rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Fixed-effects regression results for employment elasticity of growth: economic policy

|Variables||ECA|WE|PECA|PWE|EU10|BalTur|CIS|
|---|---|---|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Size of government|||||||||
|Output|Coeff.|0.300|0.350|0.075|0.123|0.398|-0.146|0.133|
||t-stat.|4.3|5.0|3.9|1.7|4.7|-0.7|0.8|
|Other variable|Coeff.|0.008|0.002|0.002|0.014|0.006|-0.011|0.003|
||t-stat.|1.9|1.0|2.5|2.5|1.2|-0.7|0.5|
||N|165|209|780|175|110|33|22|
||R2|0.46|0.52|0.26|0.63|0.62|0.57|0.76|
|Sound money|||||||||
|Output|Coeff.|0.301|0.370|0.074|0.121|0.414|-0.153|0.051|
||t-stat.|4.4|5.3|3.8|1.6|5.4|-0.7|0.3|
|Other variable|Coeff.|0.004|-0.005|-0.001|-0.001|0.006|0.005|0.001|
||t-stat.|1.8|-1.3|-1.2|-0.1|2.2|0.8|0.2|
||N|165|209|780|175|110|33|22|
||R2|0.46|0.53|0.26|0.61|0.63|0.57|0.75|
|Economic globalization fl|ows||||||||
|Output|Coeff.|0.118|0.452|0.031|0.010|0.300|-0.047|0.090|
||t-stat.|3.5|6.8|2.4|0.6|5.1|-0.3|2.6|
|Other variable|Coeff.|0.001|0.000|0.000|0.000|0.001|0.001|0.001|
||t-stat.|3.9|1.1|1.2|0.3|2.2|0.7|2.0|
||N|357|266|1316|247|126|77|154|
||R2|0.32|0.53|0.23|0.36|0.62|0.25|0.49|
|Economic globalization re|strictions||||||||
|Output|Coeff.|0.171|0.462|0.072|0.219|0.332|-0.035|0.145|
||t-stat.|4.1|7.1|4.2|3.3|5.4|-0.2|2.7|
|Other variable|Coeff.|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
||t-stat.|0.7|-1.5|1.7|-0.3|-0.4|0.4|-0.7|
||N|315|266|1068|219|126|77|112|
|Note: The dependent var<br>parameter estimate of ou<br>fixed-effects.|R2<br>iable is em<br>tput growt|0.29<br>ploymen<br>h. All reg|0.53<br>t growth.<br>ressions|0.20<br>The out<br>also incl|0.55<br>put coeffic<br>ude countr|0.60<br>ient is the<br>y and year|0.24<br>|0.45|

17
# <u>Table A 4: Impact of 3G/4G access on labor force participation by group</u>

||<br>Gen|<br>der|<br>Consu|<br>mption|<br>Re|gion|
|---|---|---|---|---|---|---|
||Male|Female|Low|High|South|North|
|_Access to_:|||||||
|Medium or strong 3G/4G|0.011|-0.026*|-0.008|-0.007|-0.030**|0.011|
||(0.014)|(0.015)|(0.015)|(0.015)|(0.014)|(0.018)|
|Medium or strong 3G/4G<br>|0.019<br>|-0.008<br>|0.012<br>|-0.004<br>|-0.002<br>|0.008<br>|
|for one year|(0.015)|(0.014)|(0.016)|(0.014)|(0.014)|(0.019)|
|Medium or strong 3G/4G|0.024*|0.029**|0.035**|0.014|0.013|0.035*|
|for two years or more|(0.014)|(0.014)|(0.015)|(0.013)|(0.014)|(0.018)|
|Medium or strong 3G/4G|0.018|0.046***|0.033**|0.033**|0.021|0.036*|
|for three years or more|(0.015)|(0.014)|(0.016)|(0.014)|(0.014)|(0.019)|
|# of observations|19,170|21,466|24,008|16,019|17,663|22,973|
||A|ge|Localit|yType|Edu|cation|
|||30 or|||Primary|Less than|
||Under 30|older|Rural|Urban|<br>or more|primary|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.019|0.010|-0.000|-0.013|-0.014|-0.007|
||(0.020)|(0.011)|(0.015)|(0.019)|(0.013)|(0.029)|
|Medium or strong 3G/4G|0.014|-0.000|0.012|-0.007|-0.001|0.030|
|for one year<br>|(0.019)|(0.011)|(0.015)|(0.016)|(0.013)|(0.033)|
|Medium or strong 3G/4G|0.039**|0.012|0.028*|0.002|0.019|0.050|
|for two years or more|(0.018)|(0.011)|(0.015)|(0.014)|(0.012)|(0.035)|
|Medium or strong 3G/4G|0.046***|0.017|0.029*|0.016|0.022*|0.049|
|for three years or more|(0.018)|(0.011)|(0.016)|(0.015)|(0.013)|(0.036)|
|# of observations|19,046|18,586|28,673|11,963|28,929|4,972|
||Lite|racy|Children|under 3|||
||Illiterate|Literate|No|Yes|||
|_Access to_:|||||||
|Medium or strong 3G/4G|0.036*|-0.019|0.008|-0.020|||
||(0.021)|(0.013)|(0.017)|(0.014)|||
|Medium or strong 3G/4G|0.022|-0.002|0.013|-0.002|||
|for one year|(0.020)|(0.013)|(0.017)|(0.014)|||
|Medium or strong 3G/4G|0.028|0.016|0.030**|0.022|||
|for two years or more|(0.021)|(0.012)|(0.015)|(0.014)|||
|Medium or strong 3G/4G|0.045**|0.014|0.042***|0.023|||
|for three years or more|(0.022)|(0.013)|(0.016)|(0.014)|||
|# of observations|11,721|28,164|17,799|22,837|||

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. All these regressions include the additional set of controls (e.g., access to electricity, ownership of dwelling, household size, and a wealth index) although the coefficients for these variables are not reported to save space. *** p<0.01, ** p<0.05, * p<0.1.

35
# **Co-Movement of Major Commodity Price Returns: Time-Series Assessment**

Francesca de Nicola<sup>i</sup> , Pierangelo De Pace<sup>ii</sup> , Manuel A. Hernandez<sup>iii</sup> JEL codes: Q02, E30, C32 Keywords: Commodity prices, co-movement, time-series models\\ i The World Bank, AFTFW ii Pomona College, Department of Economics iii International Food Policy Research Institute (IFPRI)
threshold‘ by a factor of two at least; moreover, in the Czech R., Denmark, Israel, the UK, and Belgium one 20% to 30% of informal employees work in establishment with 100 or more workers. Similar findings for Latin America are reported by Perry et al. (2007: Figure 6.1).

# **3.2 Exclusion factors**<sup>21</sup>

**_Long-term unemployment experience_ .** Prevalence of long-term unemployment experience among informal employees is much higher than among their formal counterparts (or among informal self-employed) in most countries (see Figure 14; exceptions include Hungary, Slovakia, Czech R., Norway, Greece, and Cyprus).  In 14 countries one tenth to one fifth of shadow employees have been unemployed for more than 12 months during their life, while in Latvia, Belgium, Bulgaria, Netherlands, UK, Portugal, and Italy this proportion ranges from one quarter to one third. This suggest that substantial part of the informal wage earners have been forced into informal sector by being rejected in the formal sector.

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>BG LV UA RO EE SI RU HU SK PL CZ FI SE DK NO IT PT ES IL GR CY UK NL BE DE FR AT IE CH<br>East North South West<br>Employees with contracts Employees without contracts Informal self-employed<br>% with longterm unemployment experience<br><!-- End of picture text -->

**Figure 14 Long-term unemployment experience of formal and informal workers, by country. 2004-2009**

_Note:_ The sample size of employees without contracts is less than 30 for IT, SE and FI; between 50 and 90 for CZ, RO, SK, HU, EE, BE, FR, DE and CH; between 100 and 745 for other countries. _Source:_ Calculation with ESS data.

> 21 Results of this section are based on ESS data only and for some countries should be interpreted with care due to limited number of observations on informal employees.  See Note to Figure 14  for details.

34
Controlling for expenditure, population and metropolitan city fixed effect (column 5, Table 4) does not change our results much from above although the estimated coefficient of non-workers rises further from .274 to .330. For the remaining variables, we find no significant effect of either _Expenditure_ or _Population_ on competition.<sup>15</sup> However, the metro fixed effect is significant at close to 5% level (p-value of .051). Relative to stores in other cities, the probability that a store located in a metropolitan city faces significant competition is lower by a large 17.5 percentage points. This could be due to differences in income levels or the structure of the retail markets between metropolitan and non-metropolitan cities.

One concern with the results so far could be that _Expenditure_ does not show any significant effect on competition and this holds even if we do not control for population and the metro fixed effect. If this is because the variable does not properly capture household income levels, then our results for the effect of non-workers on competition could be biased upwards (as discussed in the introduction). We discuss this issue in detail in section 5 and show exactly why the expenditure-competition relationship estimated above is weak.

# **4. Robustness**

Results from the robustness checks are reported in Table 5. We begin by first controlling for adult literacy rate ( _Literacy_ ) and the ratio of females to males ( _Sex ratio_ ). Both the variables are defined at the city level and we use their 1991 values taken from Census of

> 15 We note that the estimated coefficients of _Expenditure_ and _Population_ are insignificant at 10% or less even if we do not control for the metropolitan city fixed effect in column 5 of Table 4.

13
.

percent to 20 percent, which, revealingly, are quite similar to the average of the Asian NICs. Nicaragua is an interesting example of a country for which an improvement in tax policy actually meant a decrease in tax revenues relative to GDP froma high level of 32 percent in the mid-1980s to a more moderate 23.6 percent of GDP in 1995.

The behavior of the ratio of international trade taxes to total tax revenue also reveals a positive development for the region. A stylized fact revealed by cross-country studies is that as countries develop, international trade taxes represent a smaller share of tax revenues.” Largely motivated by tax and trade reforms, this drop in international trade taxes seems to be the Latin American trend in recent years. As Chart 15 shows, despite large increases in the volume of trade in most countries, their respective trade tax shares show a decreasing trend, with the exceptions of Guatemala, Jamaica, and Nicaragua. A comparison across countries reveals, not surprisingly, that the countries relying most heavily on trade taxes tend to be those that, because of their small size and location, are more open to international trade. Even including those countries, the share of trade taxes in total tax revenue fell on average for Latin America and the Caribbean below 20 percent; this is a remarkable achievement, especially considering that it did not weaken the countries’ fiscal stance. It must be noted, however, that the regional average for the trade-tax share of total taxes is almost 8 percentage points higher than that ofthe Asian NICs.

The productivity rate of the value-added tax is arguably the most important indicator of the efficiency of revenue generation in the region. It measures, in part, the success of the recently adopted tax system, for which the VAT is the centerpiece. In a context of weak tax administration, the VAT with its self-monitoring feature, has become the best option for revenue generation with limited distortions. Its achievements as a revenue generator have allowed the easing of the tax burden on the formal corporate sector and the reduction of trade tax rates. The VAT revenue-productivity ratio contains information on two aspects of the VAT system: the coverage of the VAT, in terms of the share of national expenditure included in the system; and the strength and efficacy of the tax administration in preventing tax evasion. Furthermore, the VAT productivity rate indicates the ability of the state to raise additional revenues from taxes when needed. .

° See Easterly and Rebelo (1993).

.

24
# **1 Introduction**

The Italian Regional and Environmental Computable General Equilibrium of the Department of Finance (IRENCGE-DF) Model is a (recursive) dynamic single-country computable general equilibrium (CGE) model. It was developed by the Italian Ministry of Economy and Finance (MEF) and the World Bank (WB). The IRENCGE-DF model is based on the MANAGE WB model of the World Bank, which is, in turn, based on the MANAGE model as in Van Der Mensbrugghe (2021). It is developed under the project "Assistance for the assessment of environmental tax reforms (20IT41)" by the World Bank for the Italian Ministry of Economy and Finance (MoEF) and funded by the European Commission. Several modeling novelties are integrated: in addition to the standard features of a single country CGE model, the IRENCGE-DF model is dynamic, using the neo-classical growth specification. Labor growth is exogenous, and capital accumulation derives from savings/investment decisions. Finally, the model has a vintage structure for capital that allows for putty/semi-putty assumptions with sluggish mobility of installed capital. IRENCGE-DF is composed of two modules: i) the environmental part, designed to focus on energy, emissions, and climate change at the national level; and ii) the regional part, built to evaluate shocks at the regional level.

In this paper, the regional module of the IRENCGE-DF model is briefly reported. One of the biggest issues in building regional CGE models is the lack of specific regional data for building a proper Social Accounting Matrix (SAM). Our work represents a key contribution to the existing literature on regionalized SAMs: we show a step by step construction procedure for a completely new regional SAM. This matrix includes all economic and fiscal flows so that it represents an important source of information to use as input data for simulating the effect of the abolition of business tax (IRAP) in Italy through the IRENCGEDF model.

The model can be calibrated to a regional SAM that follows a standard set of conventions representing the economic structure.<sup>1</sup> The key changes in IRENCGE-DF include a regional specification for Italy (20 regions at NUTS-II level), detailed demographics by age, revised tax equations with additive and multiplicative shifters, a simple debt framework similar to that of transfers to allow an analysis of distributional impacts of borrowing and debt payment, calculation of metric money utility, improved price indices in-demand system, and endogenous capital supply-to-capital stock ratio (i.e., excess capacity in the economy).

Our main goal is to simulate how fiscal shocks affect the structure of the Italian regional system, give insights to reform, modernize the tax system, and improve regional economic

> 1The model is implemented in the GAMS software, and an aggregation facility is used as a front-end to the model to allow for complete aggregation flexibility.

1
for highly educated workers. Also, program participants may have been more negatively selected in Russia, because not everyone unemployed was registered there.

_ALMP programs in Slovakia_ : Lubyova and Ours (1999) use administrative data from 20 Slovak districts to analyze whether it was beneficial for unemployed workers who wanted a regular job to accept a temporary ALMP job or enter a retraining program. Specifically, they study the effects of two ALMP programs in Slovakia targeted at registered unemployed workers on their exit rate from unemployment. The programs were targeted especially toward older workers, disabled workers and the long-term unemployed. The program provided retraining and counseling services as well as wage subsidies in two types of jobs: socially purposeful jobs (SPJ) and publicly useful jobs (PUJ). The subsidy in SPJ, which could be at private sector firms, had a minimum duration of 2 years and the subsidy at PUJ, which were typically public works jobs, had a maximum duration of 6 months.

Lubyova and Ours (1999) base their analysis on the administrative records of 100,000 individuals who entered unemployment in 1993. The records allow construction of detailed labor market histories. Using multivariate duration analysis, the authors jointly model the duration of unemployment and the duration of staying in an ALMP program, controlling for observable and unobservable heterogeneity among people. The focus of the study is on whether participation in ALMP programs affected the exit rate from unemployment to regular jobs. For women, 40% exited unemployment by finding jobs, 9% exited by entering ALMP and 51% had right-censored spells. For males, 47% exited unemployment by finding a job, 8% exited it by entering an ALMP program and 45% had a right-censored spell. On average, workers that entered the ALMP programs are found to have had a 150% increase in the exit rate into a regular job, with similar estimated program impacts for men and women. From additional analyses allowing the ALMP program effect to depend on the type of program, the authors conclude that there were positive benefits of retraining and publicly useful jobs on exiting unemployment into a job. For socially purposeful jobs, however, they find a negative effect.

_ALMP programs in Poland_ : Kluve, Lehmann and Schmidt (1999) study the effectiveness of ALMP programs in Poland. The program took three forms: publicly financed training and retraining, intervention works (wage subsidies for workers in private or public firms), and public works. The aim of training and retraining was to increase the skill set of individuals in demanded fields such as data processing, accounting, secretarial work and welding, through courses lasting 21
Policy Research Working Paper 8061

# **Abstract**

This paper uses the 2011 Cambodia Socio-Economic Survey to analyze the relationship between participation in the garment industry and household welfare. The analysis relies on propensity score matching estimators to investigate whether households that have at least one member employed in the textile and apparel sector are better off than those who do not participate in the garment industry, in terms of several monetary and non-monetary welfare indicators. The findings show that garment households are less likely to experience self-reported food insufficiency, and their children are more likely to be enrolled in school. Yet, the positive effect of the treatment is restricted to the bottom 40 percent of the consumption distribution, possibly due to the nature of garment jobs, and the fact that they represent an attractive alternative for the poorest households but not necessarily for the better-off. Using instrumental-variables, the analysis also shows that remittances originating from the textile and apparel sector relax household budget constraints, increasing expenditures in education, health, and investments in agricultural activities.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at carolinamejia@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 4.3a** **<u>(continued): Non-response bias in HFPS</u>**

||||**Ethiopia**|||||**Malawi**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**Sample**|**Co**|**ntacted**|**I**|**nterviewed**|**Sample**|**Co**|**ntacted**||**Interviewed**|
||||_Bias due to_||_Bias due to non-_|||_Bias due to_||_Bias due to non-_|
||_Mean_|_Mean_|_non-contact†_<br>_(2) -(1)_|_Mean_|_interview†_<br>_(4) -(2)_|_Mean_|_Mean_|_non-contact†_<br>_(7) -(6)_|_Mean_|_interview†_<br>_(9) -(7)_|
|**Characteristic**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**_Consumption_**|||||||||||
|Mean expenditure(LCUper capita)|16,247|17,200|953.183***|17,186|(14)||||||
|**_Quintiles_**<sup>**_a_**</sup>|||||||||||
|Quintile 1 -poorest|0.166|0.134|-0.032***|0.133|-0.001|0.106|0.081|-0.025***|0.082|0.001|
|Quintile 2 -poorer|0.186|0.164|-0.022**|0.164|0.000|0.161|0.139|-0.022***|0.141|0.002|
|Quintile 3 - middle|0.207|0.209|0.003|0.210|0.001|0.208|0.189|-0.018|0.191|0.002|
|Quintile 4 - richer|0.214|0.241|0.028**|0.242|0.001|0.250|0.265|0.015***|0.266|0.001|
|Quintile 5 - richest|0.228|0.251|0.023**|0.251|-0.001|0.275|0.326|0.051***|0.320|-0.006|
|**_Housing_**|||||||||||
|Improved roof|0.739|0.792|0.053***|0.798|0.006|0.681|0.725|0.044***|0.722|-0.003|
|Improved floor|0.228|0.264|0.036***|0.264|0.000|0.421|0.478|0.057***|0.472|-0.006|
|Improved toilet|0.367|0.394|0.028**|0.398|0.004|0.056|0.064|0.008|0.063|0.000|
|Improved water source|0.785|0.824|0.039**|0.828|0.004|0.852|0.838|-0.014|0.837|-0.001|
|Access to electricity|0.634|0.702|0.069***|0.707|0.005|0.207|0.248|0.041***|0.242|-0.006|
|**_Asset ownership_**|||||||||||
|Solar|0.148|0.151|0.003|0.154|0.003|0.252|0.262|0.01**|0.265|0.002|
|Generator|||||||||||
|TV|0.234|0.289|0.055***|0.291|0.002|0.208|0.247|0.038***|0.241|-0.005|
|Refrigerator|0.095|0.118|0.023***|0.118|0.000|0.112|0.136|0.023**|0.131|-0.004|
|Bicycle|0.016|0.022|0.006|0.022|0.000|0.453|0.476|0.023*|0.479|0.003|
|Motorbike|0.012|0.011|-0.001|0.011|0.000|0.061|0.073|0.012**|0.073|0.000|
|Car or other vehicle|0.012|0.014|0.001|0.014|0.000|0.034|0.043|0.009|0.043|0.000|
|Mobile Phone|0.706|0.793|0.086***|0.797|0.004|0.784|0.852|0.068***|0.850|-0.002|
|**_Other Activities_**|||||||||||
|Owns land|0.779|0.754|-0.026***|0.752|-0.001|0.854|0.851|-0.004|0.857|0.006|
|Cropfarming|0.611|0.570|-0.04**|0.572|0.002|0.755|0.742|-0.013**|0.748|0.006|
|Owns anylivestock|0.636|0.602|-0.035**|0.600|-0.001|0.498|0.490|-0.007|0.494|0.004|
|Operates nonfarm enterprise|0.259|0.279|0.02**|0.280|0.001|0.459|0.481|0.022**|0.482|-0.003|
|Anymember has financial account|0.547|0.620|0.073***|0.624|0.004|0.419|0.461|0.042***|0.458|0.002|
|Income from remittance(int'l & domestic)|0.161|0.150|-0.011|0.151|0.001|0.532|0.539|0.007|0.541|0.003|

aFor Malawi, the quintiles are based on a wealth index, not consumption expenditures.

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

22