rafmacalaba/datause-encoder-data huggingface dataset:
third establishment receives “0003”, and so on. Some of the companies are composed by a single establishment, which receives the 4-digit code “0001”.<sup>3</sup> The last two digits of the CNPJ correspond to the establishment economic sector, and they work more as a confirmation code. Therefore, we use the first 12 digits of the CNPJ to extract the establishment composition of each company.

To summarize, there are three reasons why the RAIS data set is particularly useful for the analysis. These are:

- (i) It identifies entry behavior each year, allowing us to build a panel of entry data that we use to control for spatial fixed effects.

- (ii) It provides spatial detail on entry decisions (necessary for discussion of policies to promote entry into lagging regions).

- (iii) It describes firm family structure (i.e., identifies parent or headquarters, which we find to be a particularly important determinant of entry).

To model spatial entry decisions, we had to select an appropriate geographic unit.

Using municipalities creates several problems: (i) there are a large number of municipalities (5,506 in the year 2000), which would significantly increase the computational burden in estimating the location decision model; (ii) many municipalities are very small (i.e., with fewer than 5,000 inhabitants) and/or in rural areas with very few establishments in the formal sector; (iii) because of the continuous creation of new municipalities in Brazil, it is necessary to coordinate different municipality maps across the

> 2  Refer to Table 5 for a list of the industries included in the analysis.

> 3 The establishment with digits “0001” in positions 9 to 12 is the initial headquarters of the company. However, should the headquarters be transferred to another municipality and the previous establishment be maintained, the new headquarters may receive a four-digit sequence different from “0001” while the previous establishment retains the “0001” four-digit code, even though it is not the headquarters anymore.  On the other hand, it may happen that if the headquarters moves to another municipality, it will keep the same CNPJ, and in this case, we still have the headquarter with the “0001” code.  Even though we cannot identify these

10
11

**Figure 3. MDG 1: Financing Gap**

<!-- Start of picture text -->
-18<br>2008 2009 2010 2011 2012 2013 2014 2015<br>-22.12 -0.83 -0.8<br>-23 -0.80<br>-0.85 -0.86<br>-0.88<br>-28 -28.93 -27.21 -0.90 -0.9<br>-29.91<br>-33.26<br>-33<br>-0.98 -32.90<br>-1.0<br>-38 -36.21<br>Millions of US$ -39.88<br>-1.07 % of non-oil GDP (secondary axis)<br>-43 -1.1<br> Source:  Authors’ calculations.<br><!-- End of picture text -->

**Table 4. MDG 1: Cost, Budget and Financing Gap (2008-2015)**

<!-- Start of picture text --> US$ 275.57 m<br>Total Cost<br>MRO 75,736 m<br>Budget US$ 113.33 m<br>Allocation MRO 31,161.8 m<br>US$ 162.24 m<br>Financing MRO 44,574 m<br>gap 0.90 % of non-oil GDP<br>US$ 9.44 p.c.<br>Source: Authors’ calculations.<br><!-- End of picture text -->

# **MDG 2: Achieve Universal Primary Education**

_Target 3: Ensure that, by 2015, children everywhere, boys and girls alike, will be able to complete a full course of primary schooling._

**_A. Current Situation:_ high enrollment but low retention rates.** Mauritania has one of the highest gross enrollment rate (GER) in the sub-region. But the retention rate is low. Rural schools that offer an incomplete cycle have been found to be amongst the major reasons for low completion rates, affecting some two-third of dropouts. Also, evidence shows that the mere existence of a school in the areas of residence is no guarantee of better retention rates: the low quality of education and cost associated to sending children to schools are also important factors affecting the high drop-our rate.

**Figure 4. MDG 2: Current Situation and Trends**

<!-- Start of picture text --> 100<br>Past performance<br>Government plan<br>Projected status quo<br>88 MDG-2<br>76 78.0<br>73.8 71.9 72.4 75.2<br>64 70.0 68.2 66.3 66.7 69.5<br>64.4 62.5 64.0<br>60.6 58.8 59.0<br>56.9<br>52 55.0 54.0<br>49.0 48.0 49.0 49.0<br>40 44.0 44.0 44.0<br>1990 1995 2000 2005 2010 2015<br>Source: Authors’ calculations.<br><!-- End of picture text -->

**_B. Trends and Likelihood:_ a low probability of achieving MDG2.** Between 1990 and 2006, while the GER at the primary level increased significantly, the retention rate deteriorated markedly – from 73.8 to 44 percent. In other words, in 2006 only 44 percent of children enrolled in Grade 1 completed successfully Grade 6. Given the current trend, the retention rate would increase only marginally by 2015, returning to the level it reached in 2000. The likelihood of achieving the ambitious government plan, therefore, is low (Figure 4).
# The Week

Maya Eden<sup>_∗_</sup>

**JEL Classification:** E23, E24, J08

**Keywords:** week, productivity, fatigue, memory, learning by doing, leisure, religious institutions, calendar reform

> _∗_ World Bank, Development Economics Research Group, Macroeconomics and Growth Team. I thank Benjamin Eden, Aart Kraay, Valerie Ramey, Lael Schooler and Luis Serven for helpful discussions. Contact: meden@worldbank.org.
**Table 6:  Testing for differences in the determinants of comparative advantage**

||Intermed<br>|iate versus all o<br>|ther exports<br>|Intermedi<br>|ate versus consu<br>|mption exports<br>|
|---|---|---|---|---|---|---|
||A.1|A.2|A.3|B.1|B.2|B.3|
|qxQ (institutions)|0.060***|0.063***|0.055***|0.064***|0.069***|0.065***|
||(15.465)|(10.175)|(8.683)|(15.098)|(9.710)|(8.765)|
|qxQxI||-0.005|0.005||-0.008|-0.003|
|||(-0.902)|(0.790)||(-1.198)|(-0.422)|
|txT (infrastructure)|0.043***|0.034***|0.030***|0.045***|0.017***|0.017***|
||(12.480)|(7.028)|(5.987)|(11.706)|(2.791)|(2.738)|
|txTxI||0.013***|0.017***||0.037***|0.036***|
|||(2.756)|(3.402)||(6.018)|(5.518)|
|kxK (capital)|0.019***|0.018***|-0.001|0.026***|0.026***|0.018**|
||(4.726)|(4.652)|(-0.114)|(5.972)|(5.953)|(2.445)|
|kxKxI|||0.026***|||0.011|
||||(4.233)|||(1.604)|
|hxH (human capital)|0.017***|0.017***|0.022***|0.018***|0.016***|0.009|
||(5.194)|(5.255)|(4.941)|(4.724)|(4.299)|(1.383)|
|hxHxI|||-0.005|||0.009|
||||(-1.238)|||(1.369)|
|I (intermediate dummy)|-0.076***|-0.077***|-0.076***|-0.01|-0.011*|-0.011*|
||(-15.832)|(-15.875)|(-15.855)|(-1.627)|(-1.659)|(-1.663)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.709|0.709|0.709|0.691|0.691|0.691|
|Number of observations|33,743|33,743|33,743|28,252|28,252|28,252|
|Number of countries|95|95|95|95|95|95|
|Number of industries|342|342|342|309|309|309|

Notes: The dependent variable is the natural log of intermediate or all other (consumption) exports in industry k by country i to the World. Coefficients are beta standardized and t-values for robust standard errors are reported in brackets. * Significant at 10%; ** at 5%; *** at 1%.

# **_B.  Robustness Checks_**

As a first test for the robustness of the results, we estimate equation (2) using alternative measures for the quality of the institutional environment and the ability to deliver in time. Results are reported in Table 7. We consistently find that the ability to deliver on time is a significant determinant of comparative advantage also when it is measured by the days required to export (columns A.1 and B.1) or by the index of transport infrastructure – calculated as an average of the quality of rail, road, air and port infrastructure (columns A.2 and B.2). We also find that the coefficients for the quality of institution is significant and of the expected sign also when the quality of institutions is proxied by the time and the cost required to enforce a contract 13
# Taxation in Decentralizing Socialist Economies: The Case of China

by Christopher J. Heady University of Bath

# and

Pradeep K Mitra World Bank

# Table of Contents

1. Introduetion

2. Indirect Taxes

|2.1 The Current Tax Structure<br>|
|---|
|2.2 Taxes and Enterprise Profitability<br>|
|2.3 Simplification of the Rate Structure<br>|
|2.4 Tax Simplification<br>|
|2.5 Cascading<br>|
|2.6 Equity<br>|
|2.7 Price Controls as Implicit Taxes|

3. Direct Taxes 1 2 2 8 9 11 12 14 16 19

- 3.1 The System of Enterprise Income Tax 3.2 Contract Responsibility System

- 3.3 Problems with the Current System

- 3.4 Statutory and Effective Rates of Tax 3.5 Resource Tax

_19_ 21 23 24 29 4. Conclusions 30

|References|
|---|

33

> * The analysis of this paper was undertaken as part of a World Bank study on revenue mobilization in China. We thank Patrick Honohan and Christine Wallich for their helpful comments on an earlier draft.
**Table 9: Value Added Production Function GLS Parameter Estimates: China**

||**Apparel**|**Consumer**<br>**Goods**|**Electronic**<br>**Component**|**Electric**<br>**Equipment**|**Vehicles**<br>**and Parts**|
|---|---|---|---|---|---|
|**Caital**|**0.19**|**0.32**|**0.37**|**0.37**|**0.44**|
|**p**|**_3.28_**|**_6.96_**|**_11.49_**|**_7.41_**|**_8.80_**|
|**Labor**|**0.73**|**0.70**|**0.52**|**0.49**|**0.58**|
||**_9.11_**|**_8.71_**|**_9.76_**|**_6.03_**|**_7.17_**|
|**NOBS**|578|426|1023|483|583|
|**R-Squared**|0.55|0.69|0.58|0.52|0.68|

_t-scores are shown below parameter estimates. Estimates significantly different from zero at the .01 level are shown in bold._

**Table 10: Value Added Production Function GLS Parameter Estimates: Brazil**

||**Textile**|**Apparel**|**Leather**|**Electronics**|**Auto Parts**|
|---|---|---|---|---|---|
|**Capital**|**0.39**|**0.47**|**0.63**|**0.50**|**0.52**|
||**_5.11_**|**_15.57_**|**_11.81_**|**_8.35_**|**_9.06_**|
|**Labor**|**0.61**|**0.65**|**0.38**|**0.68**|**0.66**|
||**_5.16_**|**_13.18_**|**_5.01_**|**_5.67_**|**_7.42_**|
|**NOBS**|264|1108|416|158|339|
|**R-Squared**|0.61|0.71|0.76|0.79|0.72|

_t-scores are shown below parameter estimates. Estimates significantly different from zero at the .01 level are shown in bold._ 15
<!-- Start of picture text --> Dhaka - Employment (1000s)<br>by sector<br>100008000<br>6000 aH 5041<br>4000<br>387 667<br>2000 3364 3290<br>0<br>Scenario A Scenario B<br>HE Manufacturing HE Tradable Services<br>HER, (Non-Tradable Services<br><!-- End of picture text --> <!-- Start of picture text --> East-Dhaka - Employment (1000s)<br>by sector<br>15001000 | 4802<br>500 1031<br>se<br>0 171<br>Scenario A Scenario B<br>HE Manufacturing HE Tradable Services<br>HERs(Non-Tradable Servicees<br><!-- End of picture text -->

<!-- Start of picture text --> ee‘R505: OT<br>15 rH<br>Dhaneast 3302<br>eal 15000<br>46 10000<br>Ss ini 5000<br>0<br>-5000<br>7 ta a -10000-15000<br>y i Lea<br>R620)<br>ro (zao0g) CO .<br>Sreenagar Munshig 4<br>R620 eeNeots WAS<br><!-- End of picture text --> <!-- Start of picture text --> ee‘R5OS- OO<br>15 rH<br>Dhaneast 3302<br>Sab 15000<br>e 10000<br>5 ini 5000<br>0<br>-5000<br>ta a -10000-15000<br>y an a,<br>R620)<br>ro (zao0g) CO<br>Sreenagar Munshig 4<br>R620 eeNeots Ws<br><!-- End of picture text --> <!-- Start of picture text --> ee ‘R505: OT<br>15 Zz<br>Dhaneast 3302<br>ab 15000<br>a 10000<br>SS ini 5000<br>0<br>-5000<br>7i ta a -10000-15000<br>y cao ay,<br>RE)<br>ro (zoom) OO<br>Sreenagar Munshig 4<br>RB20 ewaNeots WA<br><!-- End of picture text -->
of maize for smallholders. A recent assessment of maize marketing in Kenya concluded that the distribution system did not always operate effectively and that certain regions faced significant problems procuring an adequate supply of the cereal (World Bank, 1982; Maritim, 1985). While large and medium scale farmers sell almost all their maize through NCPB, only 20 to 30 percent of smallholders' maize is sold to NCPB (World Bank, 1983). Smallholders (generally women) tend to sell maize even though remaining amounts may not suffice to meet family food requirements. Their pressing need for cash at harvest time (coinciding with school fee ,.iyments), price uncertainty, and lack of on-farm storage facilities contribute to these decisions. Later in the season, these same smallholders may be forced back into the market to purchase additional maize, often at higher prices. Beans and other pulses, in addition to other minor crops, are marketed almost exclusively through informal channels.

Agricultural marketing cooperatives exist for the production, processing and marketing of the main cash crops and are estimated to handle 40 percent of the country's marketed commodities. Female membership in these cooperatives remains very low. Explanations include women's lack of title to land which is often required for membership, women's lack of regular income to pay monthly contributions, and their low educational attainment which limits their understanding of cooperative regulations and by-laws (ILO, 1986).

ii. Storage. Storage of food crops is critical to achieving food security on both a national and local level by easing price variations and assuring more stable supplies. In most of Sub-Saharan Africa, storage facilities are inadequate and ineffective. It is estimated that one-fourth of all food produced is lost due to spoilage, insects and rodents. In Kenya, farmers incur the highest level of losses due to the unsuitability of on-farm storage structures (Maritim, 1985).

Women play an important role in food storage and handling, and as such could benefit from improved methods and facilities. Improvements in traditional systems of storage and food handling could make a major contribution to food security in terms of stabilizing supplies and helping to maintain the nutritional value of stored and processed foods (Dey, 1984).

In Kenya, lack of sufficient on-farm storage hastens the sale of food at harvest time evbn if it means buying back food later in the season, often at higher prices. A recent review of storage policies in Kenya pointed out the some of the weaknesses in the system: 80 percent of producers did not have adequate storage facilities to store their maize and/or could not afford to store maize for more than two months. This resulted in a depletion of their food reserves and a limited ability to bridge the time gap between harvests (Maritim, 1985).

# 3. Women as Income Earners

# a. Farm and off-farm income

In Kenya, smallholders depend on the market to a significant extent to satisfy their consumption requirements. Surveys have shown that rural 7
29

**TABLE** _45_

|**SaeepL****_Em_**|**_ploymai_**of T<br>**inthe Enz**|eymp**y** <br>**crpdaesam**|**_Wotdn_**by<br>**ple**|**1hAsy**|
|---|---|---|---|---|
||1980|1982|1984|1985|
|Food|2,107|5,490|8,001|4,615|
|Tobacco andMatches|62|241|0|77|
|Textiles, Clothing and<br>Leather|519|704|629|752|
|Wood|1|9|S|17|
|**Paper**|**_51_**|12|61|73|
|Prnting|28|S3|_51_|40|
|Chemicals|5,140|312|212|226|
|construction|83|139|101|,86|
|Mechanical Industries|279|264|205|204|
|TOrAL|8,270|7,224|9,265|6,090|

Source: Enterprise sample.

worked on a temporary basis. Although this number is a useful indicator of worker welfare, the extent of on-the-job training and the breadth of modern sector employment, it does not distinguish between short vs. long spels of temporary employment. Table 4.6 shows the relative use of permanent and temporary labor input by firms by presenting the labor cost of temporary workers relative to that of permanent workers in the sampled firms. The data indicate that the wage bill allocated to temporary workers in each industry is relatively small when compared to the wage bill of permanent workers-on the order of _5_ percent to 12 percent for most industries. Hence, the actual number of days each temporary laborer is employed is quite small relative to the
# **Figure 10.  Part-Time and Temporary Employment in the EU Accession and Candidate Countries, 2002**

(as percent of total employment and of all employees respectively) <!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>part-time<br>10<br>fixed-term<br>8<br>6<br>4<br>2<br>0<br>percent<br>RO PL LT LV CY EE SI CZ HU BG SK ACC EU-15<br><!-- End of picture text --> Notes: ACC – EU accession and candidate countries. _Source_ : _Eurostat_ (based on 2<sup>nd</sup> quarter LFS data).

# **Box 2.  Self Employment in the Latvian Economy**

The share of self-employed among the employed the extent to which employment is based on one’s own capital resources and carried at one’s own risk. As self-employment was often restricted in socialist countries, the share of self-employment can be seen as a gauge of progress towards a market-oriented economy. Furthermore, the establishment of additional enterprises in expanding sectors of the economy is very important for job creation. Still, it is apparent that some of the self-employment in transition economies is a low productivity, semi-legal alternative to joining the ranks of the unemployed. This may be a rational response on the part of those who lose, or are in danger of losing, their attachment to the formal labor market.

The data in the table below confirm the relation between self-employment and the agricultural character of the regions. In Romania, more than 20% of employed workers are self-employed in agriculture. In Poland this fraction is 12.7 %, but in three regions of the country the share also lies at around 25 percent. In contrast, in Latvia the share of self-employment is only 5.5 percent.

The industrial sectors of all countries generally have few self-employed individuals. In the Czech Republic, selfemployment activities are most widespread in the industrial sector of regions with an unemployment rate between 4 and 6 percent. In Hungary, this group accounts for 3.7 percent of the total employment in industry. In Bulgaria, the Baltic States and Romania however, hardly any self-employed are found in the industrial sector. These selfemployed account for less than 2 percent of the employed in these countries.

In all countries, most of the self-employed work in the service sector rather than in the industrial sector. The employment share in services is particularly high in the Czech Republic with 8.9 percent, and Hungary with 8.4 percent. In Latvia, as in Lithuania, Romania and Bulgaria, the self-employment rate in the service sector is lower than in the agricultural sector.

||**Self-employment**|**C**|**ontribution of sector**|**s**|
|---|---|---|---|---|
||**rate**|**Agriculture**|**Industry**|**Services**|
|Bulgaria|14.6|7.2|1.5|5.8|
|Czech Republic|14.5|0.9|4.6|8.9|
|Estonia|8.1|2.1|1.3|4.7|

14
|Bogota Urban Services Project|Mauricio Jaime Cuellar Montoya|
|---|---|
|Bogota Urban Services Project|Mauricio Jaime Cuellar Montoya|
|Bogota Urban Services Project|Mauricio Jaime Cuellar Montoya|
|Bogota Urban Services Project|Mauricio Jaime Cuellar Montoya|
|Bogota Urban Services Project|Mauricio Jaime Cuellar Montoya|
|Bogota Urban Services Project|Mauricio Jaime Cuellar Montoya|
|Bogota Urban Services Project|Mauricio Jaime Cuellar Montoya|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|National HIV/AIDS Prevention Project|Sandra Rosenhouse|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Financial Services for the Poorest<br>|Kiatchai Sophastienphong<br>|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Financial Services for the Poorest|Kiatchai Sophastienphong|
|Tax Reform Adjustment Loan Project|Steven Benjamin Webb|
|Tax Reform Adjustment Loan Project|Steven Benjamin Webb|
|Tax Reform Adjustment Loan Project|Steven Benjamin Webb|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|State Judicial Modernization Project|David F. Varela|
|HN PRSC|Ulrich Lachler|
|HN PRSC|Ulrich Lachler|
|HN PRSC|Ulrich Lachler|
|Programatic Structural Adjustment Credit|Ulrich Lachler|
# A Survey of **Recent Zstinates** of **Price Elasticlties** of **Demand** for Transport

# I. Introductlon

There have been numerous empirical studies of the demand for transport in the past decades. With advances in computer technology, many previously inapplicable or impractical econometric methods have been applied to the field of transportation. Researchers have become more aware of pitfalls whlch can undermine empirical studies. In recognition of advances, in the ability to estimate demand functions, this survey concentrates on the major empirical studies of own-price elasticities of demand for transport that emerged in the last ten years or so. As the title of this paper suggests, only studies which contain empirical results are included. We also omit most survey papers, but generally we include studies mentioned in these surveys. For example, the review article by Winston (1985) is not recorded in our survey, but all the demand articles cited therein are included in this revlew.

# 1I. Sources of Demand Studies

With the emphasis on more recent studies, most of the studies reviewed are those which appeared in the 1980s. We have included a few studies that date back into tbe 19709, primarily because of their importancb in the literature. The earliest study included in this paper is the work by McFadden (1974). Our survey does not go back earlier hence empirical estimates published in the 1960's (e.g., the 'abstract mode' model by Quandt and Baumol, 1966) are not included.

The majority of the studies reviewed are journal articles, for the simple reason that this is the avenue moot authors use in comwunicating their research findings. We include a few studies not reported in academic journals. These entries generally are for modes or markets for which we did not find empirical estimates of elasticities ln the published academic literature.

The l1terature review began with the collection of journal articles in Waters (19P,4, 1989). Because only articles appearing in major economics journals