rafmacalaba/datause-encoder-data huggingface dataset:
6 female-headed households by up to 50 percent<sup>7</sup> (ICRW, 1988). Before continuing with the analysis, it is important to verify that our "operationalization" of the concept of female - headship -- the designation in the household survey performs reasonably well in separating households according to the sex of the person who bears the most responsibility for the economic welfare of the household.

To evaluate the meaning of headship in the survey data, we compared the classification of households on two alternate definitions of headship. The first definition of household head is the person who has the highest income in the household. The second measure of headship has been proposed by Rosenhouse (1989). and considers as head the person wr.o provides the most "efforts on behalf of and commitment to the household" (p. 12). Effort is measured by the number of market-oriented hours worked, and has the advantage over the income definition of correcting for the problem of estimating the income of the self-employed. As incomes or hours worked could be very close, we also allow for joint headship where the value of the classification variable for the two highest-scoring members differs by less than 10 percent. If the survey classification is useful, in most households the same gender will be designated as head under all classifications.

The top section of Table 2 shows the distribution of female- and male-headed households in three metropolitan areas if they are reclassified according to the income criterion.8 The last row in the table shows that overall, this criterion turns up slightly more "pure" female-headed households than the survey classification (20 percent, compared with 18.7 percent in the survey), and that in the case of 6.4 percent of the households, any classification would be arbitrary. The nature of the difference between the head reported in 7 For example, in Peru, Rosenhouse (1989) found that in the collection of the Living Standards Measurement Survey data, the oldest male member of the household was listed as the head despite the fact that in one-third of the households women were the main providers. In other cases, household survey interviewers are instructed to list the oldest male present as the head, resulting in teenage children being listed as the head of household.

8 Note that a household will be classified as having joint headship if both potential heads are the same sex.
endogeneity of state support with respect to current performance. Second, if history is of any guidance for enterprise management, using lagged values of subsidies would be a way to capture the effect of _expected_ state support and hence necessity to restructure<sup>27</sup> . This will be a good measure of expected subsidies (and to a great extent also the overall softness of budget constraint) particularly for enterprises that continue being fully state owned throughout 1995-1997. However there is still a problem with subsidies being jointly determined with ownership. In other words, some enterprises might face hard budget constraints after they are privatized, which creates a problem in measuring the relative importance of the effects of private ownership and business environment on 28 perfonnance Unfortunately using subsidies (lagged or contemporaneous) would provide only limited information about the extent of state support to enterprises and the softness of budget constraints. A more complete analysis would have to include (1) arrears that enterprises are allowed to run on various accounts (tax, social security, wages, etc), (2) cost reductions associated with subsidized credits and other inputs, (3) special contracts, among other direct and indirect ways to favor enterprises. Unfortunately the available data prevents us from keeping a complete account of state assistance to enterprises and so lagged values of subsidies will be used to proxy the full extent of state support and softness of budget constraint.

# _Selection of Sample Size_

The choice of the sample size for regression analysis is a crucial one here. Bearing in mind the importance of explaining the ownership structure at privatization (i.e. the first stage regression), only enterprises for which private share ownership at privatization can be "explained" by available pre-privatization values of instruments were selected. Since the data is available for 1995-1997 only, 1995 was set aside to provide data for 27 Kornai (1998) and subsequently Anderson, Korsun and Murrell (1999) emphasize the _expectational_ component of softness of budget constraint. They argue that decision to restructure is largely a function of expected (rather than current) state support in times when the enterprise is in trouble.

28 Controlling for endogeneity of variables describing business environment lies beyond the scope of current paper and will be attempted by subsequent research.

25
**Table 6**

|**Share of various sources of capital financing for City of Johannesburg**|
|---|
|Loan funding<br>60%|
|Developers contributions<br>7%|
|Provincial housing grants<br>25%|
|National Infrastructure grants<br>8%|

Source: Hunter (2004) As indicated above, in 2004 Johannesburg successfully floated 2 bonds of R1 billion each. In the late 1990’s, Johannesburg experienced serious financial problems. Much of this was related to the institutional changes and complexity of the two-tier system, which was initially introduced. Indeed, the Johannesburg experience was probably the crucial factor in the country deciding to create single-tier metropolitan governments from 2000.

The successful floating of the bonds was significant in that it not only confirmed Johannesburg’s recovery, but also served to demonstrate the scope for municipalities to raise finance in this way.

The first bond was a 6-year unenhanced bond and carried a spread of 230 basis points over the rate for the equivalent national bond. The second is a 12-year bond and is enhanced by a 40 per cent guarantee by the IFC and INCA. It carried a spread of 164 basis points. Both bonds were over subscribed.

# 6.3. <u>The scope for further bond issues</u>

South Africa has a relatively sophisticated financial sector, including an active bond market.

There are R527.6 billion of outstanding bonds listed and traded on the Bond Exchange of South Africa (BESA).

|Type of institution|**Table 7**<br>**Breakdown of issues**|Share of total<br>bonds issued by<br>value|
|---|---|---|
|Centralgovernment||73.5%|
|Corporates||15.1%|
|Parastatals/utilities||7.5%|
|Water Authorities||3.5%|
|Localauthorities||0.4%|

Source: Hunter (2004) A municipal bond market did exist during the _apartheid_ period but this was in the context of prescribed asset requirements, whereby insurance companies and other major investors were required to hold a proportion of their assets in instruments such as municipal and other government bonds.

The new municipal bond market is emerging in the context of no prescribed asset requirements, no national guarantees, and no tax benefits for municipal bonds. Indications are that it could become a very important source of future financing for local governments.

13
39 Table 6. Average effect of the Venezuelan migration shock on educa– tional outcomes Primary school grades (marginal effects)

||(1)<br>(2)|(3)|
|---|---|---|
|Dependent Var.|Ln(Enrollment)<br>Promotion Rate|Dropout Rate|
||**Panel A: Without Controls**||
|Foreign Males|0.0038***<br>-0.0257***<br>(0.0009)<br>(0.0080)|0.0175***<br>(0.0067)|
|Native Males|0.0010<br>-0.0187**|0.0109|
||(0.0008)<br>(0.0075)|(0.0070)|
|Foreign Females|0.0036***<br>-0.0115|0.0085|
||(0.0009)<br>(0.0088)|(0.0070)|
|Native Females|0.0008<br>-0.0167**|0.0120*|
||(0.0008)<br>(0.0078)|(0.0072)|
|R-Squared|0.8691<br>0.2449|0.2063|
||**Panel B: With Controls**||
|Foreign Males|0.0036***<br>-0.0247***|0.0162**|
||(0.0008)<br>(0.0077)|(0.0065)|
|Native Males|0.0007<br>-0.0177**|0.0095|
||(0.0007)<br>(0.0074)|(0.0070)|
|Foreign Females|0.0034***<br>-0.0105|0.0071|
||(0.0009)<br>(0.0087)<br>|(0.0069)|
|Native Females|0.0006<br>-0.0157**|0.0106|
||(0.0008)<br>(0.0077)|(0.0072)|
|R-Squared|0.8693<br>0.2453|0.2068|
|Native Males Mean|4.005<br>94.61|2.941|
|Native Males SD|1.442<br>10.24|8.492|
|Native Females Mean|3.892<br>95.91|2.470|
|Native Females SD|1.462<br>9.077|7.976|
|Foreign Males Mean|0.911<br>91.56|5.933|
|Foreign Males SD|1.017<br>22.45|19.27|
|Foreign Females Mean|0.884<br>92.70|5.529|
|Foreign Females SD|0.997<br>21.20|18.82|
|Observations|313,195<br>313,195|313,195|

Clustered standard errors by municipality in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All columns include School FE, Year FE and Department _×_ Year FE. Selected controls include the following variables interacted with year dummies: N. of Terrorist Attacks _×_ 2012, 2014 and 2016, Informal Labor _×_ 2018, UBN _×_ 2013.
- 16 -

Using the firm registry data and detailed road network data in Madagascar, the paper estimated the impacts of market and farm accessibility on agglomeration of new agrobusinesses. It is found that market accessibility is important to attract agrobusinesses. Agglomeration economies are also among the important determinants of their location. Thus, the currently existing agrobusiness clusters would likely continue to grow further. By contrast, accessibility to ports and farms does not seem to be so important for agrobusinesses to decide their locations. One of the reasons for agrobusinesses concentrating on large markets may be the better quality of labor that is available. Our findings support this. In addition, many local agrobusinesses may still retain monopsony power in their input markets. As the location model predicts, this allows them to keep relatively away from their local areas. As transport infrastructure is improved, the local agrobusiness industry would become more competitive, bringing more agrobusinesses to locate in local areas.
**Figure 20: Public ownership: size, scope and extent of control over public enterprises** a. Size of the public enterprise sector

<!-- Start of picture text --> 6<br>5<br>4<br>OECD average<br>3<br>2<br>1<br>0<br>b. Scope of the public sector<br>6.0<br>5.0<br>4.0<br>OECD average<br>3.0<br>2.0<br>1.0<br>0.0<br>c. Control of public enterprise by legislative bodies<br>5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5 OECD average<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Japan Slovak Republic Brazil Australia United States New Zealand Switzerland Luxembourg United Kingdom Portugal Canada Denmark Spain Ireland Sweden Korea Iceland Netherlands Hungary Germany Czech Republic Finland Belgium Bulgaria Mexico Italy Croatia 2007 Greece Austria Norway France Turkey Poland Albania 2007 Romania<br>United Kingdom Slovak Republic Belgium Japan Korea Iceland New Zealand Albania 2007 Denmark Ireland United States Australia Canada Netherlands Brazil Romania Greece Mexico Germany Austria Finland Hungary Spain Luxembourg Sweden Bulgaria Czech Republic Portugal Switzerland France Italy Croatia 2007 Norway Turkey Poland<br>Australia Austria Albania 2007 Japan Iceland Sweden Denmark Ireland United States Canada Romania Greece Mexico Turkey Korea Belgium France Netherlands Czech Republic Germany Spain Norway New Zealand Switzerland United Kingdom Finland Luxembourg Poland Brazil Bulgaria Slovak Republic Italy Portugal Croatia 2007 Hungary<br><!-- End of picture text -->

# b. Scope of the public sector

# c. Control of public enterprise by legislative bodies

_Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._ 22
# **Table 15.2 Impact on enrollment and child development outcomes for 1 year old cohort by parenting score at the baseline**

||Impact on thos<br>Village i<br>|e whose parents rep<br>s Batch 1<br>|ort below-average<br>Village<br>|parenting scores<br>is Batch 3<br>|
|---|---|---|---|---|
||Coefficient|Robust SE|Coefficient|Robust SE|
|**Enrollment in ECED Programs**|||||
|Whether enrolled in project playgroups|0.555***|(0.041)|0.481***|<br>(0.039)|
|Whether enrolled in non-project playgroups|-0.224***|(0.045)|-0.191***|<br>(0.046)|
|Whether enrolled in RA programs|-0.136***|(0.047)|-0.128***|<br>(0.042)|
|Whether enrolled in TK programs|0.029|(0.024)|0.067***|<br>(0.026)|
|Total Number of Months of project playgroup|8.512***|(0.745)|7.156***|<br>(0.667)|
|Total Number of Months of non-project playgroup|-3.290***|(0.713)|-2.872***|<br>(0.705)|
|Total Number of Months of RA programs|-1.305**|(0.585)|-0.997*|<br>(0.535)|
|Total Number of Months of TK programs|0.244|(0.278)|0.706**|<br>(0.319)|
|**Standardized Child Development Outcomes**|||||
|EDI: Physical Health & Well-Being|0.089|(0.090)|0.255***|<br>(0.084)|
|EDI: Social Competence|0.067|(0.076)|0.016|<br>(0.069)|
|EDI: Emotional Maturity|-0.007|(0.074)|-0.090|<br>(0.074)|
|EDI: Language & Cognitive Development|0.083|(0.072)|0.074|<br>(0.070)|
|EDI: Communication & General Knowledge|0.017|(0.072)|0.106|<br>(0.071)|
|SDQ: Emotional Symptoms|-0.030|(0.070)|0.017|<br>(0.071)|
|SDQ: Conduct Problems|-0.053|(0.087)|0.062|<br>(0.087)|
|SDQ: Hyperactivity & Inattention|0.042|(0.073)|-0.036|<br>(0.075)|
|SDQ: Peer Problems|-0.093|(0.082)|0.055|<br>(0.080)|
|SDQ: Pro-social Behavior|-0.087|(0.073)|-0.039|<br>(0.071)|
|SDQ: Total Difficulties|-0.054|(0.076)|0.041|<br>(0.082)|

Notes: *** p<0.01, ** p<0.05, * p<0.1; Batch 5 is used as the comparison group. All regressions reported here correspond to the adjusted specifications reported in Tables 10-12 51
eradicated from Tanzania, and the government reduced testing and monitoring efforts and ordered schools to reopen.<sup>17,18</sup> Case counts, hospitalization, and mortality data were unavailable; however, informal reports suggest that there were multiple waves of respiratory illness and deaths in the subsequent months. Despite this, the government did not endorse mask-wearing or vaccination.<sup>18</sup> In March 2021, President Magufuli died and was replaced by President Samia Suluhu Hassan, who announced plans to develop a coronavirus task force and urged the citizens of Tanzania to take preventive measures, including social distancing.<sup>17</sup> The President warned of a third wave in June 2021, though masks and vaccination efforts were not publicly endorsed. The Ministry of Health, Community Development, Gender, Elderly and Children (MOHCDGEC) then changed course, submitting a request to the global COVAX program, an initiative that is co-led between Gavi, the Vaccine Alliance, WHO, and the Coalition for Epidemic Preparedness Innovations to improve global equitable access to COVID-19 vaccines. Government officials subsequently reported 176 incident cases and 29 deaths during the “third wave”, recommended mask use, precautionary measures, and began a vaccination campaign.<sup>19</sup> By June 1, 2022, 33,928 cumulative cases and 803 deaths had been reported,<sup>15</sup> likely a significant underestimation of the true toll.

# **Study population and design**

The _Rewarding STI Prevention and Control in Tanzania II_ (“RESPECT II”) study<sup>20</sup> was conducted from May 2018 to January 2022 in Dar es Salaam, Tanzania, which has an estimated FSW population of 155,450 and an HIV prevalence of 26% for FSW.<sup>21</sup> The primary aim of the study was to investigate the effect of a lottery intervention on combined HIV/STI incidence among FSW. In May 2018, 2,206 participants were enrolled using respondent driven sampling to identify FSW from various locations (bars, brothels, street). Individuals meeting the following eligibility criteria were eligible to participate: 1) female; 2) exchanged sexual intercourse for money in the past 6 months; 3) HIV-negative at enrollment; 4) aged 18 years or older; 5) not currently pregnant; 6) lived in Dar es Salaam for past three months and plan to remain living in Dar es Salaam for at least next 2 years; 7) have a cell phone able to receive text messages; 8) able to adequately grant informed consent; 9) in possession of a valid coupon (obtained through Respondent Driven Sampling).

Participants were randomized in a 1:1 ratio to receive standard HIV prevention services (testing, counseling, and weekly text messages on safe sex practices), or standard HIV prevention services plus entry in a random lottery rewarding safe sex as proxied by negative test for curable STIs.

3
||Used marijuana|0.315|0.257|0.220|1.000|0.485|
|---|---|---|---|---|---|---|
||Used other drugs|0.22|0.373|0.555|1.000|0.764|
||Had sexual intercourse|0.058|0.243|0.811|1.000|0.892|
||Convicted of crime|0.141|0.355|0.691|1.000|0.845|
||drank alcohol at least<br>several tiems a month|0.039|0.325|0.904|1.000|0.904|
||smoked cigarettes<br>everyday|0.438|0.315|0.164|1.000|0.452|
||used marijuana at least 1-<br>2 tiems per week|-0.511|0.489|0.296|1.000|0.543|
||used no birth control at<br>last sex|-0.544|0.311|0.080|0.883|0.294|
||Externalizing behavior|-0.004|0.003|0.182|0.730|0.243|
|**Pilkauskas et al. (2018)**|Internalizing behavior|0.001|0.002|0.617|1.000|0.617|
||PPVT|0.007|0.002|0.000|0.002|0.001|
||Woodcock-Johnson|0.008|0.002|0.000|0.000|0.000|

18
**Table 6: Net Effects of the Illness of a Worker on Household‐Level Labor Outcomes**

||||Househo|lds with...||HH in|
|---|---|---|---|---|---|---|
|||||||which|
|Sample of households|all|2 +<br>earners at<br>baseline|0 male<br>earners<br>at<br>baseline|1+ male<br>earner at<br>baseline|2+ male<br>earners at<br>baseline|woman<br>is<br>highest<br>earner at|
|||||||baseline|
|_Panel A: Dep_|_endent Variab_|_le = Total hou_|_sehold incom_|_e_|||
|Worker ill|‐81.945***|‐95.685**|‐16.763|‐89.743***|‐131.140***|8.265|
||[24.649]|[38.754]|[42.176]|[28.818]|[43.898]|[28.938]|
|Worker ill X Female ill|11.625|51.445|‐98.06|54.586|130.164**|‐104.956*|
||[36.528]|[41.083]|[76.562]|[35.321]|[57.475]|[53.902]|
|Observations|2345|1268|889|1456|353|940|
|R‐squared|0.544|0.577|0.557|0.532|0.52|0.538|
|Mean dependent variable|202.60|259.80|126.9|248.80|307.80|183.90|
|Overall effect of female illness|‐70.320|‐44.240|‐114.8|‐35.160|‐0.976|‐96.69|
|P‐value|0.004|0.024|0.018|0.109|0.980|0.019|
|Expected male earnings loss from baseline|105.500|92.06|n/a|105.5|77.06|42.33|
|Fraction of expected loss compensated|0.224|‐0.039|n/a|0.150|‐0.702|1.195|
|Expected female earnings loss from baseline|49.040|50.07|40.06|57.4|67.5|53.68|
|Fraction of expected loss compensated|‐0.434|0.116|‐1.867|0.387|0.986|‐0.801|
|_Panel B: Depen_|_dent variable =_|_Total Househ_|_old Labor H_|_ours_|||
|<br>Worker ill|<br>‐28.569***|<br>‐38.751***|<br>‐13.503|<br>‐29.440***|‐40.587***|‐12.719|
||[5.563]|[7.894]|[11.580]|[5.776]|[9.843]|[9.688]|
|Worker ill X Female ill|12.082*|21.880**|‐5.375|13.971*|32.348***|‐4.767|
||[6.813]|[9.082]|[12.888]|[8.052]|[10.896]|[8.543]|
|Observations|2345|1268|889|1456|353|940|
|R‐squared|0.797|0.751|0.744|0.783|0.719|0.815|
|Mean dependent variable|67.03|95.70|40.00|83.54|112.60|72.24|
|Overall effect of female illness|‐16.490|‐16.870|‐18.880|‐15.470|‐8.24|‐17.490|
|P‐value|0.000|0.000|0.000|0.004|0.301|0.001|

_Notes: Worker ill = 1 if a household member reported missing work for an entire week in which s/he was planning to work that week and the reason for missing work was injury/illness. Expected earnings loss from baseline is calculated by taking the average usual reported income from the baseline survey of individuals who are unexpectedly sick over the course of the survey. All specifications include household fixed effects, week of interview dummies interacted with city, and controls for the number of household members planning to work in that round. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
**Table 6: Capital Outflows in Sudden Stops**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|������|-0.165|-0.328***|-0.334*|-0.353**|
||[1.07]|[2.90]|[1.81]|[2.70]|
|Country fixed-effects|Yes|Yes|Yes|Yes|
|Country specific trends|Yes|Yes|Yes|Yes|
|Observations|2,397|2,120|2,280|2,398|
|# of countries|34|32|32|34|
|R-squared|0.143|0.062|0.032|0.045|

_Notes:_ The dependent variables are capital flows of the respective type as a percentage of trend GDP. They are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

**Table 7: Capital Outflows in Capital Flight Episodes**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|��������|0.488***|0.627***|0.528***|1.043***|
||[3.48]|[4.36]|[5.70]|[9.83]|
|Country fixed-effects|Yes|Yes|Yes|Yes|
|Country specific trends|Yes|Yes|Yes|Yes|
|Observations|2,040|1,920|2,036|2,040|
|# of countries|33|31|32|33|
|R-squared|0.150|0.085|0.050|0.104|

_Notes:_ The dependent variables are capital flows of the respective type as a percentage of trend GDP. They are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

13
coparcener first receives his share of the joint family property, a process followed by the distribution of the deceased person’s notional share of joint property among all male and female heirs, normally in equal shares. To illustrate, letting _m_ be the number of (male) coparceners and _f_ the number of additional females who are entitled to an equal share, intestate succession of a Hindu head of household would have each of the former receive a share of [(1 _/_ ( _m_ + 1)) + (1 _/_ ( _m_ + 1)) _/_ ( _m_ + _f_ )] whereas each of the latter will receive only (1 _/_ ( _m_ + 1)) _/_ ( _m_ + _f_ ) with the difference being the coparcener share.

To eliminate the gender inequality inherent in this practice, a number of states amended the 1956 HSA by stipulating that the daughter of a coparcener will become a coparcener herself by birth, i.e. acquire a status equal to that of a son.<sup>12</sup> These amendments, which was largely identical across states, were enacted by Andhra Pradesh, Maharashtra, Karnataka, and Tamil Nadu in 1986, 1989, 1994, and 1994, respectively.<sup>13</sup> . The changes in entitlements introduced by the Hindu Succession Act Amendments (HSAAs) meant that the daughter’s share in joint family property could not be willed away by the father. The change brought about by the HSAA constitutes an interesting natural experiment that allows us to explore, empirically, whether changes in inheritance legislation improve physical and human capital assets of women. This is of interest for India since, in 2005, the HSAA’s coverage was expanded to cover the entire nation, along the lines of the amendments introduced in the southern states. In addition, the issue is of relevance for other countries where inheritance rules remain biased against women.

For legislation in this area to have an impact, those affected must be aware of content and implications of the law and compliance must provide advantages exceeding the cost of the effort needed to enforce compliance. At the same time, substitution effects need to be ruled out so that it is the spirit of the law, rather than just its letter, that is complied, e.g. by increasing the amount of physical capital transferred upon inheritance while at the same time decreasing the amount of human capital. Concerns about potential limited or even adverse effects of legal changes in inheritance legislation are reinforced by studies

> 12Inheritances, being a concurrent topic in India, both the central and the state governments have the right to amend the laws concerning it.

> 13Kerala abolished joint family property system altogether in 1976 in favor of an arrangement where all family members hold their share separately (Agarwal 1994)

10