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32
affiliates, we define a subsidiary (parent) as the lower (higher) revenue firm.<sup>55</sup> Figure A14 plots the share of firms which are subsidiaries by revenue and the linear fit on each side of the thresholds, excluding intervals around the thresholds. On average, 5% of small firms are subsidiaries of larger firms and the relation appears continuous on either sides of the threshold. The estimated discontinuities indicate that the share of subsidiaries slightly drops at the thresholds by 0.39% at the first and 0.14% at the second, however neither estimates are significant. The coefficient on excess bunching subsidiaries are small: there are only 0.31% more bunching subsidiaries than the linear prediction at the first threshold and 0.83% more at the second, and only the latter is significantly different from zero. Finally, we find no excess profitability of subsidiaries compared to non-subsidiaries at either threshold: if anything subsidiaries appear on average slightly less profitable. Hence tax motivated division of firms and profit-shifting to smaller subsidiaries can only explain a tiny fraction of bunching and none of the profits discontinuity at the thresholds. The absence of tax motivated firm division could be due to the registration fees and yearly stamp duties required to keep firms active or to the ease of over-reporting costs, making firm division suboptimal. It is also possible that the data on economic groups does not fully capture firm affiliations, in particular evading firms might use more sophisticated strategies to appear unaffiliated.

# **7.5 Timing of Revenue Realization**

The timing of revenue realization is another margin for firms to lower their taxes (le Maire and Schjerning 2013). To remain below the threshold, firms could date some of their revenue from September (end of fiscal year) to October, or stop production once they reach the threshold. Both types of responses imply that bunching firms generate a lower share of revenue at the end of the fiscal year, compared to non-bunching firms. Retiming also predicts a higher share of revenue early in the fiscal year. Since testing these hypothesis requires monthly revenue data, we use the subsample of firms liable for monthly sales taxes and run the following regressions: where _yimt_ is revenue of firm _i_ , in month _m_ and fiscal year _t_ , _αm_ are month fixed effects, and _γt_ are fiscal year fixed effects. _Bunchit_ equals one if firm _i_ ’s revenue falls in the one million interval below the threshold in year _t_ and 0 otherwise. The coefficient _β_ 1 ( _β_ 2) measures the differential monthly revenue of bunchers in September (October), compared to other months and firms. If firms retime their income or limit production at the end of the fiscal year, then _β_ 1 should be negative

> 55Results are not sensitive to the definition of subsidiaries, since empirically subsidiaries tend to be owned by much larger firms. For example results remain unchanged when we define subsidiaries as firms affiliated with a firm with revenue over 150 million CRC, far above the second threshold.

29
**Table 9. The role of labor standards for economic upgrading**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|
|**Backward links**|||||||
|_FVAX_|0.0877***<br>(0.0303)|0.0734***<br>(0.0142)|0.0627***<br>(0.0138)|0.00286<br>(0.0439)|0.0726<br>(0.0835)|0.0513***<br>(0.0172)|
|_FVADP_|0.259***|0.272***|0.282***|0.254***|0.268***|0.280***|
||(0.0276)|(0.0243)|(0.0285)|(0.0269)|(0.0342)|(0.0260)|
|_FVAX*Basic ILO conv._|-0.0023<br>(0.0031)||||||
|_FVAX*Vulnerable Empl._||-0.0003<br>(0.0004)|||||
|_FVAX*Child labor_|||-0.0002<br>(0.0010)||||
|_FVAX*Female workforce_||||0.0016<br>(0.0011)|||
|_FVAX*Wage dispersion_|||||-0.0095<br>(0.0400)||
|_FVAX*Minimum wage_||||||2.3e-05<br>(2.1e-05)|
|_Constant_|5.733***|5.532***|5.778***|5.844***|5.676***|<br>5.641***|
||(0.179)|(0.160)|(0.208)|(0.181)|(0.234)|(0.175)|
|Observations|<br>7,037|<br>6,578|<br>5,147|<br>7,156|<br>2,995|<br>5,502|
|R-squared|0.858|0.855|0.840|0.854|0.870|0.867|
|**Forward links**|||||||
|_DVAR_|0.102*<br>(0.0610)|0.252***<br>(0.0231)|0.232***<br>(0.0238)|0.156*<br>(0.0824)|0.473***<br>(0.128)|0.208***<br>(0.0271)|
|_FVADP_|0.239***|0.250***|0.264***|0.233***|0.235***|0.259***|
|_DVAR*Basic ILO conv._|(0.0261)<br>0.0115*<br>(0.0061)|(0.0234)|(0.0275)|(0.0259)|(0.0298)|(0.0247)|
|_DVAR*Vulnerable Empl._||-0.0010*<br>(0.0005)|||||
|_DVAR*Child labor_|||-0.0027**<br>(0.0012)||||
|_DVAR*Female workforce_||||0.0016<br>(0.0020)|||
|_DVAR*Wage dispersion_|||||-0.1200**<br>(0.0554)||
|_DVAR*Minimum wage_||||||3.2e-05<br>(2.8e-05)|
|_Constant_|4.899***|4.715***|5.071***|5.162***|4.886***|<br>4.980***|
||(0.180)|(0.155)|(0.212)|(0.184)|(0.222)|(0.180)|
|Observations|7,045|6,580|5,149|7,164|2,997|5,502|
|R-squared|0.875|0.875|0.861|0.872|0.886|0.886|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

# **_Social Standards_**

The identical intuition holds for social standards; table 10 shows that the empirical evidence is in line with the evidence for labor standards. A basic welfare system, measured through pension and unemployment insurance proxies, facilitates the positive effects of GVC integration as a seller on domestic value added (columns 2 and 3); and these effects are not driven by any particular income group. The positive mediating effect on the selling side supports the findings by Jetha and Linsen (2015) and the World Bank (2012).

29
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6 are deprived of the revenue stream from the deterred customers.<sup>3</sup> In this situation, nobody wins.

In theory, low connection charges for new customers accompanied by cost-reflective tariffs allow utilities to build up sufficient numbers of customers in a given location for it to become a reliable source of revenue over many years. Everybody wins. But utilities in SubSaharan Africa have very high consumer connection charges. Sometimes the connection charges are so high that they exceed a country’s annual income per person (appendix 2)—an obvious barrier to obtaining electricity (Bernard 2012). In fact, of all the countries for which information is available, Sub-Saharan African countries had the highest number of countries with connection charges more than $100 per customer at lowest rating—and correspondingly low rates of electrification (figure 1).<sup>4</sup> There are various reasons for these high charges for smaller consumers, including:

- weak commitment of utilities to provide electricity access (because of greater emphasis on high-consuming urban consumers, power generation constraints,<sup>5</sup> and inability to recover costs because of politically motivated limits on tariffs);

- lack of incentives to adopt solutions to make the connections affordable to poor populations;

- high costs of providing electricity connection, which stem from overrated technical specifications for low loads<sup>6</sup> ;

- inefficient procurement practices;

- greater distances between the household and the distribution pole in rural areas; and

- lack of financing options to make connection charges affordable.

Although many African households could afford monthly electricity bills of $3 to $7 per month (in part because the alternative monthly payments for kerosene, candles, and batteries are as high or even higher), many of them cannot afford the initial charge to obtain an electricity connection (appendix 2), which is in addition to the cost for internal wiring of the house.

> 3 A socioeconomic baseline study on a German Agency for International Cooperation (GiZ) rural electrification project in Benin emphasized affordability as a key barrier to obtaining a service connection, in addition to accessibility of the distribution network and lack of knowledge among the poor on the benefits of electricity.

> 4 There are some difficulties in comparing connection charges due to differing international practices. This study uses estimates obtained from various countries using a standard set of questions about charges for a new electricity connection. These questions were answered by people knowledgeable about the local electricity companies, or information was drawn from reputable reports.

> 5 The access programs in Ethiopia were hindered for over two years by a moratorium on connections adopted by the government at the end of 2008 due to power generation supply constraints.

> 6 − In Benin, for example, the utility charge is around $150 for a single phase, 5 30 amps. But since the consumption in rural areas is expected to be very low (below 5 amps), the use of a service conductor designed for 30 amps increases the cost.
words, is there an optimal combination of high- and low-violence treated children that maximized the aggregated effect?

As we describe in Appendix 4, the separated effects of the shares of high- and low-violence treated children are not statistically different from zero, particularly due to an increase in the standard errors. However, coefficients signs indicate that spillover effects on academic outcomes may be driven by the share of treated students with low levels of violence, while the reduction in misbehavior at school may be caused mainly by the share of treated students with higher propensity for violence. In this sense, exposing nonenrolled children to both groups with unlike levels of violence can increase the total spillover effect on different outcomes, either because treated children are role models for the rest of their class or due to a reduction in the violence focal points or disruptive behaviors.

Second, the intensity of these spillovers may change due to the exposure level—in terms of time length—of nonenrolled children to treated participants. Since nonenrolled children usually spend more time with treated students in their own class, one may expect that the spillover effects of exposure to a greater share of treated classmates are larger. In fact, our estimations indicate that spillovers on a nonenrolled student’s outcomes are led mostly by the share of treated students from her own classroom, providing evidence for our hypothesis of better school climate.

Finally, spillover effects may be different by misbehavior closeness of nonenrolled with treated students. Since the ASP’s effects are different by initial propensity for violence of treated participants, there may also exist heterogeneity in the spillover effects by initial nonenrolled students’ misbehavior at school. The estimations we present in Appendix 4 show that most of the effects are greater for students whose bad behavior at school is intermediately away—between 1 and 2 standard deviations—from the average misbehavior of the share of treated students within their classroom. Particularly, the effects of this medium closeness are greater on bad behavior reports. Thus, this result highlights that only certain levels of similarity to treated students can have positive spillover effects. Similar to Dinarte (2018)’s findings, this last result indicates that diversity plays an important role in enhancing these positive externalities because it allows children to learn from peers with different levels of misbehavior.

# **5. Emotional Regulation Drives Reductions in Violence**

Why did this intervention have such behavioral and academic impacts? We argue that the enhancement of participant capacity for emotional regulation is the main path through which to observe the ASP’s impacts. In this section, we first present neurophysiological and economic evidence that 25
_Table 7_ _Changes in Employment Status/Ownership_ _<u>for the Panel Individuals 2002-2004</u>_

||||||2004<br>Informal<br>|Self-<br>||||
|---|---|---|---|---|---|---|---|---|---|
|||Foreign|State|Private|Wage|employed|student|Unemployed|2002 Total|
||Foreign|36|6|8|11|3|4|3|71|
|2|State|12|710|33|18|57|3|19|852|
|0|Private|17|12|81|24|20|1|1|156|
|0|Informal wage|8|26|80|672|306|15|44|1151|
|2|Self-employed|15|94|94|406|5071|129|194|6003|
||Student|17|59|38|87|391|1062|75|1729|
||Unemployed|4|20|16|67|213|25|219|564|
||2004 Total|109|927|350|1285|6061|1239|555|10526|

Source: Author’s calculation based on the VHLSS 2002 and VHLSS 2004.

Notes: The ownership is based on an individual’s “most time-consuming job in the last 12 months”.

_Table 8_

# _Log Differences in Hourly and Weekly Wages for the Panel Individuals 2002-2004_

|A. LogDi|fferences in Ho<br>200|urlyWages<br>4|<br>|||B. LogD<br>20|ifferences i<br>04|n WeeklyWages<br>||
|---|---|---|---|---|---|---|---|---|---|
||||Informal|2002||||Informal|2002|
|Foreign|<br>State|Private|wage|Total|Foreign|State|Private|wage|Total|
|2<br>Foreign<br>.19|.06|.04|.07|.13|.24|.21|.17|-.03|.18|
|0<br>State<br>.13|.27|.07|-.10|.25|.25|.28|.22|-.03|.27|
|0<br>Private<br>.18|.01|.10|.17|.11|.35|.06|.15|.07|.16|
|2<br>Informal wage<br>.40|.14|.22|.20|.20|.67|.23|.27|.18|.20|
|2004 Total<br>.20|.26|.14|.19|.21|.31|.27|.21|.17|.23|
|Source: Author’s calculation b|ased on the<br>_Re_|VHLSS 2<br>_sults of _|002 and V<br> <br>_Worker-S_|HLSS 2004.<br>_Table 9_<br>_pecific Fixe_<br>(1)|_d Effects_|_Model_<br>(2)||||
||∆Foreign dum|my||.079||.19**||||
|||||(.084)||(.083)||||
||∆State dumm|y||.095||.049||||
|||||(.072)||(.059)||||
||∆Private dum|my||.023||.032||||
|||||(.047)||(.053)||||
||∆married|||-.044||-.11||||
|||||(.059)||(.068)||||
||∆Industry du|mmies||X||X||||
||Number of O|bs.||1727||1712||||
||R<sup>2</sup>|||.034||.039||||

Notes: The dependent variables are the difference in logs of hourly wages in regression (1) and the difference in logs of weekly wages in regression (2). The informal wage sector is the omitted ownership category.

** indicates that the coefficients are significant at the 5 percent level. The robust standard errors are between parentheses.

34
Table 4: Number of destinations per firm

||Me|an|Med|ian|Maxi|mum|
|---|---|---|---|---|---|---|
||LAC|All|LAC|All|LAC|All|
|2007|6.4|10.1|6.0|8.0|16|45|
|2008|6.5|10.3|6.0|8.0|16|48|
|2009|6.6|10.7|6.0|9.0|16|48|
|2010|6.7|11.2|7.0|9.0|17|51|
|2011|6.8|11.5|7.0|9.0|17|51|
|2012|6.9|11.6|7.0|9.0|17|49|
|2013|7.0|12.0|7.0|9.0|17|51|
|2014|7.0|12.3|7.0|9.0|17|61|
|2015|7.0|11.9|7.0|9.0|17|61|
|2016|7.0|11.9|7.0|9.0|17|58|
|2017|6.9|11.5|7.0|9.0|16|60|

Source: Author’s calculation based on Colombian customs data. Note: This table reports the number of destination markets by Colombian exporters over time.

# **3.2 Tariffs in LAC**

Colombian exporters saw declining tariffs in their regional destination markets between 2007 and 2017. The applied tariff decreased steadily from 6.7% in 2007 to only 4.1% in 2017, as seen in Table 5, as Colombia ratified numerous preferential trade agreements with regional partners over the period under investigation.<sup>5</sup> This is most evident for 2017 when tariff reductions of the Pacific Alliance between Colombia and Mexico, Peru and Chile, came into effect. At the same time, Most-Favored-Nation (MFN) tariffs negotiated at the World Trade Organization (WTO) declined only marginally from 9.1% in 2007 to 8.7% in 2017, reflecting the persistent deadlock in multilateral trade negotiations.

# **3.3 Non-Tariff Measures in Latin America**

The NTM dataset contains 6,211 regulations for Colombia’s 17 destination markets in LAC between 2007 and 2017. To study the variation of regu-

> 5The Pacific Alliance, signed in 2013 between Colombia and Mexico, Peru and Chile, reduced 92% of tariffs between members in 2016. Moreover, the free trade agreement (FTA) between Colombia and Costa Rica entered into force on August 1, 2016. As a result, 74% of industrial products became duty-free.

11
public sector jobs are a reward for political support. That is not to say, that motivated public servants do not exist and do not bemoan this equilibrium. For example, a health assistant in Ethiopia noted that absenteeism in public facilities was a rational response to the fact that ‘moonlighting’ in the much better paid private sector was not regulated in any way. “We observe [absenteeism] – we cannot lie about it. Not respecting working hours is something we got used to and this has created a problem for the patients. During tea time, for example, people go to work in the private sector. Even the small amount of time we have in the facility we do not use effectively” (see Lindelow and Serneels qualitative study of Ethiopian nurses, 2006). Similarly, poor infrastructure may beget low effort if the two are complements. As noted by an Ethiopian health assistant in the same study: “There is inadequate supply of material [in the public sector]. It is not possible to work efficiently and meet expectations. If you try to work with what is available, your service will be sub-standard, and eventually you might be forced to drop your ethics.”

# **Blocking Reforms That Threaten Rents**

If public sector jobs are a mechanism to distribute rents to political supporters, then reforming public service employment to be more accountable reduces rents and would potentially be interpreted as the patron reneging on his part of the bargain. This may be one (among other) reasons why teacher unions tend to be opposed to accountability reforms in the public sector – especially if these might imply salary reductions for some teachers.

However, while teacher unions tend to be opposed to pay reform, this is by no means the case for the average teacher (as shown for the US by Ballou and Podgursky, 1993)– partly because not all of them are part of the clientelistic equilibrium (for example in Kenya about 20 percent of public primary school teachers are employed by Parent-Teacher Associations rather than the government at a fraction of the pay of civil service teachers) or even within a clientelistic equilibrium, effective and motivated teachers stand to gain from such reforms. Mirroring Ballou and Podgursky’s findings in the US, Muralidharan and Sundararaman (2011) show that in their sample of government teachers in Andra Pradesh over 80 percent of teachers are in favor of merit pay and that exposure to actual incentive pay – far from demoralizing teachers – actually increases teachers’ support of it. This split in teachers’ interest became visible for example in the US, when teachers in Washington, DC, were asked to vote on 7
Policy Research Working Paper 9591

# **Abstract**

The U-shape theory argues that at early stages of development, countries experience a reduction in the female labor force participation, eventually followed by a recovery. In Morocco, female labor force participation is now lower than it was two decades ago due to several factors that are discussed in the paper. There is also a persistent 50-percentage-points gender gap in labor force participation rates, despite improvements typically related to development and female inclusion—such as a higher gross domestic product per capita, lower fertility rates, and better access to education. At the same time, urban job creation has not been able to offset rural job destruction nor the increase in the working age population for both genders. Using data from the Moroccan Labor Force Survey, the World Values Survey, and the Arab Barometer, probit models and a multinomial logit are estimated to explore the challenges affecting female insertion into the labor market. The findings show that higher educational attainment increases the probability of female participation, but this relationship has decreased over time, not being enough to offset other obstacles caused by other individual and household characteristics. Being married and the presence of other inactive women are found to decrease female participation. The educational level of the head of household (typically men) increases female inactivity, suggesting that potentially gender roles may drive women out of the labor market and slow the recovery in women’s participation.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gacevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team