rafmacalaba/data-use-mentions-extended huggingface dataset:
In conclusion, the reservation wage gap in North Macedonia is persistent and statistically significant. In cross-individual context, it is about 13% of the market wage, corroborating the notion that it represents a binding constraint for people to search for and accept a job. However, the gap – as opposed to the level – of the reservation wage, declines with education, suggesting that reservation wage rises with education, but learning and information-spreading mechanisms are more effective for low-skilled individuals to better align their expectations with reality. Still, persons who experienced periods of both unemployment and employment generally maintain a high reservation wage, with somewhat downward adjustment reflecting the learning experience. This is especially true for lower-skilled individuals.

# **7. Conclusion**

This study explores, for the first time, the nature of reservation wages in North Macedonia, by taking advantage of newly collected information from the Labor Force Survey 2016 and 2017. Given the fairly scant information on reservation wages, we mainly use it in a pooled cross-section context, while being aware of the limitations it imposes onto our conclusions. However, a number of interesting observations emerge, that suggest avenues for future research.

Results suggest that it is mainly supply factors that shape reservation wages in the country. That is, higher education sizably increases future wage expectations, especially when persons are younger and with short unemployment spells, hence with lower probability of having past work experience. Then, the reservation wage declines with age and with longer spells of unemployment. On the other hand, the insignificance of demand factors for reservation wage formation may suggest that job seekers either lack information about local conditions, or – more importantly – they are reluctant to fast accommodate, considering them substandard. The latter is especially the case for low-skilled individuals, who consistently value their skills higher than what the market offers and set too high expectations. These circumstances are aggravated in cases where the household is well-off and/or receives remittances. On the other hand, highly skilled individuals, despite maintaining a positive reservation wage
macroeconomic front, AI’s are seen serving the ultimate goal of reducing government spending, which contributes to reducing the budget deficit, inflation and government borrowing (other things equal).

This is sensed through setting up a dedicated institutional setup with the purpose of providing free services at to the society in order to contribute to maximizing welfare. This is efficiently achieved while fully binding to resource constraints as the main operational feature of this setup builds on maintaining the property itself and not consuming it through the process. In addition to helping achieve inter-generational equity, awqaf help also achieve intra generational equity through supplying services to much consecutive generations. This setup needs simple prerequisites to ensure optimal social returns; this includes honesty of caretakers, entitled legal identity and finally efficient administration. This in turn alters Timur Kuran’s imprecise judgment and assessment to awqaf. Kuran thinks the nonprofit setup of awqaf wasn’t intended as it was initially proposed for corporatization<sup>25</sup> . This is not necessarily right as, awqaf; since its inception, were meant to provide non-fee charitable services to contribute to overall welfare of society. Another significant feature is that it is intended by construction to facilitate access to assets and social services and hence is not by any mean perceived as structural stagnation.

# **Section IV. Gaps and Challenges of Enhancing Inclusion through Islamic Finance**

Although the OIC countries have experienced rapid economic growth and financial expansion during the past decade, low level of financial inclusion is still constraining the development of many Islamic economies. The argument is based on our analysis of two recent databases, the Doing Business Report 2011 by the World Bank and the Financial Access Report 2010 by the CGAP. In order to obtain a comprehensive analysis of the financial inclusion status of OIC countries, we calculated and compared the financial access related indicators of OIC countries with those of other groups of countries, i.e., GCC, OPEC, OECD, OIC excluding GCC, MENA region, low Income countries, and developing
sewage system as the (first-) best technology available. The second-best option is improved latrines (private or shared); all other traditional sanitation practices are considered _unimproved._ The Joint Monitoring Program (JMP) further distinguishes between unimproved sanitation techniques (e.g. bucket latrines) and open defecation. We investigated this distinction empirically without finding any significant difference across the two groups in our previous paper (Günther and Fink, 2010), and therefore decided to collapse the two categories for the purpose of this paper. We also deviate marginally from the JMP ladder with respect to improved sanitation. Whereas the JMP groups private flush toilets to a sewage system/septic tank together with improved onsite latrines we distinguish between these two categories, since connected private toilets are

> 8 http://www.wssinfo.org/definitions/ladders.html

> 9 Several DHS surveys do not distinguish between protected and unprotected wells and springs. Whenever it was not clear from the data whether a spring or well was improved or not we made the assumption that it is unimproved. We therefore might somewhat underestimate the access to improved water technologies and overestimate the costs to reach MDG4 with MDG7 in our estimations (see Section 3).

8
based on cross-country differences in maternity leave and firm-level survey data are relatively immune from such selection bias. This is especially so since the survey data we use target only registered private firms with 5 or more full-time workers. Thus, the issue of self-selection of firms and workers into the micro (with less than 5 employees) and unregistered sectors where maternity leave laws do not apply or are difficult to enforce than in the rest of the private sector does not arise in our sample. Fourth, there is lingering concern that women are still concentrated in lowpaying jobs such as those in the informal sector. Thus, our focus on registered private firms that provide the bulk of high-paying jobs in the country is important.

We would like to caution that our results are based on pure cross-section data and so they may suffer from omitted variable bias. Apart from controlling for several firm and country characteristics, we offer two validity checks against the omitted variable bias problem. First, as discussed above, we explore the difference between government funded vs. employer funded maternity leave. The understanding is that the maternity leave and female employment relationship should be stronger (more positive or less negative) for the case of government funded maternity leave. However, there is no reason to expect this to hold if maternity leave is merely proxying for other correlated factors such as the overall level of development, cultural factors, etc. Second, since we consider the relationship between maternity leave and the proportion of females in total (male plus female) workers at the firm, we can expect the relationship be magnified (in absolute terms) when there are more adult females relative to adult males in the country.

# **2. Data and Main Variables**

Our main data source is firm-level surveys conducted by the World Bank’s Enterprise Surveys (ES) and the World Bank’s Women, Business and the Law (WBL) project that documents countrylevel gender-specific disparities in the law. These data sources are complemented with other data 6
interest. This paper takes advantage of a large and comprehensive data set on Mexican microfirms that offers information on a broad cross section of small firm characteristics along which we may find division or segmentation and that makes generating a richer typology feasible. To this end we employ two econometric techniques, factor and cluster analysis, that permit the segmentation of the market to emerge from the data with a minimum of prior structure imposed. These techniques have been employed in the literature on industrial or labor market dualism in the industrialized countries by Oster (1979) and Anderson et. al. (1987) for the US, Sloane et. al. (1993) for the UK, and Flatau and Lewis (1993) for Australia.

# **II. Possible Sources of Heterogeneity**

In the spirit of Hart, Levenson and Maloney (1997) argue that small firm dynamics in LDCs may be well described by models now standard in the industrialized country literature rather than representing qualitatively different phenomena. These models imply heterogeneity that is unrelated to structural features of LDCs or labor market distortions. Lucas (1978) argued that the vast range of firm size among established firms can be derived from a model where entrepreneurs have differing cost structures. Building on this, Jovanovic (1982) offers a model of firm dynamics where entrepreneurs face a multiplier on their costs, x(O+c), that is a function of entrepreneurial ability or perhaps firm location, 0, and s captures random noise:

## C = x(0±+)c(q). (1)

It is assumed that c'(q)>O and c"(q)<O where q is the level of output. Entrepreneurs have only a vague idea of what their true cost structure will be at the time of start up and only with experience can they generate a more precise estimate. If they find themselves with profits above 2
|**Appendix table 1: Number and area of re**|**gistered large farms at district level (2000-2017)**<br> <br>|
|---|---|
||**No. of farms > 20 ha**<br>**Area cultivated by these**|
|Post-2008|0.191*<br>0.374*|
||(0.101)<br>(0.193)|
|Eastern province|-0.451<br>0.521|
||(0.284)<br>(0.545)|
|Post-2008*Eastern province|-0.0160<br>-0.768|
||(0.369)<br>(0.706)|
|Constant|0.934***<br>6.390***|
||(0.0836)<br>(0.160)|
|Number of observations|514<br>514|
|Mean of dep. var.|1.034<br>6.650|
|R-squared|0.0209<br>0.00800|

_Source:_ Raw data extracted from Zambia Integrated Land Management Information System (ZILMIS) in 2018. _Notes:_ Dependent variables are in logs. ZILMIS includes information on year of establishment for 2,858 farms (77 of which located in Eastern province) that were established in the 2000-2017 period. Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

29
the last week. These variables are available in all four rounds of the GHS and provide information on the extensive margin of changes in labor supply. Starting in the 2015-2016 round, the GHS began recording the number of hours individuals worked in each of these activities as well as total hours worked.

Descriptive statistics on these seven outcomes are found in Table 1 in six panels (for the pooled sample, men, and women, separately, each for both the post-planting and post-harvest period). One important thing made clear by the table is that, while the mean values of hours worked appear in a reasonable range (the mean total hours worked is 18 hours in the post-planting season and 12 in the post-harvest period), there is substantial variation in all measures of the hours worked (the standard deviation for total hours, e.g., is 22 and 20 hours in both periods, respectively). As shown in Appendix Figures A2–A4, these moments of the data are the result of bunching at zero values, with a large rightward skew, though for work outside the home, there is visual evidence of bunching at 40 hours, which makes intuitive sense under many ideas of a “full” work week. Due to the rightward skew and the incidence of zero-value responses, we also consider results using the inverse hyperbolic sine (IHS). However, the use of IHStransformed measures as outcome variables can be problematic at the point of interpretation, especially

> 26We are indebted to the World Bank’s LSMS team for producing these measures using the unmasked GPS coordinates. Note that this process involved, first, calculating exposure variables using publicly available GPS data; this process was replicated by the LSMS team using the unmasked data. To assure household anonymity, we never gained access to linked data between individual households and specific violent events.

10
vary by data source. All told, we run more than 129,600 regressions to identify which weather metrics have strong predictive power over a large set of crops, countries, obfuscation methods, and data sources.

While our approach allows us to compare various combinations of obfuscation or metric or source to another, it does not allow us to compare any combination to the objective fact that a data source is trying to measure. This is because there is no data source that records the objective fact for the households in our data set - or in most data sets used by economists. Unlike with studies that seek to define mismeasurement in land area, harvest, or seed variety planted, we are unable to measure the objective fact of precipitation and temperature. The equivalent to GPS traces of a plot, crop cutting of harvest, or DNA fingerprinting of seed would, in our case, be a rain gauge and thermometer at every household with data recorded daily. While it is clearly technically feasible to collect this sort of data, it is highly atypical data for collection as part of economic research. Hence the reason why economists have come to rely on remote sensing weather data.

As we lack a record of the objective facts of precipitation and temperature for each household, all of our comparisons of mismeasurement are comparisons of one obfuscation/metric/source combination to another. In order to make these comparisons meaningful, we adopt the prior that both rainfall and temperature have a significant impact on crop production. Some metrics, such 5
|P90/50|0.404|‐6|13|21|
||P50/10|0.311|211|23|818|
||Gini coefficient|0.078|42|‐4|27|
|Poland|P90/10|0.938|91|‐10|59|
||P90/50|0.303|26|‐9|30|
||P50/10|0.208|184|‐4|79|
||Gini coefficient|0.068|92|‐21|51|
|Sain|P90/10|2.672|312|‐10|158|
|p|P90/50|0.466|39|‐19|60|
||P50/10|0.442|772|86|114|

Source: Authors’ calculations based on LIS harmonized surveys. Note: the percentages shown in columns (1), (2) and (3) are derived using the distributions of earnings simulated according to the methods described in the respective sub‐sections of section 2.2. The change in the inequality indicators corresponds to the ones estimated in the working sample and may differ from those of the whole sample of wage workers. Observations with missing data in some of the relevant variables are excluded. The change in the Gini coefficient of labor income for the whole set of wage workers (including those with missing information) was 0.065 in Germany, 0.060 in Poland, and 0.071 in Spain The education, age and gender structure of the labor force underwent significant transformations between the early 1990s and 2013. For example, in our sample, the share of individuals with tertiary education expanded substantially in Poland, where it went from 7% to 20%, and in Spain where it went from 12% to 32%; but not so much in Germany where it increased from around 22% to 27%
Table 4 provides summary statistics for selected other indicators that have been identified in past work as relevant to nutritional outcomes, specifically GDP per capita, the national poverty rate, the female literacy rate, and access to improved water and sanitation facilities. Table 5 gives the correlation matrix for the three nutritional indicators from Table 2 and the five country-level indicators from Table 4. GDP and FLR are both negatively correlated with the nutritional indicators, as is access to water and sanitation. For GDP, the correlation is only statistically significant for stunting (r= -0.54). The FLR has a large and significant negative correlation with the wasting rate (r= -0.73), but the correlations are not statistically significant for underweight women or stunted children. The poverty rate is strongly correlated with stunting (r= 0.71). Water access is correlated with stunting (r= -0.33), while sanitation access is correlated with wasting (r= -0.31). Of course, these are only simple (pair-wise) correlations and may be deceptive. For example, if one regresses the stunting rates in Table 3 on both GDP and the poverty rate, only the latter is statistically significant.<sup>26</sup> In other words, the negative correlation between stunting incidence and GDP is due to an omitted variable bias, given that GDP is (negatively) correlated with poverty incidence, which is a strong covariate of the incidence of stunting.

For a subset of countries, the DHS also collected data on adult male anthropometrics which provide an insight into the extent of intra-household inequality. Table 6 provides summary statistics on the incidence of undernutrition for women and children stratified according to whether the male head of household is underweight or not. We see that the incidence of undernutrition among women and children is lower when the male head is adequately nourished. However, substantial inequality in nutritional status is also evident, and the gender inequality goes in both directions. The majority of
on the tax burden if the tax rates differ by sector. This is the case in Ethiopia where the presumptive tax has a complex assessment system with divergent tax rates by the activity type and turnover - with 99 activities and 19 turnover bands.

Yet, an empirical analysis of how the presumptive tax affects low-income and female entrepreneurs has received limited attention because of a lack of data and the low priority given by governments that often generate very little revenue from the presumptive tax (Mas-Montserrat et al., 2023). Despite this, the tax can be burdensome for small and microenterprises (Bucci 2020). Given governments’ keen interest to register informal small businesses and to bring them into the tax net, it is important that the tax burdens on these businesses be examined.

This paper examines the gender and equity implications of the presumptive tax on small and microenterprises in Ethiopia by estimating the effective tax rates, disaggregated by the sex of the business owner. The study uses newly available data on household nonfarm enterprises from the 2018/2019 and 2021/2022 Ethiopian Socioeconomic Surveys (ESS) and the tax code to impute tax liabilities. The ESS also collected information on self-reported tax payments by household enterprises. The effective tax rates are 2
17 Province is in the island of Luzon.

18 Data on age and income were gathered in the initial baseline survey carried out in 2016 and 2017.

19 Agrarian reform beneficiaries are the spouse who was the primary recipient of the agricultural parcel awarded through CARP.

20 We consider parcels “formally owned” when the household possesses an official title document for the land. This includes all parcels received through CARP.

31
704|704|704|704|704|704|697|704|704|
|Within R-squared<sup>**†**</sup>|0.095|0.009|0.039|0.267|0.147|0.193|0.046|0.072|0.045|0.040|

Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. Entry rate in industry j in year t is the ratio of the number of entrant firms to the average number of firms in t and t-1. Exit rate is the ratio of the number of exiting firms to the average number of firms in t and t-1. Gross job creation is the ratio of the sum of employment created by firms to the average number of employments in t and t-1. Gross job destruction is the ratio of the sum of employment reductions of firms to the average number of employments in t and t-1. Net job creation rate is the ratio of the difference between employment created and reductions by firms to the average number of employments in t and t-1. Job reallocation (churn) rate is the sum of the absolute value of changes in employment divided by the average employment between years t and t-1. Employment share of young firms is the fraction of employees in total employment who are employed by firms aged less than 5 years. Employment growth dispersion is the standard deviation of employment growth of firms in an industry, where employment growth is the change in employment divided by the average employment between years t and t-1. HHI is the Herfindahl-Hirschman Index of sector. Growth of total assets is the change in total assets divided by the average of total assets between t and t-1. Concentration calculated as the sum of squared market (revenue) share of each firm operating in a sector. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-100% in the firm. The reference group is private-owned enterprises (
in Table A2 to compute the baseline between-market mobility frictions. With this baseline mobility friction, estimated _ν_ , labor allocation data, and the wage data for the base year, we solve for the labor allocation and values at the initial steady state as described in Appendix A.5.<sup>45</sup> We calibrate the cost share of labor ( _γl_ for Brazil and _γ_ ̄ _n,l_ for all other countries), the cost share of intermediate inputs ( _γm_ for Brazil) to the base year using the World Input-Output Database (WIOD) and the share of fixed factors in value-added of Brazil from CDP. We also allow these cost shares to vary by sector.<sup>46</sup> In addition, we calibrate labor income of each labor market of Brazil ( _w_ �1<sup>_k_</sup> _,_ 0<sup>_L_</sup> 1<sup>_k_</sup> _,_ 0<sup>);</sup> GDP of each country other than Brazil ( _w_ � _n,_ 0 _Ln,_ 0); trade shares, _λ_ ’s; and sectoral expenditure shares in preference, _φ_ ’s, to exactly match the data of the base year. We further allow for _φ_ ’s to vary by country in the calibrated model. We calibrate the initial employment share of the residual market to be 0 _._ 2659 to match the informal employment share of the data in 2003. Lastly, we set _β_ = 0 _._ 95 and set the trade elasticity _θ_ = 4 following Simonovska and Waugh (2014).

Once the baseline economy is specified, we use the simulated data from the model to calibrate the ̃ elasticity of substitution between tasks, _σ_ . Since this parameter is related to the love for variety of tasks, we calibrate it to match the elasticity of the number of job options to exports in Table 5.<sup>47</