rafmacalaba/datause-encoder-data huggingface dataset:
|Policy Rese<br>**Title**|arch Working Pape<br>Author|r Serbs<br>Date|Contact<br>for paper|
|---|---|---|---|
|WPS1384 Fiscal Decentraliation and<br>Intergovemmental<br>Finances In the<br>Republic<br>of Albania|David Sewell<br>Christine I. Walich|November 1994|G. Langton<br>38392|
|WPS1385 Fiscal Federalism<br>Dimensions<br>of Tax<br>Reforn in Developing<br>Countries|Robin Boadway<br>Sandra Robeds<br>Anwar Shah|November 1994|C. Jones<br>37754|
|WPS1386 EU Bananarama<br>III|Brent Borrel|December 1994|G. llogon<br>33732|
|WPS1387 Fiscal Decentralization<br>and the Size<br>of Govemment An Extension<br>with<br>Evidence from Cross-Country<br>Data|Jaber Ehdale|December 1994|C.Jones<br>37699|
|WPS1388 Does Voice Matter? For Public<br>Accountability,<br>Yes|Samuel Paul|DOecember<br>1994|B. Moore<br>35261|
|WPS1389 Ruble Overhang and Ruble Shortage<br>Were They the Same Thing?|: Patrick Conway|Decemnber<br>1994|L Suid<br>33974|
|WPS1390 Regional integration and the Baltics:<br>Which Way?|Pirifta Sorsa|December 1994|J. Ngaine<br>37947|
|WPS1391 Where in the World Is Population<br>Growth Bad?|Jeff Kling<br>Lant Pritchett|December 1994|S. Fallon<br>38009|
|WPS1 392 Some Economic Consequences<br>of<br>the Transition fron Civil .War to Peac|Jean-Paul Azam<br>e David Bevan<br>Paul Collier<br>Stefan Dercon<br>Jan Gunning<br>Sanjay Pradhan|December 1994|C.Jones<br>37699|
|WPS1393 The Interface of Trade, Investment,<br>and Competition Policies: Issues and<br>Challenges<br>for Latin America|J. Luis Guasch<br>Sarath Rajapatirana|December<br>1994<br>|J- Troncoso<br>37826|
|WPS1394 Macroeconomic<br>Reform and Growth<br>in Africa:_Adjustment in Africa_<br>Revisited|Lawrence Bouton<br>Christine Jones<br>Miguel Kiguel|December 1994|R. Martin<br>39026|
|WPS13g5 A Typology of Foreign Exchange<br>Aucton Markets in Sub-Saharan<br>Africa|Janine Aron<br>Ibrahim Elbadawi|December1994|<br>R. Luz<br>39059|
|WPS1396 Foreign Exchange<br>Auction Markets<br>In Sub-Saharan<br>Africa Dynamic<br>Models for Auclion Exchange Rates|Janine Aron<br>Ibrahin Elbadawi|December<br>1994|R. Luz<br>39059|
developing countries.<sup>5</sup> (iii) In order to be able to detect differences between a country’s core and its non-core regions, we considered countries with at least three regions.<sup>6</sup> As a second next step, we had to determine which regions to consider as core regions. We defined these as follows:

- We determined a region’s relative size, defined as the number of firms in a region divided by the total number of firms in the country. This exercise is representative for each country, since Enterprise Surveys represent a stratified random sample with regard to regions. Note that this procedure includes both manufacturing and services firms. We set the core dummy _CORE_ = 1 for the region with the largest relative size.

- In some cases, the Enterprise Surveys defined regions around the country’s capital very narrowly, resulting in a relatively small relative size of such regions. In such cases we bypass the first rule and define these as core regions.

We set _CORE_ = 0 for all other regions. The procedure above results in 79 countries covering 467 regions, 92 of which are core regions. One downside of database is that the definition of regions is not harmonized across countries. The list of countries, year of most recent survey, number of regions by country as well as the number of firms (total and for manufacturing and services firms separately) can be found in _Appendix 1_ . _Appendix 2_ shows the names of the core regions for each of these countries.

# **2.2 Manufacturing Firms**

In a next step, we calculate weighted averages of selected indicators per firm in the core and compare these averages with non-core regions. We use sampling weights as provided by the Enterprise Survey Indicator Database. We also perform a t-test which tests for the null hypothesis that there is no significant difference between the core and non-core regions for a given indicator.<sup>7</sup> We do this separately for manufacturing and services firms. Our analysis covers 33,113 manufacturing firms which represent more than 80 percent of the overall sample (see _Appendix 3_ ).

> 5 We dropped these, since the database only included nine high-income countries which were not representative of high-income countries (some OECD countries, some non-OECD countries, and one oil exporter).

> 6 We applied a threshold of three and not two regions, because in many countries with only two regions these had similar relative sizes rendering the determination of core regions very difficult.

> 7 Since some strata only had one observation, we did not use stratification. This affects the standard errors, which in general should be larger than when the strata is specified, or in other words, this should result in a more conservative test.

8
**Table 1. Summary statistics of project playgroups that closed**

||**Mean**|**(S.D.)**|
|---|---|---|
|**Why did the playgroup close?**<sup>*****</sup>|||
|Lack of operational funds (1=Yes)|0.65|(0.48)|
|Lack of students (1=Yes)|0.37|(0.49)|
|Lack of teachers (1=Yes)|0.35|(0.48)|
|Could not get funding from community (1=Yes)|0.33|(0.48)|
|Too many ECED centers in same neighborhood (1=Yes)|0.08|(0.28)|
|**What happened to the building?**|||
|Building is not used by other ECED/community program (1=Yes)|0.70|(0.46)|
|Building is used by other community program (1=Yes)|0.15|(0.36)|
|Building does not exist anymore (1=Yes)|0.08|(0.28)|
|Building is used by other ECED program (1=Yes)|0.07|(0.25)|
|**What happened to the toys?**|||
|Toys are still left in the building (1=Yes)|0.45|(0.50)|
|Toys no longer exist or are broken (1=Yes)|0.33|(0.48)|
|Toys have moved to other ECED facility (1=Yes)|0.22|(0.42)|
|**What happened to the teachers?**|||
|No longer teaching|0.40|(0.36)|
|Teaching in other ECED center|0.26|(0.36)|
|Teaching in primary/secondary school|0.12|(0.21)|
|Don't know|0.11|(0.22)|
|Moved to other village|0.10|(0.17)|

Note: Data from 2016 Indonesia ECED Survey.<sup>*</sup> Reasons for playgroup closure are not mutually exclusive categories (multiple answers possible). N= 60

# **_Qualitative evidence_**

Interviews with former administrators and teachers from closed playgroups were also carried out to provide additional insight to the quantitative data presented in Table 1. Most described a chain of events that led to the closure of their playgroup. As one interviewee said, “ _When the project ended, we no longer had funds to pay the teachers and replace the books and toys that were damaged. So the teachers quit. And the students left. Nobody stepped up to keep the center.”_ Many of the project playgroups that eventually closed had tried to remain open for some time, for example, one former teacher described, _“After the project ended, the center continued to operate for a year. During this time, the center relied on funding from the village government. The center held a meeting with local parents, requesting them to pay tuition fees in order to maintain the center. But this was not successful.”_ Indeed, our quantitative data suggest that a strategy employed by many playgroups to sustain their programs was to charge fees. As shown in Appendix Table 2, only 32% of playgroups that would eventually close were charging fees in 2010. In contrast, 51% of playgroups that would remain open were charging fees in 2010. The proportion of playgroups charging fees rapidly expanded between 2010 and 2013, for both those that would eventually close (59%) and those that would remain open (70%). Moreover, project playgroups 8
**Table 2 – Dynamic and average treatment effect on labor outcomes**

|_Panel a:_<br>Agricultural employment<br>Estimate|SE|
|---|---|
|Effect (t0)<br>-0.106<sup>***</sup>|0.031|
|Effect (t1)<br>-0.074<sup>**</sup>|0.040|
|Effect (t2)<br>0.020|0.035|
|Effect (t3)<br>0.028|0.044|
|Effect (t4)<br>0.018|0.036|
|Average<br>-0.052|0.050|
|Effect (t-2)<br>-0.001<sup>***</sup>|0.000|
|Effect (t-3)<br>-0.023<sup>***</sup>|0.000|
|_Panel b:_<br>Manufacturingand construction||
|Estimate|SE|
|Effect (t0)<br>0.051<sup>**</sup>|0.025|
|Effect (t1)<br>0.054<sup>***</sup>|0.020|
|Effect (t2)<br>0.005|0.030|
|Effect (t3)<br>-0.035|0.027|
|Effect (t4)<br>-0.006|0.031|
|Average<br>0.032|0.053|
|Effect (t-2)<br>-0.040|0.000|
|Effect (t-3)<br>0.039|0.000|
|_Panel c:_<br>Other sectors||
|Estimate|SE|
|Effect (t0)<br>-0.006<sup>**</sup>|0.018|
|Effect (t1)<br>-0.007|0.007|
|Effect (t2)<br>-0.003|0.007|
|Effect (t3)<br>0.013|0.021|
|Effect (t4)<br>0.018|0.018|
|Average<br>0.007|0.032|
|Effect (t-2)<br>0.032<sup>***</sup>|0.000|
|Effect (t-3)<br>0.045<sup>***</sup>|0.000|
|_Panel d:_<br>Unemployment||
|Estimate|SE|
|Effect (t0)<br>0.063<sup>***</sup>|0.023|
|Effect (t1)<br>0.014|0.028|
|Effect (t2)<br>-0.014|0.038|
|Effect (t3)<br>0.014|0.019|
|Effect (t4)<br>-0.016|0.019|
|Average<br>0.028|0.038|
|Effect (t-2)<br>0.002<sup>***</sup>|0.000|
|Effect (t-3)<br>-0.061<sup>***</sup>|0.000|
|N<br>480||

_Notes:_ 95 percent CI: ***. Estimates are obtained using the `did_multiplegt` Stata command by D'Haultfoeuille and de Chaisemartin (2020). Standard errors are clustered on governorates and cohorts. Bootstrapped (250 reps.) t-1 is assumed as omitted category. Confidence intervals are at 95 percent. Governorate linear trends are included. Dynamic effects computed over -2 and 4 years.

31
assume that it has the same persistence as the process estimated for the United States annual productivity series. In particular, we use ρ� �0.815.<sup>8</sup> We assume that innovations to productivity ε�,� are i.i.d. mean-zero normally distributed. In the counterfactual experiments in which productivity shocks are present we set the volatility of productivity innovations to match the average volatility of GDP in the OECS countries.

We model government spending as an exogenous stochastic process, calibrated to match its properties in the Eastern Caribbean countries. We consider two specifications for government expenditures process – one in which government expenditures are a-cyclical with productivity; and one in which they are pro-cyclical with productivity. In the first case, we assume that government expenditures in percent deviations from their balanced growth path follow an AR(1) process: where ρ� is the autocorrelation coefficient, while ε�,� are i.i.d. mean zero normally-distributed innovations to G<sup>�</sup> �.

In the specification with pro-cyclical government expenditures we use the idea that government follows a policy rule in which government fiscal expenditures respond to the fundamental shocks in the economy given by the productivity shocks in the model. In particular, we assume the following process for �<sup>�</sup> �: where η� is a constant capturing how much government spending responds to productivity shocks.

This specification is consistent with the identification assumptions often used in the empirical VAR literature to identify the effects of fiscal shocks on output. In particular, equation (8) implies that shocks to productivity affect government expenditures with a one period lag. At the same time, shocks to government spending can have a contemporaneous effect on current output in the model.<sup>9</sup> We calibrate the parameters in each specification (7) and (8) to match the persistence and volatility of the government expenditures in the OECS countries. In particular, we target the autocorrelation coefficient of G<sup>�</sup> � equal to 0.25, and the volatility of G<sup>�</sup> � relative to output volatility equal to 2.55. In the case of pro-cyclical fiscal rule (8), we have an additional parameter to calibrate, so we also target the correlation between government spending and productivity equal to 0.39 in the OECS data.

Next, we describe the process for the interest rate. We assume that the supply of funds from the international capital market is infinitely elastic, i.e. there is a large mass of international

> 8 Note that this value of the autocorrelation coefficient corresponds to 0.95 in quarterly series.

> 9 We also considered a specification in which �� enters contemporaneously in the specification for ��, and found the quantitative results to be similar.

13
# 3.1 Data on skilled migration

Our empirical analysis is based on a recent data set on international migration by educational attainment detailed in Docquier and Marfouk (2006) (henceforth DM). DM use the receiving country r<sup>0</sup> s census or population register to extract information on immigrants country of birth, age, and skill level. Such statistics were collected for all OECD countries (Organization for Economic Cooperation and Development). Let Mt;s<sup>rdenotethestockofworking-ageindividualsborninagivencountry,ofskill</sup> level s = l; h (h stands for individuals with post-secondary certi...cates, l stands for those with less than post-secondary schooling) and living in country r at time t. The stock of emigrants from a given country for a given education level, Mt;s =<sup>P</sup> r<sup>M r</sup> t;s<sup>;</sup> is then obtained by summing over receiving countries. Emigration rates by education levels are then obtained by comparing the number of emigrants to the population at origin with similar characteristics, Nt;s. Skilled and unskilled emigration rates are denoted by mt = Nt;hM+t;Mh t;h<sup>and</sup><sup><u>m</u></sup> t<sup>=</sup> Nt;lM+tM;l t;l<sup>respectively.Thedatasetalso</sup> allows us to compute the proportion of post-secondary educated among natives (Ht = ~~P~~ Ns<sup>(</sup> t<sup>N</sup> <u>;h</u><sup>t;s</sup> +<sup>+</sup> M<sup>M</sup> t;h<sup>t;s))andamongresidents(ht=</sup> ~~P~~ Ns t<sup>N</sup> <u>;h</u><sup>t;s).</sup> The DM estimates are built according to a broad de...nition of ”skilled migrants” in that they include all foreign-born workers with post-secondary schooling wherever they acquired education.<sup>5</sup> Beine et al. (2007b) use immigrants’ age of entry as a proxy for where education has been acquired. They provide alternative measures of the brain drain by de...ning skilled immigrants as those arrived in the receiving country after age 12, 18 or 22. These corrected skilled emigration rates, which can be seen as intermediate bounds to the brain drain estimates, are by construction lower than those computed without age-of-entry restrictions by DM. Let us denote

> 5For example, Mexican-born individuals who arrived in the US at age 5 or 10 and later graduated from US high-education institutions are counted as highly-skilled Mexican immigrants.

12
- 20 -

# C. Targeting Transfers in Urban Cote d'Ivoire

Using the regressions given in Table 2 one can calculate (10), poverty after transfers, where transfers are based on predicted income levels, for urban Cote d'Ivoire. The relevant calculations are given in Table 5. A poverty line of 148,690 CFA Francs per capita per year is used, which classifies 30% of the urban population as poor.L61 Turning to the top half of Table 5, assume that 10,000,000 CFA Francs are available for transfers.l7/ The first row of Table 5 calculates poverty, using equation (11), for three different values for a. As a increases the poverty index declines, yet this has no meaning whatsoever because the only relevant comparisons of levels of poverty indices are within a given value of a. Yet, changes, in levels can be compared for different values of a, as will be done below.

The remaining rows in top half of Table 5 show poverty levels, calculated from the CILSS data, when 10 million CFA Francs are available for transfers to the poor with varying amounts of information. In the row labeled "untargeted" it is assumed that no information is available, so one has no choice but to give everyone an equal transfer regardless of expenditure level. This untargeted approach reduces poverty by a relatively small amount - between 5% and 8% for different values of a (see figures in parentheses). If instead one uses the predicted income from the regression in Column 1 of

- 61 Any poverty line embodies a value judgment regarding who is poor. We have chosen this poverty line for expositional purposes.

- 71 Since the CILSS is a random sample of approximately 0.13% of the Ivorian population, 10,000,000 CFA applied to reducing poverty in the sample is equivalent to 7.83 billion CFA (about $20 million) on a nationwide basis.
55 concern that the compliance is more related to issues that are identified as priority by the government officials, for whatever reason, rather than by the workers.

There needs to be more attention paid to raising the awareness and the knowledge of workers of their rightful working conditions, benefits and rights. Agrawal (1995) calls this avenue the "self-regulation" of companies. However, awareness alone is not sufficient--there has to be a mechanism for grievances to be addressed in a balanced and transparent way so that incidents such as labor unrest do not also become prevalent. One sound avenue is reevaluating the role of trade unions.

# _VI. 5 Role of Trade Unions and Workers 'Aspirations_

The survey finds, not surprisingly, that 60 per cent of the women workers indicate that there is an SPSI chapter and that most (85 per cent) are members. However, only 53 per cent say that their working conditions (including wages and benefits) have improved with the presence of SPSI. Membership is also much more prevalent in the large sized companies. Only 9 per cent of workers from small and medium sized companies are members of SPSI compared with 69 per cent in the large sized companies. Thus, to the extent that SPSI is effective in ensuring better working conditions and benefits, the interests of women workers in small and medium sized companies are under represented. Further analysis is needed as to whether the lower than desired unionized rate is due to the present government sanctioned one labor union policy. One needs to analyze what will happen if this sanction is lifted and whether a freer setting up of labor unions will represent workers' interests better and whether the interests of workers in small and medium enterprises will be protected.

# **VII. Concluding Notes**

This study can be considered as a preliminary one that has provided some important insights into the effectiveness of laws and regulations in ensuring adequate compensation and conditions for women working in the formal industrial sector in Indonesia. In particular, the study focused on three export oriented sectors where 45 percent of women workers can be found out of the total number of women working in the manufacturing industry (1993 Survey Industry), mainly in medium and large scale companies (i.e. formal sector).
Table 2: Descriptive Statistics, non-Treated and Treated Firms.

||Non-treated frms|Treated frms|||
|---|---|---|---|---|
||Mean|Mean|t-Stat|p<br>t|
|Sample|131|100|||
|Firm characteristics|||||
|sales_it_|19.5|20.3|-2.0|**|
|Nemployee_it_|2.9|4.7|-4.7|***|
|Export performace: intensive margin|||||
|total<br>~~e~~xport<sup>_eu_</sup><br>_it_|10.1|11.9|-6.7|***|
|total<br>~~e~~xport<sup>_ffv_</sup><br>_it_|10.0|12.0|-6.8|***|
|total<br>~~e~~xport_it_|10.7|11.9|-4.7|***|
|Export performace: extensive margin|||||
|Ndest<sup>_ffv_</sup><br>_it_|1.6|4.2|-5.9|***|
|Ndest_it_|2.4|2.5|-0.3||
|Nprod<sup>_ffv_</sup><br>_it_|1.6|4.2|-5.9|***|
|Nprod_it_|5.1|4.8|0.4||

Variables are detailed in Table 1. *, **, and *** denote statistical significance of the t statistics at the 10%, 5%, and 1% levels, respectively.

average numbers of products and destinations are very small.

# **3.2 Estimation Issues**

Estimating the effect of the PIP poses a standard missing-data problem—estimating how much smaller would have been the export flows that got assistance, had they not gotten assistance.<sup>16</sup> Formally, let and That is, _dikj_ marks the treatment group. The basic estimator for the problem at hand is the difference-in-differences (DID):

> 16Exports to non-EU destinations may be reduced because of a reallocation effect. If this is the case, the effect of the treatment on the treated would be further increased.

14
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# **Table A12. Impact of living in the GMA and, once in the GMA, of participating in the** **<u>VAG/CRB on the non-poor households</u>**

|||Treatment Regre<br>capita co|ssion mod<br>nsumptio|els for the impac<br>nexpenditure of<br>Participating|t on per<br>in|
|---|---|---|---|---|---|
|Variable|Variable description|LivinginGM|A<sup>_a_</sup>|<br>VAG/CRB|<br><sup>_b_</sup>|
|||(1)||(2)||
|park2|Kafue dummy variable|0.372|***|0.262|***|
|||(0.129)||(0.086)||
|park3|Lower Zambezi dummy variable|0.406|***|0.203|**|
|||(0.128)||(0.082)||
|park4|Luangwa dummy variable|0.456|***|0.546|***|
|||(0.100)||(0.092)||
|hage|Age of the household head|0.000||0.001||
|||(0.002)||(0.002)||
|fhhh|Female-headed dummy|-0.127|*|-0.158|*|
|||(0.071)||(0.081)||
|maxedu|Education of the most educated member|0.045|***|0.020||
||(years)|(0.015)||(0.013)||
|c0to14|Number of children less than 15 years old|-0.120|***|-0.141|***|
|||(0.013)||(0.013)||
|f15to60|Number of female members 15-60 years|-0.030||-0.052|*|
|||(0.029)||(0.030)||
|m15to60|Number of male members 15-60 years|-0.093|***|-0.075|***|
|||(0.024)||(0.024)||
|m61plus|Number of members older than 60 years|-0.178|***|-0.303|***|
|||(0.043)||(0.057)||
|kroad|Distance to the nearest main road in km|-0.004|***|-0.006|***|
|||(0.001)||(0.002)||
|kbsch|Distance to the nearest basic school in km|-0.001||0.009|***|
|||(0.003)||(0.003)||
|kheal|Distance to the nearest health centre in km|-0.001||-0.004|**|
|||(0.002)||(0.002)||
|vacast|Value of consumer durable assets in ZMK|0.036|***|0.090|***|
|||(0.012)||(0.019)||
|dcoop|Participation in cooperatives dummy|0.308|***|0.249|***|
|||(0.051)||(0.078)||
|gma or|Participation dummy variable<sup>_a,b_</sup>|0.596|***|0.536|***|
|dvcrb||(0.193)||(0.183)||
|cons|Intercept|13.055|***|13.564|***|
|||(0.201)||(0.115)||
|Number of|observations|1370||688||
|Log-likelih|ood value|-2071||-806||
|Goodness|of fit Chi-Square|252.05|***|315.46|***|
|Rho||-0.559|***|-0.427|***|

> _a_ Participation dummy variable refers to the GMA dummy, equal to 1 if the household is located in a GMA

> _b_ The participation dummy variable refers to the CRB/VAG dummy, equal to 1 if the household participates in the VAG/CRB. This model uses a sub-sample of households that are located in the GMA Significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

27