rafmacalaba/datause-encoder-data huggingface dataset:
is to treat “domestic work” and “leisure” activities as two extra sectors in an otherwise standard CGE. The rationale would be, again, that women are much more involved than men in these two extra sectors. In order to add the sectors, information on time use is needed, to be able to account for the amount of time allocated by women and men to leisure, household chores, work, etc. The originality in Arndt and Tarp (2000) is that they develop a different treatment for home consumption versus market consumption. Both types of consumption are possible for all the commodities in the model.

Neither Fontana and Wood (2000) nor Arndt and Tarp (2000) disaggregate men and women into different types of skill. Siddiqui (2007) does so, while including other improvements. Apart from considering four different levels of skill (as in the present model), Siddiqui introduces an extra interesting feature, namely, one household category for “all female-headed households in rural areas”. This is a way of incorporating intra-household reallocation of resources, capturing not only the “production” impact for women through wages but also through the “consumption” side of the economy. There is considerable evidence, however, suggesting that wages are the primary means through which women earn a living (United Nations, 2009; Arndt et al., 2006).

In our model, we capture the impact of non-market consumption through the existence of a factor called the subsistence factor, which is available in “Agricultural sectors” and in “Fishing” and “Hunting and forestry”. We offer a revised ambitious four-skill-type disaggregation of women and men. Our main contribution is, we believe, the innovative modeling of potential sources affecting females’ and males’ remunerations and their sectoral allocations through the presence of the Dixit-Stiglitz-Ethier framework and the entry of multinational firms.

# **3. Data on gender and the Tanzanian economic structure**

We use data (not typically available in most countries) from the Integrated Labour Force Surveys (ILFS) of 2001 (NBS, 2002a) for Tanzania. In each sector, factors of production are distinguished by four skill levels and by sex. At the top of Table 1, we show factor remuneration in billions of current Tanzanian Shillings across all factor categories modeled for four aggregates of sectors from the 52 sectors of our model.<sup>1</sup> The four broad sectors are: 1) business services - those in which FDI is allowed to enter and regulatory barriers are going to be reduced; 2) Dixit-Stiglitz goods, which produce under a Dixit-Stiglitz-Ethier mechanism in imperfectly competitive sectors and with increasing returns to scale; 3) agriculture, and 4) other CRTS (constant returns to scale sectors) - the remaining sectors that produce with constant returns to scale. Exact correspondences between these four sectors, the 52 sectors of the model and the sectors in Tanzanian statistics are given in Appendix 2.The sum of all factor

> 1 Table 16A in Appendix 1 presents the value added shares of all factors across the 52 sectors. Note that the last column of Table 16A offers the share in “Total value added” (or GDP).

6
**Policy Research Working Paper· Serles**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2240 The Effects of Land Registration on· ·<br>Financial Development and Economic<br>Growth: A Theoretical and Conceptual<br>Framework|FrankF;K.Byamugisha|·November 1999|E.Gelos<br>37846|
|WPS2241 How Land Registration Affects<br>Financial Development and Economic<br>GrowthinThailand|FrankF.K.Byamugisha|November 1999|E.Gelos<br>37846<br>.·.:..<br>·;.,.··,,~·|
|WPS2242 Confronting Competition: Investment<br>Resp~nseand ConstraintsinUganda|Ritva Reinikka<br>Jakob Svensson|November 1999|<br>H. SladovicF( ·<br>37698|
|WPS2243 Designing Pro-Poor Water and<br>Sewer Concessions: Early Lessons<br>from Bolivia|Kristin Komives|.<br>November 1999|M. Salehi<br>37157<br>_/_|
|WPS2244 True World Income Distribution, 1988<br>and 1993: First Calculations, Based<br>on Household Surveys Alone|Branko Milanovic|November 1999<br>r|P.Sader<br>33902|
|WPS2245 Opportunities for Improving<br>Environmental ComplianceinMexico|Susmita Dasgupta|<br>1<br>November~999|Y.D'.Souza<br>31449·|
|WPS2246 Infrastructure's Contribution to<br>Aggregate Output|David Canning|November 1999|H.Sladovich<br>37698|
|WPS2247 Does Deposit Insurance Increase<br>Banking System}3tability?<br>An Empirical Investigation|Asl1DemirgOg-Kunt<br>Enrica Detragiache|November 1999|K.Labrie<br>31001|
|WPS2248 Privatization and Regulationof<br>Transport Infrastructureinthe<br>1990s: Successes ... and Bugs to<br>Fix for the Next Millennium|Antonio Estache|November 1999 ·|G:Chenet-Smith<br>36370|
|WPS2249 Argentina's Transport Privatization<br>and Re-Regulation: Ups and Downs<br>of a Daring Decade-Long Experience|Antonio Estache<br>JoseC.Carbajo<br>Gines de Rus|November 1999|G.Chenet-Smith<br>36370<br>_.f,;_|
Table A.6: Robustness checks, excluding all western countries. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***|-0.074**|-0.014**|-0.001|
||(0.001)|(0.034)|(0.006)|(0.005)|
|Age squared|-0.000***|0.001**|0.000***|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.019***|0.485***|-0.030|-0.445***|
||(0.004)|(0.117)|(0.021)|(0.026)|
|Primary or preparatory|0.032***|0.477***|0.049**|-0.420***|
||(0.004)|(0.125)|(0.022)|(0.025)|
|Secondary|0.017***|0.360***|0.034*|-0.286***|
||(0.003)|(0.101)|(0.017)|(0.020)|
|Rural|0.009***|0.254***|0.009|-0.028*|
||(0.003)|(0.083)|(0.015)|(0.016)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.231**<br>(0.102)|||
|Confict|||0.047***<br>(0.013)||
|Illegal status||||-0.239***<br>(0.053)|
|Legal status||||0.205***<br>(0.045)|
|Observations|14,879|1,955|2,071|8,040|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.:|1.516; Illegal mig.|=1.314; Legal|mig.=1.764||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return, and excludes returnees from all western countries. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

53
population, and labor and capital by sector of activity from Brazil’s national accounts data disseminated by Brazil’s Institute for Applied Economic Research ( _Instituto de Pesquisa Econˆomica Aplicada, IPEA_ ).<sup>5</sup> Unfortunately, the data for labor and capital is available only for a subset of municipalities and years.

We use georeferenced data on roads for each decade from 1970 to 2000 obtained from Brazil’s National Department of Transport Infrastructure ( _Departamento Nacional de Infraestrutura de Transportes_ , DNIT). From the road data, we subset the road segments that are paved, duplicated, or in the progress of duplication.<sup>6</sup> For the purpose of identification (see next section), we separate out radial roads extending from Brazilia towards the 25 state capitals<sup>7</sup> along with two coastal highways based on DNIT classification.<sup>8</sup> For each time period, we construct the start and end points of the DNIT radial and coastal highway road segments that existed at the time. Our road access measure is a dummy variable equal to 1 for municipalities that have a radial road within 50 km of its centroid and 0 otherwise.

Electricity access data over the period 1970 to 2000 come from various sources including the Geographic Information System of the Electric Sector ( _Sistema de Informa ̧c ̃oes Geogr ́aficas do Setor El ́etrico, SIGEL_ ) of the Brazil National Electricity Regulatory Authority ( _Agˆencia Nacional de Energia El ́etrica, ANEEL_ ), the Business Information System for the Electricity Sector ( _Sistema de Informa ̧c ̃oes Empresariais do Setor de Energia El ́etrica, SIESE_ ) of a major Brazilian electric utility, _Eletrobras_ , and Brazil’s and Cadastral Geographic Information System of the National Interconnected System ( _Sistema De Informa ̧c ̃oes Geogr ́aficas Cadastrais Do Sistema Interligado Nacional, SINDAT_ ) of the Brazil’s Operator of the National Electricity System ( _Operador Nacional do Sistema El ́etrico, ONS_ ). As in Lipscomb, Mobarak, and Barham (2013), we define electricity access in a given year as the share of municipality area located within 50 kilometers of a transmission line, distribution substation, or autonomous power plant.

Table 1 shows variable means for population, gross-valued added, and capitallabor ratio across municipalities, overall and for the industry sector, in the study’s four periods.

> 5See `www.ipeadata.gov.br` .

> 6More precisely, the road segment is defined as one of the following: ‘Pavimentada’,‘Duplicada’, and ‘Em obra de Duplica ̧c ̃ao’.

> 7This includes the state of Tocantins and its capital, Palmas, even though Tocantins had not not yet been created in 1970.

> 8Radial highways include road segments with labels ‘010’, ‘020’, ‘030’, ‘040’, ‘050’, ‘060’, ‘070’, ‘080’, ‘090’. Coastal highways include segments with labels ‘101’ and ‘116’.

5
We begin with the first pathway, marriage, by reviewing how basic institutional rights to property ownership often depend on a woman’s marital status.<sup>5</sup> As shown in Figure 1, based on the 2016 Women, Business and the Law (WBL) database, married women face legal restrictions on property ownership in 10 out of 46 African countries for which we have data (left panel), while unmarried women typically have the same rights as unmarried men (right panel).<sup>6</sup> This demonstrates that, from a legal perspective, discriminatory provisions often do not apply to all women but to married women in particular, whose legal status changes, sometimes profoundly, upon marriage (Hallward-Driemeier and Hasan 2013).

**Figure 1: Married men and women often do not have the same property rights**

<!-- Start of picture text --> Married men and women Unmarried men and women<br>2 1 2<br>10<br>36<br>45<br>Have equal rights Do not have equal rights Have equal rights Do not have equal rights<br>No data No data<br><!-- End of picture text --> _Note:_ Shows number of countries (48 in total). Data not available for Somalia and Niger. _Source:_ WBL 2016.

Today’s property rights of women in marriage often have historical roots. British common law was particularly unfavorable to women, owing to the doctrine of ‘couverture’, whereby a woman’s legal status was subsumed by her husband upon marriage. Roman and Islamic legal traditions generally allowed married women to retain their legal personality (Deere and Doss 2006). Nowadays, women’s and men’s ability to own property during marriage and after its dissolution is governed by marital property regimes. The most common regimes are (i) full community of property, which considers all assets as joint property of the couple, (ii) partial community of property, which considers assets acquired during marriage as joint property but allows spouses to retain assets brought into the marriage, and (iii) separation of property, where all property is individually owned. Overall, women are expected to fare better under community property regimes, which recognize women’s role in the accumulation of marital property through child-rearing and other unpaid work, than under separation of property regimes, which reinforce gender gaps in economic and labor market opportunities (Deere and Doss 2006; Deere et al 2013).

Figure 2 shows default marital property regimes across Africa, defined here as the rules that govern property rights in monogamous marriages unless the spouses agreed on a different regime prior to entering

> 5 Marriage rates are high in Africa, with approximately 80 percent of the male and female population being married by their early thirties. For men, these rates remain high until well into old age, while for women they start to decline in their mid-thirties (Djuikom and van de Walle 2018).

> 6 For example, DRC’s family law (Code de la Famille) specifies that married women must obtain authorization from their husbands for legal actions (Art. 448) and that the management of joint and individual property is presumed to be entrusted to the husband (Art. 490).

5
that national and grocery store inflation was around 2% per year over this period, these effects are economically meaningful.

To assess the use of housing prices as a control variable and potential endogeneity concerns, we show that the results are robust to several additional specifications in Appendix Table N4. First, we show that the results are robust to dropping housing prices as a control variable when the full set of control variables is included, although the first-stage F-statistic drops by about half. Second, we show that our results are robust to restricting the sample to stores in a set of contiguous county pairs that straddle state borders and adding county-pair fixed effects. When dropping the housing-price control in such specifications, the first-stage F-statistic again decreases, although the point estimate remains nearly identical. Further restricting the sample to only contiguous counties in different states but in the same commuting zones, we obtain similar point estimates, although there is a further weakening of the instrument to levels below standard thresholds. We note that this is a demanding test since over 80% of the stores in the original sample are dropped and since benefits (the explanatory covariate of interest) in these selected counties are imprecisely aligned with state-level benefits (the covariate available). Reassuringly, the point estimate remains stable and the average SNAP participation rate is nearly identical in the smaller sample. In addition, our results are robust to relaxing the identifying assumption by allowing the IV to be plausibly exogenous `a la Conley et al. (2012) with the IV having a direct impact on outcomes even after controls. We elaborate on this check in Appendix Section E.

We also estimate the potential impact of retail chains that adopt uniform pricing, adding a variable that measures the average log benefits per population for each store at the retail chain level, weighted by store revenue in the pre-period of 2006, excluding stores in the same state. We find that the estimated coefficient for state-level benefits per population is only reduced by about 20% and the sum of the two coefficients is similar to our previous estimates. This finding is consistent with our earlier discussion that a large amount of grocery stores belong to chains that price flexibly or chains that are located only in a few states, which implies that most grocery stores engage in regional pricing.

Furthermore, we conduct a series of additional robustness checks in Appendix Table N5 to show the estimated price response is robust to using log benefits with or without population controls; weighting by store revenue or county population; regressing with state-level observations; using county-level benefits; using the full sample from 2006 to 2015, which includes the recovery periods; removing SNAP disaster payments; including a set of government transfer policy controls at the state-year level; and using states with each of the two main methodologies in setting the SUA. Note that we can observe how states that use lagged CPI are setting the SUA by using CPI data, and we control for contemporaneous 19
# Table <u>5b. Association of ECED timing and test scores in primary school</u>

|||**Timing**<br>|<br>|
|---|---|---|---|
||Language<br>(1)|Maths<br>(2)|Cognitive Skills<br>(3)|
|**Timing**(Base: No ECED [I])||||
|Playgroup (3-4) then primary [II]|0.0854***|0.116***|0.0650*|
||(0.0318)|(0.0312)|(0.0356)|
|Kindergarten (5-6) then primary [III]|0.238***|0.221***|0.133***|
||(0.0228)|(0.023)|(0.0269)|
|Playgroup (3-4) then kindergarten (5-6) then primary [IV]|0.418***|0.427***|0.205***|
||(0.0289)|(0.0292)|(0.0358)|
|Other combination [V]|0.440**|0.304|0.117|
||(0.174)|(0.196)|(0.251)|
|Playgroup (5-6) then primary [VI]|0.0225|0.0529|-0.0131|
||(0.0333)|(0.0327)|(0.0377)|
|Playgroup (5) then kindergarten (6) then primary [VII]|0.320***|0.308***|0.141*|
||(0.0716)|(0.0676)|(0.0844)|
|Constant|-0.559***|-0.510***|-0.352***|
||(0.0379)|(0.0381)|(0.0445)|
|Observations|12,710|12,710|12,710|
|R-squared|0.281|0.233|0.108|
|Pairwise contrasts||||
|Timing:||||
|[II] - [I]|0.0854***|0.116***|0.0650*|
|[VI] - [II]|-0.0629*|-0.0629*|-0.0781*|
|[III] - [II]|0.153***|0.105***|0.0680**|
|[IV] - [III]|0.180***|0.207***|0.0715**|
|[VII] - [IV]|-0.0983|-0.119*|-0.0634|

Note: Robust standard errors clustered at the village level in parentheses. All regressions include child characteristics (age, grade, gender, and stunting), family characteristics (household wealth, mother’s education, parenting practices), ECED characteristics (average quality, average fees), and district fixed effects.

*** p<0.01, ** p<0.05, * p<0.1

31
relative to private firms, the coefficient on the interaction term of an SOE dummy with investment climate conditions would then be negative.

# _Firm ownership:  Foreign Direct Investment_

There is a debate as to whether foreign firms are a relatively stable source of investment or whether they are footloose, happy to accept incentives to set up production but quick to move to another location should there be difficulties in operations or a better incentive package offered elsewhere. Bernard and Sjoholm (2003) examine whether foreign firms are more likely to exit, using census data from Indonesia. They argue that they are less likely to – until one controls for size and productivity, and then they are more likely to exit than domestic firms thereby providing evidence that foreign firms can be more ‘footloose’.

Firms with foreign partners may be affected differently than domestic firms by a weak investment climate. In some cases, the foreign partner may be able to assist with providing an alternative solution to cope with a weak environment. Thus, in the case of accessing finance; even if local financial markets are less developed, FDI firms should not find this as constraining. However, in other cases, it could be that foreign firms are more affected. This could be the case in navigating through red tape or dealing with corruption. Here, foreign firms may have less understanding of how to operate and so are more likely to suffer in such an environment. On the other hand, a local joint venture partner could be useful in such a situation (Smarzynska and Wei (2000)). Distinguishing joint ventures from wholly- foreign owned firms can test for the importance of this in the exit decision.

# **Empirical model of the exit decision**

14
The rest of the paper proceeds as follows: Section 2 describes the research design and identification strategy; Section 3 reports the main results; Section 4 explores potential mechanisms; and Section 5 concludes.

# **2 Study Design**

## **2.1 Background and Study Sample**

While unemployment in South Africa is considered largely a structural problem, recent research has documented significant frictions in the labor market.<sup>4</sup> The Department of Labour (DoL) is trying to address these market inefficiencies through a range of employment services including job counseling and job referrals. In the context of sluggish economic growth, however, public services are resource-constrained. In collaboration with the DoL, this study is part of an agenda to test innovative programs that are inexpensive and scalable.

Our sampling frame is the Employment Services of South Africa (ESSA) database, comprising of more than 550,000 job seekers collected by the DoL. We restricted this sampling frame to unemployed job seekers between the ages of 18 and 35, who registered with ESSA in the previous 18 months and lived within traveling distance from the three urban Labour Centres that were part of the study.<sup>5</sup> From this sample, we randomly selected work seekers and contacted them using the phone number provided in ESSA. In the telephone call, surveyors invited job seekers to participate in an employment service study at the local Labour Center on a specified day. In return, they were offered a small stipend of 30 rand (2.5 USD) that covered their travel cost. Of the individuals successfully contacted, approximately 67% agreed to participate, and of those who agreed, 63.5% came to the Labour Center on the

> 4Abel et al. (2017) document that reducing information asymmetries between hiring firms and job seekers through reference letters can improve match quality.

> 5We worked with the Labour Centres in Krugersdorp, Sandton and Soweto, in the Gauteng province.

5
17 <!-- Start of picture text --> 78<br>6<br>6 7 ~~~~~Income tax/expenditure {XX<br>Dedle 8 9 v(/ Ircome tax/income (X%)<br>10<br>| inClm t ,cms 6 CX) * Income taxlexpendit/e XX)|<br><!-- End of picture text -->

# Figure 1. Income Tax Share of Income or Expenditure.

the exclusion of in-kind income/expenditure from taxation. Such exclusion greatly reduces tax burden estimates, particularly in the mral sector where in-kind income/expenditure is more common:;. The "urban bias' in tax burden, however, can be justified on equity grounds as there appears to be a wide belief across the country that public services have not been equally distributed among urban and rural areas. In particular, social and infrastructure expenditures were lower in rural areas with the capital of the country having the loin's share.

/3JHuang's (1976) study of the distribution of the tax burden in Tazania concluded that the role of in-kind consumption is extremely important and partally explains why the tax system is both progressive and urban bias.