rafmacalaba/datause-encoder-data huggingface dataset:
# M. FORMALITY. INFORMALITY AND PSD CONSIRAINTS

Classifying private enterprises according to their form of registration provides a clear picture of Cote d'Ivoire's formality-informality continuum. The CECPS private enterprise survey, conducted among 37 firms"4 in Abidjan and Bouake, illuminates both firm characteristics and respondents' perceptions of the binding constraints to their growth and future operation. On one extreme are foreign-owned corporations paying a wide array of taxes and complying with burdensome labor regulations. On the other extreme are unregistered microenterprises paying rudimentary taxes and escaping much of the burden of labor regulations. The former benefit from access to bank financing and legal recourse, as well as from investment incentives and other government-sponsored assistance programs. The latter fend for themselves, subject to considerable arbitrariness in their exposure to revenue-raising from government representatives. Informality emerges not as the deliberate choice of entrepreneurs but as their response to a tax and regulatory system that assumes away the realities of the large majority of the existing enterprises -- as reflected, for instance, in the unrealistic recordkeeping requirements of the VAT and in the biases (favoring large enterprises) of the incentive structure. While _informal status_ characterizes only rather small enterprises, _informal behaviors_ are engaged in by enterprises of many sizes.

Naturally, the majority of enterprises fall in between the two extremes descnIbed above -- paying some taxes and managing to capture some of the benefits of formality. SMEs cover the full spectrum from informality to formality (as indicated by enterprise registration, which provides the clearest distinction in firm characteristics). Table 5 shows four indicators which point very consistently to the utility of registration status in segmenting the continuum of SMEs: education, bank financing, taxation, and employment of apprentices.

Table 5: SME Characteristics by lTpe of Registration

|I|Of Owners/Manager<br>With Secondary<br>or<br>Higher<br>Education|s<br>Pay<br>Licensin<br> <br>Tax|ng<br>g<br>VAT||I<br>Receiving<br>Bank Loans|I<br>OfApprentices<br>**in**<br>Total EopLoyment|
|---|---|---|---|---|---|
|Unregistered|43|75|0|13|47|
|Soto Proprietorship|s<br>58|85|46|36|34|
|ULLEs|100|90|90|70|17|
|Corporations|100|100|100|75|2|

**SOURCE:** CECPS Survey

**14** While 37 entrepreneurs were administered a structured questionnaire, many others were interviewed in depth in a less-structured interview format for other parts of our analysis.

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(NGOs) will participate in local consultative councils to review applications and ensure that subsidies are not granted to individuals who are employed or who already receive pensions or other unemployment benefits. A conservative estimate of the annual cost of the subsidy is AR$1.3 billion.

In order to finance the program, export taxes on grains and oilseeds were increased from 10 to 20 percent, and those applied to vegetable oils and meals increased from 5 to 20 percent in 2002. The government expected these increases to generate at least US$1 billion per year in additional revenues, nearly sufficient to finance the subsidy.

The Jefas program represents recognition of the state’s obligation to provide a minimum safety net to alleviate poverty. If it is successful in avoiding past practices of corruption and political patronage, it may help to stimulate a modest rise in consumer demand and lessen the impact of the present crisis. The Jefas program is widely seen as successful because it reduced aggregate unemployment, although it moved as many people into the workforce from inactivity as it did people who would otherwise have been unemployed (Galasso and Ravallion 2003).20 The authors also find that the Jefas program partially compensated losers from the crisis and reduced extreme poverty (the research was undertaken with the household survey dataset that only covers urban areas).

With World Bank support, the program expanded rapidly to cover about two million households by late 2002. Although the Jefas program had the best monitoring system, the information gathered by the program does not allow disaggregation of coverage into rural and urban areas.

Provinces in Argentina are divided in departments. Departmental information indicates that Jefas’s coverage is highest in departments with 2,000-6,000 inhabitants where 7 percent of residents are covered, and is lowest in departments with fewer than 2,000 inhabitants where 4 percent of residents are covered (Table 6.6). In dispersed rural areas, only 3.9 percent of household heads received Jefas in 2003: 4.2 percent of male heads and 3.5 percent of female heads (see Table 6.7). Data also reveal that major variations exist across provinces. Provinces with the least rural poverty received less Jefas than other provinces. In the provinces of Santa Fe and Mendoza, 1.0 and 2.5 percent of the household heads received Jefas while in Santiago del Estero and Chaco 5.6 and 7.3 percent, respectively, received Jefas. Moreover, 1.3 percent of nonpoor household heads (measured by income) received Jefas and 5.4 percent of income poor heads received Jefas. These findings, together with findings from Section 4 (P0 for rural households are much larger than for urban), indicate that rural areas are undercovered compared to urban areas in Argentina. Field visits to rural areas in Chaco, Mendoza, and Santiago del Estero confirm this observation.

> 20 Galasso and Ravallion (2003) assess the Jefas program in urban areas only, due to lack of data on rural households.

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Table 2. MXXISC0 Short- **and** Long-Run **lifects** on **the Relative** Price of Znvestment (in Percentage Points) Short-Run Long-Run Increase of one percentage point in pf .19 .36 Increase of one percentage point **in** QRs .104 **.21** Increase of I billion 1980 **Mex$** in **a** -.024 -.05 Figure 2 indicates that the rolative price **indez** of investment in **Mexico** has always been below the U.S. relative price of **new** capital goods in Mexican pesos. This explains that capital equipment **is indeed** a component in **the** implicit cost of investment, albeit **an** important one. Accordingly, **a** possible incentive for **investment** could come by further liberalizing trade policy, that is to reduce the cost of **a** major component, namely, capital equipment. Although **QRs** for **new** capital goods have been abolished, QRs still remain for used capital goods. Moreover, Rule 8 of the Tariff Code allows imports of parts for assembling **new** capital goods with **ad** valorem tariffs ranging from 0 to lOS, instead of the simple average nominal tariff on imports of new capital goods of **13.5Z (12.6Z** weighted). To improve efficiency in the allocation of resources, a more neutral tariff treatment for imports of new capital goods would be desirable.

On the other hand, imports of used durable goods (e.g. used capital goods) under Rule 10 **are** subject to the same tariffs as imports of new capital goods, but with a depreciation schedule that discriminates against imports of used capital goods. A more realistic depreciation schedule for imports of used capital goods would provide Mexican investors with access to cheaper capital goods, and possibly with a wider variety of
To test such hypothesis, we first check whether or not the application of PSI on imported inputs induces plants to switch towards domestically produced inputs. To that end we exploit highly disaggregated plant-level data on the use of intermediate inputs at 9 digit-level. Importantly, the data allows to distinguish between domestically sourced and imported inputs. More specifically, we estimate the following linear model: In Equation 2, _Yjmt_ is an outcome variable for input _m_ used by plant _j_ at time _t_ . The variable _Input PSImt_ is a dummy taking value 1 if PSI is applied on an intermediate input _m_ at time _t_ . The vector **X** _mt_ includes import tariffs and dummy variables for whether NTMs other than those under analysis are applied to input _m_ .<sup>24</sup> We also include 5-digit industry-year fixed effects ( _uit_ ) that control for differences in competition and other industry-level confounders, and region-year fixed effects ( _urt_ ). Plant-input fixed effects are denoted by _ujm_ . Standard errors are clustered at the plant-level.

Table 6 presents the estimates for Equation (2).<sup>25</sup> The dependent variable in column (1) is a dummy variable taking value 1 if a plant switches from an imported to a domestic variety of a given 9-digit input. The dummy takes value 1 if the imported quantity of an input decreases and the domestic quantity of the same input increases.

The coefficients in column (1) suggest that while PSI does not induce plants to switch to domestic inputs, import tariffs do. The results in column (1) are also supported by those in column (2), where the dependent variable is the share of imported quantity of an input over the total use of the same input (i.e., imported plus domestically sourced).

This switch to domestic inputs induced by tariffs may help explain the negative impact of tariffs on TFPQ, as domestically produced inputs may be of lower-quality than imported inputs. This is supported by the result in column (3), which suggests that import tariffs are associated with a reduction of the average unit price of the used inputs. Consistent with the view that PSI does not affect the quality of inputs used by the plants, the coefficients of PSI are not significant on the dependent variables presented in Table 6.

Why do import tariffs and PSI applied on imported inputs induce two different

> 24Also in this specification, we use aggregate 1-digit categories of NTMs.

> 25The number of observations is higher in such specification, due to the fact that each plant utilizes multiple inputs.

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gender and gender attitudes also remains marginally significant, in spite of the relatively small number of countries included in that analysis. Thus, our results do not appear to be driven by the correlation in grammatical structure observed within language families.

# **5 Within-Country Analysis**

## **5.1 Empirical Strategy**

Next, we explore the relationship between gender languages and women’s labor force participation at the individual level in a cultural and institutional context where both gender and non-gender languages are indigenous. There are seven countries in Africa where between 10 and 90 percent of the population speaks a gender native language: Chad, Kenya, Mauritania, Niger, Nigeria, South Sudan, and Uganda. In these countries, both gender and non-gender languages are indigenous — in contrast to, for example, several countries in South America where non-gender indigenous languages and a gender colonial language are both widely spoken. Of the seven African countries listed above, we focus on the four that have been included in at least one round of the Afrobarometer survey: Kenya, Niger, Nigeria, and Uganda. Four rounds of data are available for Kenya, Nigeria, and Uganda, while only one round of data is available for Niger.<sup>21</sup> Our sample includes 26,546 Afrobarometer respondents who speak 175 different languages.

Our individual-level analysis parallels our cross-country analysis. We consider two main outcomes: labor force participation (an indicator equal to one if a respondent either does some type of income-generating activity or is actively looking for a job) and education (indicators for having completed primary and secondary school). We report two regression specifications. First, we estimate the association between grammatical gender and labor

> 21The first round of the Afrobarometer surveys did not include sufficiently detailed data on native languages for inclusion in our analysis. Our analysis includes data from Afrobarometer Rounds 2 through 5 for Kenya, Nigeria, and Uganda. Niger was only added to the Afrobarometer in Round 5; that round is included in our analysis.

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18 In case (ii), Figures 6 and 7 illustrate the evolution of outstanding loans and required subsidies under the assumption that interest rates follow the optimistic sharp decline scenario and that wages follow inflation. The DIM results in an acceleration of installments in nominal terms. Interest subsidies would be required for only three years, and in an amount far smaller than the original figures. Further, the loans' principal--and consequently, the bonds' principal--would be totally reimbursed after 12 years.

In case of a contract revision then, the advantages of adopting the DIM option are both that it saves scarce budgetary resources, and that the problem of the subsidized portfolio disappears in the short-term. From the individual borrowers' viewpoi' option (i) is "cheaper" since subsidies remain for a longer time. However, by fixing the repa, -ne v to the wage level the DIM ensures that (in relative terms) no additional burden is placed on tne monthly income.

FIGURE 6 <!-- Start of picture text --> Revised Indexed Repayments<br>on Outstanding Loans<br>300-<br>250-<br>200-<br>LL<br>X 150-X<br>c 100-<br>a.-<br>50-<br>60- - -<br>1990 2002: end of loan repayments i<br>L initial contract - indexed repayments<br><!-- End of picture text -->
real GDP per capita<sup>7</sup> under the assumption that recent spurts in growth could have additional effects on overall quality of health and mortality rates in the country. The variable is taken from WDI.

Next, we control for the level of urbanization in the country and the density of population as these factors have often been linked to the availability of health services and disease transmissions. For urbanization, we use the percentage of population in the country that lives in urban areas. Population density is defined as total population divided by the total land area (in square kilometers) of the country. The data source for both these variables is WDI.

Participation in the labor market provides women with financial freedom, income and an outside option that improves their bargaining power within the household. Hence, such participation is likely to improve women’s standard of living, leading to lower mortality rates. To filter out this factor from affecting our results spuriously, we control for the ratio of women’s to men’s labor force participation rate, where participation rate is defined as the percentage of women and men above 15 years of age that are part of the labor force The variable is taken from WDI.

While women’s participation in the labor market is an important factor increasing women’s empowerment, it is by no means the only determinant. Greater education and political representation are also likely to be important in this regard. To this end, we use two controls. These include number of years of education among women aged 25 or above (lagged by one year) relative to men, and the proportion of seats in the lower house of the parliament that are held by women. The data source for the education variables is Gakidou et al. (2006) and Inter-Parliamentary Union (IPU) for women in parliament. We also include the quality of democracy as measured by Polity 2 variable taken from Marshall et al. (2013). One possibility here is that electoral compulsions may

> 7 The growth rate is computed from GDP per capita figures in constant local currencies.

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consist of time-variant bilateral factors such as the preferential political or economic relations between two countries that might be correlated with the explanatory variables of interest.

Following the approach suggested in Baltagi (2001) and originally due to Mundlak (1978) on individual e¤ects, we attempt to test and capture this time-variant bilateral e¤ect, denoted by �ijt, by assuming �ijt is a linear function of the averages of all the existing explanatory variables (measures of regional initiatives) across industries: where X<sup>�</sup> ijt: is a 1 � R vector of observations on the explanatory variables averaged across industries. This e¤ect is uncorrelated with the explanatory variables if and only if � = 0. As Mundlak (1978) assumed, without loss of generality, the X are deviations from their sample mean. The main equation to be estimated becomes: where P = IN � IN � IT � J<sup>�</sup> R, and the new error term has zero mean.

The estimation results with the control of unobserved time-variant bilateral e¤ect are reported in Table 4. The coe¢ cient on PX is statistically signi...cant and positive, rejecting the null of zero correlation between the unobserved e¤ect and explanatory variables. It suggests that over time a stronger bilateral relationship leads to a larger amount of sectoral trade. Furthermore, note that the magnitude of most estimates has fallen except for the coe¢ cients on HAR and MRA_RO, compared to column I in Table 2. This result shows that consideration of the unobserved e¤ect reduces the explanatory power of most of the regressors but not of intra-EU harmonization and MRA with rules of origin.<sup>23</sup> While this test cannot be regarded as conclusive, at least the inclusion of a measure of unobserved e¤ects does not alter our qualitative conclusions.

> 23 The considerable decrease in the coe¢ cients of RTA variables with the inclusion of �ijtis not surprising, since RTA variables also measure time-variant bilateral relations.

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# **VI. UHC performance in the UNICO countries – levels, changes, and trends**

In this section we apply the UHC index to the 24 UNICO countries, showing levels of UHC attainment, and changes and trends therein.

# _Data_

Our household surveys are drawn from the data initiatives mentioned above. We also use some of the results in a study of UHC in Latin America (Wagstaff et al. 2015), notably the results produced by researchers from the individual countries who analyzed household expenditure surveys that are not available through one of the aforementioned data harmonization exercises.

We ended up excluding some data on the grounds the results were implausible, given the numbers we obtained using other surveys, what other researchers have concluded about the data, and what we suspect about how the dataset was prepared before it came to us – see annex for further details.

# _Levels of, and changes in, UHC attainment_

Figure 2 shows UHC attainment for the latest year of data for the 14 UNICO countries for which we have complete data on all UHC indicators. The chart shows how each country fares on the two dimensions of UHC – financial protection and service coverage. The curves are contours of UHC attainment, indicating the combinations of the two UHC dimensions that produce the same UHC index score, given our assumption that UHC can be measured as an unweighted geometric 26
own island and village. Employers recruiting from the work-ready pool conduct interviews of the short-listed workers to decide who to take. For example, Vinepower interviewed 20 workers in a village to order to recruit 10 (Marlborough Express, 2007a). The Tongan Labor Ministry has tried to ensure that all island groups, and as many villages as possible were given the opportunity to participate in the scheme. All villages in Tonga now have at least two workers in the scheme. Table 1 shows the geographic breakdown of RSE workers to date is reasonably close to the overall population distribution across islands. Only 73 of the 816 Tongan RSE workers (9 percent) recruited by May 22, 2008 were female. Only 3 out of the 305 Tongan RSE workers arriving in New Zealand in 2007 were women, with more recruited in 2008. One reason for the increase in female participation in early 2008 appears to be the changing nature of seasonal work available, with more women being to do work packing fruit towards the end of the season.

# **3. Survey Data**

In order to evaluate the short-term development impact of the RSE on individuals, households, and communities in Tonga, the World Bank partnered with the University of Waikato and New Zealand Department of Labor to design a research study. The study aims to survey households and individuals in Tonga before RSE workers leave for New Zealand, survey these same households while the workers are away, and survey households again upon the return of the workers. The survey targets three groups of households: households with a member selected for the RSE, households with a member who is part of the work-ready pool who have not been selected to work under the RSE

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# **Table 5: OLS Estimates of Employment Determinants in Manufacturing, 1983-2002**

Dependent Variable: Log U.S. Manufacturing Sector Employment

|Variable|All|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Lagged log of low income|-0.023**|-0.045**|0.005|-0.050|
|affiliate employment|(0.01)|(0.02)|(0.02)|(0.04)|
|Lagged log of high income|0.076**|0.155**|0.201***|0.034|
|affiliate employment|(0.03)|(0.06)|(0.05)|(0.13)|
|Lagged import penetration|-0.605*|-0.226|-0.064|0.254|
|using 1979 weights|(0.32)|(0.61)|(0.35)|(1.55)|
|Lagged export share|-0.276|-0.466|0.181|0.046|
|using 1979 weights|(0.27)|(0.67)|(0.33)|(1.27)|
|Lagged log of price of|-0.075|0.344|0.106|-0.854|
|investment using 1979 weights|(0.17)|(0.25)|(0.26)|(0.73)|
|Lagged total factor productivity|-0.167**|-0.042|-0.191**|0.527|
|level using 1979 weights|(0.07)|(0.14)|(0.08)|(0.68)|
|Lagged log of real price of|0.150**|0.152|0.067|0.257|
|shipments using 1979 weights|(0.06)|(0.11)|(0.07)|(0.25)|
|Lagged computer use rates|-0.035|0.043|-0.113|-0.805|
|by industry|(0.144)|(0.259)|(0.200)|(0.482)|
|Number of observations|6,399|1,662|4,248|489|
|R-squared|0.86|0.78|0.55|0.65|

_Source_ : See Table 1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. All models include year and industry fixed effects. Low-income affiliate employment is defined according to the World Bank income categories. The sample size corresponds to 5 education groupings X 21 years X 67 industries, less missing values. The results shown in columns 2-4 are (2) industry and year combinations where more than 2/3 of the tasks are routine, (3) cells where between 1/3 and 2/3 of tasks are routine, and (4) cells with than a 1/3 of the tasks are routine.