rafmacalaba/datause-encoder-data huggingface dataset:
**Table S3.E: Results for the baseline specification with interaction terms (similar firm size x network variables) added**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|newprod|invdum|anyrd|it|pcf_tech|suprat|htrain|ftrain|
|supbuy|0.0486|-0.0468|0.0945|-0.0731|0.0770|-0.0218|0.0795|-0.0455|
||(0.126)|(0.117)|(0.108)|(0.187)|(0.240)|(0.0333)|(0.107)|(0.134)|
|samesup|-0.0199|0.000180|0.0346|-0.0869|-0.0424|0.0150|0.0342|0.00815|
||(0.0409)|(0.0464)|(0.0413)|(0.0672)|(0.0671)|(0.0150)|(0.0451)|(0.0455)|
|samecus|0.0653|-0.0249|0.00500|0.0476|-0.0762|0.0168|0.0344|0.125**|
||(0.0588)|(0.0704)|(0.0696)|(0.0943)|(0.0955)|(0.0238)|(0.0748)|(0.0622)|
|same_sec|-0.0311*|-0.0566**|-0.0213|-0.0545*|-0.0997***|-0.00225|-0.0164|-0.0301*|
||(0.0170)|(0.0259)|(0.0140)|(0.0279)|(0.0345)|(0.00484)|(0.0124)|(0.0177)|
|ldist|<br>-0.00347|<br>-0.00188|<br>-0.00988*|<br>-0.0148**|<br>-0.0143*|<br>-0.00174|<br>-0.0157***|<br>-0.0149***|
||(0.00245)|(0.00179)|(0.00543)|(0.00705)|(0.00845)|(0.00158)|(0.00301)|(0.00294)|
|ad_fmage|-0.000569|-0.000803***|-0.000984|-0.00179|-0.00319*|-0.000143|-0.000171|0.00182|
||(0.000570)|(0.000277)|(0.00105)|(0.00130)|(0.00174)|(0.000335)|(0.000716)|(0.00113)|
|ad_meduc|<br>0.00855|<br>0.0142|<br>-0.0100|<br>0.0643**|<br>0.0474**|<br>-0.00393*|<br>0.0111|<br>0.00641|
||(0.00907)|(0.00958)|(0.00830)|(0.0285)|(0.0226)|(0.00237)|(0.0184)|(0.0160)|
|ad_mexper|-0.000794|0.000124|-0.000590|-0.000939|-0.00315*|0.000208|-0.00170**|-0.000976|
||(0.000889)|(0.000387)|(0.00131)|(0.00150)|(0.00170)|(0.000479)|(0.000799)|(0.000832)|
|ad_mfemale|-0.00325|0.00138|-0.00627|0.0871*|0.00782|0.00740|0.0166|-0.0125|
||(0.0184)|(0.00605)|(0.0284)|(0.0450)|(0.0479)|(0.0112)|(0.0256)|(0.0178)|
|ad_lemp|<br>0.00199|<br>0.0238**|<br>0.0287*|<br>0.182***|<br>0.118***|<br>0.0104**|<br>0.0840***|<br>0.0975***|
||(0.0103)|(0.00934)|(0.0148)|(0.0281)|(0.0323)|(0.00521)|(0.0163)|(0.0152)|
|sizdist|-0.0115*|0.00516|-0.00483|-0.0218**|-0.00896|-0.00169|-0.00983*|-0.00352|
||(0.00616)|(0.00363)|(0.00481)|(0.00857)|(0.0108)|(0.00170)|(0.00516)|(0.00539)|
|sizlink|<br>0.000278|<br>-0.00512|<br>0.0801|<br>0.149|<br>0.156|<br>-0.0129|<br>0.0593|<br>0.0190|
||(0.0934)|(0.0844)|(0.0965)|(0.121)|(0.150)|(0.0262)|(0.101)|(0.0925)|
|Constant|0.490***|0.473***|0.267***|0.444***|1.010***|0.173***|0.328***|0.277***|
||(0.0195)|(0.0222)|(0.0434)|(0.0617)|(0.0742)|(0.0135)|(0.0371)|(0.0375)|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 9
# **1. Introduction**

Collecting data on HIV/AIDS is notoriously difficult. As a result, common data sources in most countries do not yield representative sub-national prevalence estimates. As in most Sub-Saharan African countries, Malawi monitors HIV prevalence predominantly through antenatal clinic (ANC) sentinel surveillance (National AIDS Control Program, 2000). Surveillance is conducted every one to two years using a consistent methodology in the same population group, namely pregnant women who attend antenatal clinics (ANC).<sup>2</sup> Malawi’s HIV sentinel surveillance indicates that HIV prevalence among antenatal attendees increased rapidly between the late 1980s and early 1990s. By the middle of the 1990s, prevalence stabilized and has since remained fairly constant.

ANC sentinel surveillance systems use unlinked, anonymous, methods for specimen collection and testing to avoid participation bias which can significantly affect HIV prevalence estimation. However, there remain many other potentially important biases inherent in sentinel data: ANC sites are often concentrated in more urban or readily accessible locations; pregnant women may be having unprotected sex at a greater rate than the general population of women; women with potential HIV-associated infertility are not captured; pregnant women that come to the ANC sites are likely to be more educated; and so on. In addition, extrapolations from pregnant women to the population as a whole (men and women) are often based on questionable assumptions.

It has been suggested that weighting of sentinel data can help overcome some of the above biases (Eckert et al, 2002). However, such corrections are likely to address, at best, only those biases arising from variables observed in the sentinel data, such as age, education, and place of residence. Moreover, they can only be applied to the age groups of women observed in the sentinel data. An additional problem with sentinel data encountered in many countries, including Malawi, is that not all districts, or other subnational units, have sentinel sites.<sup>3</sup> As a result there are geographical pockets for which even rough estimates of prevalence are not available. Moreover, lack of broad coverage

> 2 The system has collected data from 19 sentinel sites dating back to 1994. Some sentinel sites started data collection in 1990.

> 3 In Malawi 8 out of 26 districts do _not_ have sentinel sites.

2
- [52] McKenzie, David and Hillel Rapoport. 2010. “Self-selection patterns in Mexico-U.S. migration: the role of migration networks.” _Review of Economics and Statistics_ 92(4): 811-821.

- [53] McPherson, Michael A. 1995. “The hazards of small firms in Southern Africa.” _Journal of Development Studies_ 32(1): 31-54.

- [54] Michaels, Guy. 2008. “The effect of trade on the demand for skill: evidence from the interstate highway system.” _Review of Economics and Statistics_ 90(4): 683-701.

- [55] Miguel, Edward, Shanker Satyanath and Ernest Sergenti. 2004. “Economic shocks and civil conflict: an instrumental variables approach.” _Journal of Political Economy_ 112(4), 725-753.

- [56] Miguel, Edward and Shanker Satyanath. 2011. “Re-examining economics shocks and civil conflict.” _American Economic Journal: Applied Economics_ 3(4), 228-232.

- [57] Mueller, Val ́erie A. and Daniel Osgood. 2009. “Long-term impacts of droughts on labour markets in developing countries: evidence from Brazil.” _Journal of Development Studies_ 45(10): 1651-1662.

- [58] Munschi, Kaivan. 2003. “Networks in the modern economy: Mexican migrants in the U.S. labor market.” _Quarterly Journal of Economics_ 118(2): 549-599.

- [59] Pischke, J ̈orn-Steffen and Johannes Velling. 1997. “Employment effects of immigration to Germany: an analysis based on local labor markets.” _Review of Economics and Statistics_ 79(4): 594-604.

41
- 81 -

subtracted from gain calculations.98 The airlines themselves have not fared very well since deregulation. However, we do not know the counterfactualt the U.S. Federal Trade Commission's study of airline deregulation concluded that it had the effect of raising profitability relative to the counterfactual (FTC, 1988, p. 10); others (Dempsey, 1990) have claimed it has lowered profitability. It does not appear that deregulation has simply involved a redistribution from consumers to producers. The increased use of hub-and-spoke systems has almos. certainly led to reduced direct costs of air travel, and this ehould count among the deregulatory gains.

Airline labor has almost certainly suffered losses as a result of dexegulation. Due to the more competit - environment following deregulation, and the fact that many of thi new entrants to the market used non-union labor, unions were forced to make salary and workrule concessions (Hawk, 1989, p. 271). As in the case of trucking, however, we may appropriately ask whether preservation of above-competitive wages in an industry should be a desideratum Interestingly, travel agents appear to have benefited from deregulation. Commissions were e Eectively deregulated when the airlines were, and as -ach airline made efforts to inducs travel agents to book customers' flights on its flights, average commissions increased dramatically 98 Even here the issues are clouded. Air travel has increased dramatically since deregulation (Hawk, 1989). This in itself would increase congestion and delays, and therefore it is difficult to asses deregulation's contribution to increased delays.
pesticide overuse. Steps could include stricter enforcement of existing regulations, the promotion of integrated pest management programs, and further research on alternative pest control methods.

# **References**

- Antle, J. M. and P. L. Pingali 1994. Pesticides, Productivity, and Farmer Health: A Philippine Case Study. _American Journal of Agricultural Economics_ , **76** : 418-430.

- Bangladesh Bureau of Statistics (BBS) 2001. Statistical Yearbook of Bangladesh, December 2003, Planning Division, Ministry of Planning, Government of the Peoples Republic of Bangladesh.

- Blais, J. M., D. W. Schindler, D. C. G. Muir, L. E. Kimpe, D. B. Donald and B. Rosenberg 1998. Accumulation of Persistent Organochlorine Compounds in Mountains of Western Canada. _Nature_ **395** : 585-588.

- Cropper, M. 1994. Economic and Health Consequences of Pesticide Use in Developing Country Agriculture: Discussion. _American Journal of Agricultural Economics_ **76** : 605-607.

- Davies, V., H. Freed, and F. W. Whittemore 1982. _An Agromedical Approach to Pesticide Management: Some Health and Environmental Considerations_ . Miami, Fla., USA: University of Miami School of Medicine in cooperation with the Agency for International Development (USAID) and Consortium of International Crop Protection.

- Dung, N. H. and T. T. T. Dung 1999. Economic and Health Consequences of Pesticide Use in Paddy Production in the Mekong Delta, Vietnam.  EEPSEA Research Report Series.  Available at: <u>http://www.eepsea.org/en/ev-8427-201-1-DO_TOPIC.html.</u>

- Dung, N. H., T. C. Thien, N. V. Hong, N. T. Loc, D. V. Minh, T. D. Thau, H. T. L. Nguyen, N. T. Phong and T. T. Son 1999. Impact of Agro-Chemical Use on Productivity and Health in Vietnam.  EEPSEA Research Report Series.  Available at: <u>http://www.eepsea.org/en/ev-8427-201-1-DO_TOPIC.html.</u>

- Extension Toxicology Network (ECOTOXNET) 2004. Cholinesterase Inhibition. Toxicology Information Brief. Cornell University.  Available at: <u>http://pmep.cce.cornell.edu/profiles/extoxnet/TIB/cholinesterase.html</u>

- Fabbri, D., C. Monfardini and R. Radice 2004. Testing Exogeneity in the Bivariate Probit Model: Monte Carlo Evidence and an Application to Health Economics. Department Working Paper No. 514.  Department of Economics, University of Bologna, Italy.

16
# 4 Costs of gender gaps by age group

The purpose of this section is to to decompose the total income loss into the dierent age group gaps. As argued in World Bank (2014), gender gaps are likely to vary across age groups and this may have important implications for the future evolution of the labor market and, in particular, the distortions associated with these gaps. Table 6 displays the number of observations by country and age group.<sup>10</sup> Table 6: Number of observations by age group

||Age|15-24|Age|25-35|Age|36-50|Age|51-65|
|---|---|---|---|---|---|---|---|---|
||_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|_t_= 1|_t_= 2|
|Albania|660|492|982|1354|2167|2393|1137|1768|
|Bosnia and Herzegovina|-|3830|-|3645|-|5996|-|5616|
|Croatia|590|604|1508|1472|2712|2648|1286|1522|
|Kosovo|-|169393|-|146731|-|171452|-|99449|
|Macedonia|10150|8350|9096|7259|13523|11596|11060|11398|
|Montenegro|196|130|616|484|900|980|480|556|
|Serbia|734|442|2426|2022|4228|3704|2694|2472|
|Turkey|3816|2621|-|7681|7882|7974|3342|4475|

## 4.1 Data description

Tables 7-9 report the existing gender gaps in the labor force participation, employers and selfemployed for dierent age groups. We split our sample in four age groups: 15-24, 25-35, 36-50, and 51-65. Table 7 shows that there exist signicant gender gaps against women in labor force participation in the majority of age groups. Interestingly, the largest gaps concentrate in the youngest population (ages 15-24) and the oldest one (ages 51-65). In the 15-24 group the largest gap takes place in Albania in 2012 (65.6%). In the 25-35, 36-50, and 51-65 age groups Kosovo has the largest gaps in 2012 (64.9%, 68.5%, and 80.4% respectively).

In Table 8 it is again apparent that there are gender gaps in self-employment in almost all age groups. For this occupation, Macedonia leads the gaps in all cases, except in the age group 25-35, where Montenegro has the largest gap in 2007. On average, the age group with the largest gap is in the age bracket 51-65.

The gender gaps for employers by age are shown in Table 9. As before, most countries, years and age groups show women being underrepresented in this occupation, especially in ages between 15 and 24. In several instances the gaps are 100%, indicating that in our sample 10For a study of the changing demographic prole of women in Turkey, see also Dayo§lu and Krdar (2010).

10
**Figure 8: % of companies reporting on water‐related environmental indicators, by subsample**

<!-- Start of picture text --> Total water withdrawal by source Water sources significantly affected<br>(G4‐EN8) by withdrawal of water (G4‐EN9)<br>SME 46.1% SME 16.0%<br>Large 62.6% Large 34.9%<br>MNE 64.7% MNE 28.0%<br>All 61.4% All 30.8%<br>Percentage and total volume of Total water discharge by quality and<br>water recycled and reused (G4‐EN10) destination (G4‐EN22)<br>SME 20.5% SME 26.9%<br>Large 39.8% Large 44.5%<br>MNE 36.3% MNE 46.1%<br>All 36.6% All 43.1%<br>Water bodies & related habitats<br>affected by water discharges (G4‐<br>EN26)<br>SME 8.7%<br>Large 25.0%<br>MNE 18.3%<br>All 21.2%<br><!-- End of picture text -->

**Table 17: Water‐related environmental indicators, regressions, Probit model**

|**GRI G4**<br>**Metric**|**GRI Indicator**|**_MNE_**|**p‐value**|**_Large_**|**p‐value**|
|---|---|---|---|---|---|
|G4‐EN8|Total water withdrawal by source|0.078|0.503|**0.419****|**0.011**|
|G4‐EN9|Water sources significantly affected by withdrawal of water|‐0.181|0.147|**0.699*****|**0.001**|
|**G4‐EN10**|**Percentage and total volume of water recycled and reused**|**‐0.305****|**0.011**|**0.670*****|**0.000**|
|G4‐EN22|Total water discharge by quality and destination|0.017|0.888|**0.472****|**0.010**|
|**G4‐EN26**|**Identity, size, protected status, and biodiversity value of water bodies and related**<br>**habitats significantly affected by the organization’s discharges of water and runoff**|**‐0.378*****|<br>**0.005**|**0.741*****|**0.001**|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01. Note: Variables refer to 2015 GRI data (see section 2.2). All regressions control for country‐sector fixed effects. Standard errors are robust to heteroscedasticity.

26
**Table ‎3.2:  Industry Distribution of HE**

|1.7||2006||Change 20|01-2006(Percen|tagepoints)|
|---|---|---|---|---|---|---|
|||All|||||
||Rural|Urban|Total|Rural|All Urban|Total|
||%|%|%|%|%|%|
|Agric., Hunting, Forestry, Fishing|2.6|1.0|1.9|-1.0|0.4|-0.4|
|Mining, Manufacturing, Energy|26.6|17.5|23.0|9.5|8.9|9.5|
|Construction|2.4|2.9|2.6|-3.1|-3.1|-3.1|
|Trade, hotels, restaurants|62.1|71.1|65.6|-6.7|-4.5|-6.0|
|Transport,storage,real estate|2.2|2.8|2.4|1.7|1.5|1.6|
|Public and social/person. Services|4.2|4.7|4.4|-0.4|-3.2|-1.6|
|Total|100.0|100.0|100.0||||

_Source: Calculations based on the ILFS 2000/01 and 2006 data._ 27. Although not based on a representative sample, Table 3.3 enriches the sectoral picture by showing data from the FGD study. HEs are categorized according to three major non-agriculture sectors (manufacturing, trading, and services) on the basis of the participants‟ description of their activities. Not surprisingly, a substantial number of HEs are engaged in some form of trade. Next in importance are manufacturing enterprises. This sectoral structure is in part a reflection of the ease of entry into informal trading (see Box 3.1), but also of the constraints HEs faced in undertaking production and other activities requiring electricity, fixed premises, labor, and skills.

**Table ‎3.3:  Types of HE Activities, by Sector, 2006**

|Trading||Manufacturing||Services||
|---|---|---|---|---|---|
|_Activity_|_Incidence_|_Activity_|_Incidence_|_Activity_|_Incidence_|
|Sellingroasted/fried cashew nuts|2|Tailoring|8|Motorcycle repair|2|
|Sellingwater|3|Makingsnacks|2|Video shows|1|
|Sellingsugarcanejuice|1|Restaurant|18|Decorations|1|
|Sellingbelts for clothing|1|Food vending|4|Women‟s hair saloon|2|
|Sellingsocks and wallets|1|Juice makingand selling|1|Motor vehicle mechanics|<br>1|
|Sellingfruits and vegetables|9|Masonry|1|Transport(_bodaboda_)|3|
|Dealer in used clothes|7|Carpentry|2|Pharmacy|2|
|Selling cellular phone SIM cards<br>and air-time vouchers|4|De-husking cashew nuts|1|IT equipment and video<br>cassette hiring|3|
|Sellingcookingstoves<br>|1|Foodprocessing|2|||
|A small shop/_genge_<sup>_7_</sup>|20|Local brew making|1|||
|Fish retailing|3|Millingmachine|4|||
|Vendingwater|1|Carpentry||||
|Sellinglocal chicken/guinea fowls|2|||||
|Sellingsoft and alcoholic drinks|4|||||
|Selling petroleumproducts|1|||||
|Importing maize from Mozambique<br>and selling|2|||||
|Selling _upupu_<sup>_8_</sup><br>_Source: FGD report, 2009_|1|||||

> 7 Note that in a _genge,_ food products like sardines, tomatoes, onions, as well as other items like soap are sold.

> 8 _Upupu_ is a wild legume cooked for 10 to 15 hours and is consumed during periods when there is shortage of food. It is poisonous, if improperly cooked.
--------. 2005. _World Development Report 2006: Equity and Development_ . New York _:_ Oxford University Press

--------2006. _Uganda Poverty and Vulnerability Assessment_ October  2006, Report No. 36996-UG, Washington DC. The World Bank,

--------2007. Uganda - Moving Beyond Recovery : Investment and Behavior Change for Growth. _Country Economic Memorandum_ , Report No. 39221-UG. Washington DC, The World Bank

--------2009. _World Development Report 2009: Reshaping Economic Geography_ .  New York _:_ Oxford University Press

--------2010. _World Development Indicators Online_ . Washington, DC: The World Bank.

<u>http://devdata.worldbank.org/dataonline/</u>

--------. 2011a. _Africa’s Future and the World Bank’s Support to It_ .  New York _:_ Oxford University Press. www.worldbank.org/africastrategy

——— 2011b. ―Agriculture for Inclusive Growth in Uganda‖ Draft Policy Note March 2011, The World Bank, Washington DC.

34
# **Table A5: Robustness Checks – Water Shortages and Sales, using Logs of Water Infrastructure** **<u>Variables</u>**

|**Y = Log of sales**|**Translog**<br>**(1)**|**Cobb-**<br>**Douglas**<br>**(2)**|
|---|---|---|
|Log of labor (lnL)|0.769***|0.815***|
||(0.022)|(0.036)|
|Log of capital (replacement value) (lnK)|0.020**|0.036***|
||(0.010)|(0.009)|
|Log of raw materials (costs) (lnM)|0.238***|0.205***|
||(0.011)|(0.029)|
|Log of no of water shortages per day in a typical month|-0.257*|-0.386**|
||(0.135)|(0.155)|
|Log of average duration of water shortage|0.027|0.013|
||(0.034)|(0.037)|
|Log of no of electrical outages per day in a typical month|0.037|-0.042|
||(0.067)|(0.091)|
|Log of average duration of electrical outages (hours)|-0.006|0.027|
||(0.022)|(0.040)|
|Firm owns or shares a generator Y/N|0.101***|0.155***|
||(0.034)|(0.043)|
|Losses due to breakage/spoilage shipped domestically (% of value of the products|0.000|-0.007|
||(0.003)|(0.005)|
|Log of age of firm|0.015|0.015|
||(0.018)|(0.027)|
|Foreign ownership Y/N|0.109**|0.201***|
||(0.052)|(0.066)|
|Exports 10% or more of sales Y/N|0.103***|0.187***|
||(0.038)|(0.046)|
|Security costs (% of sales)|-0.011***|-0.019***|
||(0.004)|(0.006)|
|Establishment competes against unregistered or informal firms Y/N|-0.051|-0.128***|
||(0.032)|(0.044)|
|No. of years that firm was unregistered at start|-0.006**|-0.007**|
||(0.003)|(0.004)|
|Formal training Y/N|0.049|0.136***|

37
|O||Income|||performancevs.<br>Benchmark|
|---|---|---|---|---|---|
|Deviation of-10% or more from Benchmark||||||
|Energy: Access to ElectricityNetwork(% ofPopulation)|10|a7|15|60|Underperform|
|ICT: Mainlines Teledensity(mainlines subscribers/1000 people)|TS|261|31|213|Underperform|
|ICT: Phone Faults (reported faults/100 mainlines)|37|Ig|at|37|Underperform|
|ICT:Teledensity (totaltelephone subseribers/1000 people)|a7|6i5|90|201|Underperform|
|ICT: Cost ofPhone Call to theUS (U3 cents/3 minutes)|364|365|407|335|Underperform|
|Transport: RailLines DensityinTerms ofLand(rail-lon/1000 eqkon)|18|aT|3.63|23.12|Underperform|
|Transport: RoadDensity<br>in Terms of Land (road-kmv‘1000 sqke)|227|L076|1537|$40.6|Underperform|
|Transport: RoadDensity<br>in Terms ofPopulation(road-km1000 people)|62|92|33|67|Underperform|
|Water: Access to Improved Sanitation( ofpopulation)|41|a6|at|é4|Underperform|
|Deviation ofbetween-10% and +10% from Benchmark||||||
|Transport: PavedRoads(% oftotalroads)|35|Ss?|25|40|Average|
|Water: Access toImproved Water Sources (%6 ofpopulation)|95|of|64|#0|Average|
|Deviation of +10°%o or more from Benchmark||||||
|ElectricityAverage End-UserPrices(U3 cents‘kWh) - (Residential/Non-Energy: Residential|4g|OFF|6s|6|Outperform|
|ICT:Costof LocalPhone Call(US cente/3 minutes)|2|9|id|g|Outperform|
|ICT:CostofCellularLocalCall(U3cente/3off-peakminutes)|33|ja|42|49|Outperform|
21

# observed spending patterns.<sup>30</sup>

Similarly, the marginal propensity to spend on transportation and communication tends to be larger from unearned income among both men and women, even though the coefficients are not always precisely estimated, especially when using the household fixed effects estimator. For example, while the OLS estimates of the MPCs for men and women from unearned income in Ruvuma are clearly and statistically larger than those from earned income, when controlling for household fixed effects, the MPC in Ruvuma from male unearned income remains almost 8 times larger (0.154 compared with 0.02 for male earned income), though with a t-value of 1.613 it just fails to meet the 10 percent significance level.

Spending on alcohol and tobacco from unearned income by males is also much larger than from earned income (though again measured with some imprecision when considering the household fixed effects estimated in Kilimanjaro). Interestingly, however, when unearned income is allocated to women, it reduces spending on alcohol and tobacco in Kilimanjaro. This underscores 1) that spending preferences differ by gender and 2) that gender preferences as such do not explain the differences in MPC from unearned and earned incomes. Similarly, gift giving from unearned income is also higher than from earned income for both men and women in Ruvuma, though there is no clear pattern in Kilimanjaro. Spending on non-staple food from unearned income is larger than from earned income, with the results only significant in Kilimanjaro and more pronounced for men than for women. The disaggregation of income sources by gender did not generate clear cut patterns for spending behavior on staples and education.

Second, with the exception for clothing in Gansu and Inner Mongolia, the general finding that the MPC is larger for spending on non-basic goods (transportation and communication, alcohol and tobacco, non-staple food) and gifts, and smaller for spending on basic goods (staples) and education, also holds when looking at the anticipated component of income only (Table 7). This suggests that the effects of earned and unearned income go beyond their anticipated and unanticipated features and that they go beyond the predictions of the PIH, which are in this context only relevant for goods with a durable aspect to begin with.<sup>31</sup> It further suggests that the results are robust against concerns of measurement error or

> 30 This general pattern holds for expenditures for  men’s, women’s and children’s clothing with the only exception of female (though not male) spending on children’s clothing in Ruvuma, where there is a higher MPC of female spending on children’s clothing from earned income (Table A4).

> 31 While spending on clothing in rural China appears more driven by the earned portion of anticipated income, the reverse seems to hold when it comes to the unanticipated income, as suggested by the widening of the MPC gap between unearned and earned income in going from the OLS to the household fixed effects results when