rafmacalaba/datause-encoder-data huggingface dataset:
# **7.2 The Urban Environment and Employment Types**

To explore how the urban environment correlates with job types, we use big data and the Mozambique Census to explore how urban density, poverty, and cell service correlate with the spatial distribution of job types across five urban areas. A visual depiction of the spatial distribution of the variables is provided in Annex 2. The multivariate estimates are presented in Table 6.

**Table 6: Multivariate estimates, by job type**

|Variables|LE-SE|HE-SE|Unpaid<br>Worker|Employer|Informal<br>Employee|Formal<br>Employee|
|---|---|---|---|---|---|---|
|Log Building|0.029|0.001|0.003|-0.012***|0.010|0.019|
|Density|(0.018)|(0.003)|(0.014)|(0.004)|(0.008)|(0.021)|
|Log|0.001|-0.0001|-0.001|-0.008|0.016**|-0.041***|
|Population|(0.011)|(0.002)|(0.011)|(0.007)|(0.008)|(0.014)|
|Log|-0.146**|-0.006|-0.131*|0.024|0.043|0.235***|
|Luminosity|(0.069)|(0.015)|(0.067)|(0.018)|(0.052)|(0.118)|
|Avg<br>Electrification<br>Likelihood|-0.068<br>(0.058)|0.048**<br>(0.014)|-0.179***<br>(0.038)|0.007<br>(0.020)|0.198***<br>(0.046)|0.067<br>0.087)|
|Proportion of<br>Urban Area|-0.083<br>(0.057)|0.019**<br>(0.009)|-0.038<br>(0.056)|0.055***<br>(0.014)|-0.012<br>(0.043)|-0.067<br>(0.063)|
|Log Cell<br>Tower Count<br>per sqkm|-0.015<br>(0.009)|0.004<br>(0.003)|0.007<br>(0.007)|0.002<br>(0.003)|-0.006<br>(0.007)|0.012<br>(0.012)|
|R-squared|0.813|0.801|0.368|0.883|0.706|0.849|

Notes: Linear Models of employment-type probability. Control variables for all regressions were poverty proxies and city fixed effects. Each model is estimated with the full set of urban variables. Each column presents the results of a single regression, where only the coefficient estimate and standard error for the urban variables in the regression are reported. Sample size for each estimate is 212.

Crowded neighborhoods are also those with less formal work arrangements. The results are robust to two measures of urban density. Population density is positively and significantly correlated with the density of informal employees but negatively correlated with formal employees. Similarly, building density is negatively correlated with being an employer. The findings from the multivariate regression are consistent with literature that finds that urban density is characterized by greater congestion and thus less conducive to operating a formal sector firm (Grover and Maloney 2022).

Less poor neighborhoods are home to formal and more structured types of work. The two measures of neighborhood economic development – luminosity and electrification – tell slightly different stories.<sup>63</sup> Luminosity is positively and significantly correlated with formal employees but negatively correlated with LE-SE and unpaid family workers. Electrification

> 63 Henderson et al. (2012) find that night-time lights are a proxy for the density of poverty.

Page **42** of **60**
health of community banks are the total market shares of community banks<sup>(defined in various ways), and their</sup> weighted-average efficiency ranks estimated using cost and profit functions<sup>for the banks in each nation in each</sup> year. Importantly, these variables measure the health ofcommunity banks relative<sup>to other banks within the same</sup> nation, rather than comparisons of individual banks across nations that operate under<sup>very different conditions.As</sup> a consequence, these relative health variables are reasonably comparable across<sup>nations.</sup> We run three sets of tests separately for 21 developed nations and for the 28<sup>developing nations.First, we</sup> run reduced-form regressions of gross domestic product (GDP) growth on the relative<sup>health of community banks,</sup> controlling for other dimensions of the financial system (public debt and equity markets,<sup>regulation,legal rights,</sup> bank competition) identified in the finance-growth literature. This allows<sup>us to test whether the relative health of</sup> community banks affects economic growth.

Second, we try adding measures of the SME employment share and the ratio of<sup>overall bank lending to</sup> GDP as additional regressors to these GDP growth equations to test the transmission<sup>mechanisms.In a recursive</sup> model of the transmission mechanisms, community bank health would directly affect<sup>oneor bothof these</sup> intermediate variables and then these variables would directly affect<sup>economicgrowth.Thus, to the extent that</sup> improved SME financing is an important mechanism through which<sup>relatively strong community banks improve</sup> economic growth, then we may expect a positive measured effect of the SME employment<sup>share on GDP growth,</sup> and a substantial diminishment of the measured effects of community bank<sup>health on GDP growth when the SME</sup> employment share is controlled for in the regressions. Similarly, if greater<sup>overallflows of bankcredit is a key</sup> transmission mechanism, then we may expect a positive measured effect ofthe<sup>overall-bank-lending-to-GDP ratio</sup> and substantial diminishment of the measured effects of the relative health of<sup>community banks on GDP growth</sup> when the overall bank lending variable is included in the regressions.

Third, we regress the SME employment share and bank lending to<sup>GDP ratioon therelativehealth of</sup> community banks and the control variables. We test whether community<sup>bank healthhas positive effects on the</sup> SME sector and on overall bank lending, as predicted by the two transmission<sup>mechanisms.</sup> In addition to trying to contribute to the finance-growth literature, we<sup>try to add to the community banking</sup> literature in several ways. First, we examine the effects of community banks on<sup>overall economic performance.</sup> Community banking studies often focus on flows of credit to SMEs, but<sup>generallydonotexaminethe</sup> 2
schedules or fixed costs (e.g. commuting time or child care), and individual preferences for economic autonomy (Hill, 1989).<sup>7</sup> Yet, a persistent gap in the literature on women’s employment is that informal and unpaid work are largely undocumented and unquantified, while a disaggregated picture of female work by its nature and ‘quality’ is likely to provide a more realistic employment pattern, especially in a developing or transition economy (Paci, 2002; Lim 1996, Mehra and Gammage, 1999).<sup>8</sup> Thus, we account for the heterogeneity of female labor market constraints by distinguishing paid from unpaid work. Overall, it is well known that male and female family decision-makers have different preferences, but it is an open question how massive emigration in a traditional society affects the value of time and the ownership of an income stream by gender.

# _2.1. The Albanian context_

Albania is a particularly interesting setting where to study the impact of migration on domestic (formal and informal) labor market by gender. This country has been largely affected by the passage on the market economy at the beginning of 1990 and key changes over the process have occurred in the local labor market. Like in many other transition economies, the country experienced a substantial decline and stagnation in labor force participation in the new labor market. Public sector employment has declined enormously during the transition period but job growth in the private sector has been too slow to compensate (see Figure 1).

<!-- Start of picture text --> Active population by gender, 1998-2002 Public sector employment by gender, 1999-2003<br>1600 140<br>1400 120<br>1200<br>100<br>1000<br>80<br>800<br>60<br>600<br>40<br>400<br>200 20<br>0 0<br>1998 1999 2000 2001 2002 1998 1999 2000 2001 2002 2003 2004 2005<br>TOTAL Men Women Men Women<br>s<br>d<br>n<br>a n d s a<br>s<br>u<br>o<br>h o u s h<br>T T<br><!-- End of picture text -->

**Figure 1** : Albanian labor market trends ( _Source_ : ILO LABORSTA)

7 As pointed out by Schultz (1990), women are more likely to work in the family or informal labor market if the labor costs to firms exceed the opportunity costs of female labor to family enterprises. Firms are at a relative disadvantage compared with families in the employment of less experienced and less skilled labor. Edwards and Field-Hendrey (2002) focus specifically on the site of work and show that, even in the U.S., home-based work is an attractive option for women for whom the fixed costs of work are high.

8 From a perspective of a time allocation model, labor supply (the allocation of leisure) is an indirect measure of individual consumption. However, the impossibility to distinguish leisure from other non-market time (e.g. home production or unpaid labor), is a shortcoming of most studies, which therefore assume that a person’s utility is increasing in _all_ non-market time (including unpaid work).

6
societal pressures reduce the ability of women to migrate alone from such countries. For example, in Kuwait, where Egyptians and Syrians are among the major Arab migrant groups, Shah (2004) reports that there were twice as many migrant male Arabs as migrant females, and that only 12 percent of female Arab migrants were in the labor force, compared to 66 percent of Arab men. Most females were not working and accompanying family members. This contrasts strongly with female Asian migrants to the Gulf, who more often migrate alone as housemaids and for whom 77 percent were working.

**Table 6: Walling their Women in**

|_Married Women require their husband's permission_|_Restrictions on both married and unmarried women_|
|---|---|
|_but no restriction on unmarried women_|Afghanistan|
|Algeria (women under 18)<br>Democratic Republic of Congo|Iran<br>Jordan|
|Gabon|Kuwait (unmarried women under 21)|
|Uganda (when travelling with children)|Libya<br>Qatar (women under 30)|
|_Unmarried Women require their father's permission_|Saudi Arabia|
|_but no restriction on married women:_|Sudan|
|Egypt (women under 21)|Swaziland<br>Syria (women under 18)<br>United Arab Emirates<br>Yemen|

source: Country reports of U.S. Department of State (2005) In Table 7 we examine whether the presence of these restrictions on the freedom of women to emigrate is associated with a country having fewer migrants per capita.<sup>13</sup> Since we were unable to obtain passport costs for many of the countries with restrictions, we do not look at the joint impact of passport costs and restrictions, but instead regress migrants per capita on per capita income, the log of population, and the government effectiveness measure which was a prime determinant of passport costs. In Column 2 we also check how sensitive the results are to adding a squared term in per capita income and

> 13 Gender disaggregated migration data is not available at a sufficient level of detail to permit testing whether these restrictions have a greater effect on female migration, as one would expect.

- 18 -
**Table 2: SOFTEC information regarding the characteristics of the properties and the development.** **<u>Means and standard deviations in parentheses.</u>**

|**Variables**|**Average values**<br>**(standard deviation)**|
|---|---|
|Median housing price of properties sold in the last trimester of the year|1,144,346<br>(1,497,433)|
|Average number of years with information (how long in years the|3.109|
|development takes to be sold out)|(2.076)|
|Average number of properties sold in a month (in the same development)|3.447<br>(12.00)|
|Total number of properties sold per development|84.62<br>(460.2)|
|Area of the property (mts2)|125.8<br>(93.46)|
|Number of bathrooms|2.133<br>(1.655)|
|Number of bedrooms|2.368<br>(0.586)|
|Number of stories|5.864<br>(5.124)|
|Property has parking place|1.619<br>(0.932)|
|Property has bedrooms for employees|0.276<br>(0.447)|
|Socioeconomic classification: 1. Social 2. Affordable 3. Medium 4.|3.771|
|Residential 5. Residential plus|(0.910)|
|Development has a pool|0.125<br>(0.330)|
|Development has security surveillance|0.777<br>(0.416)|

Source: SOFTEC.

# _2.2. Transport infrastructure and accessibility measures_

In order to measure accessibility, we distinguish between “private accessibility”, which considers road network distances from each residential point to all employment subcenters, and “public accessibility” which measures distances to public transit options. Employment subcenters were identified in Atuesta et al. (2017) using McMillen’s (2001) approach. Private accessibility measures are estimated from each housing project to each subcenter. The private accessibility index uses an exponential distance decay function as follows (Song, 1996): where �� is the accessibility index for each project �; �� is the number of employees working in subcenter �; and ��� is the road distance variable from project � to employment subcenter 11
In Burkina Faso, Kenya, and Senegal, over 20 percent of the households report receiving remittances from members residing in other countries. This contrasts with Nigeria and Uganda where less than 6 percent of households report receiving international remittances.

Furthermore, the survey includes questions on households’ access to and use of formal financial services. In particular, households are asked whether any member of the household has a bank account. For households that have a member residing abroad, there is also a question asking if the household opened a bank account after the migrant left.

There is significant variation across countries in terms of households’ financial inclusion. Over 60 percent of the households sampled from Kenya report that at least a member of the household has a bank account. In Burkina Faso, only 11 percent of households sampled report having a bank account. About 44 percent of households in Nigeria report having at least a member with a bank account. The corresponding figure is 23 percent for Uganda and 20 percent for Senegal.<sup>5</sup> A relatively smaller proportion of the households with an international migrant note that a member of the household opened a bank account after the international migrant left the household. Higher values are reported in Kenya (and Uganda) where about 18 (and 8) percent of the households noted opening such account after the international migrant left the family. For the rest of the countries it is less than 5 percent.

We analyze the link between remittances and financial inclusion by estimating the following model:

> 5A nationally representative survey of household financial inclusion indicates slightly lower figures for all the countries, except for Burkina Faso (Demirguc-Kunt and Klapper, 2012). The survey indicates that only about 6 percent of the population in Senegal has an account in formal financial institutions. Similarly, just about 42 percent of households in Kenya have an account in formal financial institutions. The corresponding figures for Uganda and Nigeria are 20 and 30 percent, respectively. In Burkina Faso, about 13 percent of the households report to have such account.

6
# **Table 4: Benefit-Cost Ratios for Teaching** **<u>at the Right Level, Second and Third Grades</u>**

||Te<br>|st scores to<br>|labor earnings<br>|<br>|Test<br>|scores to ye<br>to labor e<br>|ars of school<br>arnings<br>|ing<br>|
|---|---|---|---|---|---|---|---|---|
||In-class<br>remedial,<br>with<br>assistant<br>(1)|After-<br>school,<br>remedial<br>with<br>assistant<br>(2)|Split<br>class, with<br>teacher<br>and<br>assistant<br>(3)|In-class<br>remedial,<br>no<br>assistant<br>(4)|In-class<br>remedial,<br>with<br>assistant<br>(5)|After-<br>school,<br>remedial<br>with<br>assistant<br>(6)|Split<br>class, with<br>teacher<br>and<br>assistant<br>(7)|<br>In-class<br>remedial,<br>no<br>assistant<br>(8)|
|**Annual cost per student, 2017 PPP $**|46|46|<br>44|25|46|<br>46|<br>44|<br>25|
|**Test score effect size, s.d.’s**|||||||||
|English, two-year treatment|0.129|0.174|0.079|0.085|0.129|0.174|<br>0.079|<br>0.085|
|Foundational English, two-year treatment|0.127|0.178|0.077|0.130|0.127|0.178|<br>0.077|<br>0.130|
|English, persistence|0.033|0.089|0.088|–0.020|0.033|0.089|<br>0.088|<br>–0.020|
|Foundational English, persistence|0.063|0.114|0.104|0.021|0.063|0.114|<br>0.104|<br>0.021|
|**Annual benefit per student, 2017 PPP $**|||||||||
|English, two-year treatment|220|297|135|145|242|327|<br>148|<br>160|
|Foundational English, two-year treatment|217|304|132|222|238|334|<br>145|<br>244|
|English, persistence|113|304|301|–68|124|334|<br>165|<br>–75|
|Foundational English, persistence|215|390|356|72|237|428|<br>391|<br>79|
|**Benefit-cost ratio**|||||||||
|English, two-year treatment|4.8|6.5|3.1|5.8|5.3|7.1|<br>3.4|<br>6.4|
|Foundational English, two-year treatment|4.7|6.6|3.0|8.9|5.2|7.2|<br>3.3|<br>9.7|
|English, persistence|2.4|6.6|6.8|–2.7|2.7|7.2|<br>3.7|<br>-3.0|
|Foundational English, persistence|4.7|8.5|8.1|2.9|5.1|9.3|8.8|3.1|

_Note_ : See appendix B for details on the calculations. s.d. = standard deviation. PPP = purchasing power parity. Two-year treatment: the impact for children who participated in the program for two years, in both second and third grades. Persistence: impact on children who participated only in the third grade but were tested a year later, at the end of their fourth grade.

29
school-going children were displaced from one department to another, then it is likely that it would produce an upward bias on the average education impact of war. It is also possible that the household control variables for the comparison group might have changed over time, especially if they migrate. As a result, we use an alternative identification strategy to minimize the potential bias that might arise due to these factors.

# **4.2 Identification using victimization indicators from ENV-2008**

As a next step, we use 11 victimization indicators as potential identifiers of true war victims. The victimization indicators are dummy variables, which takes the value of one for a household or individual being a victim, zero otherwise. It is possible that the self-reported victimization indicators may produce subjective bias related to a particular ethnic group or other identities. The simplest way to detect the extent of this bias is to estimate each victimization indicator as a function of the observable characteristics. The estimated outcome does not conform to any subjective bias generated by any particular variable (for reasons of space we do not show the outcome in the paper; it is available from the authors if requested).

We first estimate the standard linear OLS regression outcomes of years of education as a function of the victimization dummy and household and individual controls on a sample restricted to individuals aged between 10 and 22 (who are likely to be in the primary school during the conflict). In Table 4.3.1 we report the estimated coefficients for the eleven victimization categories (columns M1 through M11). The coefficients of all the victimization dummy variables are negative. The coefficients are statistically significant for victimized individuals or households when they registered deaths or injuries due to conflict, income dropped, lost job, lost livestock and experienced violence due to war. Overall, the estimated war outcomes on education are in line with previous findings, despite the fact that the impact of war is now identified by a set of victimization indicators based on the subjective evaluation of war impact by the survey respondents.

Table 4.3.1 OLS Regression outcomes on Average Years of Education

|M1<br>M2<br>M3<br>M4<br>M5<br>M6<br>M7<br>M8<br>M9<br>M10|M11|
|---|---|

14
Power is higher against starker alternatives, such as that rural wages do not respond at all to agricultural prices. More precisely, I can use the bootstrapped _t_ -distribution to answer the question: How likely would I have been to reject _H_ 0 : _γ_ = 1 had the true _γ_ been at or very near zero? Panels (a) and (b) of Figure 2 plot empirical power functions over the range of alternatives _γ ∈_ [0 _,_ 1] for the specifications in column 3 and 5, respectively. (Alongside are the corresponding power functions based on the standard normal test-statistic; consistent with Bester et al., 2011, the asymptotic approximation usually leads to over-rejection.) These bootstrap-based power functions indicate that at a true _γ_ of zero, _H_ 0 : _γ_ = 1 would be rejected with 95% certainty in the column 3 specification, and with closer to 90% certainty in the column 5 specification. In this sense, then, power is reasonably good.

In sum, the evidence does not support the view that rural wages are unresponsive to agricultural price changes over a half-decade period. Moreover, the magnitude of the wage responses are consistent with at least one version of the specific factors model. Next, I assess how this model fares against a particular alternative, that of segmented labor markets.

# **3.7 Test of sectoral labor mobility**

To test perfect intersectoral mobility of labor, I use the same procedure described above to construct log-wage district fixed effects for the 2004-05 and 2009-10 NSS-EUS rounds, except in this case using only wage data for _nonagricultural_ jobs. The dependent variable in panel (b) of Table 3 is thus the time difference of these district fixed effects scaled by _ψd_ . Relative to the previous analysis, 17 districts are missing the dependent variable because of lack of data on nonagricultural wage jobs and are thus dropped.

The estimates in columns 1-5 of panel (b) do not differ much from their counterparts in panel (a), nor can I reject _H_ 0 : _γ_ = 1 in the specifications with the least restrictive identifying assumptions. While this non-rejection suggests that perfect mobility of manual labor between agriculture and non-agriculture is a tolerable approximation for rural India, the issue of power is again salient.

19
14 Table 2. Regression Coefficients for Probability of Dying by Age Five<sup>on Time Period Dwunny</sup> Variables and Socioeconomic Categories for 10 Countries of Sub-Saharan Africa

# Absolute Decline<sup>Model</sup>

|Socioeconomic||Regres|sion coeff|icients for|||R2|
|---|---|---|---|---|---|---|---|
|category|Constant|Base<br>value|1980-84|1985-89|Pre-<br>Adjust<br>ment|Post-<br>Adjust-<br>ment||
|Total|68.9<br>(2.2)|0.713<br>(10.5)|<br>-9.5<br>(-1.3)|-10.9<br>(-2.0)|-3.8<br>(-0.4)|-3.8<br>(-0.4)|0.92|
|Urban|36.8<br>(0.9)|0.729<br> <br>(6.1)|<br>-0.5<br>(-0.1)|-4.0<br>(-0.6)|-10.9<br>(-0.9)|9.3<br>(0.8)|0.77|
|Rural|90.3<br>(2.8)|0.675<br>(10.9)|-13.2<br>(-1.7)|-13.9<br>(-2.4)|-5.4<br>(-0.5)|-4.1<br>(-0.4)|0.92|
|No education|81.2<br>(2.2)|0.669<br> <br>(8.2)|<br>-7.5<br>(-0.9)|-12.9<br>(-2.0)|-7.0<br>(-0.6)|-3.0<br>(-0.3)|0.88|
|Primary education|<br>76.5<br>(2.1)|0.684<br> <br>(6.8)|-11.0<br>(-1.3)|-9.8<br>(-1.5)|-6.0<br>(-0.5)|1.3<br>(0.1)|0.82|
|Secondary+educ|48.6<br>(1.4)|0.718<br> <br>(3.2)|<br>-4.9<br>(-0.5)|-3.0<br>(-0.4)|1.5<br>(0.1)|-1.4<br>(0.1)|0.48|
|Rural, no educ|100.8<br>(2.7)|0.643<br> <br>(8.7)|-11.1<br>(-1.3)|-15.5<br>(-2.3)|-9.5<br>(-0.8)|-2.6<br>(-0.2)|0.89|
|Urban, no educ|27.0|0.727|<br>3.3|-2.9|-2.8|4.8|0.64|
||(0.5)|<br>(4.5)|(0.3)|(-0.3)|(-0.2)|(0.3)||

Model specification: Sqo(i)- constant + 5qo(base) + years of period i in 1980-84 + years of period i in 1985-89 + years of i in 3 years before start of adjustment + years of period i in 3 years after start of adjustment.