rafmacalaba/datause-encoder-data huggingface dataset:
Then we estimate separately for each gender: The results are shown in Table 4. The top two rows of the table show the categories A and B which de...ne strata. Columns (1) and (2) show the OLS and ...xed e¤ects estimates of treatment heterogeneity by the gender mix of the industry ...rms work in. De Mel et al. (2009a) found some evidence in Sri Lanka that the impact of grants was less for women in female-dominated industries than those in mixed industries. In Ghana, panel A of column (2) shows that with ...xed e¤ects, the cash treatment has a -6.9 cedis e¤ect in female-dominated industries versus a 1.8 cedis e¤ect in mixed industries, and the in-kind treatment has a 25.4 cedis e¤ect in female-dominated industries compared to a 39.8 cedis e¤ect in mixed industries. The point estimates are therefore consistent with the idea that the grants may have more e¤ect on the businesses of women who operate in mixed industries. However, the di¤erences in treatment e¤ects by industry category are not statistically signi...cant. Likewise panel B shows no signi...cant heterogeneity by industry category for men.

Columns (3) and (4) examine heterogeneity according to the baseline measure of capture. Recall that individuals in the “high capture” category state that whenever they have money on hand their family members are likely to request some of it, and that people who do well in business get requests from others for help. We do not obtain signi...cant heterogeneity according to this variable for either men or women, with large standard errors and the point estimates varying quite a lot between the OLS and ...xed e¤ects speci...cations. Later in the paper we examine alternative measures of capture to see whether this lack of signi...cance is due to the particular choice of measure being used.

Finally we look at heterogeneity according to the initial size of the ...rm. Columns (5) and (6) consider this in terms of the initial capital stock of the ...rm, as ...rms were strati...ed as being above or below median baseline capital stock, while columns (7) and (8) de...ne initial size in terms of initial pro...ts. Since wave 2 pro...ts was matched to form quadruplets, we ...rst calculate the maximum wave 2 pro...t within a quadruplet or group, and then de...ne ...rms as being in a low pro...ts group if the maximum wave 2 pro...ts for the group is less than 138 cedis (the median of pro...ts over the whole sample). This classi...es 62 percent of females and 45 percent of males as being in the low pro...ts group. The results con...rm the visual impression in Figures 1 and 2.

26
increase in wage growth. This would have almost negated the decline in mean wage growth observed in South African manufacturing industries 2010 and 2014 (-0.05 percent).

At the same time, sales share is positively associated with wage growth. By contrast, greater firm size (as measured by employment levels) appears to be highly negatively related to wage growth. The larger is the current number of employees, the lower is the wage growth. This might indicate that larger firms are able to rely more on reputation, job security or other non-wage factors to attract workers.

_Table_ 5 _: Effects of weaker competition on wage growth in South Africa manufacturing industries_

||Model 1|Model 2|
|---|---|---|
|Log of lagged markup|-0.407<sup>***</sup>|-0.463<sup>***</sup>|
||(0.048)|(0.047)|
|Lagged TFP growth|0.418<sup>***</sup>|0.412<sup>***</sup>|
||(0.031)|(0.031)|
|Log of lagged TFP|-0.200<sup>***</sup>|-0.132<sup>***</sup>|
||(0.041)|(0.038)|
|Low-intensity exporters|0.023|0.021|
||(0.015)|(0.015)|
|Medium-intensity exporters|0.010|0.008|
||(0.027)|(0.027)|
|High-intensity exporters|0.053|0.053|
||(0.034)|(0.034)|
|Sales share|2.920<sup>***</sup>|2.881<sup>***</sup>|
||(0.854)|(0.849)|
|Age: 6-10 years|-0.016|-0.019|
||(0.019)|(0.019)|
|Age: 11-20 years|-0.009|-0.012|
||(0.025)|(0.025)|
|Age: 21-30 years|0.047|0.044|
||(0.036)|(0.036)|
|Age: 31-40 years|0.030|0.034|
||(0.057)|(0.057)|
|Age: 41 years or more|0.122|0.128|
||(0.090)|(0.089)|

22
Table 7: Aid & bilateral TRADE – 2<sup>nd</sup> stage

|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Ln(Fatalities)_|0.017|0.014|0.014|0.013|0.018|
||(0.014)|(0.015)|(0.015)|(0.015)|(0.015)|
|_Ln(Affected)_|0.035***<br>(0.004)|0.028***<br>(0.005)|0.028***<br>(0.005)|0.025***<br>(0.006)|0.022***<br>(0.006)|
|_Ln(GDP p.c.)_|-0.147***|-0.133***|-0.148***|-0.149***|-0.154***|
||(0.040)|(0.044)|(0.045)|(0.048)|(0.049)|
|_Ln(Population)_|-0.254***|-0.244***|-0.257***|-0.240***|-0.253***|
||(0.029)|(0.031)|(0.031)|(0.031)|(0.032)|
|_Trade (% of GDP)_|-0.002**|-0.002*|-0.002**|-0.002**|-0.002*|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Distance_|-0.055***|-0.064***|-0.062***|-0.063***|-0.062***|
||(0.008)|(0.009)|(0.009)|(0.010)|(0.010)|
|_Affinity index_|0.469*|0.387|0.356|0.388|0.360|
||(0.271)|(0.296)|(0.298)|(0.298)|(0.298)|
|_Common religion_|-0.007|0.003|-0.001|0.001|0.008|
||(0.064)|(0.075)|(0.076)|(0.075)|(0.076)|
|_Common language_|0.165**|0.049|0.036|0.066|0.015|
||(0.079)|(0.105)|(0.104)|(0.104)|(0.105)|
|_Former colony_|-0.222***|-0.257***|-0.271***|-0.258***|-0.236**|
||(0.084)|(0.093)|(0.092)|(0.093)|(0.094)|
|_Ln(GDP p.c.) donor_|-0.159***|-0.150***|-0.160***|-0.156***|-0.173***|
||(0.048)|(0.048)|(0.048)|(0.048)|(0.051)|
|_Trade with donor_|0.124***|0.119***|0.142***|0.120***|0.188***|
||(0.014)|(0.015)|(0.017)|(0.015)|(0.023)|
|_Regulatory quality_||0.027|-0.184*|||
|||(0.069)|(0.106)|||
|_Trade with donor_×|||0.055***|||
|_Regulatory quality_|||(0.021)|||
|_Corruption control_||||0.102|-0.398**|
|||||(0.088)|(0.158)|
|_Trade with donor_×|||||0.117***|
|_Corruption control_|||||(0.028)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|0.973***|0.970***|0.950***|0.964***|0.918***|
||(0.130)|(0.132)|(0.132)|(0.135)|(0.140)|
|Constant|3.840***|3.735***|4.071***|3.957***|4.052***|
||(0.750)|(0.803)|(0.811)|(0.843)|(0.852)|
|Log likelihood|-7546.738|-6398.397|-6394.050|-6386.357|-6356.830|
|N|3284|2757|2757|2757|2757|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

21
**Figure 11: Evolution of GVC Exports Participation, Sophistication and Concentration trends, 1988‐2014**

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>EXPRELY_new HHI_X Normalized year 2001 GDP (world GVC‐exports‐weighted average)<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> <u>Notes: Values in ratios from original variables expressed in thousand current US$ and calculated on the total number of 181</u> countries/geographies with available data on the variables necessary for the construction of both indexes and non‐missing normalized GDP, in at least some years of the period 1988‐2014, even though some have missing data in some years over the GVC mirror data series over the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits). Normalized GDPs are calculated for each country and year as a ratio relative to the USA GDP in the fixed year 2001, with both countries' GDPs measured in constant 2005 USD (normalized GDP for the USA equals 1). All trade data is drawn from Comtrade on WITS, and countries' GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

**Figure 12: EXPRELY of World GVC Exports, by GVC category, 1988‐2014**

<!-- Start of picture text --> 0.7<br>0.6 Apparel ‐ Intermediate Good<br>Apparel ‐ Final Good<br>0.5<br>Motor Vehicles ‐ Intermediate Good<br>0.4<br>Motor Vehicles ‐ Final Good<br>0.3 Electronics ‐ Intermediate Good<br>Electronics ‐ Final Good<br>0.2<br>Footwear ‐ Intermediate Good<br>0.1<br>Footwear ‐ Final Good<br>0 Textiles ‐ Final Good<br>1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> <u>Notes: Values in ratios from original variables expressed in thousand current US$ and calculated on the total number of 181</u> countries/geographies with available data on the variables necessary for the construction of both indexes and non‐missing normalized GDP, in at least some years of the period 1988‐2014, even though some have missing data in some years over the GVC mirror data series over the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits). Normalized GDPs are calculated for each country and year as a ratio relative to the USA GDP in the fixed year 2001, with both countries’ GDPs measured in constant 2005 USD (normalized GDP for the USA equals 1). All trade data is drawn from Comtrade on WITS, and countries’ GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

29
TABLE 6: Composition of the Optimal International Portfolio Without Commodity Assets

|Rate of<br>Retumrn<br>(Re)|USA|UK<br>.|JAP|GER|swz|FRA|WK|GTAI|KOR|MEX|THI|BRZ|IND|Sum of<br>Weights|Portfolio<br>Standard<br>Deviation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1.00|.100||.527||.286||||||||.087|1|4.57|
|1.25|.334||.337||.161||||||||M.68|I|3.89|
|1.47*|.407*||.217*|.028*|.061*||.012*|.014*|.063*||.001*||.196*|1*|3.77*|
|1.50|.405||.208|.030|.049||.020|.016|.069||.006||.197|1|3.78|
|1.75|.381||.129|.029|||.071|.033|.108|-041||008|.199|1|3.86|
|2.00|.342||.053|.007|||.114|.048|.137|.016|064|016|.202|1|4.06|
|2.25|.264|||||.017|.151|.055|.160|.048|.078|.023|.203|1|4.36|
|2.50|.136|||||.008|.205|.059|.171|.093|.083|.030|.214|1|4.75|
|2.75|.008||||||.259|.063|.182|.138|.088|.038|.224|1|5.24|
|13.00|||||||.270|.069|.120|.229|.049|.048|.215|1|5.88|

# NOTE: Weights may not sum exactly to I because of rounding up.

*Starred values represent return, weights and standard deviation of the minimum-risk portfolio.

Data sources: See Tables I and 2.
- 44 -

where T-R/X **and PX.+*)** represents the new acquisition of raw materials. The first order conditions for this problem reduce to where **y** , the rate of change in the nominal price of the good held as inventory. This expression gives the marginal benefit of a unit of inventory holdings. To convert it to a rate of return, we subtract the real capital loss on holding a unit of inventory (which is the analog of true depreciation here), so: where **OMp4P-u** , the rate of change in the price of inventory relative to that of other goods in the economy.

_C. Depletable resources_ We with inventories, we must make some simplifying assumption to render the problem of exploiting non-renewable resources manageable. We consider a firm which is simultaneously involved in exploration, investment in mining facilities, and extraction. Inventories are excluded so that sales equal extraction; it would be relatively straightforward to add inventories. The taxation of resources is notoriously complex in practice. For illustrative purposes we consider a relatively simple scheme which incorporates most of the key issues.<sup>2 9</sup> In the exploration state the firm hires current inputs L at a price W and produces a depletable asset according to the strictly concave production function S(L). It then invests in mining capital K at a price Q to make the asset ready for extraction. The production function is Z(K, F) where F is the current use of the previously discovered asset. This is the only state at which depreciable capital is used, though it would be straightforward to allow for it at either of the other two states. Finally, the firm extracts an amount Y of the resource according to the strictly convex nominal cost function C(Y) and sells it at a price P. The dividend flow resulting from this three-stage process is: 29n# **folwing anlysis** is adopted **from** Boadway, Bmce, McKenzie and Mintz **(1987).**
32

# **Mobility and Motorization**

# **Motor Vehicle Registrations**

Since 1950, the global growth rate for automobiles has averaged 5.9% per annum; for trucks and buses, it has been only slightly less at 5.6% per annum. Although the overall rate of growth over this period declined slightly, commercial vehicles increased more rapidly than automobiles. For example, since 1970, automobile growth has averaged 4.7% per annum while truck and bus registrations have averaged 5.1 %. As a result of this sustained growth, the total worldwide vehicle population now exceeds 500 million, with about 400 million automobiles [MVMA 1988].

A geographical distribution of the motor vehicle population (in 1986) is summarized in Table 12. Europe and North America each had a 40% share of the world's automobile population, with the remaining 20% accounted by Asia, South America, Oceania and Africa. Regarding trucks and buses, North America had a 41% share followed closely by Asia and Europe. Within Asia, Japan accounted for 70% of automobiles and 62% of trucks and buses. The developing countries, as a group, had a 10% share of the world's automobiles and about 20% of trucks and buses. The global population of motorized 2- and 3-wheeled vehicles (motorcycles, rickshaws) is not known, although they account for a substantial part of the motorized vehicle fleets in developing countries and contribute significantly to mobile emissions. In Taiwan, for example, motorcycles accounted for 76% of the registered motor vehicle fleet in 1988 and were responsible for 36% of total HC emissions and over 15% of CO emissions from all sources [Shen and Huang 1989].

**Table 12. Global distribution of motor vehicles in 1986**

|Region|Automobiles (%)|Trucks &<br>Buses(%')|
|---|---|---|
|North America|40.0|41.4|
|Europe|39.8|21.0|
|Asia|11.1|27.8|
|South America|4.8|3.7|
|Oceania|2.2|2.3|
|**Africa**|**2.1**|3.7|

Source: [MVMA 1988] The world motor vehicle fleet has been growing faster than the overall population. In global terms, automobiles per capita increased by over 20% during the last decade [MVMA 1988]. Short term projections suggest that the greatest gains in motor vehicle registrations will
2017). But in that setting, significant indemnity payouts had occurred in the second year in which contracts were sold following the catastrophic 2011 regional drought, so average indemnity payouts substantially exceeded average premium expenses. Those results could, therefore, be purely the result of stochastic ordering of loss events and associated indemnity payments. Those indemnity payouts had sizable behavioral and welfare effects (Janzen & Carter, 2013). Because there were no indemnity payments in southern Ethiopia, our study isolates the welfare effects of insurance that arise purely from reduced _ex ante_ risk exposure, that is, just the peace of mind effects that arise from buyers’ risk aversion, abstracted from the complication of indemnity payments. The Ethiopia IBLI pilot and associated data enable us to get at these important issues in a novel way that sheds considerable light more generally on the value of insurance coverage.

# **3 Data and Descriptive Statistics**

## **3.1 Data and Study Design**

A baseline survey (R1) was designed and fielded in February-March 2012 before IBLI was developed or announced. Data on a broad range of household characteristics, livestock and other assets, livelihood activities, consumption, social networks, expectations and subjective well-being were collected. A year later, following sales period 2, a follow-up survey round (R2) of the original sample households was fielded in March-April 2013. Following sales period 4, a third round (R3) of survey data was then conducted in March 2014 from the same respondents as the first two survey rounds. We therefore have pre-experiment baseline data (R1), followed by two survey rounds (R2 and R3) with the same respondents. In R2, IBLI contracts purchased in sales periods 1 and 2 were in force. In R3 contracts from sales period 1 and 2 had lapsed but contracts purchased in sales periods 3 and 4 were in force (Figure 2).

### Figure 2 here

The sampling was clustered at the _reera_ level.<sup>7</sup> _Reeras_ were purposively selected based on geographic distribution, variation in market access, and agro-ecological variation across the

> 7 _Reera_ is the fourth level administrative division in Oromia region below zone, _woreda_ , and _kebele_ .

7
9 Fig WAGES + BENEITS shares from cost s,de <!-- Start of picture text --> 0.5F<br>0.4~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0<br>0.2 0<br>0.1.<br>191 2Q9 391 4c091 1Q92 2Q92 3Q92<br>w3 ce bil :onuiS fUnd socicl ris, tcx emPbYnent fund<br>perion fund so icl fuLd<br><!-- End of picture text -->
may live in households where other members have accounts), whereas nearer to 72% of adults in the developing world do not have accounts. Even at the very low level of precision possible in this type of exercise, these numbers indicate a major gap that has not yet even begun to be addressed by the many policy initiatives currently underway or by the microfinance movement. Finally, we investigate the relationship between deposit, loan, and bank branch penetration with other variables and find significant associations of deposit and loan penetration with per capita income, physical and financial infrastructure, and macro-economic stability, but no significant association with policies which have financial inclusion as a central goal.

Having appropriate data is crucial to understanding and measuring financial inclusion. The data introduced in the _Financial Access Database_ should be viewed as an attempt to generate consistent cross-country indicators of financial penetration around the world. Yet, as an effort to document access to financial services worldwide, it faces many challenges. Many countries do not collect information on key variables and most have incomplete data on the non-banking sector. There is the need to improve the quality and availability of financial access data, both by improving and extending cross-country indicators as well as employing country-specific-indepth diagnostics. Additionally, data are often collected on an ad-hoc basis. To be useful, indicators must be collected on a repeated, regular basis, so that policymakers can set priorities and track changes. Finally, supply-side data must be complemented with other efforts, mainly through household surveys, to estimate accurately the characteristics of the population with access to the financial system. Since this could be a time and resource-intensive exercise, an alternative could be the use of quick financial access surveys or ‘snapshots’. These snapshots should not be intended to replace deeper country-level surveys, but could provide a usefully broad framework in which to set other sources of information. In the end, these efforts should complement each other to provide a broader picture of financial inclusion in order to identify obstacles and design policies to overcome them and ultimately expand access.

36
**Table 8:  IV-FE estimates of the impacts of microenterprise adoption on expenditure and**

||**poverty**<br>||||
|---|---|---|---|---|
|**Explanatory variables**|**Log per capita**<br>**income (Tk./**<br>**month)**|**Log per capita**<br>**exp. (Tk./**<br>**month)**|**Moderate**<br>**poverty**<br>**headcount**|**Extreme**<br>**poverty**<br>**headcount**|
|Year|0.002|-1.767|1.370|1.346|
||(0.01)|(-2.64)|(1.98)|(2.20)|
|Household has microenterprise activity as|0.626|0.505|-0.235|-0.357|
|an additional activity (1=yes, 0=no)|(1.89)|(2.69)|(-1.21)|(-1.89)|
|Head’s education (years)|0.022|0.020|-0.013|-0.009|
||(12.05)|(19.46)|(-12.01)|(-8.63)|
|Log of land asset (decimal)|0.071|0.071|-0.045|-0.048|
||(4.51)|(7.89)|(-4.90)|(-5.31)|
|Log of non-land asset (Tk.)|0.150|0.092|-0.093|-0.062|
||(4.43)|(4.81)|(-4.66)|(-3.19)|
|Village has electricity|0.057|0.002|0.006|0.023|
||(1.00)|(0.05)|(0.18)|(0.71)|
|Proportion of irrigated land in village|-0.104|0.139|0.008|-0.104|
||(-0.69)|(1.62)|(0.09)|(-1.21)|
|Village has FFW program (1=yes, 0=no)|0.130|-0.082|0.011|0.050|
||(1.95)|(-2.17)|(0.28)|(1.32)|
|Village has VGF program (1=yes, 0=no)|-0.033|0.073|-0.133|-0.014|
||(-0.39)|(1.52)|(-2.68)|(-0.29)|
|R<sup>2</sup>|0.201|0.287|0.240|-|
|Endogeneity test statistics|χ<sup>2</sup>(1)=1.950|χ<sup>2</sup>(1)=7.810|χ<sup>2</sup>(1)=1.119|χ<sup>2</sup>(1)=3.934|
||p=0.162|p=0.005|p=0.290|p=0.047|
|N|11,200|11,200|11,200|11,200|

Note: The FE unit is thana times year. A household’s adoption of microenterprise activity is considered endogenous and so is instrumented using village-level placement of banks and microcredit programs and their interactions with householdlevel exogenous variables as instruments. For the estimation, data from 2000, 2005 and 2010 rounds of HIES are combined and then households from only those thanas that are common to all three rounds are kept. Figures in parentheses are t-statistics. Regressions additionally include community prices of consumer goods, daily wage, etc., and agroclimate characteristics (land elevation, average number of sunny months, and excess rain amount per month). Sources: HIES surveys, 2000, 2005 and 2010.

40
26

# Table 11: Sources of Growth

(Percentage share of output growth accounted for by factor gro'wth and total factor productivity)

||19|60-197|3|197|3-1987||1960-|1987||
|---|---|---|---|---|---|---|---|---|---|
||Capital|Labor|TFP*|Capital|Labor|TFP|Capital|Labor|TFP|
|Africa|59|22|17|92|37|-27|73|28|0|
|East Asia|50|16|35|62|17|20|57|16|28|
|South Asia|81|20|0|55|19|24|67|20|14|
|Latin America|55|20|25|94|51|-47|67|30|0|

Source: World Bank Development Report 1991 ( Table 2.3) * TFP: Total Factor Productivity
|(8)|(-0.53∗∗∗)<br>-(0.13)∗∗∗|(-0.62∗∗∗)|-(0.02)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-13,156∗∗∗|-0.12∗∗∗<br>wo dependent variables<br>ousehold-head controls<br>s: ∗∗∗p<0.01, ∗∗p<0.05,|
|---|---|---|---|---|---|---|---|---|
|ng Wealth<br>(7)|(-0.40∗∗∗)<br>-(0.13)∗∗∗|(-0.92∗∗∗)|-(0.03)∗∗∗|(-Yes∗∗∗)|(-Yes∗∗∗)|(-No∗∗∗)|-13,156∗∗∗|-0.22∗∗∗<br>theses. The t<br> Household/H<br>fcance level|
|Non-Housi<br>(6)|(-0.55∗∗∗)<br>-(0.13)∗∗∗|(-0.62∗∗∗)|-(0.02)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13,156∗∗∗|-0.11∗∗∗<br>ors are in paren<br>livestock-debt). <br>dwelling. Signi|
|(5)|(-0.42∗∗∗)<br>-(0.13)∗∗∗|(-0.60∗∗∗)|-(0.02)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13,156∗∗∗|-0.00∗∗∗<br>t standard err<br>gold+silver+<br>d, and type of|
|(4)|(-3.29∗∗∗)<br>-(0.38)∗∗∗|(-0.49∗∗∗)|-(0.07)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-8,230∗∗∗|-0.27∗∗∗<br>3 = 0. Robus<br>e expenditure+<br>per househol|
|GSL<br>(2)<br>(3)|(-3.35∗∗∗)<br>(-2.33∗∗∗)<br>-(0.38)∗∗∗<br>-(0.40)∗∗∗|(-0.48∗∗∗)<br>(-0.47∗∗∗)|-(0.07)∗∗∗<br>-(0.08)∗∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-8,230∗∗∗<br>-8,230∗∗∗|-0.26∗∗∗<br>-0.34∗∗∗<br>ion (2) after setting β2 = β<br>Wealth (income-non-durabl<br>ehold size, number of males|
|(1)|(-3.16∗∗∗)<br>-(0.31)∗∗∗|(-0.32∗∗∗)|-(0.07)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-8,230∗∗∗|-0.10∗∗∗<br>ents of equat<br>on-Housing<br>cation, hous|
|Variables|Variance of Income (log)|Income (log)||Household Controls|Province Fixed Efects|Confict|Observations|Adjusted R2<br>The table shows the estimated coefci<br>are GSL (gold+silver+livestock) and N<br>include gender, age, marital status, edu<br>∗p<0.1.|

24