rafmacalaba/datause-encoder-data huggingface dataset:
- 42

# Behaviour of Firms

difference in the likely discount rate to be applied<sup>to the hunan capital -and hence ultimately to the</sup> efficiency of job search - from paying de facto<sup>unemployment benefits inside rather than outside the</sup> firm. In the absence of adequate mobility, for example,<sup>and other labour market rigidities, the inside-</sup> firm option may have merits that in other settings would<sup>not be obvious. In summary, the core of the</sup> Russian industrial firm - the so-called production worker<sup>-remains untouched. The clear implication</sup> is that a large 'employment overhang' existed at the end of 1992.<sup>The next phase of transition will be</sup> suitably difficult.

The wage path revealed in the survey responses matches<sup>well with the aggregate information. Cut</sup> sharply back by the large price shock of January 1992,<sup>real statistical wages then consistently climbed</sup> through the remainder of 1992 back toward early 1991 levels.<sup>Relative wages began to move somewhat</sup> but to a smaller extent than might have been expected.<sup>7te inertia in the system should not be ignored.</sup> Benefits provided by firms are shown to account for large<sup>shares of labour income and firm costs;</sup> to the order of 40/45 _%_ in the latter instance. There are<sup>signs that firms have tried to squeeze benefits -</sup> - particularly in housing - but that Social Fund allocations<sup>have generally stayed constant in the levels</sup> and shares. Housing programmes are already being divested<sup>and annual maintenance outlays curtailed.</sup> Yet simply to cover annual maitenance charges would<sup>require raising current rents to levels equivalent</sup> to between 15/30% of 1992.3 wages.

The overall conclusion that we derive from this small survey<sup>exercise is that matters on the ground</sup> are both more diverse and perhaps rather more original<sup>than conventional economic intution night have</sup> led us to believe. Firms did suffer for the most part negative<sup>shocks to production in 1992 but this was</sup> not generally translated into comparable declines in profitability.<sup>Of course, the survey is localized and</sup> does not really cover the large uni-firm towns or military-industrial<sup>complexes that are elsewhere reported</sup> to be in widespread financial distress. Further, the 1991<sup>comparators suggest that average sales and gross</sup> profits in our sample were high and this likely results<sup>in a more positve picture of firm financial results.</sup> But the results drive home the sad observation that what<sup>is true in some sense for the firm may clearly</sup> be deleterious for the economy at large. Rapid adjustments<sup>to product prices and a stable pricing rule,</sup> combined with a strong acceleration in nominal wage claims<sup>and an accomodating monetary policy, are</sup> key ingredients for high and sustained inflation.
31 <!-- Start of picture text --> Figure I Influences on the<br>Fertility Rate<br><!-- End of picture text -->

# i) The Effects of Altruism

The altruism parameter indicates the importance that parents attach to their children's welfare relative to their own <!-- Start of picture text --> 2.<br>2A ..........................<br>2.<br>Is<br>1.4- ..... ....................<br>/<br>I 060. 0.38 0.39 0.<br>AhulnFaso<br><!-- End of picture text --> welfare.

- iI) The Impact of the Cost of Raising , Children

The cost is measured as the fraction of adult labour time allocated per child.

<!-- Start of picture text --> 3S<br>...................<br>2 , ....................... _ . I _.._..<br>II 2... .......... .<br>1S .. ......................... .. ......<br>ai o0 obe 04 G 01 0ob 7 o 0 o C C .1<br>Lab_ COIdChdWrbn<br><!-- End of picture text -->

- iii) The Effect of Growth in _per Capita_ Income

<!-- Start of picture text --> 12<br>25<br>.<br>.......... .... % . a............<br>1.5 ...... .. .. e 2. 2.. 2.3 2:4 2.5.2<br><!-- End of picture text -->
||ii||
|---|---|---|
|Fig.<br>2.|Eonomies of Scale in Constructing Inter-Urban Roads .....<br>....|........<br>12|
|Fig.<br>3.|Rdationship Between Road Capacity and Number of Lanes ....|..........<br>14|
|Fig.<br>4.|Effect of Marginal and Average Cost Pricing<br>on Total Surplus .20||
|Fig.<br>_5._|Threshold Value of the Marginal Cost of Public Funds as<br>a Function of the Proportional Fall Price, Given<br>Different Price Elasticities of Demand .21||
|Fig.<br>6.|Loss of Consumer Surplus Associated with Covering<br>Collective Common Costs .<br>.<br>...........|27|
|Fig.<br>7.|Using the Inverse Elasticity Rule to Compute the<br>Mark-ups Needed to Cover Collective Common Costs ....|29|
|Table 1.|Changing Condition of Brazil's Road Network ........|10|
|Table 2.|Actual Costs and Those Covered by Optimal Pricing<br>Model: Tunisia, 1982 ..........|18|
|Table 3.|Optimal User Charges and Ad Valorem Mark-up Needed<br>to Cover Unfunded Road Expenditures: Tunisia, 1982 .22||
|Table 4.|Analysis of Costs in Terms of their Avoidability:<br>Tunisia, 1982 .25||
|Table 5.|Calculation of User Charges Using Avoidable Cost<br>Principles and the Inverse Elasticity Rule:<br>Tunilsia, 1982 .34||
|Table 6.|User Charges Calculated Using Avoidable Costs<br>Compared with those Used in Tunisia .35||
|Pacfic Islands Regional Ocenscape Program Forum Fisheries AgencyCaryAnne Cadman|
|---|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|KM-ElectricitySect. RecoveryProj(FY14)<br>Jan Friedrich Kappen|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Skills Development Project<br>Nobuyuki Tanaka|
|Rwanda Second Support to Social Protection System<br>Alex Kamurase|
|Rwanda Second Support to Social Protection System<br>Alex Kamurase|
|StrengtheningPublic Financial Management in Armenia<br>K. Migara O. De Silva|
|Comoros Economic Governance Reform Grant<br>Shireen Mahdi|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Coastal Watersheds Conservation in the Context of Climate Change Adriana Goncalves Moreira|
|Caribbean Climate Innovation Center<br>Elaine A. Tinsley|
|Caribbean Climate Innovation Center<br>Elaine A. Tinsley|
|Caribbean Climate Innovation Center<br>Elaine A. Tinsley|
|First Programmatic Development PolicyLoan<br>Marco Antonio Hernandez Ore|
|First Programmatic Development PolicyLoan<br>Marco Antonio Hernandez Ore|
|First Programmatic Development PolicyLoan<br>Marco Antonio Hernandez Ore|
|Eastern Dedicated Freight Corridor - II<br>SarojAyush|
|Eastern Dedicated Freight Corridor - II<br>SarojAyush|
|Eastern Dedicated Freight Corridor - II<br>SarojAyush|
different occupations. This is exceptional, as such performance differences exist in all comparison countries. Household income is significantly and strongly related to student performance in Colombia.<sup>14</sup> The same is true in all other countries with income data. However, due to differing national currencies and thus income categories, the estimates are not directly comparable across countries.

In contrast to all comparison countries, there is no statistically significant difference in student performance between any category of school location in Argentina. In the other LPCs, students in rural areas perform systematically worse than students in urban areas, and even than students in large cities of more than half a million inhabitants.

Not considering the imputation controls, the student background variables considered in the model together account for as much as 27% of the total variation in student performance both in Argentina and in Colombia. This is similar to Greece and England, slightly smaller than in Germany and Macedonia, but substantially larger than in Turkey and Italy.

# **5. School Characteristics and Educational Achievement**

Tables 4a and 4b report the relationship between school characteristics and student achievement, estimated using equation (2) above, which controls for the effects of student background characteristics. The considered school characteristics fall into two broad categories, namely measures of the resource endowment of classrooms and schools and measures of the institutional features of schools.

## **_5.1 Resource Endowment_**

In all considered countries, there is no consistent evidence of significant relationships between resource endowments and student performance. The few statistically significant estimates of a positive relationship tend to be matched by statistically significant estimates of negative relationships. This is true for Argentina and Colombia as much as for the comparison countries in the LPC and HPC group.

In no country are smaller classes related to better performance in a statistically significant way. However, estimates of class-size effects are particularly prone to endogeneity bias. For example, if class size is allocated in a compensatory way, so that weak students are placed in smaller classes, results from observational data will be biased against finding class-size effects (cf., e.g., Wößmann and West 2004). By contrast, if strong performers are placed into

> 14 Note that in Colombia, national category 5 refers to “I do not know”.

18
|Weight-<br>foreign<br>index|Weight -<br>domestic<br>index|Score|Restriction|
|---|---|---|---|
|0.0500|0.0500||Fee setting|
|||1.00|Mandatoryminimumor maximumfees|
|||0.50|Restrictions for some groups or activities|
|||0.00|No restrictions|
|0.0200|||Licensing requirements on Management|
||||All directors/managers or at least<br>a majority ofthemmust be nationals|
|||1.00|or residents|
|||0.75|Atleastonedirector/managersmustbenationalsofresidents|
|||0.50|<br>Directors and managers mmstbe locallylicensed|
|||0.25|Directorsandmanagersmustbedomiciled|
|||0.00|<br>No restrictions|
|0.0200|||Otherrestrictions(scoresadditive)|
|||0.33|<br>Restrictionsonhiringprofessionals|
|||0.33|<br>Restrictions on the use offinm's intemational names|
|||0.33|Governmentprocurement-restrictionstowardsforeigners|
|||0.00|<br>No restrictions|
|0.0100|||Movement ofpeople -Temporary|
|||1.00|No temporary entry ofexecutives, seniormanagers or specialists|
|||0.75|Temporary entry ofexecutives, senior managers or specialists up to<br>30 days|
|||0.50|Temporary entry ofexecutives, senior managers or specialists up to<br>60days|
|||0.25|<br>Temporary entry ofexecutives, senior managers or specialists up to<br>90days|
|||0.00|<br>Temporary entry ofexecutives, senior managers or specialists over 90<br>days|
|1.0000|0.3800||Total|

Source: Newyen-Hong (2000).
the sample did not complete the survey because the firm was liquidated, while the remaining four percent proved untraceable or had moved outside Dushanbe. There were no significant differences in attrition across treatment groups at the endline survey (Appendix Table A1). In addition to these data, the study relies on extensive interviews and focus groups with tax officials and firms at different stages of the project to design the intervention, understand potential channels of impact and interpret findings.

Table 1 shows summary statistics for some variables from the administrative data and the baseline survey. Firms have a median of two employees (including the entrepreneur) and an average of three. In 40 percent of firms, the entrepreneur is the only employee. About 42 percent each are in the trade and services sector. Women own eight percent of firms. Table 1 also shows that randomization achieved balance across the different treatment groups for most variables.<sup>18</sup> P-values of joint orthogonality tests obtained from regressing the treatment variable against different firm characteristics also indicate that the variables are not systematically correlated with the treatment. Since the analysis will emphasize heterogeneous results by firms’ baseline risk score, Table 1 also includes randomization checks for the two subgroups (firms above and below the median risk score). As Section 7 will describe, the information and training only treatment has no significant impact on e-filing adoption; therefore Table 1 combines Groups B and C as the control and compares with Group A. Appendix Table A2 provides randomization balance tables for the original three groups. Analogous to Table 1, Appendix Table A3 shows that randomization balance persists in the sample surveyed at endline consistent with the non-differential attrition across groups.

At baseline, we measure firms’ tax compliance costs using the number of visits to the

> 18 Firms’ compliance costs are a notable exception. Firms in Group A visited the tax office 6.53 times compared to 6.4 times in the control group. Results from the endline survey show a reduction in the number of visits in Group A so, if anything, the results may underestimate the reduction in compliance costs due to e-filing.

18
Figure 3: Comparison of location of lagging regions and Doing Business performance in Italy and Poland _Lagging and non‐lagging regions in Italy Doing Business performance in Italian regions_ <!-- Start of picture text --> (0.94,1.00]<br>(0.88,0.94]<br>(0.80,0.88]<br>(0.73,0.80]<br>Low growth RegionsNon-lagging regions (0.69,0.73](0.64,0.69][0.62,0.64]<br>No data No data<br><!-- End of picture text --> <!-- Start of picture text --> Lagging and non‐lagging regions in Poland<br><!-- End of picture text --> _Doing Business performance in Polish regions_ <!-- Start of picture text --> (0.89,1.00]<br>(0.75,0.89]<br>(0.72,0.75]<br>(0.71,0.72]<br>Low income regions (0.70,0.71](0.68,0.70]<br>Non-lagging Regions [0.59,0.68]<br><!-- End of picture text --> _Note: The figure reports the lagging regions (left‐hand) and doing business (right‐hand) in Italy and Poland. White shading indicates regions where no data is available for Doing Business variables._ Looking more specifically at the individual SDB variables Table 5 summarizes the results of simple bilateral correlations within countries, reporting the average time required, the difference in the averages of lagging and non‐lagging regions, and the test of the significance of the difference (t‐statistic). Note that a positive difference indicates worse SDB performance in lagging regions. The results show significant variation both across countries and across indicators. Only in Italy are the results unambiguous, with lagging regions performing worse in all three indicators and significantly so in two of them. In terms of _starting a business_ , lagging regions underperform in all countries but Spain, although the difference is only significant in Italy. On _obtaining a construction permit_ , lagging regions perform worse in ‘low growth’ regions but better in ‘low income’ regions; again, differences are significant only in Italy. On _enforcing a contract,_ the same results apply (although Spain is not included).

13
Figure 25: Education profile of Temporary employees. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Georgia Kyrgyzstan Turkey Armenia<br>Without Instruction (SP) Without Instruction (EP) Primary (SP)<br>Primary (EP) Secondary (SP) Secondary (EP)<br>Tertiary (SP) Tertiary (EP)<br><!-- End of picture text --> Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point.

It should be noted, however, that, like in the case of Latin American countries, the improvement in the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in the Annex of the paper.

When we analyze what has happened at the salary level in the period of consideration (Figure 26), two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by the economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.10

> 10As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time). However, since there are non-standard forms of employment not identified in the database, our standard employment category could, in fact, incorporate non-standard workers.

Page 32 of 51
|13|Manufacture of furniture|94|19|16.8|1.0|67|7|9.5|0.3|
|---|---|---|---|---|---|---|---|---|---|
|14|Insurance, reinsurance and<br>pension funding|21|6|22.2|0.3|23|7|23.3|0.3|
|15|<br>Fishing and aquaculture|91|3|3.2|0.2|101|6|5.6|0.3|
|16|Telecommunications|26|10|27.8|0.5|12|6|33.3|0.3|
|17|Manufacture of beverages|7|0|0.0|0.0|10|1|9.1|0.0|
|18|Manufacture of basic<br>pharmaceutical products & preps|27|15|36.6|0.8|||||
|19|Water collection, treatment and<br>supply|9|2|18.2|0.1|||||
|20|Employment activities|52|9|14.8|0.5|||||
||Total employees (in thousand)|3663|1928|34.5|100|3766|2021|34.9|100|

Source: ILOSTAT database, International Labor Organization Services sectors consists of around 40 percent of women in the work force, however, within the services sector male workers dominate all the sectors except education, health and manufacturing. Women predominate in the expected sectors of health and education, as most teachers, nursing and medical staff are female, and are well represented in industry groups that rely on professional services such as insurance and banking. Even service sectors such as accommodation (hospitality), wholesale and retail, and agriculture where women could reasonably be expected to be employed in larger number, employ more male workers.<sup>23</sup> Just like other countries in the region, the gender wage gap is high in Sri Lanka as well. The Global Gender Gap report (2017) highlights that Sri Lanka has widened its gender gap in wage equality for similar work despite a modest increase in parity in estimated earned income.

**Table 8: Gender Wage Gap by Economic Activity in Sri Lanka in 2010**

|**S.No.**|**Economic activity**|**Men**|**Women**|**Wage**<br>**Gap (%)**|
|---|---|---|---|---|
|||**Nomina**<br>**earning**|**l annual**<br>**s (in US$)**||
|1|Hotels and restaurants|1596|816|51.1|
|2|Electricity, gas and water supply|2292|1440|62.8|
|3|Manufacturing|1536|984|64.1|
|4|Activities of private households as employers|1128|732|64.9|
|5|Extraterritorial organizations and bodies|2592|1728|66.7|
|6|Not elsewhere classified|888|600|67.6|
|7|Agriculture, hunting and forestry|924|672|72.7|
|8|Financial intermediation|2940|2220|75.5|

> 23 International Labor Organization 2016. Factors affecting women’s Labor force participation in Sri Lanka, ILO Country Office for Sri Lanka and the Maldives. Colombo.

21
der has a causal impact on women’s labor force participation and, in parts of Sub-Saharan Africa, women’s educational attainment. Nevertheless, this approach — like instrumental variables — relies on fundamentally untestable assumptions. Though modern gender attitudes could not plausibly have impacted the grammatical structure of language, we cannot fully rule out the possibility that cultural factors shaped both grammatical structure and gender norms. As in all studies of history and culture, it is not possible to run experiments and relevant sample sizes are fairly small; some measure of caution about causal claims is therefore certainly warranted.

# **7 Conclusion**

Using a new data set on the grammatical gender structure of more than 4,000 languages, we document a robust negative association between gender languages and women’s labor force participation. At the country level, an increase in the proportion of the population whose native language is a gender language is associated with lower female labor force participation and — perhaps more importantly — larger gender differences in labor force participation. Using data from the World Values Survey, we show that grammatical gender also predicts support for traditional gender roles. However, the prevalence of gender languages does not explain cross-country differences in women’s educational attainment.

Focusing on four African countries where both gender and non-gender languages are indigenous, we show that a similar pattern holds within countries. Speaking a gender native language is associated with lower labor force participation and educational attainment among women, both in absolute terms and relative to men from the same ethnolinguistic group. Both our cross-country and our individual-level regressions are robust to the inclusion of controls that could not plausibly have been impacted by treatment; if one is willing to assume that the relationship between unobserved omitted factors, treatment, and the outcomes of interest is similar to the observed relationship between controls, treatment, and the outcomes of interest, our estimates suggest that grammatical gender has a large 34