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

Calvo, Guillermo, Alejandro Izquierdo and Luis-Fernando Mejia (2004), “On the Empirics of Sudden Stops: The Relevance of Balance-Sheet Effects,” NBER Working Paper no. 10520 (May).

Calvo, Guillermo, Alejandro Izquierdo and Ernesto Talvi (2003), “Sudden Stops, the Real Exchange Rate and Fiscal Sustainability: Argentina’s Lessons,” NBER Working Paper no. 9828 (July).

Cavallo, Eduardo and Jeffrey Frankel (2008), “Does Openness to Trade Make Countries More Vulnerable to Sudden Stops, or Less? Using Gravity to Establish Causality,” _Journal of International Money and Finance_ 27, pp.1430-1452.

Cavallo Eduardo, Andrew Powell, Mathieu Pedemonte and Pilar Tavella (2013), “A New Taxonomy of Sudden Stops: Which Sudden Stops Should Countries Be Most Concerned About?” Inter-American Development Bank Working Paper no.430.

Demirgüç-Kunt, Asli, and Enrica Detragiache (2000), “Financial Liberalization and Financial Fragility,” in Gerard Caprio, Patrick Honohan and Joseph Stiglitz (eds), _Financial Liberalization: How Far? How Fast?_ New York: Cambridge University Press, pp.96–122.

Edwards, Sebastian (2004a), “Financial Openness, Sudden Stops, and Current Account Reversals,” _American Economic Review_ 94(2):59-64.

Edwards, Sebastian (2004b), “Thirty Years of Current Account Imbalances, Current Account Reversals and Sudden Stops,” NBER Working Paper no. 10276 (February).

Efremidze, Levan, Sungsoo Kim, Ozan Sula and Thomas Willett (2015), “The Relationships Among Capital Flow Surges, Reversals and Sudden Stops,” unpublished manuscript, Claremont Institute for Economic Policy Studies (December).

Eichengreen, Barry, Andrew Rose and Charles Wyplosz (1995), “Exchange Market Mayhem: The Antecedents and Aftermath of Speculative Attacks,” _Economic Policy_ 21, pp.249-315.

Forbes, Kristin J. and Francis E. Warnock (2012), "Capital Flow Waves: Surges, Stops, Flight, and Retrenchment," _Journal of International Economics_ 88, pp.235-251 Gourinchas, Pierre-Olivier and Maurice Obstfeld (2012), “Stories of the Twentieth Century for the Twenty-First,” _American Economic Journal: Macroeconomics_ 4, pp.226–265 International Monetary Fund (2012), _The Liberalization and Management of Capital Flows-An Institutional View,_ Washington, D.C.: IMF.

International Monetary Fund (various years), _Annual Report on Exchange Arrangements and Exchange Restrictions_ , Washington, D.C.: IMF.

14
**Figure 1: Intermediate Goods Prices and National Income.** Green markers indicate economies in continental Africa. Prices are measured in US dollars at market exchange rates. The dashed line shows the fit of a local linear regression of each price on national income. The units at which prices are measured and sources are, for cement, one metric ton of ordinary Portland cement (World Bank, 2020b); for steel reinforcement bar, one metric ton of high-yield steel 16mm diameter reinforcement bars (World Bank, 2020b); for urea fertilizer, one metric ton in the month of March AfricaFertilizer.org (2019), and for internet, a month’s subscription to wired broadband service International Telecommunications Union (2019). Plots show data from 2017, except for the urea fertilizer plot, which shows data from 2014 and 2013, the last years the United States Department of Agriculture (2019) reports the farm price of urea in the United States for comparison.

<!-- Start of picture text --> NOR<br>LBR SLB JAM BMU<br>4 KIR BDI BRB KNA<br>1/161/41 BDI NERMOZMWIMDGSLERWATGOETHUGAAFGBFAMLIGINTZALSOCOMBENHTIVUTTLSSENZWETJKZMBCMRNPLKHMKENBGDPNGSDNPAKMMRG CIV MRTKGZDJINGAHH N AWINDPSETONUZBCPVICDMARAGO LASMBLP GSWZBOLSOZHLTMLVJAMNAMJORTUNIRMNGDMABTNIDNMDAECUDUKRGUYARM PE PRYLKAQZAFALB ZA LBYGEO T FJICBIHBLR C AZE B GABRMKDKMBRBOLHNGRDLBNRADOMBWATHAMDVSURMEXATGCRIMUBGRGNQARGCHLKAZRUSMYSTTHRVROU S TURGOMNLVHPOLSVKYCP ROU P A ESTLTU A PRICBHSVNNRTNCYPABWCZEEIJ S MLTNZLITA SF PSGBRRSAUBHRCANAUSPFINRANKWTSWEBELDEUAUTDNKNLDSM ISL HKGUSABRNNORACHECYM R IRLEQATSGPLUXMAC 21 CAFCOD LBRNERMWIMDGTCDSLEGNBRWATGOGMBUGABFAMLIGINTZLSOCBENHTI A SENZWEOMZCMRCIVNPLMKHMSTPBGDCOGSDNBPAKMMRMRT DJ NGAHNDIINDPSECPVVNMMARLAOPHLSWZBOLNAMJOIRQDMABTNIDNECUDZAGUY R VCTPPRYLKAZAFEFJCHNBGABLCARGRD R DOM I BWAMASURATG DVMUS BGRGNQURYARGRUSMYSSYCOMNPAN PRT BHSSVNABWGBRSAUBHRFINKWT B DEUNLDEHKGUSA BRL CYMN QATSGP<br>1/64 VNM 1/2 MNG THA TTO ARE<br>1/64 1/32 1/16 1/8 1/4 1/2 1 2 1/64 1/32 1/16 1/8 1/4 1/2 1 2<br>PPP GDP per capita (USA = 1) PPP GDP per capita (USA = 1)<br>2 2<br>GUY ABWBEL<br>TGO BEN<br>TUN UGA<br>GMB ZWE NOR<br>1 CODNERMWI BFAMLIGINLSOSENCMRCIVCOGNGA SWZBOLJAMNAMVCTZAFBRAGABLCABRBGRDBWA KNA FINUSACYM MOZ MWI CIV<br>1/2 RWAETHUGATZACOMHTIZMBKHMBGDSDNPAKMMRGHA DJ IINDPSECPVVNMMARLAOPHLJORIRQEGYMNGDMABTNIDNECUDZALKAFC JIHNDO THAMDVMBGRRUSMYSOMNPANPRTSVNGBRSAUBHRKWTDEUNLDHKGBRNAREQAT BFAMLI TZA SENBEN KEN GHA NGA<br>SGP 1<br>1/64 1/32 1/16 1/8 1/4 1/2 1 2 1/64 1/32 1/16<br>PPP GDP per capita (USA = 1) PPP GDP per capita (USA = 1)<br>Cement price (USA = 1)<br>Broadband internet price (USA = 1)<br>Steel rebar price (USA = 1)<br>Urea fertilizer price (USA = 1)<br><!-- End of picture text -->
TABLE 7. Estimation with 2 year lags using Olley-Pakes productivities: Productivity in domestic manufacturing firms and reform in upstream services sectors

|Dependent Var|iable: ln(TFP)|I|II|III|IV|V|VI|VII|VIII|IX|X|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Services input|EBRD|||-0.036|0.114|0.375|0.471|0.218|0.296|-0.163|-0.117|
|linkages||||(0.865)|(0.864)|(0.864)|(0.854)|(0.851)|(0.836)|(0.937)|(0.948)|
||FDI (share)|2.054||2.082||2.719*||3.541||3.225**||
|||(1.753)||(1.754)||(1.579)||(2.443)||(1.578)||
||FDI (threshold)||1.631*||1.617*||1.523*||2.111||1.944**|
||||(0.821)||(0.813)||(0.777)||(1.477)||(0.832)|
||Concentration|||||7.332*|7.409|7.773*|7.388|7.352|6.583|
|||||||(4.371)|(5.056)|(4.543)|(5.082)|(4.517)|(5.178)|
||Privatization (share)|||||||-3.346|-4.177|||
|||||||||(6.623)|(8.261)|||
||Privatization (threshold)|||||||||-41.61<br>(32.277)|-40.323<br>(31.488)|
|Manufacturing|FDI (share)|0.664||0.665||0.245||0.185||0.031||
|input linkages||(0.428)||(0.427)||(0.491)||(0.501)||(0.536)||
||FDI (threshold)||0.01||0.017||-0.316||-0.271||-0.324|
||||(0.428)||(0.430)||(0.457)||(0.492)||(0.463)|
||MFN Tariff|0.146|0.056|0.146|0.054|0.101|0.077|0.094|0.066|0.113|0.086|
|||(0.106)|(0.110)|(0.106)|(0.112)|(0.103)|(0.111)|(0.106)|(0.120)|(0.107)|(0.114)|
|Within sector|FDI (share)|-0.355*||-0.355*||-0.17||-0.154||-0.102||
|effects||(0.181)||(0.181)||(0.219)||(0.218)||(0.226)||
||FDI (threshold)||-0.184||-0.187||0.009||-0.009||0.007|
||||(0.124)||(0.126)||(0.179)||(0.193)||(0.184)|
||MFN Tariff|-0.06|-0.017|-0.06|-0.016|-0.028|-0.017|-0.021|-0.009|-0.034|-0.025|
|||(0.051)|(0.052)|(0.051)|(0.053)|(0.050)|(0.052)|(0.053)|(0.058)|(0.053)|(0.053)|
||No. of obs.|5662|5662|5662|5662|5662|5662|5662|5662|5662|5662|
||R<sup>2</sup>|0.042|0.042|0.042|0.042|0.042|0.042|0.042|0.042|0.043|0.043|

The Inputs Linkage variables are weighted by the respective I/O coefficient. All regressors are lagged two years. All models include firm and year fixed effects. Robust standard errors clustered on industry-year combinations are presented in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1% level.

19
2009 Serbia Household Budget Survey (HBS) is shown in Table 1. Note that although around 40 variables are needed for the imputation model, as few as 21 questions are needed to construct these variables because some variables can be constructed from one question, like location dummies.

**Table 1: Estimation Model and Variables used**

|**2009 model**|||**Nat**|**ional me**|**an**|
|---|---|---|---|---|---|
|**Variable description**|**Coeff.**|**t**|**2008**|**2009**|**2010**|
|Intercept|12.3458|414.3079||||
|<br>Household: Number of children under 14 years of|0.0462|5.1854|0.402|0.402|0.370|
|Household: Dependency Ratio|-0.0217|-7.474|1.207|1.702|1.297|
|Household: Number of Household Members|-0.2807|-22.6336|3.037|3.001|2.939|
|Household: Number of Household Members|0.0156|10.6496|11.895|11.826|11.39|
|Highest education level: Vocational school|0.1239|8.3677|0.180|0.182|0.199|
|Highest education level: High school|0.192|6.6128|0.365|0.368|0.342|
|Highest education level: Higher|0.2275|12.1566|0.233|0.214|0.214|
|Highest education level: Post-graduate|0.3132|5.9431|0.007|0.009|0.011|
|Income: Main source is pensions|0.0703|5.2307|0.375|0.425|0.457|
|Month worked by HHH 12 moth|0.0056|5.1801|5.503|5.238|4.703|
|Share of unemployed members|-0.1365|-7.0911|0.125|0.136|0.164|
|Purchases: Household bought clothes|0.0905|6.4555|0.622|0.622|0.586|
|Purchases: Household bought men’s clothes|0.0457|3.6777|0.369|0.366|0.338|
|Purchases: Household bought shoes|0.0737|6.3058|0.530|0.510|0.436|
|Monthly income meet needs: fully or generally|0.1189|9.7607|0.257|0.242|0.220|
|Durables: number of air conditioner owned|0.0652|4.8355|0.173|0.187|0.204|
|Durables: number of car owned|0.1949|18.4621|0.498|0.488|0.459|
|Durables: number of computer owned|0.0822|6.8392|0.329|0.374|0.389|
|Durables: number of dishwashers owned|0.0847|3.9108|0.059|0.060|0.067|
|Durables: number of DVD owned|0.0519|4.4486|0.280|0.332|0.290|
|Durables: number of microwave owned|0.0536|3.5667|0.155|0.156|0.158|
|Durables: number of two-axis tractor owned|0.0611|4.7834|0.166|0.169|0.163|
|Durables: number of TV owned|0.0325|3.4243|1.269|1.326|1.303|
|Durables: number of washing machine|0.1132|7.604|0.881|0.904|0.903|
|In STRATUM_22|-0.1328|-5.1361|0.038|0.038|0.038|
|In STRATUM_24|-0.0775|-4.76|0.095|0.095|0.095|
|In STRATUM_25|-0.3952|-24.5707|0.081|0.081|0.081|
|In STRATUM_27|-0.2856|-12.233|0.051|0.051|0.051|
|In STRATUM_28|-0.1085|-5.4496|0.040|0.040|0.040|
|<br>In STRATUM_29|-0.0665|-3.2613|0.070|0.070|0.070|
|<br>In STRATUM_32|-0.1438|-7.8711|0.093|0.093|0.093|
|In STRATUM_33|0.16|6.1912|0.042|0.042|0.042|
|In rural areas|-0.0586|-5.0735|0.411|0.411|0.411|

Note: Sample size is 4,592 households. R-square is 0.50.

9
The estimate makes use of information provided by Zhi Liu of the World Bank, based on personal contacts with officials at the Bangalore Metropolitan Transit Corporation. The information shows that Bangalore bus fares are distance-based, and that the average cost per km traveled is 0.40 Rs. Adding this figure to the above time cost estimate yields an overall commuting cost of 0.78 Rs. per km.

This number must be multiplied by 2 to put it on a round-trip basis and multiplied again by 300 work days per year to convert it to an annual basis.<sup>9</sup> These calculations yield a commuting cost of 468 Rs. per km per year, using late-1990 prices.

If commuting distance for the edge resident falls by 3 km, as suggested by Figure 7, then the reduction in commuting cost equals 1,404 Rs. per year. If the smaller decline of 2 km is relevant, then the commuting-cost reduction is 936 Rs. per year. The magnitude of these savings can be judged by comparing them to per capita income for Bangalore in 1999, which equaled approximately 28,300 Rs.<sup>10</sup> The commuting-cost savings range from 3.3 to 5.0 percent of per capita income, representing a significant share of individual resources.

While per capita income provides a convenient benchmark, its use may overstate the importance of the commuting-cost savings on a household basis given that not all members are wage earners. Recognizing this possibility, an alternative way of gauging the magnitude of these savings is to compare them to total household consumption expenditure, which is measured via survey methods. The NSS Consumer Expenditure Survey for 1999-2000 documents an average household consumption for urban India of 3867 Rs. per month, or 46,400 Rs. per year. If a household contains two wage earners, with each experiencing the above savings in commuting cost, then the combined savings range between 1,872 and 2,802 Rs., representing between 4.0 and 6.0 percent of household consumption. If the average number of wage earners is instead 1.5, then 21
and responsibilities. Central to such schemes are incentives that link pay and/or tenure directly with performance. For example, teachers’ compensation is linked with factors that influence student achievement, such as teacher attendance and working at a low-performing school, or teachers’ compensation is directly linked with students’ achievement gains. In recent years, both OECD and developing countries have introduced two particular teacher incentive policy reforms, contract tenure reforms and pay-for-performance reforms to strengthen teachers’ accountability for performance. Such incentive reforms, either offering positive rewards or strengthening the threat of sanctions, built up the existing policy framework (Bruns, Filmer and Patrinos 2011).

Typically, experiments in India and Kenya found that the contract teachers were strongly associated with decreased teacher absenteeism and increased student test scores. A study in West Africa where contract teachers are widely used shows that the presence of a contract teacher was positively correlated with the learning performance of low-ability students in the early grades (Bruns, Filmer and Patrinos 2011). In addition, eight rigorous evaluation programs in India, Israel, Brazil and Kenya observed significant improvement in students’ achievement in schools implementing pay for performance (Ferraz and Bruns Forthcoming; Glewwe, Ilias and Kremer 2010; Lavy 2009; Muralidharan and Sundararaman 2009).

The pay-for-performance reform has been introduced into many developed countries as well, although there have been very few rigorously evaluated programs or research results so far. In Australia, under the recent Teacher Quality National Partnership Agreement aiming at quality of teaching, teacher pay was restructured to reward quality teaching as well as those teachers and leaders who work in disadvantaged indigenous, rural/remote and hard-to-staff schools. Likewise, Finland instituted a new salary system in 2007, which linked teachers’ salaries with the tasks, requirements and the results of the work, the professionalism of the staff and work experience (OECD 2010: 285-286).

# <u>Quality Element 4: Attention to Teachers</u>

The quality of an education system cannot exceed the quality of its teachers. A survey of the top-performing school systems suggests that the main driver of the variation in student learning at schools is the quality of the teachers. It was articulated that the success of these school systems is largely attributable to: (a) selecting the right people into the profession; (b) teachers’ salary structure; and (c) developing teachers into effective instructors (McKinsey & Company 2007; Hanushek and Rivkin 2003).

First, these systems all develop effective mechanism for selecting teachers. According to the survey, the top-performing school systems recruit their teachers from the top third of each graduate cohort of their systems: the top 5 percent in South Korea, the top 10 percent in Finland, and the top 30 percent in Singapore and Hong Kong SAR, China. These countries usually 12
# **Table F1: Investment Probit**

||<br>|**Investment**<br>|**Probit - Ma**<br>|**rginal Eff**<br>|**ects**<br>||||
|---|---|---|---|---|---|---|---|---|
||**Bangl **<br>|**adesh**|**Ethi**<br>|**opia**|**Indon**<br>|**esia**|**Sri Lan**<br>|**a**|
||**coef**|**se**|**coef**|**se**|**coef**|**se**|**coef**|**se**|
|||**M**<br>|**ODEL 1: G**<br>|**ENDER O**<br>|**NLY**<br>||||
|mngr is male|0.299|(0.217)|0.183|(0.130)|-0.001|(0.133)|-0.252**|(0.128)|
|Observations|2,480||649||1,369||1,315||
|Pseudo R2|0003||0004||0000||0005||
|<br>|.<br>**MO**<br>|**DEL 2: GE**<br>|.<br>**NDER, IN**<br>|**ITIAL SI**<br>|.<br>**ZE AND SE**<br>|**CTOR**<br>|.<br>||
|mngr is male|0.021|(0.242)|0.228|(0.150)|-0.274|(0.220)|-0.198|(0.150)|
|ln Lt-1|0.164**|(0.076)|0.263**|(0.103)|0.187|(0.123)|-0.050|(0.087)|
|services|0.002|(0.181)|0.157|(0.326)|-0.388|(0.238)|-0.343**|(0.162)|
|trade|-0.452***|<br>(0.148)|-0.002|<br>(0.320)|-0.029|<br>(0.279)|-0.106|<br>(0.134)|
|hh enterprise|-0.347*|(0.193)|0.228|(0.150)|-0.215|(0.249)|0.085|(0.139)|
|Observations|2,301||648||776||1,128||
|Pseudo R2|0037||0032||0040||0016||
|<br>|.<br>|**M**<br>|.<br>**ODEL 3: A**<br>|**LL CONT**<br>|.<br>**ROLS**<br>||.<br>||
|mngr is male|-0.181|(0.259)|0.291|(0.237)|-0.378*|(0.204)|-0.074|(0.136)|
|ln Lt-1|0.181*|(0.093)|0.142|(0.087)|0.220*|(0.117)|0.075|(0.098)|
|services|-0.034|(0.193)|0.243|(0.304)|-0.610**|(0.302)|-0.289*|(0.168)|
|trade|-0.436***|<br>(0.168)|-0.021|<br>(0.276)|-0.182|<br>(0.278)|-0.086|<br>(0.139)|
|hh enterprise|-0.397*|<br>(0.225)|||-0.071|<br>(0.288)|0.086|<br>(0.139)|
|mngr age|-0.017|(0.031)|-0.008|(0.043)|0.122***|(0.043)|-0.000|(0.016)|
|mngr age<sup>2</sup>/100|1.477|(3.654)|-0.000|(0.043)|-0.142***|(0.048)|-0.003|(0.052)|
|mngr primary|-0.172|(0.231)|0.144|(0.208)|-0.457|(0.330)|-0.396|(0.513)|
|mngr secondary|-0.204|(0.209)|0.294|(0.251)|0.037|(0.284)|-0.375|(0.513)|
|mngr tertiary|-0.108|(0.417)|||-0.426|(0.472)|-0.393|(0.634)|
|electricity usage|0.325|(0.206)|1.227***|(0.243)|0.144|(0.251)|0.196*|(0.110)|
|ln firm age|0.039|(0.075)|0.191***|(0.070)|-0.029|(0.119)|-0.151***|(0.054)|
|<br>town|0.055|<br>(0.148)|-0.137|<br>(0.286)|0.384|<br>(0.248)|||
|ln dist market|0.430***|(0.163)|-0.155|(0.152)|0.005|(0.102)|-0.024|(0.092)|
|credit institution|0.240|(0.177)|-0.388|(0.273)|0.224|(0.279)|0.091|(0.152)|
|ln local wage|-0.482|(0.299)|-0.043|(0.278)|0.626**|(0.262)|-1.574***|(0.334)|
|Observations|1,855||574||69|7|993||
|Pseudo R2|0.069||0.106||0.15|2|0.073||

Note: *** p<0.01, ** p<0.05, * p<0.1. Regressions are weighted. Standard errors are heteroscedasticity robust and clustered at the village level.

65
# **CReferences**

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- **Avdjiev, Stefan, Leonardo Gambacorta, Linda S Goldberg, and Stefano Schiaffi.** 2017a. “The Shifting Drivers of Global Liquidity.” Working Paper 23565, National Bureau of Economic Research.

- **Avdjiev, Stefan, Bryan Hardy, Sebnem Kalemli-Ozcan, and Luis Servén.** 2017b. “Gross Capital Flows by Banks, Corporates and Sovereigns.” Working Paper 23116, National Bureau of Economic Research.

- **Bai, Jushan, and Serena Ng.** 2002. “Determining the Number of Factors in Approximate Factor Models.” _Econometrica_ , 70(1): 191–221.

- **Bailey, Natalia, Sean Holly, and M. Hashem Pesaran.** 2016a. “A TwoStage Approach to Spatio-Temporal Analysis with Strong and Weak Cross-Sectional Dependence.” _Journal of Applied Econometrics_ , 31(1): 249– 280.

- **Bailey, Natalia, George Kapetanios, and M. Hashem Pesaran.** 2016b. “Exponent of Cross-Sectional Dependence: Estimation and Inference.” _Journal of Applied Econometrics_ , 31(6): 929–960.

- **Barrot, Luis-Diego, César Calderón, and Luis Servén.** 2018. “Openness, specialization, and the external vulnerability of developing countries.” _Journal of Development Economics_ , 134 310 – 328.

- **Barrot, Luis-Diego, and Luis Serven.** 2018. “Gross capital flows, common factors, and the global financial cycle.” Policy Research Working Paper Series WPS 354, The World Bank.

- **Bekaert, Geert, Eric C. Engstrom, and Nancy Xu.** 2019. “The Time Variation in Risk Appetite and Uncertainty.” Working Paper 17-108, Columbia Business School.

71
Leme and Wajnman presented a comprehensive review of the main results as regards the gender wage gap studies for the case of Brazil and pointed to the narrowing of wage differentials between men and women over the period 1977 to 1997 from 70% to just 25%.<sup>11</sup> This result, the authors claimed, is in line with a worldwide trend in gender wage gaps. In their paper, the authors used information on cohorts borne in 1952 and 1962 to identify the determinants of the narrower gender wage differentials over the period 1977 to 1997 in Brazil. Their results, based on the standard Oaxaca decomposition, suggested that some 17% of the male-female wage differential were eliminated over this period because of reduced discrimination against women.

Evaluating and comparing the existing studies is difficult because they use different data sets and different specifications for the earnings function. However, it is clear that when we look at a single occupation, particularly an occupation such as teaching where public sector employment is very significant, then the finding that over 80% of the wage differential is attributed to endowment differences is not surprising. On the other hand when nationally representative samples for all the labor force are used, at least for the 1980s, up to three-quarters of the gender wage differential remains unexplained. This might be taken as an upper bound for the extent of discrimination against females in Brazil. But, as noted, the most recent research has suggested that the degree of discrimination may be declining. This is an important suggestion which merits further investigation.

6
<!-- Start of picture text --> Form of work Activities – Women Activities - Men<br>for crops; irrigation; fumigation), including<br>“patio” fruit and vegetable gardens<br>• Build house ii) Production of ‘services’:<br>ii) Production of ‘services’: • Help with children<br>• Prepare and bring breakfast to men in • Clear up the yard<br>fields • Fix house<br>• Clean house • Fix wire fence<br>• Wash/iron clothes<br>• Food preparation<br>• Grind corn for flour (for own consumption)<br>• Childcare<br>• Maintain dwelling with adobe<br>(b) Employment work • Collect and sell blackberries • Seasonal vegetables (to sell)<br>comprising work • Livestock/poultry rearing (to sell) • Vegetable business (middleman)<br>performed for • Laundry services for pay • Construction work<br>others in exchange • Domestic work for others (irregular/ • Cow milk (to sell)<br>for pay or profit; permanent) • Day laborer<br>• Work in grocery store o Work in ranch<br>• Run grain mill (from home) o Cut wood<br>• Prepare and sell food (tortillas, o Cut grass<br>nacatamales, sweet breads) o Clear pastures<br>o Sow plantation fields<br>o Spray pesticides<br>(c) Volunteer work • Schools – prepare meals, bake sales • Help sow fields of elderly parents<br>comprising non- • Health centers – weighing children • Help brother mount gates<br>compulsory work • Community organizations – women’s groups, • Build eco stove for a neighbor<br>performed for cooperatives<br>others without pay;<br><!-- End of picture text --> Note: Table does not include responses of men and women from the cooperatives.

**What explains the discrepancy between answers to the household survey and performed activities by women in rural communities?**

The results of the study suggest that there are two explanations behind the observed discrepancy between responses to the survey questions regarding women’s employment and the activities in which women in rural communities engage. The first and immediate explanation behind that discrepancy is related to women’s and men’s perceived identities: Women and men each identify with specific spheres of life. Women commonly identify with the private sphere – and hence, the domestic sphere – while men identify with the public sphere – which in a rural context implies the agricultural sector. This is in line with traditional divisions of gendered life spheres according to which women’s role belongs to the private sphere and men’s role belongs to the public sphere, including market work and political voice and representation (Adams 2011). While these identities are understood to be mutually exclusive, for the women in this study there is a significant overlap between activities undertaken in the private sphere and economic activities.

A second explanation this study suggests is that women identify ‘employment’ with a set of necessary characteristics. Those characteristics may not all apply to their own individual cases. They are as follows: 15