rafmacalaba/datause-encoder-data huggingface dataset:
be partly due to the lower average age, as the age bracket definition differs from the rest of the studied countries; however, anecdotal evidence suggests a similar relevance of private rental for young households, as in Poland.

Excluding Poland, where the private rental share is almost invariant over age brackets, housing starters in the ECA region are between 2.6 and 6.5 times as likely to be private rental tenants as the population as a whole. For such households, subsidized rental housing such as university dormitories and company housing is available only in isolated cases. Consequently, starters in the region are relegated to seek rental tenure in the private market, with its frequently much higher rents (see below).<sup>50</sup> In the category of young families (Table 6 and Figure 5), with household heads between ages 30 and 44, the owner-occupied ratio rises; but still the private rental sector is of high relevance, catering to between 5 and 27 percent of households. Given the likely high degree of underreporting of private rental status in the surveys for Romania and Russia discussed earlier, one should consider the figures obtained for these countries in all age brackets as their lower boundaries.

**Table 6. Urban Tenure by Young Households in Studied Countries**

|||**Armenia**|||**Lithuania***||
|---|---|---|---|---|---|---|
|**% of households**|**Total**|**Starters**|**Young**<br>**families**|**Total**|**Starters**|**Young**<br>**families**|
|Owner-occupied|91.6|68.2|85.8|82.6|60.4|83.0|
|Other tenure|8.4|31.8|14.2|17.4|39.6|17.0|
|Public rental|5.9|17.1|9.0|3.8|4.0|4.5|
|Private rental|2.5|14.7|5.2|12.2|33.9|11.4|
|Free orother|n/a|n/a|n/a|1.4|1.6|1.1|
|||**Poland**|||**Romania**||
|Owner-occupied|55.4|43.9|50.0|95.2|79.2|94.6|
|Other tenure|44.6|56.1|50.0|4.8|20.8|5.4|
|Public rental|20.9|32.0|22.7|1.5|1.9|1.8|
|Private rental|23.7|24.2|27.3|1.9|11.9|1.9|
|Free orother||||1.4|7.0|1.7|
|||**Russia**||**Ser**|**bia and Monten**|**egro**|
|Owner-occupied|56.0|47.7|51.7|84.7|73.9|86.8|
|Other tenure|44.0|52.3|48.3|15.3|26.2|13.2|
|Public rental|40.7|32.6|43.1|3.9||5.8|
|Private rental|3.0|19.4|4.7|7.1|18.1|4.9|
|Free orother|0.4|0.4|0.6|4.4|8.1|2.5|

_Source_ : Household surveys (Table 1).

*Lithuanian Free Market Institute (2002) _._

_Note_ : Urban apartment sector, except Lithuania (entire housing sector). Private rental includes company housing and excludes in-kind and free housing. Age definitions: starters, 16–29 years; young families: 30–44 years (with at least one child). Lithuania Household Survey age definitions: starters, all under 20 years; young households, 30–39 years (regardless of number of children).

> 50  Sub-renting is typically at market rents, while formal renting is often subject to rent controls—or at least to rent review controls, as in Poland.

28
# **Table 11: Fertility and Risky Behaviors, Among Women Aged 18-25 at Baseline**

## **OLS estimates, standard errors in parentheses, p-values in braces**

||**Condom use:**<br>**Often/Always**|**Other**<br>**Contraceptive**|**Frequency**<br>**of Sex**|**Frequency of**<br>**Unprotected Sex**|**Unwanted**<br>**Sex**|**Transactional**<br>**Sex**|**Pregnancy**|**Out-of-Wedlock**<br>**Pregnancy**|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|**Disruption Effect**|-.009|-.027|-.794|-.046|-.038|-.002|.017|.053|
||(.011)|(.062)|(1.01)|(.818)|(.024)|(.018)|(.049)|(.035)|
|**Treatment Effect in High Disruption**|.008|.137**|-.577|-.950|.054**|.054***|-.047|-.051|
||(.010)|(.063)|(.825)|(.730)|(.023)|(.020)|(.048)|(.032)|
|**Treatment Effect in Low Disruption**|-.000|.034|-.053|-.142|.004|.017|.008|.024|
||(.009)|(.030)|(.542)|(.388)|(.013)|(.011)|(.028)|(.018)|
|**Difference [β3, p-value]**|{.525}|{.150}|{.596}|{.331}|{.063}|{.125}|{.333}|{.042}|
|**Control Mean at BL**|.049|.468|7.31|3.96|.154|.047|.844|.369|
|**Observations**|2,314|2,314|2,196|2,171|2,243|2,243|2,400|2,400|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. The Other Contraceptive category includes all methods other than condoms. Frequency of sex is measured over a 30 day period. Frequency of unprotected sex is defined as intercourse frequency for respondents that do not use any form of contraceptive, and equal to zero for those that do (to provide conservative estimates, respondents that ever use condoms are assumed to engage in protected sex). Unwanted Sex is defined as, "Been involved in any sexual intercourse that you were not willing to do". Transactional Sex is defined as, "Receiving anything such as money, gifts, help with school work or something else in exchange for sexual intercourse". Columns 1 through 4 do not control for baseline outcome values, while all other specifications do. Columns 7 and 8 control for any pregnancy at baseline (in or out-of-wedlock). The main results are estimated with an ANCOVA specification. Control variables for all specifications include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
Even though the results in this paper cannot provide a conclusive answer to the question of what is leading price dispersion in multiple-destinations exporters, it opens the way for future research in this area. In fact, even though it is a standard practice to treat agricultural commodities as homogeneous goods, these commodities are perceived by consumers as differentiable in quality, according to production practices, seeds, geographical locations of production, sanitary and phyto-sanitary measures and food safety requirements. For this reason, differences in brand, quality and other aspects of heterogeneity make these products differentiated in international trade<sup>4</sup> . Allowing for the possibility that food products can be vertically differentiable amplifies the spectrum of existing opportunities for developing countries to exploit product differentiation and market niches in countries where consumers are willing to pay more for products that satisfy specific quality dimensions.

The remainder of the paper is organized as follows: Section 2 reviews the theoretical and empirical framework upon which this work is built. Section 3 reports how Tanzanian trade performed during the 2003-2009 period. Section 4 introduces the data used in this analysis and highlights some preliminary characteristics. Section 5 studies the variation in prices across firms and destinations to investigate whether the Tanzanian data shows evidence of price or quality competition. Section 6 concludes.

# **2. Literature Review**

Before starting the empirical analysis, it is useful to classify the main models of trade and heterogeneous firms and review the predictions that are most relevant for this paper. This section will focus on the predicted relationship between firms’ fob export prices and: revenue, quantity, GDP, GDP per capita and distance. This brief review draws on Baldwin and Harrigan (2011) and Manova and Zhang (2012). The classification of the reviewed models is summarized in Table 1. Even though it is based on Baldwin and Harrigan (2011), unlike their analysis the fob export price in Table 1 is intended as the unit price of a product at the _firm level_ , not the unit price of a particular good _averaged across firms_<sup>_5_</sup> . For this reason, Table 1 should be compared to the results of the firm-level work in Manova and Zhang (2012).

The workhorse model of heterogeneous firms, Melitz (2003), predicts that only firms with productivity above a certain threshold level can export. In the classification summarized in Table 1 Melitz (2003) is defined as a price competition model (or efficiency sorting model) with constant mark-up: more productive firms face lower marginal costs and charge lower prices. The Melitz model incorporates firm level productivity differences: before deciding to enter an industry, firms draw a productivity level and the marginal production cost associated to it. The model assumes constant elasticity of substitution (CES)

> 4 Saker and Surry (2006)

> 5 Ito (2011).

4
<u>Table 3. Global inequality extraction ratio, 1820-2002</u>

||Using<br>Maddison<br>GDP data<br>and<br>Milanovic<br>global<br>inequality<br>estimate|Using Madd|ison GDP per|capita at 199|0 PPPs, and B<br>estimates|ourguignon-M|orrisson globa|l inequality|Using 2002<br>WDI GDPs and<br>2005 benchmark<br>PPPs and<br>Milanovic<br>global inequality<br>estimate|
|---|---|---|---|---|---|---|---|---|---|
||1820|1850|1870|1913|1929|1950|1960|1980|2002|
|(1) Global average GDP<br>percapita (in$PPP)|689|916|911|1599|1941|2111|2778|4520|5886|
|(2) Global average<br>income to subsistence<br>(ratio)|2.30|3.05|3.04|5.33|6.47|7.04|9.26|15.07|19.62|
|(3) Maximum feasible<br>Gini(in%)|56|67|67|81|85|86|89|93|95|
|(4) Maximum feasible<br>Theil(in%)|83|112|111|167|187|195|223|271|298|
|(5)Estimatedglobal Gini|43|53.2|56.0|61.0|61.6|64|63.5|65.7|70.7|
|(6) Estimated global<br>Theil(0)|58|49|54|67|69|78|77|85|105|
|(7) Estimated global Gini<br>inequality extraction ratio<br>(5/3;in%)|77|79|83|75|73|75|71|70|73|
|(8) Estimated global<br>Theil inequality<br>extraction ratio (6/4; in<br>%)|70|43|49|40|37|40|34|31|35|

Sources: 1820-1980 global average income calculated from Maddison’s data; global Gini from Maddison and Bourguignon (2002) who base themselves on Maddison’s data (all data in 1990 PPPs). 2002, global income calculated from World Development indicators (in 2002 PPPs, based on the benchmark 2005 ICP). Global inequality for 2002 calculated from Milanovic World Income Distribution (WYD) database; all in 2002 PPPs, based on the benchmark 2005 ICP. Therefore, the PPPs used for year 2002 differ from Maddison’s 1990 Geary-Khamis PPPs by US CPI increase between 1990 and 2002 (38 percent) as well as by major revisions in relative price levels that occurred in 2005 International Comparison Project. The data for 2002 ($PPP 5886) correct for the former (that is, formally they are expressed in 1990 international prices) but cannot correct for the latter.

18
# **Table 4. Sibling Sex Composition and Competitiveness**

Dependent variable =1 if participant chooses tournament in Round 3, =0 if instead chooses piece‐rate.

Linear probability model estimates in Columns 1‐3, Probit estimates in Column 4, standard errors reported in parentheses.

|||**LPM Estimates**||**Probit Estimates**|
|---|---|---|---|---|
||**(1) No**<br>**controls**|**(2) Experimental**<br>**controls**|**(3) Other**<br>**controls**|**(4) All**<br>**controls**|
|**_Panel A. Girls_**|||||
|**Impact of sisters in control communities [**��**]**|.028|.037|.043|.037|
||(.053)|(.045)|(.044)|(.039)|
|**Impact of sisters in treatment communities [**�����**]**|‐.011|‐.020|‐.019|‐.018|
||(.036)|(.034)|(.034)|(.032)|
|**Siblings fixed effects**|Yes|Yes|Yes|Yes|
|**Experimental controls**|No|Yes|Yes|Yes|
|**Other controls (socio‐demographics)**|No|No|Yes|Yes|
|**Test (p‐value):**����|[.523]|[.274]|[.584]|[.248]|
|**R‐Squared**|.043|.221|.227|–|
|**Observations**|389|389|389|389|
|**_Panel B. Boys_**|||||
|**Impact of sisters in control communities [**��**]**|‐.103**|‐.089**|‐.087**|‐.088**|
||(.043)|(.043)|(.044)|(.044)|
|**Impact of sisters in treatment communities [**�����**]**|.077**|.094***|.098***|.105***|
||(.038)|(.033)|(.030)|(.029)|
|**Siblings fixed effects**|Yes|Yes|Yes|Yes|
|**Experimental controls**|No|Yes|Yes|Yes|
|**Other controls (socio‐demographics)**|No|No|Yes|Yes|
|**P‐value:**����|[.002]|[.002]|[.002]|[.000]|
|**R‐Squared**|.090|.259|.268|–|
|**Observations**|304|304|304|303|

**Notes:** *** (**) (*) indicates significance at 1% (5%) (10%) level. Each column corresponds to a separate regression. In all columns the dependent variable is a dummy equal to 1 if the participant chooses competitive tournament in round 3, and 0 if instead chooses uncompetitive piece‐rate. Linear probability model (OLS) estimates are reported in Columns 1‐3. Probit marginal estimates are reported in Column 4. Robust standard errors clustered by experimental session reported in parentheses (50 clusters). Column 1 only controls for the number of sisters, the treatment dummy, their interaction, and sibship size fixed effects (in isolation and interacted with the treatment dummy). Column 2 additionally includes experimental controls: participant’s performance in round 1, improvement in performance from round 1 to round 2, a dummy equal to 1 if the participant believes to have ranked first during round 1 performance and 0 otherwise, and a dummy equal to 1 if the participant chooses to submit past performance in round 1 to tournament and 0 to piece‐rate. Columns 3 and 4 additionally control for participant‐level socio‐demographic characteristics: age, years of schooling, indicators for whether the participant is out of school, is married, and has children.
28

# Table VIII

# Growth and Initial Financial Depth: 1960-89

|Independent<br>Variable|(1)|(2)<br>(3)|(4)|
|---|---|---|---|
|C|0.042***<br>(0.005)|0.035***<br>0.033***<br>(0.007)<br>(0.009)|0.035***<br>(0.010)|
|LYO|-0.014***<br>(0.003)|-0.016***<br>-0.016***<br>(0.003)<br>(0.003)|-0.014***<br>(0.003)|
|LSEC|0.013***<br>(0.002)|0.013***<br>0.013***<br>(0.002)<br>(0.002)|0.010***<br>(0.003)|
|GOV in 1960||0.070*<br>0.072*<br>(0.035)<br>(0.036)|0.044<br>(0.040)|
|PI<br>in 1960||0.037<br>0.032<br>(0.031)<br>(0.033)|0.040<br>(0.033)|
|TRD in 1960||-0.003<br>-0.004<br>(0.006)<br>(0.006)|0.001<br>(0.001)|
|Index of Civil<br>Liberties||0.001<br>(0.002)|0.001<br>(0.002)|
|Number of<br>Revolutions||-0.010<br>(0.009)|-0.010<br>(0.009)|
|Number of<br>Assassinations||-0.001<br>(0.004)|0.001<br>(0.003)|
|Sub-Saharan<br>Africa Dummy|||-0.011<br>(0.007)|
|Latin American<br>Dummy|||-0.010*<br>(0.005)|
|LLY in 1960|0.030***<br>(0.007)|0.028***<br>0.028***<br>(0.00-;<br>(0.008)|0.020**<br>(0.009)|
|R<sup>2</sup>|0.57|0.61<br>0.63|0.66|
||(standard e|rrors in parentheses)||
|Dependent variable:<br>Observations:|GYP-<br>Real pe<br>57|r capita GDP growth 1960-1989||

* significant at 0.10 level ** significant at 0.05 level *** significant at 0.01 level LYO = log of initial real per capita GDP in 1960 LSEC = log of secondary school enrollment rate in 1960 GOV = government consumption / GDP PI = inflation rate TRD = (imports + exports) / GDP
**Table 7: Effect of participation in the non-farm activities on various outcomes in rural Malawi**

||**Effects of non-farm wage e**<br>|**mploymentpart**<br>|**icipation**<br>|**Effects of non-farm self-**<br> <br>|**employmentpar**<br>|**ticipation**<br>|
|---|---|---|---|---|---|---|
|VARIABLES|CRE Probit/fractional Probit /<br>ordered Probit|CRE Tobit|Linear FE|<br>CRE Probit/fractional Probit /<br>ordered Probit|CRE Tobit|Linear FE|
|**Objective welfare outcomes (N=5321)**|||||||
|HHPCE (1000 MKW)|||4.476|||7.003***|
||||[0.158]|||[0.000]|
|Log HHPCE|||0.102**|||0.129***|
||||[0.022]|||[0.000]|
|Poverty incidence|-0.074**|.|-0.072**|-0.085***||-0.083***|
||[0.012]||[0.022]|[0.000]||[0.000]|
|Poverty gap|-0.034***|-0.033**|-0.034**|-0.035***|-0.035***|-0.030***|
||[0.007]|[0.011]|[0.018]|[0.000]|[0.000]|[0.002]|
|Squared poverty gap|-0.018**|-0.017**|-0.019**|-0.018***|-0.017***|-0.014**|
||[0.021]|[0.017]|[0.045]|[0.000]|[0.000]|[0.011]|
|**Subjective welfare outcomes**|||||||
|**(N=5321)**|||||||
|Food insecurity (0/1)|-0.047+||-0.065**|-0.034+||-0.027|
||[0.125]||[0.041]|[0.140]||[0.281]|
|Food consumption adequacy (1-3)|0.177**||0.104**|0.057||0.025|
||[0.037]||[0.021]|[0.379]||[0.482]|
|**Agricultural outcomes (N=5038)**|||||||
|Fertilizer purchase decision (0/1)|0.039||0.037|0.045**||0.055**|
||[0.190]||[0.303]|[0.043]||[0.036]|
|Inputs purchase decision (0/1)|0.046||0.037|0.053**||0.060**|
||[0.160]||[0.260]|[0.031]||[0.039]|
|Value of fertilizer purchase (1000||0.156|-0.638||0.388|0.136|
|MKW)|||||||
|||[0.745]|[0.419]||[0.225]|[0.786]|
|Value of inputs purchase (1000 MKW)||-0.124|-0.874||0.234|0.085|
|||[0.802]|[0.305]||[0.495]|[0.870]|
|Land cultivated (1000 acres)||-0.292|0.274||0.710|-0.576|
|||[0.699]|[0.800]||[0.561]|[0.893]|

Source: Generated by authors using LSMS data Note: ***, **, *, and + indicate that the corresponding regression APE is statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. The table presents only APEs on our main treatment variables. The APEs from the CRE Probit models are estimated using GEE approach. The CRE fractional Probit APEs are from GLM estimation. The unconditional APEs from CRE Tobit are estimated using pooled Tobit regression methods. The linear FE results are presented for comparison purpose. The full results tables are in appendices 2, 4, 5, and 6. P-values between brackets are based on cluster robust standard errors.

33
# **<u>Table 4: Determinants of generation III’s rates of educational completion</u>**

||<br>**Prim**|**ary**|**Seco**|**ndary**|
|---|---|---|---|---|
|Female|-0.355***<br>(0.006)|-0.373***<br>(0.005)|-0.186***<br>(0.012)|-0.165**<br>(0.020)|
|Female*Mother’s father died after 1994 or alive|0.028*|0.032|0.042|0.069**|
||(0.009)|(0.015)|(0.028)|(0.016)|
|Female*Education gap mother-father||-0.005<br>(0.003)||0.008<br>(0.005)|
|Female*Share of mother’s gifts at marriage||-0.042<br>(0.143)||-0.276***<br>(0.024)|
|Reform*Female|0.146***|0.202***|-0.278***|-0.279***|
||(0.006)|(0.005)|(0.012)|(0.020)|
|Reform*Female*Mother’s father died after 1994 or alive|0.080**|0.065**|0.131**|0.089**|
||(0.009)|(0.015)|(0.028)|(0.016)|
|Reform*Female*Education gap mother-father||0.017**<br>(0.003)||0.022**<br>(0.005)|
|Reform*Female*Share of mother’s gifts at marriage||0.044||0.321***|
|||(0.143)||(0.024)|
|Observations|1,919|1,919|1,634|1,634|
|R-squared|0.738|0.739|0.736|0.739|

_Note:_ As explained in the text, the sample comprises generation III individuals 15-30 and 19-34 years old in 2011 (<14 years or < 18 in 1994) for primary and secondary education, respectively. All regressions include gender and state specific year of birth fixed effects and household fixed effects. Robust standard errors clustered by state in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

19
increased from 26% in 2000 to 37% in 2005. On an employment-weighted basis, the rate increased from 17% to 25%. The female ownership rates across major cities have a distribution that is mostly similar to the distribution across states.

In Table 2b’s services analysis, states with the highest female ownership rates are Kerala, Tamil Nadu, and Andhra Pradesh, with average female ownership shares exceeding 12%. The lowest female ownership rates are in Rajasthan, Bihar, Orissa, and Uttar Pradesh, each with 6% or less. The average female business-ownership share, with and without employment weights, was between 8% and 9% for 2001 and 2006. Female ownership rates in major cities tend to be higher than overall state averages in services. The correlation of state gender ratios between manufacturing and services is about 0.5 on a count basis and above 0.9 on an employmentweighted basis.

Tables 3a and 3b provide similar female business-ownership shares by two-digit NIC industry in manufacturing and services, respectively. Within manufacturing, female shares are highest and typically exceed 50% in industries related to chemicals and chemical products, tobacco products, and paper and paper products. At the opposite end, female shares of 2% or less are evident in industries related to computers, motor vehicles, fabricated metal products, and machinery and equipment. Among services industries, female ownership shares exceed 30% in industries related to sanitation and education. Industries related to research and development, water transport, and land transport have the lowest rates at 1% or less. Female-owned businesses are smaller in terms of employment than male-owned business in manufacturing, while they are larger in services.

# **Spatial Determinants of Female Entrepreneurship**

We now describe the spatial factors that we relate to female entrepreneurship patterns. We first identify general district traits that can influence the gender ratio of business owners. We then construct indices of local industrial conditions for a specific district-industry.

## _District-Level Conditions_

Our initial explanatory measures naturally focus on basic traits of the district: population levels (and its square), population density, age profile, and average education levels. Given our interest on gender balance, we also include the district’s female literacy rate, total fertility rate, and sex ratio. These traits are important as entrepreneurs tend to start their businesses in their current local area and are even disproportionately found in their region of birth (e.g., Figueiredo et al. 2002, Michelacci and Silva 2007,Dahl and Sorenson 2007). These controls will pick up this 7
(Insert Table 6 about here)

# **6. Robustness**

# _6.1. Interactions with Investment Environment Variables_

Examining the interactive effect of FPI with investment environment variables gives us a better idea of the effect of FPI on small firm access to capital across levels of investment environment (i.e., levels of investment, law and order, and corruption variables). Examining the implications of FPI when interacted with these variables on the entire dataset (across firm size and property rights development) offers further insight into the importance of the investment environment.

The results are similar to those of La Porta et al. (1997), Bekaert and Harvey (2003), and Wurgler (2000). The impact of FPI including investment (a variable that refers to a government’s attitude toward foreign investment and the resulting risk of foreign investment in that country) is one that implies decreasing margin to return. For those governments that are hostile to foreign investment (e.g., investment = sample mean minus the sample standard deviation), the marginal impact of FPI on access to capital is large, 8.71% for each standard deviation increase in FPI. For those governments already open to FPI, the marginal effect of becoming more so is less. Those governments with an investment index equal to the sample mean plus the sample standard deviation would see only a 0.92% marginal benefit of a one standard deviation increase in FPI.

The impact of FPI given corruption is also influential. The cumulative impact suggests that the impact of FPI in the presence of this variable is pivotal. For example, looking to those nations where corruption is rampant (e.g., corruption = sample mean minus sample standard deviation), the cumulative impact of a one standard deviation increase in FPI is positive for firm access to capital (3.09%). In those nations where corruption is well under control (e.g., corruption = sample mean plus sample standard deviation), the impact is equally large with regard to magnitude, but negative at -2.98%. These results suggest that there are decreasing returns to scale for corruption as well. In fact, nations with a corruption 20