rafmacalaba/data-use-mentions-extended huggingface dataset:
and Vijverberg, 1989). After more than 20 years from the contribution of Van der Gaag, Stelcner and Vijverberg, econometric analysis can take advantage of the large amount of household data currently available for Peru. We compare public sector and formal private sector wages by using 2009 data from the Encuesta Nacional de Hogares (ENAHO). The ENAHO is a rich source of information. Using ENAHO data, the analysis of public-private sector wage differentials can be based on more than 16,000 observations. The large amount of information available allowed controlling for variables which might affect the wage levels, such as the level of education, the working experience, the gender, the marital status and the location (urban/rural) of the employees included in the sample.

Finally, this paper investigates how much of the difference between public and formal private sector wages is actually driven by the specific characteristics of the individuals employed in each sector. The evaluation of the remaining part, i.e. the “unexplained difference” of the wage differential will provide valuable information to determine whether public sector employees in Peru earn a wage premium which is independent from their specific characteristics and skills. To breakdown the wage differentials into an explained and an unexplained part, the OaxacaBlinder approach is used.

The topic of wage differential in Peru has been already considered in the past. In the late 1980s, Van der Gaag, Stelcner and Vijverberg (1989) used a switching regression model to show that public sector wages offered in Peru are well below those in the private sector. The rationale behind public sector penalties might be that lower wages in the public sector compensate a less competitive working environment and a level of job security which is obviously higher in the public sector. More recently, empirical contributions which based their estimations on 1990s household survey data (Panizza and Quiang, 1999; Panizza, 2001) found evidence of a public sector premium in Latin America, particularly for female workers. Empirical evidence of a public sector premium has been found also outside Latin
of additional schooling. Therefore, in order to capture the impact of education on growth better, a more complete picture will be presented by analyzing the role of primary, secondary and tertiary schooling.

After making some modifications to account for the statistical circumstances in Guatemala, the following procedure for constructing estimates of the human capital stock is used, based on the attainment census method advocated by Barro and Lee (2001). The use of a perpetual inventory method that employs census and survey information on educational attainment as benchmark figure can be seen as a major advantage over previous methodologies. The benchmarks are taken from various national censuses and surveys, see Table 3. Guatemalan statistics report distributional attainment stratified by age and sex in five cases: no formal education, first cycle of primary, second cycle of primary, first cycle of secondary, second cycle of primary and tertiary education. The data has been summarized into 4 broad categories, that is, no school, some primary, some secondary and some tertiary education.

The procedure starts to construct current flows of adult population, which are added to the initial benchmark stocks of the labor force (taken for 1950 from the Barro and Lee 2001 data set). The formulas for the three levels of schooling for the labor force aged 15 and over are as follows: where

- _HN j_ = number of the economically active population for whom j is the highest level of schooling attained (j=0 for no school, j=1 for primary, j=2 for secondary and j=3 for higher education)

- _PRI_ = enrollment ratio for primary education

- _SEC_ = enrollment ratio for secondary education

- _TER_ = enrollment ratio for tertiary education

- _L_ = number of the economically active population

- _L15_ = number of persons aged 15

- _L20_ = number of persons aged 20

-17-
almost 4 percent of arable lands are subject to sea water flooding during rainy season, is of medium fertility and suitable for rice cultivation if there is no salt intrusion; (iv) Bolilands account for 2 percent of arable lands are seasonally flooded plains of inherently low fertility and difficult to cultivate without proper machinery; (v) Riverine Grasslands, which account for about 2 percent of arable land are flooded during the rainy season, but otherwise fertile and suitable for rice cultivation. Table 1 below shows the constraints on the availability of arable land by ecology and the five rice ecosystems.

**Table 1: Availability of arable land by ecology**

|**Ecology**|**Ecosystem**|**Arable land area(ha)**|**% of Arable land**|
|---|---|---|---|
|Upland|Upland|4,300,000|80.23|
|Lowland|Inland Valley Swamp|630,000|11.75|
||Mangrove Swamp|200,000|3.73|
||Boliland|120,000|2.24|
||Riverain Grassland|110,000|2.05|
|**Total**||**5,360,000**|**100**|

Source: National Rice Development Strategy, Sierra Leone, 2009 According to the 2015 census, the majority (85.4 percent) of Sierra Leone households own or operate a crop farm. About 62 percent of agricultural households grow upland rice, while 31.6 percent of households cultivate lowland rice, which includes inland valley swamp, boliland, mangrove swamp and riverine ecologies. All-together, 93.6 percent of agricultural households grow either upland or lowland rice. Of the total land area of 3,244,214 ha under food crop production, 35 percent is under upland rice production, 17.3 percent is used for
year observation the sum of undistributed yearly profits in the corporations where they are shareholders, in proportion of their shares.

### **4.2.3 Some caveats**

When considering the pre-tax income aggregate used to measure inequality in our study, some caveats should be kept in mind. First, similarly to all studies that combine survey data and tax records, our income aggregates are not fully comparable between the two sources. Survey data does not capture all income sources (dividends, for example) nor captures the same income sources every year (interest income is only included in the survey instrument for some periods). On the other hand, tax records do not include pension income or imputed rent, and we do not try to impute those for tax record observations. Since capital income is very small at the bottom of the distribution and imputed rent very small at the top, we do not expect our results to be very sensitive to those choices. The extent to which pension income is relevant at the top of the distribution is less clear – it is certainly much less relevant than in high-income countries with stronger pension systems and an older population, but we are unable to precisely assess the relevance in Honduras in the absence of administrative data on pension distribution. In Appendix B, we also discuss how assigned undistributed measure. profits are computed from a _taxable profits_ Due to meaningful changes in CIT forms over the period we study, we opt to use a measure that is more comparable over the period instead of using a broader measure of total profits.

# **5 Methods & Diagnostics**

Once we obtained distributions of income in both the and tax administrative survey data, our next step is to combine these two sources to obtain a comprehensive distribution of income. In this section we describe the approach used to obtain this combined distribution and provide a series of diagnostics on the steps taken.

## **5.1 Merging survey and tax records**

Similar to other countries where labor informality
)|(0.168)|
|log maintenance|-0.01|-0.04|-0.03|-0.03|-0.04|
||(0.130)|(0.132)|(0.140)|(0.140)|(0.140)|
|_N_|20544|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of (log) employment on export intensity (exports/sales) only for exporting firms (the intensive margin). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

22
||**Online experiment**<br>**(unweighted)**|**Phone survey**<br>**(unweighted)**|**National**<br>**population***|
|---|---|---|---|
|Male|73.2%|73.3%|51%|
|Adultpopulation aged 40years or older|25.9%|36.1%|37.0%|
|Highlands Region|25.5%|33.2%|39.2%|
|Islands Region|21.6%|21.2%|15.1%|
|Momase Region|41.7%|29.8%|25.7%|
|Southern Region|11.2%|15.8%|20.0%|

*Based on the 2011 Census as it provides data at the individual level.

To make comparisons between the insights that the phone survey and online experiment reveal about the general population in PNG it is necessary to apply a common approach to the survey weights. As mentioned in the preceding subsections, the phone survey was designed with the intention of household weights being used based on the 2016–18 DHS, which would allow for representative analysis at the household level about a range of COVID-19 related topics. In contrast, the online experiment, as is standard practice for this type of work, was designed for data analysis to be primarily conducted at the individual level without applying any survey weights (Haaland et al., forthcoming). However, it is common practice for survey weights to be applied to online experiments to provide an estimate of how generalizable the findings are likely to be.

Inverse probability weights at the individual level based on the 2011 population census were calculated for both the phone survey and online experiment so as to be able to compare representative findings across the two
is 1.7 percent, and for those countries with statistics for children under age 14 the prevalence rate is 0.7 (see Annex Tables for a summary of the data from DISTAT).

Perhaps surprisingly, of the 11 surveys analyzed here, those that list more types of impairments do not systematically identify a higher percentage of the population as disabled. For example in the six countries that include visual, hearing, speech, and physical disabilities the percentages are 1.51 (Cambodia 1999), 0.64 (Indonesia); 2.77 (Jamaica 2000); 1.19 (Mozambique); 1.32 (Romania); and 0.55 (Sierra Leone) which span close to the entire range of prevalence across all the surveys. In Mongolia which inquires only about visual/hearing impairments the prevalence is the highest observed in this collection of datasets (3.2 percent), while in Burundi and the 2000 DHS in Cambodia which cover only physical disabilities the prevalence rates are 1.24 and 0.86 percent respectively.

**<u>Table 4. The prevalence of disability among youth ages 6 to 17</u>**<sup>**+**</sup> **<u>by household economic status quintile</u>**

||_All_|_Poorest_<br>_quintile_|_2_<sup>_nd_</sup><br>_quintile_|_3rd_<br>_quintile_|_4th_<br>_quintile_|_Richest_<br>_quintile_|_Concen_<br>_-tration_<br>_Index_|_Std._<br>_Error_<br>_of CI _|
|---|---|---|---|---|---|---|---|---|
|Burundi 2000|1.24|1.28|1.19|1.11|1.36|1.
(used to calculate the producer price index, PPI). In this category we do not look for the availability of each data point, though such information would be informative, but for the availability of item level price series.

We divide the _individual/household data category_ into three subcategories as follows: _consumption (welfare) data; labor force data and health data_ and consider various possibilities under each subcategory. For the _consumption data sub- category_ , we look for household budget surveys, household income surveys and/or living standard measurement survey – these surveys are typically used to measure household spending and income and are the underlying source data used to estimate poverty statistics. For the _labor force data sub-category_ , we consider labor force surveys which are the underlying source data used to monitor labor supply and estimate various labor market statistics including labor force participation rate and employment rate. For the _health data sub-category_ , we consider two possibilities, demographic and health surveys and multiple indicator cluster survey or any equivalent which provides the source data to estimate key health statistics including fertility, mortality, nutritional status, and various disease incidences.

Finally, we divide the _census data category_ into two subcategories namely _: population and economic censuses_ . Census data help define the structure and key characteristics of the population and economy and provide the <mark>framework needed for sampling different surveys. Censuses are rarely published in their entirety but many NSOs, i</mark> ncluding in the United States, Canada and Britain, publish randomized 5%-10% samples from their censuses.

3.2 Definition of recent microdata and classification of microdata accessibility To allow for the possibility that microdata are not released because they have not been collected recently, we first establish the availability of recently collected data in each category, whereby recently is defined based on the data at hand. For establishment, consumption, labor force and health surveys, we expect data to be collected at least once every 5 years. This is lenient: the 2016 State of Development Data Funding
proposed that important knowledge transfers primarily occur across industries and the diversity of local industry mix is important for these externality benefits. These benefits are related to the diversity of the industrial mix within a city. These include access to specialized industries and services, availability of a large labor pool with multiple specializations, inter-industry information transfers and the availability of less costly general infrastructure. Cities also provide a larger home market for end products, make it easier to attract skilled employees who are attracted by urban amenities not available in smaller towns, and support a large number of complementary service providers such as financial and legal advisers, advertising and real estate services.

The table below lists the top 5 cities in terms of intra-industry and industrial diversity in 2012. Clearly, Addis Ababa tops both rankings – not only is the strength of intraindustry clustering (measured by the localization variable) strongest within the city, but it also ranks the lowest on the Herfindahl Index, indicating high levels of industrial diversity.

> 10 Firms in a given industry could also benefit from firms in related industries. Such inter-industry agglomeration is often related to inter-industry linkages, buyer-supplier networks and opportunities for efficient subcontracting (see Venables 1996). However, owing to a lack of a national input-output matrix, it has not been possible to study inter-industry clustering.

20
 _i_ (2) 18 The design of our survey was informed by assessing Tanzanian CWIQ 2006 data which indicated that the average Tanzanian household in this area has between two to three adults who could serve as a proxy with a minimum age of 15. Our sample households had 2.7 members 15 years and older on average.

19 An alternative research design to assess the effect of proxy respondents would have been to interview two members of the household who report on their own labor activities and proxy report on the other. We did not implement such a design because it proved to be too difficult to ensure a proper implementation for a medium to large sample. After consultation with counterparts in Tanzania, we concluded that it would be difficult to assure that proxy and self responses would be independent and would remain unaffected by the knowledge that another household member reports on the same information, given the normally social nature of this setting. The specific concern was that the design (and open communication about this design within the village) would trigger either a coordinated response by household pairs and/or accommodation of response to other’s expectations, which would introduce potentially much larger (unobserved) respondent biases.

20 If desired we can also exploit the information we have about the relationship between the proxy respondent and the individual on whom the proxy informs to assess whether there are systematic response patterns that depend on the proxy chosen. Because of small household sizes, in actuality the spouse of the head of the household was in the vast majority (77%) of cases chosen, corresponding to the usual proxy respondent in surveys. Therefore, not surprisingly, our results are similar when limiting our sample for proxy respondents to spouses only (results not reported).

21 All survey assignments received in addition to the labor module also five other modules: household roster, assets, dwelling characteristics, land, food consumption, and non‐food expenditures. The questions followed the same sequence and the same phrasing and recall periods.

10
We will use this framework, the interaction of shocks and institutions, to explain the recent evolution of the labor market in the ECA countries.

# **_The regression model_**

To capture the interaction between shocks and institutions, the simplest model is as follows: where _u_ is the unemployment or employment rate in country _i_ at time _t_ , _c_ are the country dummies, _d_ are time dummies, that is, unidentified shocks assumed to be equal across countries<sup>11</sup> , and _X_ is the time-invariant value over the period of the institution _j_ in country _i_ .

What matters in the estimation is not the value of the shock or the institution but the interaction between both of them. This is the most general specification since no specific shocks are imposed; we only assume that a number of shocks over time, of equal dimension across countries, have hit the economies. Hence, it allows isolating the impact of the (different) institutions from that of the shocks.

The next step in the analysis will be to identify the shock, that is, to allow the shock to be specific to each country. Transition economies have been hit by a similar shock over the last decade, namely, a transition shock. But their labor markets have reacted differently. Some countries have managed to go through with low unemployment while others have ended up with a poor employment score. Why is that? The answer is that the impact of the shock depends on whether the institutions of the country are more or less friendly to employment creation.

Substituting the time dummy in (1) by the transition shock, which differs across countries, we can estimate how the different institutional framework of each country has facilitated or obstructed the creation of a new private sector after the fall of the communism.

# **_Data_**

To estimate the coefficients corresponding to each of the institutions in model (1), we use an unbalanced panel of 28 ECA countries over 14 years: from 1988 to 2002.
presented in Panel E, have 0.07 and 0.05 less births in this environment. Type 3s who receive a positive test result have 0.57 fewer births than in the baseline environment.

# 7 Conclusion

In this paper, I specied and structurally estimated a dynamic model of fertility in an environment with high HIV prevalence. My analysis takes into account uncertainty about HIV status and the discrepancies between perceived and actual exposure to HIV infection. Women's perceptions about their exposure to HIV infection and about their own HIV status aect their beliefs about their own and their children's life expectancy. Beliefs about life expectancies, in turn, aect fertility choices by changing the prole of expected lifetime utilities associated with each choice. I estimate the model parameters by maximum likelihood with longitudinal data from the Malawi Diusion and Ideational Change Project, which contain measures of subjective beliefs about own HIV status. The model ts well the fertility patterns in the data, as well as the distribution of reported beliefs about own HIV status.

Model simulations are informative about how HIV aects fertility and about the impact of policies aimed at reducing HIV, such as HIV testing programs and provision of antiviral therapy, on fertility and child mortality. Results show that the presence of HIV reduces fertility, both for women who are infected and who are not infected. The presence of HIV reduces the average number of life-cycle births by 0.15. I also nd that HIV testing is eective at reducing fertility of infected women, leading to a reduction in child mortality and orphan hood; however, the partial updating of beliefs following a test mitigates this eect. I also nd that prevention of mother-to-child transmission through dissemination of antiviral drugs would have a negligible eect on the average number of life-cycle births, although it reduces the incidence of child mortality.

33
practices such as accounting and financial management, marketing, and human resource management.

In contrast, personal initiative training is a psychology-based approach that aims at creating a selfstarting, future-oriented, and persistent proactive mindset (Mensmann and Frese, 2017). The training contains exercises designed to foster innovation, as well as looking for opportunities to learn from setbacks and to differentiate oneself from other businesses. Another key component of the training emphasizes financial bootstrapping, through looking for ways to address financial challenges without taking on bank loans or using microcredit. Exercises have entrepreneurs brainstorm to think through approaches like securing advance payments from customers or more favorable terms from suppliers, and self-funding towards meeting a business goal. Campos et al. (2017) and Mensmann and Frese (2017) provide more details of the training content.

# **C. Data and Attrition**

Our short-term results in Campos et al. (2017) use the baseline survey taken in December 2013, and four rounds of follow-up surveys collected between September 2014 and September 2016. Averaging these rounds together gave profit and sales impacts over the first 2 years and five months after training. Response rates for these follow-up rounds ranged from 89 to 95 percent (Table A1). Long-term follow-up surveys to measure impacts 7 to 7.5 years post-training were conducted by the survey organization AdKontact Togo in 2021. We began with a phone survey in March-April 2021, and were able to interview 1035 entrepreneurs (69%). This was followed by an in-person survey in September-October 2021, which interviewed 1131 entrepreneurs (75%). Combining the two surveys, we have interviews for 1250 owners (83.3%), and in addition were able to ascertain the operating status (including closures) of a further 91 firms, so that the operating status of 1341 firms (89.4%) is known.

Although this overall response rate is high, Table A1 shows that it varies with treatment status, with response rates for the control group 7 to 8 percentage points lower