rafmacalaba/datause-encoder-data huggingface dataset:
 ( _predicted_ ln _OPt_  ( _t_  10),  )   ''   1'' * _GDPpc_    2'' * _OPEN_    3'' * _ENGLISH_    4'' * _DIST_    5'' * _CONFLICT_    6'' * _COM_    '', _t_ , _t_  1980,1990,2000,   _country_ (7) For the 1975 arrival, there are 6 regressions (3 OP definitions * 2 periods over which improvement can be evaluated – between Censuses 1980 and 1990; between Censuses 1990 and 2000). For the 1985 arrival no more than 3 regressions can be run, because the performance of individuals is observed only in 1990 and 2000.

The final specification (equation 8) combines the conditional convergence estimations described in sub-section 4.1 with equation (7), and implies running 6 regressions for the 1975 arrival, and 3 for the 1985 arrival.

 ( _predicted_ ln _OPt_  ( _t_  10),  )   '''   ''' * _predicted_ ln _OPt_ ,    2''' * _OPEN_    4''' * _DIST_    5''' * _CONFLICT_    6''' * _COM_    '', _t_ , _t_  1980,1990,2000,   _country_ (8) GDP and English are not included in specification (8) because they are correlated to a large degree with the initial predicted occupational placement. Other biases stemming from the strength of the β-convergence, and from the high explanatory power of the country of origin attributes vis-à-vis initial performance (equation 7) will be discussed in more detail in the next section.

# **_5.2. EMPIRICAL RESULTS_**

The country specific attributes employed to explain the predicted initial occupational placement and the predicted improvement in occupational placement are described in the previous section. Table 6 presents the summary statistics associated with these variables. The set referring to 1980-1989 is assumed to influence the performance of the 1985 arrivals, while the set for period 1970-1979 refers to the 1975 arrivals.

The results of the estimations are presented in Table 7. They are robust to alternative definitions of occupational placement, as shown by Annex tables A2 and A3. The coefficients and standard errors are small because of the log specification of the dependent variable. Columns 1 through 3 and 7 through 9 describe the estimates related to performance during the 1990s of the 1985 and 1975 arrivals. Columns 4 through 6 refer to the performance during the 1980s of the 1975 arrivals.

Columns 1, 4 and 7 use as a dependent variable the predicted initial occupational placement. The results in column 1 confirm findings in Mattoo et al. (2008) according to which country attributes explain a significant part of skilled immigrants’ performance level in 1990, provided other things are kept constant. Both qualitative and selection factors have a significant 26
**Table 19. Correlates of survey attrition**

Dependent variable: inpanel (=1 if the individual is observed at baseline and followup,= 0 if baseline only)

||2010‐|2012 Pooled|Cohorts|
|---|---|---|---|
|||ITT||
||(1)|(2)|(3)|
|treated|0.052|0.042|0.090|
||(0.056)|(0.056)|(0.090)|
|Female||0.472***|0.485***|
|||(0.116)|(0.168)|
|AGEI||0.065|0.018|
|||(0.105)|(0.109)|
|parent||0.062|0.006|
|||(0.104)|(0.107)|
|married||0.136|0.084|
|||(0.101)|(0.101)|
|dalit||0.259***|‐0.250**|
|||(0.097)|(0.112)|
|janjati||‐0.048|‐0.039|
|||(0.062)|(0.066)|
|any IGA at baseline||0.204***|0.150**|
|||(0.061)|(0.062)|
|age: under 25||‐0.217**|‐0.242***|
|||(0.088)|(0.093)|
|N|4677|4487|4487|
|District,TE dummies|No|No|Yes|

Notes: All regressions use probit models. "District, TE dummies" indicates that the regression controls for district and training provider effects. Columns 2 and 3 also include training category dummies (not shown). All standard errors are clustered by event. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

42
- Qian, Y. (1999). „The institutional foundations of China's market transition‟, Annual Bank Conference on Development Economics, Washington, DC: The World Bank.

Rodrik, D. (2004). „Getting institutions right‟, CESifo DICE Report.

- Sabirianova, K., Svenjar, J. and Terrell, K. (2005). „Distance to the efficiency frontier and foreign direct investment spillover‟, Journal of the European Economic Association, 3, pp. 576-86.

- Stein, E. and Daude, C. (2007). „Longitude matters: time zones and the location of foreign direct investment‟, Journal of International Economics, 71, pp. 96-112.

- Wei, S. (2000). „Local corruption and global capital flows‟, Brookings Paper on Economic Activity, pp. 303-54.

- Wei, Y., Liu, X., Parker, D. and Vaidya, K. (1999). „The regional distribution of foreign direct investment in China‟, Regional Studies, 33, pp. 857-67.

- Zhang, K. H. (2001). „How does foreign direct investment affect economic growth in China?‟ Economics of Transition, 9, pp. 679-93.

24
regressive structure-the structure is regressive but the percentage of income absorbed by payments is small on average. If the poverty line is below the break-even level, the poverty impact is greater under the regressive payment structure, but if the poverty line is above the break-even level, the poverty impact is greater under the _progressive_ structure. Thus, if a progressive structure absorbs a large proportion of pre-payment income, it may well give rise to a larger poverty impact than a regressive structure absorbing a small proportion of pre-payment income.

# _6.3._ **_How do out-of-pocket payments add to poverty in Vietnam?_**

There are two obvious candidates for the poverty line that emerge from our earlier discussion of the deductions D(x). The first is a food poverty line giving the cost of reaching 2100 calories a day. This is often termed an extreme poverty line. Clearly, this is applicable whether income is pre-payment or post-payment. In each case, one is asking whether the person's pre- or post-payment income is sufficient for them to purchase enough food to produce 2100 calories per day. Clearly, some individuals may cross such a poverty line as the result of spending on health care, and some may sink further below it. By comparing the headcounts and poverty gaps before and after health care spending, one can get a sense of its impoverishing effects, whether in terms of additions to the number of people classified as extremely poor or in terms of deepening poverty among the extreme poor.

The second obvious poverty line is the amount used above in the more generous deduction for food and non-food items. The difficulty here is that the poverty line for prepayment income ought to include an element for health spending, whilst the poverty line for post-payment income ought not. As in computing the deduction D(x), one needs to extract an amount from the poverty line corresponding to health spending to arrive at the post-payment poverty line _zP"'._ This means that whilst some people may not be poor before health spending and be poor after it, there will be some who are marginally poor before health spending but _not_ poor after it (they spend nothing on health care or they spend appreciably less than the health spending component of the pre-payment poverty line). Thus, whereas in the case where the extreme poverty line is used poverty will necessarily be higher "after" health spending than "before", in the case where the poverty line covers food _and_ non-food items, poverty may, in fact, be higher pre-payment than post-payment.

In applying these methods to the data on out-of-pocket payments in Vietnam, we employed a food (extreme) poverty line and a broader-based poverty line using the amounts used in the deductions in the fairness analysis above. In the case of the food _28_
It is important to note, however, that not all analysts agree that India’s labor laws have made for a rigid labor market. In particular, a counter-argument to the views discussed above is that India’s labor regulations relating to job-security have been either ignored (see Nagaraj, 2002) or circumvented through the increased usage of temporary or contract labor (see, in particular, Dutta, 2003). Ultimately, whether India’s labor laws have created significant rigidities in labor markets or not is an empirical issue. It is hard to imagine that they have not created any rigidities or have not constrained entrepreneurs at all in adjusting to shocks.

# **_Measuring Labor-Market Flexibility_**

We use the partitioning of states, in terms of whether they have flexible labor markets or not, in Hasan, Mitra and Ramaswamy (2006). They start with Besley and Burgess’ (2002) coding of amendments to the Industrial Disputes Act between 1958 and 1992 as pro-employee, antiemployee, or neutral. Hasan, Mitra and Ramaswamy (2006) find the natural partition of states based on Besley and Burgess (2002) (to treat states with anti-employee amendments, in net year terms, to the IDA as those with flexible labor markets) somewhat problematic. They make changes based on the recent survey work by Dollar, Iarossi, and Mengistae (2002) and World Bank (2003) that strongly calls for some modifications. Maharashtra and Gujarat, two of India’s most industrialized states that have passed pro-employee amendments to the IDA, are perceived by Indian businesses to be good locations for setting up manufacturing plants, and are states where overmanning of plants is not prevalent. Kerala is just the opposite case, with net proemployer amendments but with an overall perception of not being very business friendly, and with substantial over-manning. Table 2 provides a list of the states as well as how they are classified.<sup>7</sup>

> 7 See Hasan, Mitra and Ramaswamy (2003) and Hasan, Mitra and Ramaswamy (2006) for details.

12
# **Figure A.3. NIRP announcements: changes in financial sector variables up to one month**

<!-- Start of picture text --> A. Financial sector CDS B. Financial sector equities<br>Basis points Percent<br>Median<br>15 10 Median<br>10<br>5<br>5<br>0<br>0<br>-5<br>-10<br>-5<br>-15<br>-20 -10<br>1 2 3 4 1 2 3 4<br>Weeks after Weeks after<br>announcement announcement<br><!-- End of picture text --> Sources: Bloomberg, World Bank.

Notes: Basis points or percent change from closing values on the day before the NIRP announcement. Line indicates median across 17 NIRP announcements. Grey area indicates 10<sup>th</sup> and 90<sup>th</sup> percentile range. Each event is extended out for 30 calendar days. Financial sector CDS is, for all the events caused by central banks in Europe, a Europe-wide senior bank debt CDS index, and, analogously for Japan. Financial sector equities are the broad financial sector index for each nation or area.

53
- 30 -

Our enthusiasm for ET taxes may have to be tempered by a consideration of external effects in consumption. At least in the case of telephones consumers' enjoyment from phoning may depend on who else has access to a phone. Up to a point, the utility of owning a telephone may be a function of the number of subscribers, in which case an efficiency argument can be made for subsidizing rather than taxing telephone use. Once more, efficiency and equity concerns clash in the design of an appropriate tax policy.

Future work in this area can profitably proceed in several areas. First, it would be useful to disaggregate large expenditure aggregates such as public transportation, alcoholic be-erages and telephones in order to see if some components have different distributive characteristics and are not close substitutes in consumption. For example, differential excise taxes on beer versus fine wine, or long distance versus local phone calls, may be used to fine-tune the system of excise taxes. Secondly, more work on the efficiency dimensions of excise taxation would be desirable. Finally, a useful extension to the analysis would incorporate the horizontal equity aspects of excise taxation.

**MasI*A4JWelf&rc Dominanca** & **the** Design of **ExciLe** Tezatiom in **the** Cott D'Zvoire/W2hiz.k:eb/5/18688
moved or were traveling and in one case a family refused to further take part in the study. Newborn children in the treatment households were enrolled in Action for Health either by the CHW or by a program officer who visited study households at least every three months. This follows the standard protocol for Action for Health, and since the caretakers’ treatment choices and resulting health improvements are our outcome of interest, we consider these children part of the sample. It is possible that the treatment allocation affected fertility, but we consider the risk to representativeness greater if we were to exclude these children.

Figure 3 in Appendix A includes a CONSORT diagram detailing both attrition and addition of new children throughout the study. After the first follow-up in 2013, which included 1,768 children, 217 children left the survey for various reasons, 12% of the sample. An ANOVA test fails to reject the hypothesis that attrition is equal across treatment groups. At the same time, 98 children were added to the sample. Again, ANOVA fails to reject the hypothesis that additions were equal across treatments. Table 1 tests for balance for a set of covariates at the child, household, and compound level. Each column is a regression on three treatment arm dummies with stratum fixed effects and clustered standard errors, using the same specification as in the regressions below. Only one coefficient in the table is significantly different from zero. We control for these covariates in the main regressions.

# **3.2 Framework and Hypotheses**

Figure 1 shows in schematic form what effects we might expect from the two treatment components, subsidies and CHW visits. Subsidies that reduce the cost of care at the CSCom are likely to primarily increase care seeking with a formal provider in the case of acute illness. Positive effects arise from better access to curative care, provided that the child indeed receives this care when it is needed.<sup>7</sup> The CHW visits are more likely to improve preventive

> 7We are able to analyze in detail how the subsidy changes the utilization of (formal) curative care conditional on the child’s health status, and in particular how it affects the risk of health care overuse (vertical arrow on left in Figure 1). This risk arises more frequently in curative care than in preventive care, because formal care only benefits a child with an illness that actually requires care and responds to treatment, whereas non-indicated treatment may have no, or even negative, effects on health outcomes, not to mention that it wastes resources. The 2013 data collection and analysis in Sautmann et al. [2020] pursue these questions in detail.

11
the DBI.<sup>39</sup> The Minister picked the key indicators, and by a few relatively simple legislative steps, was able to improve the country‘s ranking. But these indicators were only _de jure_ policy intentions, with potentially little bearing on actual policy implementation at the firm level. Deeper characteristics of the business and investment climate in the country did not apparently change in any fundamental way, and the Minister felt that there was no genuine impact on the country‘s development.

Nor should it be presumed that efforts to improve a county‘s ranking by manipulating the few proxies for poor performance that happened to get selected for the mashup are costless. Targeting reform efforts on a few partial indicators, which on their own may bring little gain, can have an opportunity cost. This has been an issue with DBI. Arrunada (2007) argues that an exclusive focus on (for example) simplifying the procedures for business start-ups risks distorting policy by not putting any weight on the benefits (to firms and the public at large) derived from formal registration procedures.

There are also applications of mashup indices, along with other composite indices, as explanatory variables in policy-relevant models for outcomes of interest. For example, the Doing Business indices have been widely used in a (large) academic literature as explanatory variables for ( _inter alia_ ) productivity, entrepreneurship and corruption.<sup>40</sup> Such applications are potentially important, although arguably it is the component series that should be the regressors not the composite index, letting the regression coefficients set the weights, appropriate to the specific application.<sup>41</sup> In this case the dependent variable provides the relevant basis for setting weights, and the mashup index can be discarded.

It is not obvious how useful an aggregate (country-level) mashup index is for policy making in a specific country. Development policy making has increasingly turned instead to micro data on households, firms and facilities. These are data on both the outcomes of interest and instrumentally important factors, including exposure to policy actions. Such micro data invariably reveal heterogeneity in outcomes and policies within countries. As HallwardDriemeier, Khun-Jush and Pritchett (2010) argue, the _de jure_ representation of policies at

> 39 Høyland et al. (2010) give other examples of such rank-seeking behavior.

> 40  A useful compendium of research using these data can be found on the Doing Business web site. Also see Djankov‘s (2009) survey.

> 41  See Lubotsky and Wittenberg (2006) for a formal exposition of this argument.

27
Table 9 Distribution of AD cases with a Final Determination, per Number of Domestic Firms

|Determination/No of firms|N = 1|(%)|2 ≤ N ≤ 5|(%)|N > 5|(%)|Total|
|---|---|---|---|---|---|---|---|
|Accepted|42|41.2|42|41.2|18|17.6|102|
|Price Undertaking|2|40.0|0|0.0|3|60.0|5|
|Rejected|49|57.0|27|31.4|10|11.6|86|
|Total|93|48.2|69|35.8|31|16.1|193|

Source: Customs, Compendium of Laws on Foreign Trade. Author's calculations.

Finally, in Table 10, in order to examine whether the application of antidumping duties had undergone any change in the four sub-periods in which the import policy was analyzed, four indicators were selected, namely: the number and percentage of actions resulting in affirmative determinations, the average length of the investigation conducted (the number of days from filing date until final determination) and the antidumping duties and dumping margin ratios. The 19941998 period witnessed a substantial increase in the percentage of accepted cases. However, on average, the investigations were increasingly protracted, and the ratio between antidumping duties and dumping margins decreased during the three sub-periods. This reveals that establishing time frames for analyzing disputes, as prescribed in 1995 by special law, failed to accelerate the course of investigations and that a more moderate approach prevailed in the application of antidumping duties during the periods under study.

Table 10 Antidumping Cases and Selected Indicators, by Sub-periods

|Sub-period|No of cases|Accepted|(%)|Duration (days)|DDA/DM (%)|
|---|---|---|---|---|---|
|1988-1989|4|4|100.0|213|na|
|1990-1993|50|18|36.0|292|83.1|
|1994-1998|69|43|62.3|440|68.1|
|1999-2002|70|42|60.0|480|47.1|
|Total|193|107|55.4|422|61.3|

Source: Customs, Compendium of Laws on Foreign Trade. Author's calculations.

28
dominates, increasing firm-size may be positively or negatively correlated with the likelihood of having a woman manager. More importantly, the relationship between women’s education access and the presence of female top managers may be conditional on the size of the firm. Thus we capture any firm size heterogeneities by separately estimating the relationship between education and presence of women top managers.

We have thus laid out the conceptual framework for the analysis, highlighting the importance of access to education and indicating the scenarios where it may prove important for women obtaining top managerial positions and where it may not. We have also provided a brief discussion of the mechanisms at play for various macro and micro-level factors. The relative importance of the factors remains an empirical question.

# **3. Data and Main Variables**

The main data source we use consists of firm-level surveys for 73 developing countries conducted by the World Bank’s Enterprise Surveys between 2007 and 2010.<sup>3</sup> A common sampling methodology – stratified random sampling – was followed in all the surveys along with a common questionnaire. The sample for each country was stratified by industry, firm-size, and location within the country. Weights are provided in the survey to ensure that the sample is representative of the non-agricultural private sector of the economy. We focus on the sample of manufacturing firms.<sup>4</sup> We complement these firm-level surveys with other data sources (listed below) such as World Development Indicators (World Bank), Inter-Parliamentary Union (IPU), etc.

Regression results discussed below are marginal effects obtained from logit estimation with Huber-White robust standard errors and clustered on the country. All marginal effects are

> 3Sample size in the regressions varies due to missing data.

> 4 Enterprise Surveys do not cover the primary sector, mining, and services sectors such as education and health. Data on some of our main variables used in the regressions is missing for the services sectors covered in the survey for some countries.

11
reallocation effects of FDI on market composition (Alfaro, 2016), but FDI’s effects on social development are largely ignored. In this paper, we investigate how FDI inflow affects an important aspect of social changes, female entrepreneurship, in the world, along with the interaction between FDI inflow and various economic barriers for female entrepreneurship, and of female empowerment. Female employment and leadership have important ramifications for development of poor countries. More work and leadership opportunities for women would raise the return to women’s time and human capital investment, induce women to emphasize the quality of children at the expense of the quantity of children (Becker and Lewis 1973; Galor 2005), reduce fertility rates and facilitate the critical transition from the Malthusian regime to the modern growth regime (Galor and Weil 2000; Galor 2005).

In this paper, we study whether and how FDI affects female entrepreneurship, and how the FDI effects interact with gender-specific barriers and empowerment. We mainly use the World Bank Enterprise Survey data, and supplement it with complementary indicators of barriers to female entrepreneurship and women’s empowerment. We allow the FDI effects to have two components: the country-level FDI level, and the countryindustry level FDI (i.e., country-specific measure of FDI intensity in an industry, intraindustry FDI for short). The overall FDI effect captures the comprehensive effect of FDI across all industries, and the intra-industry FDI allows for within-industry spillover and/or “crowding out” effects. We define female entrepreneurship by a firm having both women participation in firm ownership _and_ a female being the top manager of the firm, capturing both ownership and control by women for the firm (Atrido and Hallward-Dreimeier, 2011). By this measure, female entrepreneurship varies from zero in some countries to close to 40 percent in Mongolia.

FDI might affect female entrepreneurship via several channels. Via FDI, there can be social norms transmitted from investing countries to host countries. There can be knowledge spillover, both across and within industries, spurred by the new knowledge stock brought by FDI firms (Acs _et al_ ., 2009, 2011). There can be crowding out effects on female entrepreneurship due to women’s lack of access to key resources to compete with FDI firms within FDI-intensive industries. It is thus an empirical question as to whether and how FDI affects female entrepreneurship. With a data set of firms in around 130 3