rafmacalaba/data-use-mentions-extended huggingface dataset:
salary increases for their performance as classroom teachers, school directors, supervisors and technicaladministrators. Teacher evaluation is based on performance (35 points), experience (10 points), professional skills (25 points), educational attainment (15 points) and completion of accredited courses. There are five levels of promotion (“A”, “B”, “C”, “D”, “E”). The salary rewards allocated to each one of these levels represent an increase but do not represent a change in post assignment. The promotion ladder attaches considerable importance to seniority within _Carrera Magisterial_ , rural posts or teaching in under-developed areas. Promotion within _Carrera Magisterial_ is complex because of the different levels ( _escalafones)._ Government is the dominant provider of basic educational services. It owns close to 91 percent of primary and secondary schools, which account for 90 percent of total enrollment.<sup>4</sup> At the university level, however, the private sector plays a much bigger role, accounting for close to half of the enrollment (46 percent). The educational system in Mexico is now so extensive that there are over 483,000 schools (excluding preschools) staffed by over a million teachers, of which 84.3 percent are in public schools.

In 1999, public schools teachers<sup>5</sup> held 43 percent of total government positions. All teachers in basic public education are affiliated with SNTE. All teachers in upper secondary and tertiary education have a union or are independent (in Autonomous or State Universities).

# **3. THE DATA**

The National Household Income and Expenditures Survey ( _Encuesta Nacional de Ingresos y Gastos de los Hogares_ , ENIGH) is collected by the National Institute of Statistics, Geography, and Information ( _Instituto Nacional de Estadística, Geografía e Informática_ , INEGI). This survey is available for 1984, 1989, 1992, 1994 and 1996. Each survey is representative at national level, and distinguishes urban from rural
|_Sample_<br>_Full Sample_<br>**Table 10**<br>**FG and financial recoupling: EPF**<br>This table presents estimations of the relationship between <br>globalization. All results are from panel estimations with co<br>absolute values of net inflows to a country by global an<br>normalized by market capitalization. Dependent variables <br>(MSCI country indexes, sovereign spreads, and exchange ra<br>2005-09/2010 due to data availability. Normal and turmoil di<br>(or zero) and negative returns from the global asset respe<br>parentheses. *, **, and *** denotes significance at the 10, 5<br>MSCI, GEM World Bank, Bloomberg, US Treasury, EPF<br>Bank, IMF IFS Statistics.|_Turmoil_<br>**R Flow Data**<br> financial recoupl<br>untry fixed effec<br>d global emergi<br> are dlog returns <br>te, LCU/USD). S<br>vides the sample<br>ctively. Robust s<br>and 1% level resp<br>R, Barclays Capi|_Normal_<br>ing and financial<br>ts. FG Flows are<br>ng mutual funds<br> from EM assets<br>ample goes from<br>between positive<br>tandard errors in<br>ectively. Source:<br>tal, GEM World|
|---|---|---|
|**Equity**|||
|S&P Returns<br>0.586***|0.579***|0.841***|
|(0.058)|(0.119)|(0.137)|
|S&P Returns*FG Flow<br>4.204
two different crops, wheat and rice, have led to persistent differences in culture, which are reflected in the mental models that individuals draw upon, their perception, and their behavior.

Behavioral economics is replete with examples of how context and framing matter. The effects have been demonstrated in laboratory experiments, in the field, and recently even in policy. They are generating extensive replication to test the generality of the underlying mechanisms and the possibility of scaled-up interventions.

A field experiment in Kenya (Dupas and Robinson 2013) has results consistent with those of the money prime laboratory experiment. All the subjects received information about preventative health products, and all were encouraged to save in order to purchase them. Each individual in a randomly chosen group was given two additional things: a lockable box and a passbook to label with product that the individual hoped to purchase with his savings. Having the box and passbook had large effects on behavior. The proportion of people who reached their savings goals was only one-third in the control group (without the box and passbook but with the encouragement) but one-half for those who also had the box and passbook. The amount of spending on preventative health products in the past 12 months was 66-75 percent higher for those with the lockable box compared to those with only verbal encouragement to save for health needs.

How did the subjects manage to increase their spending on health products? Post-interview surveys suggest that the lockable box and labelled passbook helped the individuals _mentally_ allocate money for savings.<sup>22</sup> The box and passbook also made the individuals feel less obligated to share available money with others in need. A survey revealed that the group with the lockable box felt much less obligated than those without the box to share with others when their only available cash was in the box.<sup>23</sup> The mental accounting changed the way the subjects addressed both temptations to spend and pressures to share.

# 3.2  Social context and priming

Behavior is often best described _as
lower than for other more resource-based measures of women’s power, such as income or group participation. This may be an advantage of our measure.

Second, an alternative explanation of our results, that women who are more likely to be optimistic about their decision-making power are also more positive about outcomes, is countered by the increase in reported violence. Third, to mitigate concerns that our results are entirely driven by unobservable characteristics, in Appendix Tables 2 to 5 we rerun all the results above, additionally controlling for a set of women’s status and empowerment proxies. This lets other dimensions of bargaining power load into our analysis. We note lower significance in our estimates, but the coefficients remain largely unchanged, providing further evidence that examining disagreement is picking up a different facet of power.

# **5. Discussion**

By exploiting cross-reporting by wives and husbands on women’s decision-making ability over large purchases in 23 nationally representative surveys in Sub-Saharan Africa, we present a novel measure of women’s intra-household power. Through its close conceptual alignment with the power literature and its enduring signal in our analysis even when adjusting for households’ and women’s resources, we argue that it captures a unique and understudied dimension of women’s power.

We show that women’s power matters not only for gender equality, but also for other important well-being outcomes at the individual and household level. We find that the case of husbands and wives existing in an uncontested state of women’s power appears to matter in particular for health outcomes, which is unsurprising given its link to well-being outcomes in the public health 28
RUSMOD by about 25 percent, expenditures on monthly cash payments are _under-reported_ by 65 percent, while simulated PIT, SIC and VAT receipts are _under-estimated_ by the model by 30-35 percent—vis-à-vis the official/external statistics. Gini index is estimated at 34.8 versus 41.0, the official (Rosstat) number. On the other hand, for several programs (e.g. unemployment benefit, maternity capital, child allowances up to 16 (18) years, housing privileges and subsidies) the match is quite accurate.

There are numerous reasons for the mismatch. First, a large share of incomes may remain unaccounted for in the survey due to non-response or underreporting. Also, some benefits (such as a monthly cash payment) may be paid together with other benefits (pensions from the Pension Fund, in this case), and not distinguished separately by recipients. On the other hand, official/external statistics may use other sources to compile the data—for example, in the case of the Gini index and national poverty headcount, Rosstat adjusts the survey number by imputing a top-end of the income distribution, which is believed to be missing from the data.

The mismatch with official data is not a fatal flaw. First, in general, surveys are used precisely to enrich and triangulate other statistics, and may not necessarily be a less accurate reflection of the real world. Second, the discrepancies are not large for EUROMOD standards. Nevertheless, the modeling approach in this paper attempts to reflect as close as possible the existing statutory rules, while at the same trying to compute economic rather than statutory incidence of taxes and benefits. In particular, we (i) excluded informal sector workers from simulations of PIT and SIC, (ii) imputed unreported income, and (iii) assigned non-take up probabilities for means-tested benefits.<sup>4</sup> Moreover, where appropriate, _changes_ produced by the model, which are presumed to be more accurate, are used to

> 4 This model
<mark>indicators from the labor surveys. The average across countries of the relative values of the absolute deviation of the employment-to-population ratio from the online survey to that of the labor force survey data is 30.0 percent. The same figure for formal share of employment is 39.1 percent, and for self-employment it is 166.9 percent. The reweighting procedure worsens the accuracy of online estimates of the employment-to-population ratio by 18 percent on average, and the formal employment share by 1.2 percent. In the case of self-employment rate estimates, model-based re-weighting reduced the bias by 26.4 percent.</mark> <mark>We conclude that the bias from non-representative Internet survey data can be substantial. Corrections using propensity score-based weights based on a small set of demographic variables can actually worsen this bias, and therefore, the reliance on non-representative internet surveys could be misleading. However, Internet surveys may be useful to identify trends in times of emergency among the population that they represent (mostly men, younger individuals, and the highly educated) for specific purposes. On the other hand, phone surveys are much preferable to Internet surveys because they can be drawn from a nationally representative sample or utilize RDD when mobile phone coverage is high.</mark> 23
private bank, and 10% a foreign bank.

Panel C shows the local market characteristics used to account for differences in the local economy and supply of banking services, using the Indian state as the local market. CMIE provides information on each firm’s “headquarters district,” the main location of firm operations. Sample firms are located in 25 different states. We also use information on the total number of banks in the market from the RBI Branch Banking Statistics (2002). We find that the average local market has 61 banks, ranging from a minimum of 6 for one very small state to a maximum of 68 ( _Number of Banks_ ). In the model for main bank ownership type below, we include a dummy for the presence of at least one foreign bank in the state to control for whether the firm has a convenient choice of a foreign main bank **.** At least one foreign bank operates in 16 out of the 25 states of India represented in our sample. However, because most firms are in those 16 states, the mean for this variable ( _At least 1 foreign bank present_ ) is 0.97. In the regressions where we estimate the likelihood that firms will diversify across bank ownership types, we control for the degree of concentration in the bank ownership types operating in the local market. _Bank ownership concentration_ is defined as the sum of squared shares of the number of banks of our four ownership types. The mean for this variable is 0.3. We use Census India 2001 to collect population density to proxy for rural versus urban areas ( _Population Density_ ). The average population density per square kilometer is 1,441 people, with a wide range from 109 to 9,294 people. All data are for 2001.

# **5. Empirical Methodology**

_Determinants of main bank ownership type_ Our first model tests the effects of firm and local market characteristics in determining the ownership type of the firm’s main bank: _Main bank ownership type
14 coordinating tax reforms: can lost tariffs be recouped?

immediate revenue that leads stakeholders to resist trade reforms in poor countries, the focus is on short-term impacts.

The academic literature on coordinated trade and fiscal reforms in Nepal is scant. Khanal (2006) finds econometrically that trade reform in Nepal over the period 1990-2005 did not lower trade tax revenue. Cockburn (2006) uses a Computable General Equilibrium (CGE) model to study the poverty impact of tariff elimination. His innovation is to incorporate household data in the model to capture complex income and consumption effects. When tariffs are eliminated but 1.1 compensated by a uniform percent increase in consumption tax, he shows that urban poverty falls and rural poverty increases because initial tariffs protected agriculture.

# 1.3.1 _Theoretical Motivation_

In an economy with multiple distortions, reduction of one or a subset of distortions (such as tariffs) may not lead to Pareto welfare gains. This is the essence of the theory of second-best launched by Meade (1955) and Lipsey & Lancaster (1956). Welfare may also not be increasing in the number of reforms that are undertaken because of second-best interactions, except when _all_ distortions are simultaneously reduced. However, it is impossible to know all distortions and their cross-effects. The challenge in trade policy reform, therefore, is to “design small, feasible changes in the existing tariff structure that will result in a welfare improvement when the first-best policy of free trade is not feasible” (Turunen-Red and Woodland 1993, p. 145).<sup>20</sup> A more realistic objective of governments is to maximize revenue which can be used in ways to improve national welfare. When the condition that revenue

> 20 An example of such a feasible change is to remove the biggest distortions first (“Concertina” tariff reform rule). As shown by Bertrand & Vanek (1971), Hatta (1977) and Lloyd (1974), if the highest tariff is reduced to the next highest level, welfare can
# 3.1 Labor force data

Our main data source is the Database on Immigrants in OECD countries (DIOC), com ~~-~~ piled jointly by the OECD and the World Bank. The database is the output of a project with the purpose of compiling highly standardized population census and register data on immigrants and natives in OECD countries, based on the 2000/01 census rounds. It was subsequently extended to include a large number of no ~~n-~~ OECD countries and the extension is referred to as DIOC ~~-E~~ . The 2010 DIOC dataset draws again primarily from national censuses and population registers, supplemented with labor force surveys where necessary.

Significant efforts were made to standardize the data since the original sources con ~~-~~ tain significant variation due to differences in national data collection and dissemination policies (Arslan et al, 2015). Censuses survey the entire population or a representative sub ~~-~~ sample, as is the case with the micro ~~-~~ census of Germany, at a single point in time.’ For each OECD country, DIOC provides aggregate stocks by educational attainment and various age groups, gender, labor market status, and migration status.* Using this in ~~-~~ formation, we are able to distinguish the nativ ~~e-~~ born from the foreign ~~-~~ born, the tertiary educated from the non ~~-t~~ ertiary educated, and older from younger workers in each OECD country, and to determine the size of each labor group. Since the dataset is in a bilateral format, we can also construct the emigrant labor stocks of the OECD countries. We should emphasize that the bilateral nature of the DIOC (and DIOC ~~-E~~ ) data is a very unique and valuable feature. Otherwise we could not obtain emigration numbers from the censuses of individual destination countries as they only give information on people who are present at
Traore for sharing the Ivorian panel data with us.

> 2 In addition, we have establishment-level data for 2010, 2011, 2015, and 2016.  We hope to match the establishments in those years to our existing dataset so that we have a panel covering the period 1996 to 2016.  Similarly, we have one additional year of data for Tanzania (2013) and hope to extend the Tanzanian panel from 2008 to 2013.

3
Finally, we construct loan-level and loan-officer-level data sets for use in the regression analyses. The loan-level data set is comprised of information on loan size, liability for repayment (group or individual), maturity, interest rate, incidence of overdue repayment, amount overdue, days overdue, characteristics of the loan officer who originated the loan, and the per capita income of the rural population in the county in which the loan was originated for 206,875 loans disbursed from 2006 to 2010. We also aggregate the loan-level data into calendar years for each loan officer and therefore generate an unbalanced longitudinal panel data set (since not all of the officers were at CFPA for all years from 2006 to 2010). As our key variable of interest, loan size is averaged over all the loans disbursed in a given year for each surveyed loan officer. Using the same approach, we generate the average maturity and average annual interest rate on loans disbursed in a given year for every loan officer. We construct measures of the summed amount of payments overdue (either by more than one day or by more than 30 days) relative to total loan disbursement in a year for each officer. In a similar way, we aggregate the amount of individual liability loans and loans used for various purposes and calculate ratios relative to the total amount of loans disbursed in each year for each officer. The yearly data for each loan officer are designed to help track changes in their lending behavior and the quality of their loans over time.

# 4. Data Analysis and Results

# _4.1. Recruitment of Loan Officers by CFPA Microfinance_

In the process of expansion, CFPA microfinance increasingly recruited frontline loan officers from rural China. When CFPA microfinance became independent in 2006, 60 loan officers were employed in ten branches. They then recruited an additional 24 officers in 2007 for those branches. At that point, about eight loan officers were employed on average in each of the county branches. From 2008 onwards, as CFPA microfinance expanded quickly and established new county branches
Source: Author’s elaboration from Enterprise Survey (2014) <u>Table 16. Main Reasons for Process Innovation (% of process innovation firms)</u>

|**Reasons**|**Bangladesh**|**India**|**Nepal**|**Pakistan**|**Africa**|
|---|---|---|---|---|---|
|Increase quality products|95%|81%|66%|92%|81%|
|Increase production|87%|66%|56%|63%|85%|
|Increase flexibility|65%|58%|65%|46%|78%|
|Increase speed production|89%|68%|62%|64%|78%|
|Increase speed delivery|60%|42%|45%|63%|71%|
|Decrease production costs|44%|41%|27%|41%|47%|
|Reduce waste|74%|54%|66%|63%|56%|

Source: Author’s elaboration from Enterprise Survey (2014)

# 5.6 Characteristics of the Innovation Process

In the South Asia region, most of the product and process innovations are in-house. However, process innovations are related to more collaboration than product innovations. External cooperation is in most of the cases linked to other firms, although cooperation with the private sector plays a more important role for process innovation than for product innovation (Figure 3). The primary sources of information for leaders are in-house or the private sector, while customers dominate in laggard countries (Figure 4).

Figure 3. In-house versus Collaboration <!-- Start of picture text --> How was product innovation developed?<br>How was process innovation developed?<br>(% of product innovation
2 KENYA AND CHINA’S TRADE RELATIONSHIP that local producers are suffering from cheap Chinese goods. Some even argue that imports are hurting Kenya’s prospects of industrialization.

<!-- Start of picture text --> 20000<br>China, P.R.: Mainland<br>World<br>10000<br>0<br>2012 2013 2014<br>Year<br>Imports (USD millions)<br><!-- End of picture text --> Figure 2: China takes a sizeable share of total Kenyan imports (2012-2014) Source: IMF World Economic Outlook Database 2014 <!-- Start of picture text --> 3000 3000<br>China, P.R.: Mainland China, P.R.: Mainland<br>2000 2000<br>India India<br>Japan Japan<br>1000 South Africa 1000 South Africa<br>United Arab Emirates United Arab Emirates<br>0<br>2004 2006 2008 2010 2012<br>Year Country<br>(a) China and India are a major source of imports (b) China and India were top two sources of imports (2013)<br>(2004-2013)<br>Imports (USD Millions) Imports (USD Millions)<br><!-- End of picture text --> Figure 3: China and India are major sources of imports (2004-2013) Figure 3a shows trends in Kenya’s top import partners between 2004 and 2013. With strong growth since 2004, China and India have become major sources of imports to Kenya. China ranks second to India in number of exports to Kenya, and its low production costs and better positioning in global value chains may help it become the top source of imports for Kenya (figure 3b). Chinese imports grew at an annual rate of 33 percent, and Indian imports also grew quickly at 30 percent per year.

Kenya likely imports even more from
wage inadequacy is most probably right;

- conversely, the relatively low multiplier for OECD countries is related to the higher participation rate as well as the much higher GDP and smaller skill differentials between public employees and the rest of the working population. As noted earlier, the low figure for ECA is in part explained by the existence of a variety of benefits-in-kind which are not captured in the available data. But the reality of government wage inadequacy in the region (especially the former Soviet Union) is undeniable;

- the above findings are supported by data for 16 countries, from a variety of sources, concerning the relationship of public to private wages for comparable skill-mixes. Although the data are fragmentary and for a small sample, the picture conforms roughly with the findings from the other indicators. Generally, government employment carries a "discount" of 10-20% -which is not unreasonable considering the greater job security. The lowest relative public wages are found in ECA; in this case, the figure is less misleading than the

Another indicator of relative public wages exists as well: the ratio of public wages to average wages in manufacturing. This **indicator** permits much broader country coverage, because manufacturing wage data are collected and published by the ILO for a large number of countries on a regular basis. However, it gives a mnisleading comparison, as public wages are generally for white-collar skills, **while** wages **in** manufacturing are generally for lower-paid blue-collar jobs. The comparison (which we have carried out and is included in Table A-3 but is not shown in the text of **fthis** paper) is also misleading from a policy viewpoint-for it produces the false impression of a relatively well-off civil service.
# **Table 5: Private and Social Returns to Investment in Tertiary**

# **Education by Full Discount Method (%)**

<!-- Start of picture text --> Private return paying<br>Private Social<br>tuition<br>9.7 5.4 5.6<br><!-- End of picture text --> Source: Authors’ calculations using AMSSW 2015 Attending tertiary education in a public institution in Azerbaijan leads to two different returns to investment in education estimates, depending on whether the student paid tuition or not. The distinction is created by the tuition costs, where 64 percent of the students pay tuition and 36 percent of the students receive a scholarship and do not pay any tuition. For those students who pay tuition, the rate of return to investment in education is almost 50 percent lower than for those students who receive a scholarship.

17