rafmacalaba/datause-encoder-data huggingface dataset:
easier way for getting at this true cost than aggregating up from plant level estimates.<sup>10</sup> We therefore would like to think of our (plant-level) approach to the analysis of the effects of business environment as a natural complement, rather than alternative, to aggregate analyses at the industry or sector level.

One advantage of estimating productivity effects of business environment at the level of the plant rather than the industry is that it enables us to identify these along side the effects of geographic variables. Simultaneous identification of the effects of institutions and economic geography would seem to be more and more difficult as we work at higher and higher levels of aggregation. It would indeed be impossible in cases where the identification of the effects of business environment has to rely on (individual) fixed effect techniques in which the effects of economic geography would have to be “differenced out”.

On the other hand, as Dollar et al (2004) point out, plant level analysis of the productivity effects of the business environment needs to address econometric problems that would not arise in the analysis of longitudinal aggregate data. One of these is the possibility of selectivity bias in least squares estimates of the effect of business environment on productivity arising from the correlation of unobservable location or firm characteristics influencing plant productivity with those affecting location decisions. There is also the possibility that the firm level incidence of business environmental bottlenecks could be endogenous even in the absence of selectivity, say, because inherently more productive firms somehow cope better with adversity in their external environment.<sup>11</sup> 10 Long run effects can be identified along side short run effects based on plant level analysis. However, this requires longitudinal census data. Even then it is difficult to imagine an easier way of identifying long run effects as defined here than aggregating estimation at least at the industry level. The reason for this is that most competitive industries seem to be subject to simultaneous entry and exit, for which the spatial analogue would seem to be that new plants are observed to open in a local economy at the same time as others shutdown in the same line of business. Isolating the long run effects while keeping the analysis at the plant level then would mean identifying the effects of business environment on the productivity of the typical new entry and on that of the typical closing plant.

> 11 This second form of endogeneity is also discussed in Dollar et al (2004) and has  to be addressed in analysis based on industry aggregates as well

7
# **IV. Use of assessments to design in-service training programs**

# _Identification of More Pertinent Training Content_

Based on available funding and preestablished yearly training targets, administrators of the SIPROFE program reach out to universities (or other training providers) to have them propose training content. As mentioned, the training curricula for these courses are not based on a systematic assessment of needs, but rather on the university’s expertise, the supply of courses, the availability of trainers, and general delivery capacity (that is, courses are supply-driven).

According to data provided by the National Directorate of Continuous Education (a division under the MINEDUC’s Undersecretariat of Professional Development), since its introduction in 2008, the SIPROFE program has offered, on average, 15 in-service training courses per year. These training programs are categorized into two main groups: (a) complementary programs, which are largely focused on building the capacity of teachers to revise and update the curricula; and (b) remedial programs, which aim to address teachers’ knowledge gaps. Table 3 lists the remedial courses most recurrently offered by the SIPROFE program. Results indicate that courses offered are highly focused on pedagogy and general education, with a limited focus on improving cognitive skills.

**Table 3. Remedial Courses Offered under the SIPROFE Program, 2012–16**

|**Course Name**|**Training Hours**|
|---|---|
|Pedagogy for Natural Sciences (BGE)|40|
|Pedagogy for Social Sciences (BGE)|40|
|Pedagogy for Mathematics (BGE)|40|
|Pedagogy for promoting critical thinking in the classroom|40|
|Pre-K education|50|
|Strategies to evaluate student learning|60|
|Inclusive education|10|
|Introduction of information technology and communications|50|
|Critical reading|60|
|General pedagogy|60|
|Prevention of student risks (sexual abuse, violence, etc.)|30|

_Source_ : MINEDUC 2016.

Program administrators and participating training providers could use the information of the _Ser Maestro_ assessments to make courses more demand-driven and responsive to teachers’ observed cognitive skills gaps. As mentioned, the assessment identifies both the subjects and curricular areas where teachers display cognitive deficits. Administrators, for instance, could develop reports using the _Ser Maestro_ results that show the observed performance of teachers by 12
Black, McKinnnish, and Sanders (2005) was one of the earliest studies departing from the cross-country growth regressions (table AB.2). They use county-level data from the coal-rich areas in the Appalachia region of the United States to examine the short-term economic impact of the coal boom in the 1970s and the subsequent bust in the early 1980s. This study treats resource booms and busts as shocks to the local demand for labor. Black, McKinnish, and Sanders find evidence of modest employment spillovers. The boom is associated with an increase in population, especially males in prime age, and in nonmining employment and wages. These effects are reversed during the bust. Their results, however, do not support the hypothesis that the coal boom crowded out other industries.

Using data from western Canada, Marchand (2012) documents similar effects on local labor markets (that is, modest employment spillovers, mostly in construction, services, and retail) associated with the 1970s energy boom and bust. Fleming and Measham (2014) also find positive employment spillovers associated with the recent boom of coal seam gas in Australia.

In a related paper, Michaels (2011) examines the long-term economic impact of oil discoveries in the southern United States. He uses county-level data covering 1940 to 1990 and exploits geological variation in oil abundance. He finds that discovery of oil increases specialization in oil production, but does not seem to have led to deindustrialization. There is, for instance, no change in the employment share of manufacturing. On the contrary, oil discovery increases the overall size of manufacturing (measured as number of manufacturing jobs per square mile). The increase in density of manufacturing employment seems to be associated with the increase in population size. These results are the opposite of what we could expect from the standard Dutch disease argument. Instead, they are suggestive of agglomeration economies. The effect on real income is less clear. Michaels (2011) finds that nominal income increases, but this increase may have been offset by higher housing costs.

Allcott and Keniston (2014) extend the study by Michaels (2011) to rural counties in the entire United States. They find similar results: oil discoveries increase population size, income growth, and wages, but do not seem to negatively affect manufacturing. If anything, oil discoveries have a positive effect on manufacturing employment and output. Using rich data at the firm level, they also evaluate the effect on total factor productivity, but find no significant increase. They interpret this evidence as suggesting that some manufacturing firms benefit from oil booms due to the increase in size of local markets and not due to productivity gains 25
<!-- Start of picture text --> Ln[Women-owned establishments per worker, 1994]<br>5<br>®co<br>5<br>z<br>5© . ; eT<br>oO£ °<br>oY ° $ .<br>=<br>% . o Py . ;<br>3 ° $ ¢ . °<br>oNs ‘ . ; : °<br>ro} ; o e<br>¢5 ° °i : e .<br>=° :<br>+ 1992 1996, 2000 2004<br>Year of First Election Under 73rd Amendment<br><!-- End of picture text --> <!-- Start of picture text --> Women-owned share of establishments, 1994<br>3 - o o > « o e<br>rep)@<br>2<br>Sa ; °$ t<br>&= : 3 e<br>=>QSo « :°¢ E ‘ —__<br>oo - ° . od3 °a . ry °<br>Oo o . 3 3 Ad<br>Py . .<br>o s ° $ .<br>= * s bd ° Py<br>® ‘ { . : °<br>Cc o ° e e<br>zo . foi.<br>= $ 3 4 ° ry<br>So= $+ 3i H j ‘<br>1992 1996 2000 2004<br>Year of First Election Under 73rd Amendment<br><!-- End of picture text --> <!-- Start of picture text --> = Ln[new women-owned establishments per worker, 1994]<br>3<br>g<br>Fay<br>=2 eT<br>& .<br>aN °<br>oOo °<br>3 . °<br>= °<br>so ; i :<br>Cc ° Pg<br>z5 3 . i ry:<br>5°<br>= 1992 1996 2000 2004<br>= Year of First Election Under 73rd Amendment<br><!-- End of picture text --> <!-- Start of picture text --> Women-owned share of new establishments, 1994<br>Tt<br>fe))<br>ae - o ° e o o .<br>- s e<br>£<br>Eo5 . [. ¢<br>= 7 e<br>re=oO * b@ 4 ¢t °o ———_—_.<br>oo2 $of° 3 ° o<br>c bd : o<br>5 ° Py . °<br>ow ° ¢<br>£ . .<br>° 3 + ¢ °<br>te . ry 3 3 . °<br>cs - 3 io; 3 .<br>8<br>5r}= .sb ia zi . ‘3 .<br>1992 1996 2000 2004<br>Year of First Election Under 73rd Amendment<br><!-- End of picture text -->
Table A.5: Physical Intimate Partner Violence: Reduced-form Estimates

||F|ull Sampl|e|Rural|Urban|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Fraction of female leaders in close elections|0.316**|0.323**|0.358**|0.484***|-0.018|
||(0.161)|(0.155)|(0.157)|(0.185)|(0.216)|
|Observations|65,292|65,292|62,122|43,981|18,141|
|Outcome mean|0.000|0.000|0.006|0.031|-0.052|
|State fxed efects|x|x|x|x|x|
|District characteristics||x|x|x|x|
|Individual characteristics|||x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents the reduced-form regression results. Columns (1)-(3) include all women in the regressions, while columns (4) and (5) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

32
corruption hypothesis but no support for the fair wage-shirking hypothesis. Specifically, workers whose perceived fair wage is much lower than their actual wage are more likely to engage in informal charging but they are not more likely to moonlight.

The paper is organized as follows. Section 2 reviews the literature relevant to this paper. Section 3 describes the Tajikistan PETS data used in the paper and presents summary statistics. Section 4 states the econometric strategy used to identify the impact of gender on informal charges and moonlighting. Section 5 presents the results and section 6 concludes.

# **2. Related Literature**

This section reviews two strands of literature to which this paper is linked. We first address the specific literature of corruption in the health sector before turning to the gender and corruption literature.

# _2.1 Corruption in Health_

The health sector often ranks among the most corrupt in many developing countries. In a survey on corruption perceptions in 23 countries the health sector ranked in the top four in ten countries. The many forms of corruption in the health sector range from the commercialization of public positions to staff absenteeism (see Lewis 2006). Corruption also arises at several levels, from the central government to the health facilities including all intermediary administrative layers. This sub-section focuses on corruption at the front-line service delivery level.

The most pervasive and studied form of corruption at the point of service delivery is informal payments. Informal payments can be broadly defined as direct unofficial payments, in-kind or in cash, from patients to health workers for the latter’s personal benefit. The study of informal payments poses major challenges, the first of which is measurement. Users are the most common source of information on informal payments through household surveys and patients’ exit polls. Unfortunately, users sometimes have a hard time distinguishing between 3
Overall, we find that FDI does not stimulate growth in economies with excessive business and labor regulations, after controlling for some other relevant determinants of observed changes in GDP growth rates. However, this outcome is restricted to the top 20 or 30 percent most regulated countries, indicating that there is a threshold level for which our results do hold up. This outcome has some powerful policy implications, as governments have first to improve the regulatory quality in their countries to be able to benefit from increased openness to foreign capital in the form of direct investment.

The paper is structured as follows: The next section describes the regulation data used, whereas Section 3 presents the specification of the econometric model. We use standard OLS regression analysis and, since a number of the independent variables are likely to be endogenous, an instrumental variable approach. The results will be presented in Section 4 and finally, Section 5 summarizes the main results and concludes with some policy implications.

# **2. Measuring Regulations**

For a long time, researchers who examined the impact of institutions and regulations across countries had to rely on a few sources, such as the indicators on political risk provided by the PRS Group (2005) in their International Country Risk Guide or the government effectiveness indicators from the annual Global Competitiveness Report supplied by the World Economic Forum (2005). These organizations publish a large variety of relevant indicators, for instance, on the level of corruption or the quality of the bureaucracy, which are clearly relevant for the effects of institutions and government regulations. These indicators, however, are derived from the results of executive opinion surveys and thus presents the perceived level of corruption or the rule of law. Predominantly, they do not use factual information to measure differences in institutions and regulations across countries.

Recently, the World Bank started to publish results from their Doing Business project (World Bank 2004). The Doing Business database provides objective measures of business regulations and their enforcement. The Doing Business indicators are comparable across economies and indicate the regulatory costs of business. They allow us to obtain information on regulatory outcomes, such as time and money spent on bureaucratic procedures, and thus to investigate the efficiency of the government institutions in place. By focusing on evidence 6
sized ones, report facing lower financing obstacles in countries having higher levels of foreign bank presence.

In summary, the evidence on the implications of foreign bank participation for access to credit suggests that foreign banks are generally less inclined to lend to small and opaque borrowers relative to domestic banks. Nevertheless, there is also evidence that their presence can have overall positive effects on access to credit, even if foreign banks themselves are not lending to small firms.

# **4. Foreign bank participation and crises**

This section studies the link between foreign bank presence and crises. We begin by presenting data on the prevalence of banking crises and on the post-crisis levels of foreign participation across regions. Then, we use cross-country regressions to test whether countries that had crises also had higher levels of foreign participation in banking. Next, we present panel estimations with country-level fixed effects that allow us to control for unobserved country characteristics and enable us to pinpoint better the timing of the increases in foreign bank presence. In this way, we can assess whether foreign participation occurred in response to banking crises. Finally, we conduct panel regressions that test whether higher levels of foreign participation coincided with increased provision of credit to the private sector.

# **4.1 Crises and Foreign Bank Participation: General Patterns**

Banking crises are a fact of life in developing economies. Since the mid-1990s, 77 crises episodes have taken place in 72 developing countries. Table 2 shows crisis episodes as identified by Caprio and Klingebiel (2003). The table illustrates that banking crises have occurred in at least one country in each region of the developing world.

Crises represent enormous challenges to policy-makers in developing countries. As shown in Table 2, the costs of dealing with crises (e.g., paying for deposit losses, recapitalizing banks, and building banking systems that are more resilient to shocks) can be very large. Moreover, most crises have macroeconomic roots and take place in environments where governments have already difficult fiscal situations. Table 3 shows the average budget deficit to GDP in each region three years before and three years after crises. This table illustrates how even 15
not working or studying; i.e. the ―jobless‖. We can define the ―jobless rate‖ as the share of girls age 15-24 who are unemployed, not working in labor market activities, and not in school. Thus, the difference between the inactivity rate and the jobless rate is that the former includes students while the latter excludes students.

Unfortunately, no data source provides ―joblessness‖ rates so we estimate them for the purposes of this paper. To purge students from the ILO’s KILM inactivity rate, we use EDStats secondary school attendance rates to roughly estimate the percentage of girls 15-24 who are in school.<sup>11</sup> We generate two joblessness rates, under different assumptions. First, we assume that girls age 15 and older are only in upper secondary. We use gross upper secondary school enrollment rates to calculate the number of girls aged 15-24 who are in secondary school and divide by the number by the female population age 15-24. We call this the ―lower bound‖ because it does not account for girls over age 15 who are delayed in school (i.e., in primary or lower secondary). Second, we assume that all girls in upper secondary are 15 and older and that half of the girls in lower secondary are over age 15. We divide this number by the female population, age 15-24 to estimate an ―upper bound‖ since we are likely to be overestimating the number of girls in lower secondary who are over-age. We then subtract the lower bound and the upper bound from the KILM activity rate to get two measures for girls’ joblessness rates.<sup>12</sup> We present the opportunity cost both of girls’ inactivity and of girls’ joblessness relative to women’s and boys’ inactivity and joblessness. We use the more precisely measured but less conceptually correct inactivity rate as our base case. Then we present – and compare – the costs when using the less precisely measured but more conceptually correct jobless rates.

As in our calculations for early school leaving, we adjust for labor demand elasticities, assuming that an increase in the number of workers will decrease market wages. In this case, we apply the same elasticities described in the last section.

> 11 The age range 15-24 was chosen due to data availability. Ideally, girls aged 15 should be still pursuing their studies and not working.

> 12 It should be noted that females might engage in valuable home-based production activities, and this is not corrected by the ILO’s inactivity rates.

10
**Figure** **_5a:_** Transmission to Non-Oil Developing Countries of U.S. Deficit Reduction

<!-- Start of picture text --> LDC Trade Balance<br>_ -0.02<br>a0.a<br>-0.26 -El - Coprt r<br>4L-0.22 --. -- E ) Spend;nqcu<br>-0.26 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ - -C orpoar te Tax<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> LDC Exports<br>0.5<br>- 02<br>c. 0.1<br>~~ -01 ~ ~ ~ ~ ~ A<br>z -0.2 ES Sp.nding cut<br>-0. IIncome Tom<br>-0.q5 .. . * . X . | W . s . w. _ * . | . -4-Corporate Tax<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 20T2<br>Year<br><!-- End of picture text -->

12
later, it slowed down the transition process and did not achieve the critical mass of changes. Moreover, the inability to start large-scale privatization has led to the return of a policy of soft budget constraints in relation to state owned enterprises and to the significant erosion of macroeconomic policy in 1993-94.

5. A few countries undertook no significant systemic changes and made little progress towards liberalization and privatization of the economy until the end of 1994. This lead sooner (Ukraine) or later (Belarus, some Central Asian states) to economic destabilization and high inflation or hyperinflation.

6. Countries at war (Bosnia and Herzegovina, Serbia and Montenegro, Georgia, Armenia, Azerbeijan, Tadjikistan, and Croatia) where economic reforns were never launched or were halted and where war-related expenditures and the destruction and disorganization of the economy have led to hyperinflation make up this group. Like the fourth category, this one is made up of countries with important differences among themselves in the level of economic development, intended economic strategies, and the size of real war damages. For example, Armenia, Croatia, and Serbia started reforms but stopped them. The inability to implement a comprehensive transformation package could be seen, however, as the common denominator of this group.

The categorization proposed here differs significantly from the standard dichotomy between shock therapy and a **gradual approach** that will be discussed later in this paper. Yet the real picture is much more complicated than described. In practice, there is more than one variant both of the radical _(shock)_ scenario and of gradualism. Some cases escape neat classification. For example, in the beginning of 1994, the new Yugoslavia<sup>5</sup> and Croatia had taken ambitious anti-inflationary measures. At the end of 1994, large transition packages were discussed in Ukraine, Georgia, Armenia, and Belarus.

# 2. Economic effectiveness of different strategies

Tables 1-3 present three basic economic indicators-GDP growth or decline, the annual rate of inflation, and the unemployment rate-of 24 countries in transition. (See Tables 1, 2, and 3 at the end of this paper.) These countries represent all the categories characterized in the previous section except East Germany. To interpret those data, one must remember, of course, that the quality of transition statistics is often problematic. For example, before the transition started, the GDP level was very often overestimated; after the beginning of transition, GDP has usually been underestimated for many methodological reasons [see Bratkowski, 1993a]. The inflation statistics in the FSU before 1992 were very poor. Even now their quality remains problematic in some FSU countries, in part because persistent market shortages (i.e. repressed form of inflation) remain in some transition economies.

> 5 The new Yugoslavia is Serbia and Macedonia.

9