rafmacalaba/datause-encoder-data huggingface dataset:
# **_4.3 Overlapping deprivations that particularly affect girls and women_**

This section considers selected indicators that particularly affect the wellbeing of girls and women. Of the total 11 indicators considered in this study three are particularly important for the wellbeing of girls and women: female circumcision, institutional birth and girls’ education.

There has been substantial progress in investments in education for girls aged between 7 and 15. In 2000, more than three-quarters of rural households with school-aged girls had at least one girl not in school, but by 2011 this had fallen to less than half of all rural households. In urban Ethiopia progress was also observed, albeit from a much better baseline: In 2000, 22 percent of households with school-aged girls had at least one girl out of school and this fell to 14 percent in 2011. This progress is reflected in primary school net attendance ratios for girls which rose from 28 percent in 2000 to 62 percent in 2011 (Carranza and Gallegos, 2013). Remarkably, in the period 2000-11, the original gap in primary school net enrollment rates observed in favor of boys disappeared.

Very few women report giving birth in a health facility although the number of women receiving antenatal visits increased. Almost no rural women recorded giving birth in a health facility in 2011 (4 percent) and 1 in 2 urban women were similarly deprived. This represents a considerable health challenge in Ethiopia today. The WIDE-3 studies documented that despite a government campaign to encourage all babies to be delivered at health centers launched early in 2013, most births were still taking place at home with the assistance of traditional birth attendants and in some places Health Extension Workers due to practical and cultural preferences (Bevan, Dom and Pankhurst, 2014). However, the DHS data shows that the proportion of women who had an antenatal visit during their most recent pregnancy in the 18
# **_Slow Yield Growth_**

It is the commercial profitability of a crop that acts as a spring in the modernization of the production process, which in turn improves the yield growth rate (Krueger, Schiff and Valdes, 1992). Cotton is no exception to this rule. Unfortunately, the unattractiveness of the domestic cotton market price has contributed to stagnant yield growth of cotton in India. The problem is particularly serious in Maharashtra, where cotton distribution is monopolized by the state trader **(Table 6).**

**Table 6:** **_Yield Growth_** _of_ **_Cotton_**

|**Table 6:****_Yield Gro_**|**_wth_**_of_**_Cotton_**|||||(kgs_I_ha)|
|---|---|---|---|---|---|---|
|State|1989/90|1990/91|1991/92|1992/93|1993/94|1994/95|
|Andhra Pradesh|458|485|494|506|610|650|
|Gujarat|229|277|212|308|351|339|
|Haryana|498|400|488|440|347|354|
|**Maharashtra**|**_135_**|**93**|**79**|**141**|**87**|**99**|
|Punjab|592|418|593|533|340|421|
|**All India Average**|**300**|**269**|**266**|**311**|**278**|**300**|

Source: Office of the Textile Commissioner, GOI The yield of cotton is still far lower than in many other countries, although agricultural technological progress has made it possible for India to produce long-staple cotton. The average yield of cotton per hectare of India is only half of the world average of 600 kgs per ha (Table 7). The cotton yields of countries such as the US, China and Egypt are somewhere between 500 to 750 kgs per ha. The yield of Pakistan is higher than that of Punjab in India, although both regions have sirnilar climate, soil and irrigation conditions. Of course, the export restriction on cotton is not the only factor contributing to low yields. Other factors, such as the low percentage of irrigated areas, the lack of availability of good seeds and damage due to pest attacks, also led to stagnation of cotton yields in India. However, all these factors are in some way related to the price incentive.

17
to systemic distress and that time to crisis is longer in more competitive banking systems (Table 2).<sup>7</sup> Unlike Beck, Demirguc-Kunt and Levine, however, they do not find an independent link between bank concentration and systemic banking fragility. The differences in this finding, however, could be due to the smaller sample utilized by Schaeck, Cihak and Wolfe. Schaeck and Cihak (2007) identify bank capitalization as one of the channels through which competition fosters stability. Utilizing data for more than 2,600 European banks, they show that banks have higher capital ratios in more competitive environments.

Finally, there is cross-country evidence that regulatory policies that restrict entry and banks’ activities are negatively associated with bank stability. Specifically, Barth, Caprio and Levine (2004) and Beck et al. (2006 a,b) find that banking systems with more restrictions on banks’ activities and barriers to bank entry are more likely to suffer systemic banking distress, while capital regulations are not significantly associated with the likelihood of suffering a crisis. Limiting contestability of the banking sector thus seems to undermine rather than to strengthen bank stability, a result contradicting the charter value hypothesis.

Overall, the cross-country evidence points mostly to a positive relationship between bank competition and stability, but yields mixed results on the relationship between concentration and stability. This also underlines that market structure measures, such as concentration ratios are inadequate measures of bank competition. Higher concentration might result in more stability through channels other than lack of competitiveness, such as improved risk diversification. The rather clear picture arising from the cross-country studies is somewhat in contrast to the ambiguous findings emerging from country-specific bank-level studies, which can be explained by the fact that the latter do not control for the regulatory framework.

# **5. Bank Competition and Stability: Policy Implications**

> 7 Levy Yeyati and Micco (2007) find different results for a smaller sample of eight Latin American countries in the 1990s.  Specifically, they find that banks in more competitive banking systems are more fragile, as measured by the z-score and the non-performing loan ratio. This contrasting result might be explained by the contemporaneous increase in foreign bank penetration in these countries, which resulted in lower competition.
# **Labor Regulations and Industrial Relations in Indonesia***

## Alejandra Cox Edwards

**Poverty and Social Policy Department Human Capital development and Operations Policy The World Bank**

* This paper was written as part of a broader labor market study on Indonesia undertaken by Country Department III, East Asia and Pacific Region. It was presented at a joint Ministry of Manpower-World Bank workshop entitled "Indonesian Workers in the 21st Century: Workshop for Economic Reforms and labor market Restructuring for Indonesia", Jakarta, April 2-4, 1996.
Since the price without restrictions must be non-negative, we impose the following constraint: _PI if_ > -1, and _PI im_ > -1.

**2.4.4 Professional Services.** In professional services, as the only available study on professional services, we use the study by Nguyen-Hong (2000) on engineering firms as our proxy for legal and accounting services. Nguyen-Hong used a cross-sectional data set of 84 engineering firms in 20 countries. He used price- cost margins as his dependent variable, which he regressed against the firm’s market share, cost of capital, sales growth, diversification index, three types of Trade Restrictiveness indices and some other variables. Based on his methodology, the price raising or rent creating impact of the foreign discriminatory restrictions in country i can be estimated from the following equation<sup>21</sup> : (5) Price Impact(i)= [p <u>p0</u> ~~]~~ (i) = (p)(βe+βo) (1-m) STRI(i) = 1.33 * (0.37+0.26) * 0.94 * STRI(i) 0 p− where  p and p0 refer to prices with and without restrictions, respectively, c is variable costs pp cc

(assumed constant), βe and βo are the estimated coefficients of the impact of the indices of market access and operations barriers on foreign firms, respectively, and m is the sample mean of firms’ market shares. We have only one STRI that combines both types of barriers of NguyenHong, so we use our STRI for both coefficients, and we use sample means of Nguyen-Hong for <u>p</u> m and . Since equation 5 ignores the cost-raising impact of policy restrictions, in principle, it is a biased down measure of the AVEs of the impacts. Nonetheless, table 15 shows that the AVEs cc in professional services are among the highest of all the sectors.

**2.4.5 Distribution Services.** Kalirajan (2000) employed a methodology very similar to Nguyen-Hong, starting with his estimation of price-cost margins based on many variables. The principle methodological differences were that Kalirajan estimated the cost raising impacts of the barriers and developed a single STRI. Based on his estimating equation, we may calculate the AVE of the discriminatory restrictions from the the cost raising impact, that is, as:

> 21 This methodology underestimates the costs of the barriers since it ignores the impact of the barriers on costs of the service.

13
of ROIC in a year. These results suggest that the markups are really used to support the high intangible capital investment in these firms. In unreported tests, we find similar results replacing the industry skill RMAN with CPS (complex problem solving skills) or NRCOG (non-routine cognitive analytical skills).

# **3.4 Markups, Output, and Investment in Star Firms**

A central policy question is whether high profits, and in particular, star firm status, are the result of monopoly power. Markups could rise due to outright monopoly or due to monopolistic competition where with high markups but also high fixed costs, firms actually earn low profits. Grullon et al. [2017] also argue that the increase in industry concentration in the US is related to high return on assets and this is mainly driven by firm’s higher profit margins rather than asset utilization.<sup>16</sup> In Figure 7, we explore the sensitivity of ROIC to markups over time by plotting the regression coefficients from the following regression: The coefficient of interest is _β_ 4 which plots the sensitivity of ROIC to markups over time. The figure shows that ROIC is less responsive to markups over time. In Figure 8 we examine splits across industries with high and low intangible capital to assets ratios and find that the declines are steeper in the industries with high intangible capital to asset ratios. These results provide suggestive evidence that pricing power increases are used for the high investment in intangible capital.

Thus, our results above on the relation between star status and markups are more nuanced. We find that a measure of pricing power, a high markup, does statistically predict star status for a firm, even when we correct for intangible capital. But, when we do correct for intangible capital

> 16Blonigen and Pierce [2016] study mergers and show that mergers are not necessarily associated with increase in efficiency but rather with an increase in market power as seen in the 15-50% increase in markups associated with mergers.

21
with WTO accession negotiations. Our findings on TTBs also relate to a separate study on TTB use by the United States, in which Bown and Crowley (2013b) provide evidence that economic incentives at the sector level shape antidumping and safeguard use and thus US participation in cooperative, selfenforcing trade agreements such as the WTO, an idea first formalized theoretically by Bagwell and Staiger (1990).

The rest of the paper proceeds as follows. Section 2 summarizes the theoretical work regarding macroeconomic shocks and new import protection, and it characterizes the institutional environment facing emerging economies’ trade policies under the WTO during 1995-2010. Section 3 introduces our empirical model and describes our panel dataset. Section 4 presents our baseline results regarding the relationship between macroeconomic fluctuations and new import restrictions for emerging economies under the WTO covering the years 1995-2010. In Section 5, we extend the data set back to 1989 where possible and compare emerging economy TTB use under the WTO relative to the prior GATT regime. Finally, Section 6 concludes.

# **2 Theory, Institutional Environment, Empirical Model, and Data**

# **2.1 Theory**

An extensive empirical literature documents evidence of counter-cyclical trade policy in industrialized economies. Nevertheless, there are relatively few theoretical contributions that explicitly model the channels through which such import protection arises.<sup>8</sup> Political economy models face two empirical difficulties: first, changes in political parameters do not necessarily match the speed of economic fluctuations; second, there is little evidence that the government’s preference for the welfare of import-competing sectors relative to consumers or export-oriented sectors rises during recessions.

Greater success in matching some of the stylized facts on time-varying trade restrictions comes from terms-of-trade-driven models of import protection. Consider first the approach of Bagwell and Staiger (1990); they present a dynamic, repeated-game model of the trade policy choices of two large countries that participate in a trade agreement. While global welfare is higher in such a framework when countries pursue a cooperative agreement that involves more liberal trade,

> 8 See the extensive list of empirical research referenced in Bagwell and Staiger (2003), Rose (2013), and Bown and Crowley (2013a) for historical evidence. Irwin (2011, 2012) provides a recent analysis of the channels through which the shocks of the Great Depression are associated with the counter-cyclical increases in import protection of the 1930s.

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# - 82 -

from the sale of ALS licenses averages S$472,000 in 1975 for the last four months.<sup>After</sup> netting out the operating costs of ticket sales, car parks and enforcement, the monthly<sup>net</sup> revenue was about S$420,000, resulting in a net financial return to the authorities of about 76% (Watson and Holland, 1978, pp. 38-39). As a result of a one dollar increase in daily<sup>license</sup> fees on January 1, 1976, the gross revenue increased to a monthly figure of<sup>S$568,000, which,</sup> together with comparable operating costs of S$52,000, yield a net financial rate of return<sup>of</sup> 94 %. Note that the capital cost of $6.6 million is less than that of building an urban expressway two kilometers long (Watson and Holland, 1978, p. 37)!

88. Since the construction of car parks, bus shelters, provision of utilities and landscaping account for over nine tenths of the capital cost of the ALS, and since the park-and-ride<sup>facilities</sup> were considered a failure and converted to more productive uses, the capital cost of<sup>the ALS</sup> itself - which is nothing more than overhead gantry structures, electric signs,<sup>and 15 or so</sup> booths for the sales of licenses - is only S$316,000 (Watson and Holland,<sup>1978, Table 3.6).</sup> This means that the net financial rate of return is 1,590%, or an equivalent revenue-cost<sup>ratio</sup> of 16.9 (see also Wilson, 1988). These figures can be compared with<sup>the net financial rate of</sup> return figure calculated on the basis of data the author compiled for 1975-89.<sup>There the net</sup> financial rate of return, based on annualized capital and operating cost and annwal revenue-cost ratios, comes to 590% _with_ the revenues and costs of _opafting_ the car parks<sup>and 1,080%</sup> _without_ the associated revenues and costs of the car parks. The difference<sup>between the 'short-</sup> term' figures collected within the first year and a half and dhe 'long-term' figures<sup>collected over</sup> a 14-year period can perhaps be partally explained by: a) the amortzation<sup>of capital costs over</sup> a longer time period, b) the reduction in opeating cost of the ALS through learning<sup>by doing,</sup> and c) thL, 'long-term' figures include enforcement costs hitherto not taken into<sup>account. Based</sup> on an average daily car traffic volume of 45,000 in 1989 during the morning<sup>peak period and</sup> the number of operating days of the ALS of 301 (including Saturdays) in a year,<sup>the cost per</sup> transaction comes to S12.1 cents for the period 1975-89 [US9.9 cents in<sup>1990 figures]</sup><sup>_with_car</sup> parks and S6.8 cents for the period 1975-89 _[US5.6_ cents in 1990 figure]<sup>_without_car parks.</sup>

**Works Dqatment snd** Regisky of Vehicles **_for_ the** peiod 1975-89.
The poverty headcount is reduced slightly,<sup>44</sup> but the simulated impact is very small. The results are robust to the choice of the poverty line.

**Table 4. Simulated Impact of PIT reduction on poverty and Inequality** _Top Bracket of PIT decreased from 23 to 18 percent_

||**Market**<br>**Income +**<br>**Pensions**<br>_(1)_|**Gross**<br>**Income**<br>_(2)=(1)+_<br>_Direct_<br>_Transfer_<br>_s_|**Net**<br>**Market**<br>**Income**<br>_(3)= (1)-_<br>_Direct Taxes_|**Disposable**<br>**Income**<br>_(4)=(3)+Dire_<br>_ct Transfers_|**Consumab**<br>**le Income**<br>_(5)=(4)-_<br>_Indirect_<br>_Taxes_|**Final**<br>**Income**<br>_(6)=(5)+In-_<br>_kind transfers_|
|---|---|---|---|---|---|---|
|**_Gini_**|||||||
|2015 Baseline|0.3693|0.3545|0.3602|0.3452|0.3453|0.3170|
|Simulation|<br>0.3693|0.3545|0.3605|0.3455|0.3457|0.3175|
|**_Poverty (2.5 US$ per day)_**|||||||
|2015 Baseline|7.5150|4.6310|7.8221|4.8986|8.1217|3.5050|
|Simulation|<br>7.5150|4.6310|7.8221|4.8626|8.0773|3.4672|
|**_Poverty (4.0 US$ per day)_**<br>2015 Baseline|22.5838|19.7411|24.0637|21.2895|29.1802|20.4315|
|Simulation|<br>22.5838|19.7411|24.0637|21.2335|29.1028|20.3592|

Note: Own estimates based on 2015 HBS.

**Simulation results show that such a PIT reform would reduce the redistributive effect of the tax, but the extent of the change cannot be observed using household survey data solely, as it does not include top incomes.** Simulations show that the PIT becomes less progressive, captured in a slightly lower but still positive Kakwani index (Figure 20), and would have a smaller redistributive effect (Figure 21). Not surprisingly, there is no difference in the poverty effect, as the change takes place in the top bracket of the direct tax. However, given lack of data on top incomes, the extent of the change cannot be fully explored. As previously mentioned, this observed effect is a lower bound and it would be expected that if top incomes were considered, the reduction in progressivity would be higher and the redistributive effect smaller.

**Figure 20. Simulation: Progressivity of Direct Taxes and Contributions in Albania (Kakwani Index)**

**Figure 21. Marginal Contribution to poverty and inequality of current vs simulated PIT**

> 44 This poverty-reduction effect from reducing the top bracket of the PIT comes from the fact that the income thresholds for paying PIT in Albania are relatively low. For instance, 19.8 percent of the individuals living in poor households ($1.25-$2.5, thus excluding the extreme poor) were paying PIT in 2015.

29
# **4.1 Spatial price gaps**

Under the theoretical framework described in section 3.1, if markups did not vary across locations the pass-through rate would be constant across trading pairs and equal to one, i.e. _ρ_<sup>_k_</sup> _od_<sup>= 1.Fromequation(3),thisrestrictionimplies</sup> We estimate equation (8) through OLS imposing a linear relationship between distance and trade costs, i.e. _τ_ ( _xodt_ ) = _βxod_ + _γt_<sup>_k_+</sup><sup>_χk_</sup> _odt_<sup>,where</sup><sup>_γ_</sup> _t_<sup>_k_areproduct-timefixedeffects</sup> that capture economy-wide product-specific shocks to trade costs and _χ_<sup>_k_</sup> _odt_<sup>capturesother</sup> components of trade costs that are unrelated to distance. With the assumption of perfect competition, differences in prices across locations should reflect trade costs and, therefore, we would expect _β_<sup>ˆ</sup> to be positive. A negative _β_<sup>ˆ</sup> could arise in the more general case of imperfect competition with variable markups. Yet another potential explanation for observing price gaps that decrease with distance is that local distribution or logistic costs in the destination are higher at destination locations that are closer to the origin. Results are presented in Table 3. Except for Rwanda, the results show a positive relationship between origin-destination price gaps and distance.

Table 3: Estimates of the relationship between distance and trade costs using origindestination price gaps

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Distance to origin (log km)|0_._070<sup>_∗∗∗_</sup>|0_._071<sup>_∗∗∗_</sup>|0_._030<sup>_∗∗∗_</sup>|_−_0_._082<sup>_∗∗∗_</sup>|0_._026<sup>_∗∗∗_</sup>|0_._028<sup>_∗∗_</sup>|
||(0_._006)|(0_._005)|(0_._003)|(0_._005)|(0_._002)|(0_._009)|
|Observations|9_,_280|5_,_997|48_,_379|26_,_215|50_,_583|6_,_173|
|Adj. _R_<sup>2</sup>|0_._258|0_._408|0_._249|0_._344|0_._238|0_._585|

_Notes_ : This table presents the relationship between distance and price gaps across regions, under the assumption that markups do not vary across locations. All regressions use the (unadjusted) price gap between trading pairs and include product-time fixed effects. Standard errors in parentheses.<sup>_∗∗∗_</sup> significant at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **4.2 Pass-through rates**

The first step of the procedure for estimating intra-national trade costs outlined in section 3.2 involves estimating pass-through rates. Pass-through rates are informative of how (exogenous) price changes at origin locations translate into (endogenous) price changes in destination locations, in the way described by equation (4), which we reproduce here: Recall that, in equation (9), _a_<sup>_k_</sup> _dt_<sup>arenon-observabledemandshifters.FollowingAtkin</sup> 11