.3|74.3|64.3|73.6|77|99|55|47|
|Honduras|1974|92.0|59.4|48.2|58.2|26|31|18|15|
|Honduras|1988|92.6|64.6|54.7|63.5|41|48|29|24|
|Honduras|2001|89.9|62.0|52.0|60.5|69|89|51|45|
|Jamaica|1982|90.9|65.0|53.7|64.2|20|25|14|12|
|Jamaica|1991|91.7|67.5|57.8|67.6|20|28|17|14|
|Jamaica|2001|88.5|62.5|53.6|61.6|17|23|14|11|
|Mexico|1970|94.7|58.2|44.0|56.8|44|51|29|24|
|Mexico|1990|94.0|66.5|55.3|65.8|958|1191|689|579|
|Mexico|2000|93.0|66.8|57.4|66.4|1079|1442|869|708|
|Mexico|2010|94.2|73.3|64.7|72.8|1282|1634|949|801|
|Nicaragua|1971|93.1|61.8|49.6|60.8|18|20|12|10|
|Nicaragua|1995|93.5|69.4|60.4|68.8|46|56|33|28|
|Nicaragua|2005|91.2|68.1|60.5|67.6|56|80|49|40|
|Panama|1960|91.3|52.8|40.6|52.3|4|5|3|3|
|Panama|1970|91.7|57.8|46.4|56.5|12|16|10|8|
|Panama|1980|92.7|65.9|55.1|65.1|19|24|14|12|
|Panama|1990|93.2|69.9|61.0|69.6|21|30|18|15|
|Panama|2000|93.3|68.8|60.4|68.4|24|33|21|16|
|Panama|2010|91.8|68.6|61.0|68.5|29|41|25|20|
|Paraguay|1962|95.7|63.1|51.9|64.3|6|8|5|4|
|Paraguay|1972|96.0|66.5|55.3|67.1|20|23|14|11|
|Paraguay|1982|94.7|67.6|57.9|67.8|27|37|23|19|
|Paraguay|1992|93.2|61.8|52.0|60.9|32|44|27|22|
|Paraguay|2002|95.1|72.0|62.5|71.5|51|64|37|32|
|Peru|1993|94.0|69.5|60.9|69.1|196|267|165|135|
|Peru|2007|92.7|70.0|62.5|69.6|240|337|206|166|
|Saint Lucia|1980|95.3|64.5|51.5|63.2|1|1|1|1|
|Saint Lucia|1991|94.0|67.7|58.6|66.9|1|2|1|1|
|Suriname|2012|95.7|81.2|75.6|82.2|4|5|3|3|
|Trinidad and Tobago|1970|97.0|72.1|59.5|72.0|7|8|4|4|
|Trinidad and Tobago|1980|95.2|73.2|63.2|73.6|12|16|9|8|
|Trinidad and Tobago|1990|95.8|76.9|69.4|78.0|10|14|9|7|
|Trinidad and Tobago|2000|96.4|81.4|74.2|81.2|12|15|8|7|
|Trinidad and Tobago|2011|96.5|84.4|80.1|86.2|8|14|9|7|
|Uruguay|1963|97.1|70.6|60.0|70.3|16|23|14|11|
|Uruguay|1975|96.5|67.6|56.0|66.6|19|27|16|13|
|Uruguay|1985|96.9|67.1|57.5|67.6|19|29|19|15|
|Uruguay|1996|94.0|69.3|60.5|69.3|23|34|21|17|
|Uruguay|2006|95.0|74.9|65.7|74.6|21|27|16|13|
|Uruguay|2011|94.0|65.6|56.5|64.8|25|36|22|18|
|||||||Con|tinued|on ne|xt page|

43
**Table A6: Impact of unilateral facilitation on alternate migration measures**

Column 1 sample includes baseline respondents with respondent, proxy, and log endline surveys and nonmissing outcome variables. Column 2 migration outcome is based on 2010-2012 offers confirmed in 2013 follow-up survey, which was conducted among all households with at least one job offer overseas at 2012 endline. Stratification-cell fixed effects and baseline covariates described in Table 2 are included. HuberWhite standard errors reported in brackets.

46
the second war from 1983 to 1987. Atlas and Licklider (1999) depict the first war as a “blackwhite civil war” in which the coalition of two guerrilla forces maintained cohesion against a common white enemy and the second war as “a black-on-black armed conflict between former civil war allies” who “had fought to bring Ian Smith’s Rhodesia to an end” (Atlas and Licklider 1999, 40). However, Atlas and Licklider also note that the relationship between the two guerrilla parties and their leaders was fraught even during the first war. Atlas and Licklider’s efforts to justify separating the two wars they identify for Zimbabwe actually point to the close connections and inseparability of the two wars. More important, the preoccupation with death counts comes at the expense of understanding the history of how the two violent episodes are connected (Kriger 2003).

A number of general points can be made based on further examination of the PRIO/Uppsala data presented in table 1. Points 1–4 relate to nonrandom errors in the classification of the dependent variable, and point 5 addresses the question of autocorrelation.

1. The strictest rule for coding a civil war that seems applicable to the PRIO/Uppsala data is “internal armed conflict” (type 3, denoted by X and O) and “war” intensity (the threshold based on 1,000 battle deaths in any one year, reported in row 1 for each country that has two rows). Countries with at least one civil war with this criterion are Algeria, Angola, Burundi, Chad, Congo-Kinshasa, Ethiopia, Liberia, Morocco, Mozambique, Nigeria, Rwanda, Sierra Leone, Somalia, South Africa, Sudan, Uganda, and Zimbabwe. These instances of civil war are far fewer than suggested by a cursory reading of the narrative literature on Africa. Cameroon, Central African Republic, Congo-Brazzaville, Côte d’Ivoire, Kenya (Shifta War, 1963 to 1967), Mali, Niger, and Senegal are all plausible cases of civil war, some more than others (however, as we argue in section 3, almost all countries in both groups better fit the definition of participants

12
Chile; these are geared toward financing working capital. These products are followed by leasing and investment loans (Figure 4). Pre-trade financing is also considered important in both countries. Document and check discounting is the second leading product in Argentina but is not mentioned at all by Chilean banks, while factoring is a very important product in Chile but is not considered significant by Argentine banks. However, these products are similar since they enable companies to receive payments in advance at a certain discount. The main difference between check discounting and factoring lies in the instrument that is being discounted: in Argentina it is relatively easy for the bearer of a check to claim the corresponding payment since the check is an “executive title,” while the bearer of a company receipt is unable to do so because company receipts are typically not executive titles.<sup>18</sup> In Chile “the use of factoring has been facilitated after a recent legal reform that made the _factura_ an executive title.”<sup>19</sup> Based on the data received from banks in Argentina, working capital loans represent approximately 60% of the SME loans, while investment loans account for almost 20% of the SME loans. In Chile, the distribution appears to be different since short-term and long-term products represent a similar proportion of total financing to SMEs; working capital and investment loans each represent around 40% of the SME portfolio. As for the term of investment loans, in Argentina the average term is 1,000 days while in Chile it is 3,700 days (Figure 5). These stylized facts suggest that the SME lending market is more developed in Chile than in Argentina.

Banks usually require some basic collateral to make loans. Approximately 70% of the loans require collateral, and the collateral requirement represents, on

> 18 An instrument is an “executive title” if it is established by law that the payment obligation it represents must be met. This category cannot be established by the parties involved in a transaction, but rather it can only be designated by law since it reflects a public interest that the obligations in certain types of instruments are fulfilled. Therefore, the holder of an “executive title” can forcefully demand the compliance of its payment. In Argentina some receipts can become executive titles if they are certified, which are then called “facturas conformadas,” but this is not usually done in practice. 19 Quoted from bank interviews in Chile.

9
# **6. Other transactional insurance motives**

An alternative explanation to the observed lack of reciprocity could be that migrants insure non-migrants in exchange for other benefits. By concentrating on consumption differences we have considered only current pay-offs from any risk-sharing arrangement. It is quite possible that the benefits are still to accrue to the migrant in the more distant future. Lucas and Stark (1985) mention that there could be exchange motives for insurance provision relating to the desire for non-migrants to look after local assets, the intention to return home and the aspiration to inherit. In a context that lacks technology to allow future income to be consumed now, we could confuse unilateral insurance with postponed reciprocity. Fortunately, the KHDS questionnaire is particularly rich and we are thus able to explore some of these issues.

The questionnaire asks each migrant about asset holdings in the baseline village. As our outcome variable is consumption growth we cannot use these asset holdings as explanatory variables: current wealth is surely endogenous to growth in wealth. We attempt to circumvent this problem by looking at the share of assets in the current portfolio that are located in the village. While it remains possible that portfolio composition is endogenous to consumption growth, we believe the results are informative enough to report.

About 28 percent of migrants have assets in the baseline village and 25 percent of migrants own land in the baseline village. For land we have exact area measurements, but not monetary values. If migrants engage in risk sharing with those who remain at home for the purpose of maintaining land and ensuring their continued entitlement to the land (which is important in a country with few formal land deeds), then we would expect more responsiveness to network shocks from people with a larger share of their land holdings in the baseline village. The first column of Table 9 explores this. As before, the dependent variable is logged per capita consumption growth. We interact the non-migrant network shocks with a variable measuring the share of the land in the baseline village. The coefficient on this interacted variable turns out insignificant implying that the share of land in the baseline village neither increases nor decreases the insurance provision.

19
leads to a much more diverse and accurate assessment of the output changes at the sector level. Previous efforts at simulating sector output changes from trade facilitation used uniform ad valorem equivalents across sectors, which led to more uniform impacts on sector output. The data set we have used has the barriers varying by product and by country of origin or destination (due to the product mix of exporting and importing). Consequently, the predicted changes in output at the sector level vary considerably. For example, in Uganda, one of the sectors with the highest AVE of the time costs of exporting (importing) within EACU is agricultural products, where the Ugandan AVE is about 40 (30) percent, depending on the destination (origin) country.<sup>24</sup> Reduction of the time costs of trade barriers then leads to an expansion of agriculture in Uganda relative to other sectors.

**5.5.2 Political Economy of Regional Trade Liberalization** The trade liberalization scenario we consider is the liberalization of non-tariff barriers. We find that preferential liberalization results in a substantially muted output change compared with multilateral liberalization. Take Tanzania as an example (which has the highest AVEs of its NTBs in our data set). The gains from multilateral reduction of NTB barriers alone are about 25 times greater than the gains from liberalization within EACU alone. But, the maximum output decline at the sector level from NTB liberalization within EACU alone is 2 percent, compared with output declines of 9.5 percent for textiles and apparel, 11.8 percent for other manufacturing and 13.8 percent for wood and paper products. Thus, although the welfare gains of preferential liberalization are dramatically smaller than multilateral liberalization, the adjustment costs are also smaller. Empirical studies have shown that the adjustment costs of trade liberalization are dramatically smaller than the welfare gains.<sup>25</sup> However, policy makers often receive strong lobbying from those who suffer adjustment costs from trade liberalization, while those who gain are more diverse and may not realize they gain from trade liberalization, and typically do not lobby for liberalization or lobby much less vigorously. Thus, these results explain some of the appeal of regional liberalization to policy makers, despite the larger net gains of broader liberalization.

# **6. Sensitivity Analysis**

> 24 The partner country AVE is also relevant in assessing impacts. 25

> Matusz and Tarr (2000) summarize the evidence on the adjustment costs of trade liberalization.

27
# **2.1 Overview of the World Management Survey methodology**

The WMS was developed to measure adoption of structured management best practices in establishments across a range of countries and industries.<sup>5</sup> The rigorous data collection is based on double-blind, semi-structured interviews conducted by highly-trained analysts and monitored by supervisors experienced on the survey methodology. Following its successful implementation in the private sector, the WMS was subsequently extended to public sector organizations [Bloom et al., 2015a, 2019b]; in this paper, we focus on the latter.

The public-sector WMS covers 20 topics across two main areas: _operations management_ and _people management_ . Broadly speaking, operations management in schools covers practices including: whether the school has standardization of instructional processes across classrooms while allowing for within-classroom personalization of learning; whether and how the school uses assessments and data; and whether and how the school sets and uses targets and keeps track of progress. People management covers practices in handling good and bad performance measuring whether there is a systematic approach to identifying good and bad performance, rewarding school teachers proportionately, dealing with underperformers, and promoting and retaining good performers.

For each WMS topic, there is a scoring grid ranging from 1 to 5, which serves as a guide to evaluate answers to questions during the semi-structured interviews. A score between 1 to 2 refers to a school with practically no structured management practices or very weak management practices implemented; a score between 2 to 3 refers to a school with some informal practices implemented, but these practices consist mostly of a reactive approach to managing the school; a score between 3 to 4 refers to a school where a good, formal management process is in place (though not yet consistent enough) and these practices consist mostly of a proactive approach to managing a school; and a score between 4 to 5 refers to well-defined, strong processes in place which are often seen as best practices in education. The overall management index, which measures the level of adoption of structured management best practices, is simply the average of the scores for these 20 topics.

The practices measured by the survey seem to matter: Bloom et al. [2015a] show that their school management score is strongly positively correlated with school-level student outcomes across 6

> 5See Bloom and Van Reenen [2007] for the survey’s inception and Bloom et al. [2016] for a recent review.

6
- [22] Davoodi, Hamid, Paul Elger, Alexandra Fotiou, Daniel Garcia-Macia, Andresa Lagerborg, Raphael Lam, and Sharanya Pillai, “Fiscal rules dataset: 1985-2021,” IMF (2022).

- [23] Dolan, Julie, “The budget-minimizing bureaucrat? Empirical evidence from the senior executive service,”Public Administration Review, Vol. 62 (2002), pp. 42-50.

- [24] Dzhambova, Krastina, “When it rains, it pours: Fiscal policy, credit constraints and business cycle in emerging and developed economies,” Journal of Macroeconomics, Vol. 69 (2021), pp. 1-19.

- [25] Fasano, Ugo, “Review of the experience with oil stabilizations and savings funds in selected countries,”IMF Working Paper No. 00/112 (2000).

- [26] Fernandez, Andres, Daniel Guzman, Ruy Lama, and Carlos A. Vegh, “Procyclical fiscal policy and asset market incompleteness,”NBER Working Paper No. 29149 (2021).

- [27] Frankel, Jeffrey A., “A solution to fiscal procyclicality: The structural budget institutions pioneered by Chile,”NBER Working Paper No. 16945 (2011).

- [28] Frankel, Jeffrey, Carlos A. Vegh, and Guillermo Vuletin, “On graduation from fiscal procyclicality,” Journal of Development Economics, Vol. 100 (2013), pp. 32-47.

- [29] Ghassibe, Mishel, and Franceso Zanetti, “State dependence of fiscal multipliers: The source of fluctuations matters,” Journal of Monetary Economics, Vol. 132 (2022), pp. 1-23.

- [30] Gruss, Bertrand, and Suhaib Kebhaj, “Commodity terms of trade: A new database,”IMF Working Paper 19/21 (2019).

- [31] Guay, Alain, and Pierre St.-Amant, “Do the Hodrick-Prescott and Baxter-King filters provide a good approximation of business cycles? Annales d’Economie et de Statistique, Vol. 77 (2005), pp. 133-155.

- [32] Henisz, Witold, “The institutional environment for economic growth,”Economics and Politics, Vol. 12 (2000), pp. 1-31.

- [33] Henisz, Witold, and Bennet A. Zelner, “Measures of political risk,”unpublished manuscript (undated), University of Pennsylvania and Georgetown University.

32
<!-- Start of picture text --> Chart 4C: Uphill flows of sophisticated exports from non-OECD Countries, 1990-<br>2006<br><!-- End of picture text --> <!-- Start of picture text --> 4.0%<br>14.0%<br>above median<br>3.5%<br>12.0% above 75th pctile (right scale)<br>3.0%<br>10.0%<br>2.5%<br>8.0%<br>2.0%<br>6.0%<br>1.5%<br>4.0%<br>1.0%<br>2.0% 0.5%<br>0.0% 0.0%<br>1980 1985 1990 1995 2000 2002 2004 2005 2006<br><!-- End of picture text --> For each country, the measure of uphill flows is exports of sophisticated goods to countries richer than itself as a share of its total sophisticated exports. These are added for all non-OECD countries.

18
relaxation of the first lockdown restrictions in Germany, a region’s WfH potential did not matter for its employment outcomes, even if remote work remained a common practice (Arntz et al., 2022). The difference in our results from that observed in Germany (acknowledging that the outcome variable and data are different) is suggestive that while WfH via digital adoption among firms in developing countries was possibly boosted by the COVID-19 shock, it may have still been below the optimal level such that the returns to WfH continued through all periods in our database.

Table 5: **Heterogenous effects, by time**

|||**Internet**|**Users**|**In**|**ternet Secur**|**e Servers**|
|---|---|---|---|---|---|---|
||Full|Exporters|Non-Exporters|Full|Exporters|Non-Exporters|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Sh. remote workers x Internet x Apr-Jun ’20|1_._175|2_._096|1_._098|0_._405|1_._723|_−_0_._103|
||(0_._781)|(1_._888)|(0_._848)|(0_._846)|(1_._529)|(1_._008)|
|Sh. remote workers x Internet x Jul-Sep ’20|2_._704<sup>_∗∗∗_</sup>|2_._974<sup>_∗∗∗_</sup>|2_._545<sup>_∗∗∗_</sup>|1_._555<sup>_∗∗∗_</sup>|1_._079|2_._022<sup>_∗∗∗_</sup>|
||(0_._557)|(1_._148)|(0_._637)|(0_._435)|(0_._693)|(0_._549)|
|Sh. remote workers x Internet x Oct ’20-Mar ’21|1_._476<sup>_∗∗∗_</sup>|1_._609<sup>_∗∗_</sup>|1_._579<sup>_∗∗∗_</sup>|0_._417<sup>_∗_</sup>|1_._116<sup>_∗∗∗_</sup>|0_._178|
||(0_._415)|(0_._695)|(0_._481)|(0_._227)|(0_._303)|(0_._321)|
|Sh. remote workers x Internet x Apr-Sep ’21|2_._088<sup>_∗∗∗_</sup>|3_._058<sup>_∗_</sup>|1_._760<sup>_∗∗∗_</sup>|2_._344<sup>_∗∗_</sup>|3_._336<sup>_∗∗_</sup>|1_._677|
||(0_._579)|(1_._711)|(0_._605)|(1_._022)|(1_._630)|(1_._224)|
|Country F.E.|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector F.E.|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0_._155|0_._221|0_._135|0_._158|0_._222|0_._137|
|Observations|61_,_951|11_,_622|50_,_329|59_,_886|11_,_412|48_,_474|

Estimations also control for (log) firm’s size, round F.E, and Google’s Mobility index. Estimations are weighted using country weights. The sample only includes country-quarters with more than 100 observations.

Standard errors in parentheses. Significance levels ***, **, and *, correspond to p _<_ 0.01, p _<_ 0.05, p _<_ 0.1, respectively.

15