rafmacalaba/datause-encoder-data huggingface dataset:
We pool the data, giving us a sample size of 368,453 over the eight years of data. While these data are useful since they show a long-term dynamic process, they are of limited usefulness in that it is unlikely that we are capturing short-run movements between labor market states.

The Brazilian _Pesquisa Mensual de Emprego_ (PME) for 1982-2002 is designed similarly to that in Argentina, but the same sample is interviewed once a month for four months.<sup>6</sup> Again, to avoid summer vacations, we only include observations that make the September to October transition. This gives us a sample size of 340,000,000 over the 20 year period. The structure of this data set is particularly useful to our analysis since the time between observations is so short that it is unlikely that there are intermediate transitions between states that we are not observing. Thus, we are likely capturing all the movements that people make in the short run.

The Mexican _Encuesta Nacional de Empleo Urbano_ for 1987-2003 has a design similar to that in Brazil and Argentina, but there is a three month period between each interview, for a total of five interviews per cohort. Thus, we are able to observe the employment status of each cohort over a one year period. We do not include transition from the second to the third quarter or the third to the fourth quarter since those overlap with the school “summer vacation” period, leaving us with a sample size of 952,664.

We define three different age groups for analysis. _Youth_ are defined as those age 1518 years of age. They are still of secondary school age, so they are the newest entrants to the labor force of any age group and they are more tied to school than any other group. _Young adults_ are those who are age 19-24 years of age. They are beyond secondary school age (though, due to grade repetition, they still may be attending secondary school) and are, as a group, the most likely to be new entrants to the labor market. Finally, we consider _prime age adults_ , who are age 25-44, and are in the prime of their working life. We choose to only include those younger than age 45 to avoid including the years when retirement starts becoming an option. This last group is included in our analysis to serve as a counterfactual against which we can compare youth and young adult unemployment patterns.

> 6 The full panel has a 4-8-4 structure, where the sample is interviewed for four consecutive months, not interviewed for the following eight months, then interviewed another four consecutive months.  Due to attrition bias arising after the eight month hiatus, we chose to only use the information from the first four months.  Further, this is the only of our three survey that allows for observations over such a short time period, i.e. month by month, so we take advantage of that information.

8
44

# References

Agora Telematica website of political parties. Wilfried Dierksen, editor. (www.agora.stm.it/elections/parties.htm.)

- Alesina, Alberto, Sule Ozler, Nouriel Roubini, and Philip Swagel (1996). “Political Instability and Economic Growth.” Journal ofEconomic Growth 1 (June).

- Banks, Arthur S. The Political Handbook of the World, 1986-1996. CSA Publications. Binghampton, 1986-1996.

- Clague, Christopher, Philip Keefer, Stephen Knack and Mancur Olson (1996). “Property and Contract Rights under Democracy and Dictatorship.” TheJournal ofEconomic Growth 1:2, 243-276 (June).

- Collier, Paul and Anke Hoeffler (1999). “Justice-seeking and loot-seeking in civil war.” Working paper, Development Research Group, The World Bank.

- Cox, Gary W. (1997). Making Votes Count: Strategic Coordination in the World’s Electoral Systems. New York: Cambridge.

- ______ and Mathew D. McCubbins (forthcoming). “The Institutional Determinants of Economic Policy Outcomes,” in Structure and Policy in Presidential Democracies, Stephan Haggard and Mathew D. McCubbins, eds.

- Dixit, Avinash and John Londregan (1996). “The Determinants of Success of Special Interests in Redistributive Politics.” TheJournal ofPolitics 58:4, 1132-55.

,

- Durham, J. Benson (1999). “Economic Growth and Political Regimes.” Journal ofEconomic Growth 4, 81-111 (March).

- The Europa Year Book, Vols. 1 & 2, 1977-1984, 1990-1994. Europa Publications Ltd. London, 1977-1984, 1990-1994.

- Ferree, Karen E. and Smita Singh (1999). "Institutional Change and Economic Performance in Africa, 1970-1995." Paper presented at the 1999 meetings of the American Political

- - Science Association, Atlanta, Georgia.

- Gurr, Ted Robert, Keith Jaggers and Will H. Moore (1998). Polity III Codebook. Center for Comparative Politics, Department of Political Science, University of Colorado, Boulder. Polity If available through the Inter-University Consortium for Political and Social Research.

|

- Heinisz, Witold (1997). "The Institutional Environment for Growth." Mimeo, The Wharton School.
Table 7: Significant second-stage coefficients as a percentage of eligible first-stage regressions, combining the results for all 7 energy policy packages.

||**Group**|**Total eligible**<br>**first-stage**<br>**regressions**|**Share with significant**<br>**second-stage**<br>**coefficients**|**Share with positive and**<br>**significant second-stage**<br>**coefficients**|
|---|---|---|---|---|
||SSA|224|22.8%|6.7%|
||EAP|86|4.7%|1.2%|
|on|ECA|50|44.0%|18.0%|
|egi|LAC|116|6.0%|0.0%|
|R|MENA|36|2.8%|2.8%|
||SAS|28|0.0%|0.0%|
||Total|540|15.7%|4.8%|
||High|49|0.0%|0.0%|
|e|Upper Middle|113|15.0%|0.9%|
|com|Lower Middle|246|15.4%|5.3%|
|In|Low|132|22.7%|9.1%|
||Total|540|15.7%|4.8%|

_Notes: Regression specification: RISE index, UNGA affinity IV with five years moving averages. SSA: Sub-Saharan Africa. EAP: East Asia & the Pacific. ECA: Europe & Central Asia. LAC: Latin America & Caribbean. MENA: the Middle East and North Africa. SAS: South Asia._ While the aggregate results suggest that renewable energy policies had a modest significant effect on the energy mix, they fall short of explaining the effect of each of these policies separately and over time. Figure 1 illustrates the results of our main research question, which shows distributional boxplots of the effects of each energy policy package aggregated across all regions.

In addition to the default lag of 3 years, we consider the possibility that the effect of each energy policy package changes with time and analyze lags of 5 and 7 years. When breaking down by energy policy package, it becomes important that coefficients related to different energy mix outcomes for the same country cluster together (e.g., Kenya, Eritrea, and Angola in Appendix Figure A.6) due to the inherent similarity of the outcome variables.

23
INTROOUCTuON LX)4N

|Infant<br>moetality,<br>196-90|**Age-sex**structure,<br>1985|Most recent<br>contraceptive<br>prevasence|Country,<br>**economy,**or<br>territory|
|---|---|---|---|
|UNPVSR_10/68_(official<br>**est.)**|<br>U.N. 1988 revision|--|German<br>Dam. Rep.|
|Ewuo.tat1989|Eurostat<br>1989|U.N. 1988|Germny, Federal<br>Rep. of|
|U.N. 1990 revision|Blsed on 1970 census|DHS 1988|<br>Ghana|
||||Gibraltar|
|UNPVSR 10/89 (official<br>*t.)|Eurostat 1989|-|Greece|
||||**Greenland**|
|Bank**st.**|Bank est.|U.N. 1990|Grenada|
|U.N. 1990revision|U.N. 1988 revision|U.N. 1990|Guadeloupe|
|US40C1966|Bank**est.**|-|Guam|
|U.N. 1990 revision<br>|U.N. 1988 revision<br>|DHS 1997<br>|Guatemala<br>|
|U.N. 1990 revision|U.N. 1988 revision|IPPF|Guinea|
|Bankst.|Bankest.|IPPF|Guinea-Bissau|
|U.N.1990 revision|U.N. 1988 revision|Pop. Ref. Bureau 1990|Guyan|
|U.N. 196revision|U.N. 1988 revision|Ross et**at.**1988|Naiti|
||||NoLySee|
|U.N. 1990 revIsion<br>|U.N. 19<br>revision<br>|Ross**atat.**1988<br>|Honduras|
|UNPVSR10/89 (official<br>ast.)|U.N. 1988 revision|Ross at***L.**1988|Hong Kong|
|<br>UNYWSI 10/88 (offfcial<br>ast.)|1980 census|CPS 1986|Nungary|
|UNPVSR 10/89(official<br>st.)|U.N. 1988 revision||Iceland|
|U.N. 1990revision|Bamed on 1981 census|Ross at aL. 1988|India|
|Basedon ONS 1987|OfficiaL<br>**est.,**<br>Biro Pus.t|DNS 1987|Indonesia|
||Statistik<br>1988|||
|lank st.|Bank est.|Ross**at**aL. 1988|Iran, Islamic<br>|
||||Rep. of|
|U.N. 1990 revision|U.N. 1988 revision|Ross et at. 1988|<br>Iraq|
|Eurostat<br>19j19|Eurostat<br>1989|Pop. Ref. Bureau 1990|Ireland|
||||Isle of Man|
|UNPVSR 10/89<br>(official<br>est.)|U.N. 1988 revision|--|Israel|
|Eurostat<br>1989<br>|Eurostat<br>1989|U.N. 1988|Italy|
|**oased**on UIPVSR1/88|U.N. 1988 revision|CPS 1989|Jamaica|
|U.N. 1990revision|U.N. 1988 revision|CPS 1986|Japan|
||||Johnston<br>Island|
|U.N. 1990 revision|U.N. 1988 revision|Ross**at**aL. 1988|Jordan (East<br>lank)|
|U.N. 1990 revision|U.N. 1988 revision|--|KBanukheDe.|
|U.N. 1990 revision|U.N. 1988 revision|DNS 1989|Kenya|
|<br>USBOC 1988|<br>Bankest.|-|Kiribati|
|U.N. 1990 revision|U.N. 1988 revision|--|Korea,People's|
||||Dem. Rep. of|
|U.N. 990 revision|U.N. 1988 revision|CPS 1985|<br>Korea, Rep. Of|
|Based on official<br>1986**est.**|U.N.1988revision|--|**Kuwait**|
|U.N. 1990revision|U.N.1988 r.<br>vision|--|Lao**People's**|
||||Dem.Rep.|
Section 3 I discuss the possible consequences of such errors and how they affect interpretation of the estimates.

# **3 Results: The Effect of Payouts on Take-up**

In this section I address the central question: is receiving an insurance payout correlated with repurchasing insurance the following year? To do this I examine BASIX’s customers in 2005 and 2006, and regress repurchasing on payout reception and a year dummy. The basic econometric specification is as follows: Here _yi,t_ +1represents whether subject _i_ purchases insurance at time _t_ + 1, and _Pi,t_ is a dummy variable that takes a value of 1 if person _i_ receives an insurance payout at time _t_ .<sup>8</sup> The sample is all buyers of insurance from 2005 and 2006, and I include a dummy ( _D_ 2006) that takes a value of 1 for purchasers in the year 2006 to control for time effects. Also, I only include purchasers who have weather insurance contracts available in their area in the following year.<sup>9</sup> These results are presented in Table 4, and Column 1 reports the baseline OLS results. It shows that receiving a payout is associated with a 9% increased chance of repurchasing insurance the following year, which means that those who receive an insurance payout are more than twice as likely to purchase insurance the following year as those who did not receive a payout. The dummy for 2006 is negative and significant, which is expected due to the minimum sum insured rules imposed in 2007.

One point of concern with these results is that there are many cases where there are multiple purchasers of insurance in a certain village in one year, and then zero in the next year. While this could be the result of people simply being unsatisfied with insurance, the large amount of villages that suddenly drop to zero purchasers is suspicious, especially since the BASIX data does not contain information about whether marketing activities took place in a given village in a given year. For all the villages that had purchasers in one year and then none in the next year, it is quite likely that no BASIX representative visited the village, and therefore the customer did not really have a chance to purchase the insurance. If this was the case it would make sense to exclude these villages from the

> 8It makes sense to assume that the error _εt,i_ is correlated for the same person across time, as well as across people in a given year. Ideally, we would like to include individual fixed effects to account for individual heterogeneity. However, in order to exploit this variation we would need to look at customers who purchased insurance in both 2005 and 2006, and received payouts in only one of those years. Unfortunately, due to the very low repurchase rate, this results in very little variation and is therefore an unsuitable method of analysis.

> 9Basix’s insurance coverage area varied somewhat from year to year. Results do not change significantly if all areas are included in the regression.

6
# **Table 5C. Impacts of IPAs on Bilateral Patenting by Industry Cluster: CORETRIPS3 IPAs**

|VARIABLES|(1)<br>Low-IP|(2)<br>AI|(3)<br>BIO|(4)<br>CHEM|(5)<br>ICT|(6)<br>MED|(7)<br>PT|(8)<br>Other|
|---|---|---|---|---|---|---|---|---|
|Intra (member to member)|0.187***|0.254|0.315***|0.269***|0.438***|0.388***|0.095|0.262***|
||(0.060)|(0.157)|(0.077)|(0.082)|(0.162)|(0.114)|(0.099)|(0.074)|
|Extra1(between HI member and|0.092|0.020|0.152|0.116|0.204**|0.120|-0.021|0.021|
|HI non-member)|(0.116)|(0.086)|(0.095)|(0.081)|(0.087)|(0.127)|(0.119)|(0.096)|
|Extra2(NHI destination in IPA from|0.266**|0.259**|0.382***|0.322**|0.489***|0.545***|0.301*|0.420***|
|all non-members)|(0.122)|(0.124)|(0.125)|(0.145)|(0.121)|(0.148)|(0.175)|(0.140)|
|Extra3 (NHI source in IPA to|-0.305|-0.266|-0.402*|-0.427|-0.445|-0.852***|-0.399|-0.548*|
|all non-members)|(0.297)|(0.290)|(0.222)|(0.403)|(0.344)|(0.229)|(0.444)|(0.286)|
|Any IPR provision not meeting|0.275**|0.309|0.128|0.097|0.361**|0.192|0.313*|0.330**|
|WTO LE2|(0.136)|(0.198)|(0.096)|(0.098)|(0.170)|(0.148)|(0.162)|(0.161)|
|Any bilateral investment agreement|0.065|0.034|-0.042|-0.024|-0.003|0.052|0.098|0.032|
||(0.058)|(0.119)|(0.052)|(0.063)|(0.118)|(0.062)|(0.072)|(0.055)|
|Observations|664,665|664,665|664,665|664,665|664,665|664,665|664,665|664,665|
|Origin-sector-year FEs|Y|Y|Y|Y|Y|Y|Y|Y|
|Dest-sector-year FEs|Y|Y|Y|Y|Y|Y|Y|Y|
|Pair-sector FEs|Y|Y|Y|Y|Y|Y|Y|Y|
|Pseudo R2|0.994|0.994|0.994|0.994|0.994|0.994|0.994|0.994|

Notes: see Table 4.

26
updated `rnormal()` RNG, in Columns (9) – (12). For each Stata RNG, four methods are shown: draws are made after a single initialization; re-initializing after every 20 draws; reinitializing after every draw; or skipping 10,000 draws between each draw included in the stream.

For MATLAB, the three underlying generators tested are the older `’v4’` and `’v5’` RNGs, in Columns (13) through (16), and the newer `’default’` RNG in Columns (17) and (18). Only two methods are shown for each MATLAB RNG: draws are made after a single initialization; or re-initialization to an incremented seed occurs after every draw.

Columns (1), (5), (9), (13), (15), and (17) show that all six RNGs pass these basic tests when initialized only once. Comparing Columns (2), (6), and (10), that the original Stata `rnormal()` RNG behaved particularly poorly when a small number of draws was made before re-initialization: sequences of draws made immediately after re-initializing to a new seed do not behave as if they are independent random sequences. This problem has clearly been resolved in the new version of `rnormal()` , shown in Column (10).

Columns (3), (7), (11), (14), (16), and (18) show that of all six RNGs under consideration, only MATLAB’s current default RNG behaves well when re-initialized after every draw. (Note that I am not recommending re-initializing after every draw; I include this as a crude test of the relative independence of separate streams, each initialized with a different seed.) As an alternative to re-initialization, though, one might discard draws in order to advance down a single stream of random numbers, thereby effectively splitting the stream into multiple shorter ones. This possibility is explored in Columns (4), (8), and (12). By the standards set by these weak tests, this approach appears to have all the same desirable statistical properties of the single-initialization approach.

The “advancing” approach presents computational drawbacks, however. As mentioned above, Stata provides no facility for quickly advancing the state of the RNG without actually making random draws, so this process could take minutes, hours, or longer, depending on how far ahead one wants to advance.

8
# **ANNEX 1: Lessons Learned for Syria, Algeria and Libya**

**Syria, Algeria and Libya have relied less on debt markets to fund their debt.** This is partially due to revenues from oil exports. However, these countries may consider developing domestic debt markets in the future to support monetary policy and financial sector development. The case for Syria is more urgent given its higher macroeconomic vulnerability with a fiscal deficit of 3.2 percent, public debt at 40.3 percent of GDP, a current account deficit of 4 percent, and lower oil reserves that it peers.<sup>53</sup>

**Table 3: Main economic indicators for Algeria, Libya and Syria**

||**GDP**|**Deficit/GDP**|**Debt/GDP**|**Ext**|**CA/GDP**|
|---|---|---|---|---|---|
|Algeria|159.7|+11.4|7.2|2.7|+23.2|
|Libya|100.1|+36.7|0.0|0.0|39.2|
|Syria|54.8|-3.2|30.8|10.5|-4.0|

Source: IMF Regional Economic Outlook, Middle East and Central Asia, May 2009

**The three countries present similar structural constraints as their peers in the region: excess liquidity, lack of money markets, and concentrated banking sector dominated by State-owned banks, low loan-to-deposit ratios but high NPLs.** With this profile, banks could be expected to invest heavily in public debt. Therefore, there would be a case for similar policies in efficient liquidity management for money market development and measures to limit the concentration of demand.

## **The fact that no debt has been issued is a major advantage in relation to their peers in the regions for three reasons:**

- There is no legacy of illiquid debt in the portfolios of banks to deal with.

- The lack of debt burden would enable these countries to start building the yield curve with short term issues without refinancing risk.

- They can deal with concentration of demand by putting a cap on maximum auction allocations because they don ́t have funding constraints.

**A key challenge is to commit to a truly competitive primary market and not rely on captive demand.** Prices in the auction would be the only source of market prices available for some time until secondary markets develop. Therefore, transparent price discovery in the primary market would preempt many of the current problems of opaquely valued debt portfolios in peer countries.

> 53 Syria held its first government securities auction for Treasury bills in December 2010 and for Treasury bonds in January 2011.

49
G20 High-Level Principles for Digital Financial Inclusion. Among others, these Principles focus on enacting an enabling regulatory framework for digital financial inclusion, expanding the financial and information and communications technology infrastructure, and managing potential risks imposed by innovative digital financial service initiatives.

Despite such commitments, financial systems still fall short in many developing countries. To date, around 2 billion adults in the world do not have access to basic financial services such as savings and checking accounts. Except for high-income countries and the Europe and Central Asia region, in most of the regions and income groups, on average half or more of the population are excluded from the formal financial system (Figure 1). Common obstacles include physical distance from providers, lack of trust and lack of the necessary documentation. Businesses also face constraints. More than 200 million micro, small and medium-size enterprises in developing economies are either financially unserved or underserved due to lack of collateral, limited or no credit history and their informal status (IFC, 2013).

**Figure 1.** Share of adults with an account at a financial institution

<!-- Start of picture text --> 83.5<br>55.0<br>62.8<br>49.5 50.8<br>32.8<br>38.2<br>18.4<br>21.7<br>19.1<br>High income Upper Lower Low income<br>middle middle<br>EAP ECA LAC MENA SA SSA income income<br><!-- End of picture text --> Source: Findex 2014 Such unsatisfactory figures have motivated researchers to better understand the mechanisms to achieve better financial inclusion. Sarma and Pais (2011) find that financial inclusion is positively related to socioeconomic variables such as income, employment, lower inequality, and literacy and to physical infrastructure such as electronic connectivity and road networks. They also find that the proportion of nonperforming loans and the capital adequacy ratio are negatively associated with financial inclusion. Government ownership of banks is not significantly associated with financial inclusion, while foreign ownership is found to be negatively associated. Beck, Demirguc-Kunt and Peria (2007) investigate the role of institutions and find that a positive institutional environment is more conducive to financial outreach and 3
# **Appendix 4: Robustness Checks**

# Attrition Tests

_Table A4.1. Attrition in Survey Data_

||Has Baseline Survey<br>(1)|Has Endline Survey<br>(2)|Has Both Surveys<br>(3)|
|---|---|---|---|
|Pre-Treatment GPA|0.017***|0.010***|0.016***|
||_(0.003)_|_(0.004)_|_(0.004)_|
|Treatment 1 “Student Self-Learning”|-0.001|-0.149***|-0.120**|
||_(0.034)_|_(0.045)_|_(0.047)_|
|Treatment 2 “Teacher Delivery”|-0.010|-0.063*|-0.068*|
||_(0.029)_|_(0.036)_|_(0.039)_|
|Age 12|0.002|0.018|0.026|
||_(0.013)_|_(0.017)_|_(0.019)_|
|Age 13|-0.017|-0.033|-0.020|
||_(0.018)_|_(0.023)_|_(0.025)_|
|Age 14|-0.052*|-0.059|-0.044|
||_(0.028)_|_(0.037)_|_(0.038)_|
|Female|0.004|0.005|0.009*|
||_(0.004)_|_(0.005)_|_(0.005)_|
|Sixth Grader|-0.022|-0.071***|-0.067***|
||_(0.015)_|_(0.018)_|_(0.019)_|
|Albanian|-0.118***|-0.291***|-0.289***|
||_(0.042)_|_(0.059)_|_(0.060)_|
|Roma|-0.141***|-0.242***|-0.271***|
||_(0.052)_|_(0.062)_|_(0.062)_|
|Other|-0.056*|-0.090**|-0.079**|
||_(0.032)_|_(0.036)_|_(0.037)_|
|N|33,454|33,454|33,454|
|N Control|12,426|12,426|12,426|
|N Treatment 1|10,995|10,995|10,995|
|N Treatment 2|10,033|10,033|10,033|
|_R2_|0.082|0.146|0.140|
|* p <|0.05, ** p < 0.01, *** p|< 0.001||

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Own survey data, administrative data, school year 2015/2016, own calculations.

40
report concluded there was no longer anything to eat in the south, and peasants were already eating the leaves and roots of plants.

The second report was written by another grass-roots organization, Conseil de Concertation des Organisations d’Appui aux Initiatives de Base (CCOAIB), but the authors were unable to get a copy of this report (Twizeyimana and Uwimana, 1989). The third report describes a local agricultural survey of 300 households conducted by the Ministry of Agriculture in several communes in Gikongoro (Gascon, 1992). The report stated that 25 percent of the surveyed households were indigent and female headed households suffered the most. Two of the reports use the term famine to describe the situation in southern Rwanda, and the third speaks of starvation. Factors mentioned that contributed to the crop failure were rainfall variation as well as crop disease.

_Kinyamateka_ , the most prominent independent newspaper in Rwanda, also published information on the food crisis in 1989, making the famine known to the Rwandan public. André Sibomana, editor of the newspaper in 1989, expressed it as follows: _“In 1989, a terrible famine struck the south of the country. There was a natural explanation for this phenomenon, but the authorities did nothing to improve the situation. Worse still, I had evidence that part of the government’s assistance which was intended for the population at risk had been diverted. It was a scandal. I decided to publish this information. We were threatened and we were called liars, until I published photographs which were overwhelming. This had an immediate effect. Readers wrote in to express their satisfaction: at last the truth was being told.”_ (Sibomana, 1999) Based on the film, the non-governmental organizations’ reports, and the newspaper articles, we create a variable indicating which regions in Rwanda experienced crop failure during this time period. This crop failure area included all of the communes of Gikongoro province and the communes of Butare province bordering Gikongoro province (see Figure 1).

6