rafmacalaba/datause-encoder-data huggingface dataset:
# References

Alessandria, G., J. P. Kaboski, and V. Midrigan. 2010. “The Great Trade Collapse of 2008–09: An Inventory Adjustment?” IMF Economic Review 58(2): 254-294.

Allen, R. E. 2009. Financial Crises and Recession in the Global Economy. Third Edition. Northampton: Edward Elgar.

Amiti, M., and D. E. Weinstein. 2011. “Exports and Financial Shocks.” Quarterly Journal of Economics 126(4): 1841-1877.

Arteta, C., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Coming U.S. Interest Rate Tightening Cycle: Smooth Sailing or Stormy Waters?” Policy Research Note 15/02, World Bank, Washington, DC.

Bacchetta, P., and E. van Wincoop. 2016. “The Great Recession: A Self-Fulfilling Global Panic.” American Economic Journal: Macroeconomics 8(4): 177-194.

Baffes, J., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Great Plunge in Oil Prices: Causes, Consequences, and Policy Responses.” Policy Research Note 15/01, World Bank, Washington, DC.

Baker, S. R., and N. Bloom. 2013. “Does Uncertainty Reduce Growth? Using Disasters as Natural Experiments.” NBER Working Paper 19475, National Bureau of Economic Research, Cambridge.

Balke, N. S., and M. A. Wynne. 1995. “Recessions and Recoveries in Real Business Cycle Models.” Economic Inquiry 33(4): 640-663.

Barsky, R. B., and L. Kilian. 2004. “Oil and the Macroeconomy since the 1970s.” Journal of Economic Perspectives 18(4): 115-134.

Bec, F., O. Bouabdallah, and L. Ferrara. 2015. “Comparing the Shape of Recoveries: France, the UK and the US.” Economic Modelling 44: 327-334.

Bems, R., R. C. Johnson, and K.-M. Yi. 2010. “Demand Spillovers and the Collapse of Trade in the Global Recession.” IMF Economic Review 58(2): 295-326.

Bernanke, B. S., and C. S. Lown. 1991. “The Credit Crunch.” Brookings Papers on Economic Activity 1991(2): 205-247.

Bry, G., and C. Boschan. 1971. Cyclical Analysis of Time Series: Selected Procedures and Computer Programs. New York: National Bureau of Economic Research.

Burns, A. F., and W. C. Mitchell. 1946. Measuring Business Cycles. New York: National Bureau of Economic Research.

Caldara, D., M. Iacoviello, P. Molligo, A. Prestipino, and A. Raffo. 2019. “Does Trade Policy Uncertainty Affect Global Economic Activity?” FEDS Notes, September 4, Board of Governors of the Federal Reserve System, Washington, DC.

Calderón, C., and J. R. Fuentes. 2014. “Have Business Cycles Changed over the Last Two Decades? An Empirical Investigation.” Journal of Development Economics 109: 98-123.

Callen, T. 2007. “PPP versus the Market: Which Weight Matters?” Finance & Development 44(1): 50-51.

Camacho, M., and J. Martinez-Martin. 2015. “Monitoring the World Business Cycle.” Economic Modelling 51: 617-625.

24
all else constant, increases time to work outside the house. There is a negative association with having a working spouse and a positive relation with proxies of household income stability. The former likely captures an income effect, as it is robust to controlling for the role of social norms. Access and use of labor-saving technology are positively associated to FLFP.

The paper is organized as follows. Section 2 describes the data used in the paper and shows descriptive statistics. Section 3 presents the empirical model, while Sections 4 and 5 set out our main findings. Section 6 concludes.

# **2. Data and Descriptive Statistics**

We combine different sources of data. For the decomposition of the change in FLFP between 2002 and 2018, we exploit data from the 2002 and 2018 National Censuses. The censuses were collected by INE and are representative at the municipality level. This allows the measurement of withincountry variation in FLFP, and several of its drivers at the municipal level.<sup>6</sup> FLFP is defined as the number of women ages 25 to 49 who report working or seeking work during the previous week divided by the total number of women in the same age range. We do not consider women of younger ages to avoid selection problems due to educational decisions. We consider all women below 50 years old as we are interested in analyzing the association between FLFP and motherhood, among other factors.<sup>7</sup> We restrict the sample to woman living in households where they or their partner is the household head.<sup>8</sup> We align with ILO definition and include work across all sectors of activity (primary, construction, manufacturing, white collar services and other services), and the informal economy. We explore information on individual characteristics (age, level of education, race, marital status), household structure (number of children younger than 13 years old, number of household members 12 years old or more, presence of mother or mother-inlaw), whether female is the household head, whether the spouse works, type of employment of other household members, dwelling characteristics, location, and sectoral structure of municipal employment.

Next, we assess the importance of a wide set of drivers of FLFP within Guatemala in 2018, relying on the variables defined above and adding characteristics that are not available in the 2002 census. These include the use of internet, availability of home appliances (washing machine), remittances,

> 6 In Section I of the supplementary materials, we present a brief conceptual framework and a review of the literature on the factors driving FLFP.

> 7 We follow Berlinski and Galiani (2007) and others and use 49 years old as an upper- bound. They use this age threshold to analyze the relationship between FLFP or other labor market outcomes and having children of young age and public policies such as the construction of preprimary schools. 8 The Census data can identify mothers and children only in households where women are household heads or when their spouses are.

7
**Table 1: Stages of Export Diversification**

<u>(Country FEs estimation; data from 1992-2003)</u>

||Herfindahl Index|
|---|---|
|GDPpercapita|-.0000117|
||(2.41)**|
|GDPpercapita<sup>2</sup>|2.60e-10|
||(2.46)**|
|Diversification/Specialization Transition<br>Point|$22,500|
|Observations|1140|
|NumberofCountries|130|

Absolute value of t statistics in parentheses ** significant at 5%

**<u>Table 2: Export Discoveries, Patents and the Level of Development</u>**

||Export Discoveries: counts|Patents:<br>counts|
|---|---|---|
|ln(GDPpercapita)|8.667|-16.237|
||(6.53)***|(2.96)***|
|ln(GDPpercapita)<sup>2</sup>|-0.514|1.059|
||(6.67)***|(3.43)***|
|Constant|-32.289|65.872|
||(5.71)***|(2.74)***|
|Observations|73|68|

Absolute value of z statistics in parentheses *** significant at 1%

**Table 3: Export Discoveries and GDP per Capita by Leamer (1984) Commodity Groups**

|**Leamer**|**Number of**|**Discovery Curve**<br>**Maximum Point (GDP**|
|---|---|---|
|**Commodity Group**|**Discoveries**<sup>**N1**</sup>|**per capita)**|
|Petroleum|1.17|2052**|
|Raw Materials|0.85|4901***|
|Forest Products|0.73|4416***|
|Tropical Agriculture|0.49|4486***|
|Animal Products|0.73|4109***|
|Cereals, etc.|0.72|4055***|
|Labor Intensitve|0.38|3626***|
|Capital Intensive|0.56|4546***|
|Machinery|0.61|4578***|
|Chemical|0.78|6838***|

N1: normalized by the number of lines in the HS 1989/1992 nomenclature composing that category. ** significant at 5%; *** at 1%. Country fixed effects are included, but not reported. Source: Author’s Calculations.

26
their accounts to careful scrutiny.<sup>3</sup> The institutions thus represent some of the best hopes for achieving poverty reduction with profit (or at least without ongoing subsidy). Still, the average share of funding (total liabilities plus total equity) made up of subsidized resources exceeds 20 percent in this sample.

Empirical progress on understanding the trade-offs in microfinance has been held back by the lack of variation in prices and program elements necessary for identification of key parameters. Most financial institutions offer their clients a uniform set of products and they seldom change the product mix, price, or design—or institutions change policies in ways that make it difficult for researchers to disentangle patterns of product changes versus other contemporaneous changes.<sup>4</sup> The cross-country data here, however, provide substantial variation in contractual types, prices, institutional sizes and locations, and target markets. The variation provides a means to describe the nature and trade-offs of lending relationships.

The data set allows us to examine several important claims for the first time in a large comparative survey. Does raising interest rates exacerbate agency problems as detected by lower loan repayment rates and less profitability? Is there evidence of a trade-off between the depth of outreach to the poor and the pursuit of profitability? Has “mission drift” occurred—i.e., have microbanks moved away from serving their poorer clients in pursuit of commercial viability?

> 3 The data were collected as part of the _MicroBanking Bulletin_ project.  Summary statistics on the institutions have been made available in the _Bulletin_ .  This paper is based on the underlying data set, access to which has kindly been provided (with confidentiality safeguards) to the authors.  The _Bulletin_ is available at www.mixmarket.org.

> 4 New work using field experiments (e.g., Karlan and Zinman, 2005) or natural experiments (e.g., Dehejia, Montgomery, and Morduch, 2005) shows promise in ways to either exploit the variation that exists or to create variation as part of a research program.

3
# **Table 8 – Baseline Descriptive Results, 2016**

|_Indicator_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|_All_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Average Household Monthly per capita Expenditure (Rupia)|233,370|310,272<br>|374,363<br>|449,352|532,701|631,259|764,097|947,436|1,286,670|3,952,778|_1,031,795_|
|Proportion of exp. on all tobacco productsa|7.5|8.8|9.2|9.5|9.5|9.3|8.5|7.6|6.2|3.3|_6.8_|
|Proportion of exp. white cigarettes|0.3|0.4|0.5|0.6|0.6|0.7|0.7|0.8|0.8|0.6|_0.6_|
|Proportion of exp. clove cigarettes|7.2|8.3|8.7|8.9|8.9|8.6|7.8|6.8|5.3|2.7|_6.2_|
|Households that smoke any tobacco product (%)|57.1|64.0|66.6|69.3|69.5|68.5|66.9|65.0|60.5|51.9|_63.9_|
|Households that smoke white cigarettes (%)|2.3|3.3|3.7|4.6|4.9|5.5|5.9|7.1|8.6|9.5|_5.6_|
|Households that smoke clove cigarettes (%)|55.3|61.8|64.0|66.1|65.9|64.5|62.4|59.6|53.5|43.4|_59.7_|
|Woman-headed households (%)|16.5|15.0|14.6|13.8|14.2|14.2|15.0|14.6|15.2|17.1|_15.0_|
|Age, household head|50.3|48.9|47.9|47.8|47.7|47.4|47.6|47.1|46.4|45.4|_47.7_|
|Share of kids ages 0-5 in household (%)|12.0|10.7|10.7|10.1|9.6|8.8|8.2|7.8|7.0|5.3|_9.0_|
|Share of individuals ages 6-17 in household (%)|22.4|22.2|22.1|20.9|20.2|19.7|18.1|17.1|15.3|12.2|_19.0_|
|Share of adults ages 18-49 in household (%)|39.2|42.4|44.5|45.9|47.1|48.5|49.4|51.1|54.4|58.0|_48.1_|
|Share of individuals ages 50+ in household(%)|26.4|24.7|22.7|23.1|23.1|23.0|24.3|24.0|23.3|24.5|_23.9_|

_Source:_ Estimates based on Indonesia National Socioeconomic Survey 2016. _Note:_ Deciles were created using per capita household expenditure.

a. Proportional to total budget per household in each decile; households that smoke = 1 if a household member reports spending on tobacco products.

12
**Figure A8 – Respondents’ agreement with the statement “Most of my family and friends will get vaccinated”, disaggregated by willingness to get the COVID-19 vaccine**

<!-- Start of picture text --> Most of my family and friends will get vaccinated<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Agree 60.4 Agree 4.2 Agree 5.9<br>Neither 37.0 Neither 54.4 Neither 77.6<br>Disagree 2.6 Disagree 41.4 Disagree 16.6<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 10 in Appendix B.

<!-- Start of picture text --> Most Papua New Guineans will get vaccinated<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Agree 49.5 Agree 3.9 Agree 3.0<br>Neither 47.2 Neither 62.0 Neither 80.4<br>Disagree 3.3 Disagree 34.1 Disagree 16.6<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 11 in Appendix B.

34
statistical significance only narrowly. In sum, we find strong evidence of assistance effects in global value chains for FDI spillovers.

**Table 15: The Effect of Assistance on the Probability of Starting to Export due to Relationship with Foreign Firm, Part 2, Probit**

|Dependent variabl|e:_exp_starti_|_sc_||||||||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_auditisc_|0.8834*<br>(0.079)|0.9558*<br>(0.061)|0.8693*<br>(0.075)|0.7906*<br>(0.099)|0.8267*<br>(0.087)|0.8267*<br>(0.087)|0.8751*<br>(0.079)|0.7924*<br>(0.099)|1.0472*<br>(0.053)|
|_imprisc_|0.0178<br>(0.973)|0.0100<br>(0.985)|-0.1796<br>(0.740)|0.0824<br>(0.875)|-0.1168<br>(0.826)|-0.1168<br>(0.826)|-0.1327<br>(0.802)|-0.1387<br>(0.794)|-0.1363<br>(0.798)|
|_devisc_|<br>0.8849*|<br>0.8012|<br>0.6690|<br>0.8825*|<br>0.9440*|<br>0.9440*|<br>0.8656*|<br>0.8684*|<br>0.6131|
||(0.081)|(0.134)|(0.236)|(0.091)|(0.065)|(0.065)|(0.089)|(0.099)|(0.258)|
|_licenseisc_|0.6547|0.7734|0.7828|0.6869|0.6901|0.6901|0.7473|0.5457|0.7957|
||(0.254)|(0.141)|(0.179)|(0.236)|(0.228)|(0.228)|(0.185)|(0.330)|(0.136)|
|_assist_maintisc_<br>_assist_licenseisc_<br>_assist_orgaisc_|0.6738<br>(0.260)|1.4250**<br>(0.016)|1.0601*<br>(0.060)|||||||
|_assist_qualisc_||||1.0160**<br>(0.041)||||||
|_assist_inventisc_|||||0.6007<br>(0.387)|||||
|_assist_auditisc_||||||0.6007<br>(0.387)||||
|_assist_stratisc_<br>_assist_expisc_<br>_assist_hseisc_|||||||0.6723<br>(0.145)|1.2943**<br>(0.027)|1.4993**<br>(0.014)|
|_constantisc_|-7.8728***<br>(0.000)|-7.8537***<br>(0.000)|-7.4454***<br>(0.000)|-7.8423***<br>(0.000)|-7.7406***<br>(0.000)|-7.7406***<br>(0.000)|-7.6948***<br>(0.000)|-7.6089***<br>(0.000)|<br>-7.6106***<br>(0.000)|
|Country-sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Adj. R2<sup>1)</sup>|-0.204|-0.150|0.183|-0.183|-0.209|-0.209|-0.199|-0.179|-0.139|
|Observations|55|55|55|55|55|55|55|55|55|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses).<sup>1)</sup> McFadden’s adjusted pseudo R2. Note: All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity.

30
- 17 -

performance in the case of export of agricultural products and raw materials which on the whole did not show any tendency towards "underpricing." This finding is not surprising since primary commodities are relatively few in numbers and they are much more homogeneous than manufactured products. As a result, primary products are highly dependent on price competitiveness. The effect of NTBs on manufactured exports is likely to be more subdued in view of the important role of nonprice factors in determining the competitiveness of exports. In contrast to the postulates of the theory, however, price '.ncreases due to product upgrading is not evident from this analysis. On the contrary, NTBs were correlated with lower export prices of the CPEs.

# X. EFFECT OF CPEs' PRICE POLICY

Another factor which could have affected our results might have been the commercial policy of the CPEs to increase their shares in the EEC. The observed "underpricing" of manufactured exports might have been the result of a deliberate policy of socialist countries to penetrate Western markets. The market shares of socialist countries have been seriously eroded in the past in the face of rising competition of not only developed countries but also developing countries. The price responsiveness to declining market shares would imply flexible and generally export-oriented strategy, which are likely to be more successful in maintaining the country's market shares in comparison to inward and generally less-flexible strategies (Bark and de Melo, 1987).

We have, therefore, carried out a detailed analysis of export prices of socialist countries in different markets of the EEC region and tried to ascertain whether market shares exhibited any influence on the price performance of socialist countries. We shall hypothesize that the loss of market shares was most damaging in the case of commodities, which exhibit the greatest quality deficiencies. Pari passu, we shall hypothe3ize that socialist countries were able at to retain the largest market shares in the case of commodities which suffered least from quality deficiencies. The results are summarized in Table 6. The table shows the average relative prices in the two years for exports with different market shares. Only exports which were not subject to discriminatory NTBs were included in the sample.
Mejía, D. (2008); The War on Illegal Drugs. Mimeo, Universidad de los Andes.

Mejía, D. and P. Restrepo (2008); The War on Illegal Drug Production and Trafficking: An Economic Evaluation of Plan Colombia. Mimeo, Universidad de los Andes.

National Institute of Drug Abuse (2006); NIDA Info Facts: Cocaine.

Available at: http://www.nida.nih.gov/infofacts/cocaine.html OEA (2005); Estudio de los efectos del Programa de Erradicación de Cultivos Ilícitos mediante la aspersión aérea con el herbicida Glifosato (PECIG) y de los cultivos ilícitos en la salud humana y en el medio ambiente. Washington, DC, March.

ONDCP (2002); Estimation of Cocaine Availability 1996-2000, Washington, DC, March.

ONDCP (2004); National Drug Control Strategy, Washington, DC, March. Available at: <u>http://www.whitehousedrugpolicy.gov/publications/policy/ndcs04/2004n dcs.pdf</u>

---- (2005); 2004 Coca and Opium Poppy Estimates for Colombia and the Andes, Press Release, March 25<sup>th</sup> .

Available at: <u>http://www.whitehousedrugpolicy.gov/news/press05/032505.html</u> Rabassa, Angel and Peter Chalk (2001); Colombian Labyrinth. RAND.

47
profits. Women and men say that “firms like theirs” under report profits by very nearly the same amount (7850 from females vs. 8067 for males, p=0.55). We also have data on who was present at the interview for each round after the baseline survey. If unempowered women are deliberately under reporting gains following treatment, we should expect to find lower reported profits when the spouse is present at the time the interview is conducted. The last column of Table 5 does show some evidence that the presence of the spouse matters for the general level of reporting profits – males underreport profits by 433 rupees and females by 216 rupees when their spouse is present. As a percentage of average profits, this is approximately the same for both groups. More importantly, however, the effect is the same in treated and untreated households. When we interact the presence of the spouse with treatment, the interaction term is small and statistically insignificant.

# _5.3. Differences in Access to Capital_

One of the main reasons for the traditional emphasis of microfinance on women is a belief that women have less access to formal finance than men. In the baseline survey, firms were asked whether they had ever had a loan from banks, the government, microfinance, and different government programs. Only 1.8 percent have had a loan from a private bank, and 2.1 percent from a microfinance organization. 9.4 percent have had a loan from the government, 4.2 percent from the Sanasa Development Bank (an umbrella organization of small credit unions), 6.5 percent from the government’s Samurdhi program, and 0.5 percent from other government programs such as IDRP and REAP. Combined across all of these sources, we find no significant gender difference: 23.4 percent of females and 23.5 percent of males have ever had a loan from one of these formal sources (Table 6).

The characteristics of females who had ever received formal credit are similar in many respects to those of female microenterprise owners who had never received formal credit. In particular, they do not significantly differ in age, education, digit-span recall, household asset ownership, household size, or the number of wage workers in the household. It is thus not the case that credit is more likely to go to firm owners with characteristics that our model suggests would be associated with the highest returns.

20

-  -
||1|2|3|4|5|6|7|8|9|10|11|12|13|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||New<br>product<br>line|Upgraded<br>existing<br>product<br>line|New<br>Techno<br>logy|Discontinued<br>at least 1<br>product|Opened<br>a new<br>plant|Closed<br>at least<br>one<br>existing<br>plant|New JV<br>with<br>foreign<br>partner|New<br>Licensing<br>Agreement|Outsourced<br>a major<br>activity|Brought<br>in-house a<br>previously<br>out-<br>sourced<br>activity|CORE|INDEX8|INDEX2|
|Domestic||||||||||||||
|Corporation|0.069|0.217<sup>c</sup>|0.164|0.369<sup>a</sup>|-0.025|0.312|0.504<sup>b</sup>|-0.026|0.168|0.119|0.143|0.132|0.355<sup>a</sup>|
||[0.121]|[0.130]|[0.122]|[0.139]|[0.184]|[0.217]|[0.234]|[0.171]|[0.167]|[0.187]|[0.109]|[0.102]|[0.131]|
|F-Test||||||||||||||
|All Coefficients are<br>equal to each other|0.0205|0.3851|0.0001|0.1535|0.0119|0.7753|0.0143|0.0763|0.2344|0.1541|0.0001|0.0279|0.3287|
|a,b, andcrepresent signi|ficance at 1%|, 5% and 10|% respective|ly||||||||||

41
After aggregating EDGAR emissions for the grid cells within each FUA, we estimate a regression model that relates annual EDGAR emissions to annual local concentration anomalies for 5,991 FUAs. The result, report in Table 16, is highly significant and indicates that 1 ppb of the local concentration anomaly for an FUA is associated with 394 tons of CO2 emissions. The R<sup>2</sup> in this case is extremely small, because the “noise” (random variation) in the local anomaly measure is much larger than the “signal” associated with local emissions.

<!-- Start of picture text --> Table 16: Regression results: EDGAR emissions vs. local CO2 anomalies<br> (‘000 tons CO2)<br> Dependent variable: EDGAR FUA Emissions (‘000 tons)<br>Local Concentration Anomaly 0.394***<br> (ppb) (8.88)<br>Const 3023.2***<br>(37.42)<br>R 2 : 0.003<br>N 23,964 a<br>a 5,991 FUAs x 4 years<br>t statistics in parentheses<br>* p<0.05 ** p<0.01 *** p<0.001<br><!-- End of picture text --> Table 17 applies the results in Table 16 to the regression residuals from model (1), converting the residuals to deviations from expected emissions. After ranking urban areas by estimated CO2 emissions, Table 17 presents the top-40 positive and negative cases. For each urban area, we express the emissions deviation as a percent of emissions. Among the cities with emissions above expected levels (positive deviations), 3 of the cities with deviations greater than 10% are in China (Fuzhou, Jieyang, Xinxiang) and 3 are in the United States (Los Angeles, Seattle, Phoenix), along with Tehran, Islamic Republic of Iran. Two cities have emissions that are 10% or more below expected levels: Buenos Aires, Argentina and Indianapolis, United States.