rafmacalaba/datause-encoder-data huggingface dataset:
Table C.3 – _Continued from previous page_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Written Agreement|||||||-0.303***|-0.358***|
||||||||(0.104)|(0.107)|
|Non-written Agreement|||||||-0.440***|-0.470***|
||||||||(0.143)|(0.142)|
|Affected by food|||||-0.562||||
||||||(0.387)||||
|Affected by food_×_Job density|||||0.315||||
||||||(0.200)||||
|Flood event is likely||||||-0.383|||
|||||||(0.662)|||
|Flood event is likely_×_Job density||||||0.299|||
|||||||(0.316)|||
|Observations|466|466|466|466|466|466|466|466|
|R2|0.32|0.34|0.33|0.34|0.33|0.34|0.33|0.34|

_Note_ : The dependent variable is the log of rent (combined rent paid and self-estimated rent), measured in 2017 PPP US$by year. The variables _Number of rooms_ and _Number of rooms squared_ are added only in estimations (7) and (8) because of missing values reducing the total number of observations. The variable _Dwelling tenure status_ is not added because all the observations for rent are only for renters. Robust standard errors are clustered at the enumeration area level. *** p _<_ 0.01, ** p _<_ 0.05, and * p _<_ 0.1.
Policy Research Working Paper 5828

# **Abstract**

The authors study firm dynamics using a novel database of all formally registered firms in Cote d'Ivoire from 1977 to 1997, which account for about 60 percent of gross domestic product. First, they examine entry and exit patterns and the role of new and exiting firms versus incumbents in job creation and destruction. They find that while the rate of job creation at new firms is quiet high—at 8 percent on average—the number of jobs added by new firms is small in absolute terms. Next, they examine survival rates and find that the probability of survival increases monotonically with firm size, but manufacturing and foreign-owned firms face higher likelihoods of exit compared with service oriented and domestically owned firms. They find that higher growth of gross domestic product increases the probability of firm survival, but this is a broad impact with no firm size disproportionately affected. In robustness checks, they find that after 1987 size is no longer a significant determinant of firm survival for new entrants, suggesting that the operating environment for firms changed. Finally, they find that trade and fiscal reform episodes raised the probability of firm exit and attenuated the survival disadvantages faced by smaller firms, but exchange rate revaluation and pro-private sector reforms did not significantly lower the likelihood of exit.

This paper is a product of the Finance and Private Sector Development 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 author may be contacted at lklapper@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
7 60 for men. This could be explained by the development of, and easy access to the education sector and the low retirement age. At young ages, the labor force behavior observed in the FSU was close to the one observed in European countries in which large public investments in education took place and labor force participation rates were significantly below those observed in Anglo-Saxon countries (OECD, 1994).

The transition brought about several changes. As a response to the sharp income decline and cuts in public spending, **labor force participation rates** increased sharply for both men and women in the 15-19 age group and at old ages (see **Chart 1).** For example, in the age group 6064, rates increased from 32 to 77 percent between 1989 and 1995 for men, and from 14 to 67 percent for women.

**Chart 1. Ukraine Labor Force Participation Rates**

<!-- Start of picture text --> 100%<br>90%<br>70%<br>60%<br>30% \ -- Fmb18<br>50% A Male 1989<br>40% °Ot-°Male \ 1995<br>30% - -- Female 1989<br>20% -- Female 1995<br>10%<br>- N ~ ~ I?~ L D CD 9<br><!-- End of picture text --> Source: World Bank household Survey (1995) Another change has been the increased **importance of informal activities.** One important form of such activities in Ukraine consists of work on land plots, cattle/poultry breeding etc.. which represent significant sources of additional income. These activities are performed either as a complement to employment in the formal economy or have become the main occupation. In 1995, over 65 percent of men and women employed by a state or private enterprise report spending additional time working on land plots. And over half of those who do not report being employed in the formal sector indicate that they work on land plots. In other words, in addition to a large proportion of those employed who allocate part of their time to 4 This number largely exceeds the number of hidden unemployed estimated to about 2 million workers in 1995 (IMF 1996).
Remarkably, Figure 2b shows that the close relationship can hold even when cooking is done outdoors. Table 3 provides log-log regression results for various 2- minute lag structures. The implied adjustment lag is very short, and the overall fit is obviously very strong. Summary evidence for all households indicates that the observations in Figure 2 are very common in our sample. The correlation coefficient for the PDRam households is .93 for median PM10 concentrations in kitchens and living rooms; Figure 3 illustrates the strength of the relationship. To summarize, the PDRam evidence strongly indicates that air pollution from cooking diffuses into living spaces very rapidly, and at similar intensity.

# **Figure 2a:  Comparative PM10 Concentrations in Four Bangladeshi Houses: Kitchens and Living Rooms**

<!-- Start of picture text --> (1) (2)<br>0 5 10 15 20 25<br>24-Hour Time 0 5 10 15 20 25<br>24-Hour Time<br>Log Kitchen Concentration Log Living Room Concentration<br>Log Kitchen Concentration Log Living Room Concentration<br>(3) (4)<br>0 5 10 15 20 25 0 5 10 15 20 25<br>24-Hour Time 24-Hour Time<br>Log Kitchen Concentration Log Living Room Concentration Log Kitchen Concentration Log Living Room Concentration<br>9<br>7<br>8<br>6<br>7<br>5<br>6<br>4<br>5<br>3 4<br>Log Kitchen Concentration/Log Living Room Concentration Log Kitchen Concentration/Log Living Room Concentration<br>7 8<br>6.5<br>7<br>6<br>6<br>5.5<br>5<br>5<br>4.5 4<br>Log Kitchen Concentration/Log Living Room Concentration Log Kitchen Concentration/Log Living Room Concentration<br><!-- End of picture text --> 12
16 economy‘. None of the most cited items in ̳Public health‘ exceeds 1,000 citations but it is led by two classic applied papers: Becker, Grossman and Murphy (1994) on cigarette addiction (placed here rather than ̳Determinants‘ on account of its emphasis on smoking reduction), and Cutler, Glaeser and Shapiro (2003) on American obesity. This category also includes economic analyses of risk factors, heat waves, AIDS and violence. ̳Health and the economy‘ is led by Rodrik (2004) on economic development and DiMasi et al. (2003) on pharmaceutical innovation. The other papers explore further the two-way links between health (or health care) and the wider economy: growth, the economic cycle, unemployment, and private and public expenditure.

̳Supply of health services‘ has a historically characteristic focus on hospitals as nonprofit institutions, managed health care organizations and the ways in which such institutions respond to changes in their financial environments. None of the items in this category reach the rates of citation of the most cited papers. The most cited paper in this group (Cleary and McNeil, 1988) was 32<sup>nd</sup> in the overall ranking, the next most highly cited paper was 40<sup>th</sup> (Scott and et al., 2000). Newhouse‘s (1970) pioneering paper and Pauly‘s (1973) classic are two of the earliest papers referenced in health economics. Most of the papers in this group originate from the US and reflect US institutional concerns. ̳Medical insurance‘ is long-standing health economics territory and the first four items in the group were each cited more than 1000 times. These include Arrow (1963) (cf. Arrow, 2001, reprinted on several other occasions too), which many consider the founding paper of the sub-discipline, Manning et al. (1987) reporting of the RAND Health Insurance Experiment, Pauly‘s (1968; 1991) comment on Arrow (though not Arrow‘s reply!), and Barsky et al. (1997) with their famous paper on risk tolerance, time preference, and intertemporal substitution.

̳Efficiency and equity‘ as a group has relatively few US contributions and, given the dominance of US economists in the field, this may also explain why the citation rates are not higher. The group includes both methodological contributions and applied studies, including international comparisons, and covers equity in both the financing and delivery of health services. ̳Health statistics and econometrics‘ contains six contributions with citation rates above 1,000 and relatively few published before 2000. Some of these publications (e.g. Angrist et al., 1996) simply have health as an application and the usefulness of the paper extends well beyond the health field. In other cases (e.g. Duan and et al., 1983) the paper sets out to address an
# **I. INTRODUCTION**

1 Governments influence the price and quality of transport services both through their ix'vestments in transport infrastructure and through a range of regulations. In rural areas, these decisions affect the spatial pattern of agricultural production with consequences for the efficiency of agriculture, the well-being of agricultufalists and the revenues obtained from the sector by the government.

2. In many African countries, decisions on the pricing of transport services are made implicitly as a consequence of government interventions in the marketing of agricultural produce. Often, these governments establish public or semi-public agencies (parastatals) that handle marketing, and the ways in which they pay farmers at different locations for their produce are equivalent to a set of regulations on the pricing and quality of transport services. One commonly adopted component of parastatal policy is panterritorial pricing, in which all farmers are paid the same price regardless of where their produtce is purchased. This is the practice of the Compa6nie Ivoirienne pour le Developpement des Textiles (CIDT), the parastatal that is involved with cotton production and marketing in the Cote d'Ivoire and which provides the main example used in this paper to illustrate and assess some of the policies on rural transport that have been implemented in Africa.' 3. In this paper, I set out some practical diagnostic tests of the efficiency of (implicit) transport policies emboJied in state marketing and their implications for infrastructural investment, tests that can be calculated using data that are readily available in many African countries and for many crops. The next section presents a conceptual framework for looking at rural transport and state marketing,<sup>2</sup> and uses it to organize some basic facts about the operations of the CIDT. Section 3 models the implications of panterritorial pricing for transport policy, and applies the model to assess this aspect of the activities of the CIDT. Section 4 looks at the timeliness of transportation and presents evidence on the performance of the CIDT in this dimension. Section 5 gives special attention to choices about the location of purchasing depots used by parastatals and the consequences for the implicit price of transport. In principle, there is a wide variety of options here, and the CIDT exemplifies only one of the choices that I consider. A final section makes some concluding remarks.

' For my information on the CIDT, I rely on Beenhakker and Bruzelius (1985), DCGT (1986 and 1988), and CIDT(various years).

> **2** The approach that I use builC3 on the work by Walters (1968) on the Ellet model. Gersovitz (1989) applies this framework to some topics in optimal taxation and agricultural marketing.
30 concentrated in so few primary commodities. This argument is relevant for SSA countries where export concentration is high. As of 1988, two commodities provided at least 60 percent of export earnings in 30 out of the 43 SSA countries for which export data by commodity are available. (Table A4). Faini and de Melo's results assume that the terms of trade are given. It is important to note, however, that in the econometric approach, it is difficult to determine which channel. suggested by the theory is more important since the analysis focuses on the net effect of devaluation on output.

Evidence from macro-simulation models provide information that distinguishes the effects of devaluation on output through different channels. Gylfason and Risager (1984) provides evidence from cross-country macro-models on the impact of devaluation on real output. Their study shows that, while a devaluation is likely to have a positive effect on output in developed countries, it is generally contractionary in developing countries. Using the same approach, Gylfason and Radetzki (1985) obtained a similar result: contractionary devaluation is more likely to occur in developing countries than in developed countries-even more so when demand elasticities are low.1Us/ To summarize, many studies exist on the impact of devaluation on output in developing countries. (See Table 5). The conclusions are as follows. First, contractionary devaluation is true in some countries but not in others. Second, where contractionary devaluation occurs, it is short lived; in the long run, devaluation effects seem neutral. Third, contractionary devaluation is more likely to occur in developing rather than in developed countries-more so when demand elasticities are low. Finally, the impact of devaluation on output appears to be less effective (or even ineffective) in countries exporting primary products than in countries exporting manufactured goods.

# C. Empirical Evidence on Contractionary Devaluation in SSA Countries

Are conclusions drawn from studies done on developing countries in general relevant for SSA countries? There is a _priori_ belief (especially among some policymakers in Africa) that _IB/_ Although Gylfason **and** Risager's **study** emphasies **the roe of** intercst 'tes, the Gylfason and RadelizW's paper emphasizes the role of wage indexing.
**Table 3: Impacts of Increased Biofuel Production on Food Prices**

|**Study**|**Coverage & Key**<br>**Assumptions**|**Key Impacts of Biofuels on Food Prices**|
|---|---|---|
|**Banse et al. (2008)**|2001‐2010; Reference<br>scenario without mandatory<br>biofuel blending , 5.75%<br>mandatory blending scenario<br>(in EU member states), 11.5%<br>mandatory blending scenario<br>(in EU member states),|Price change under reference scenario , 5.75%<br>blending, and 11.5% blending, respectively:<br>Cereals: ‐4.5%, ‐1.75%,+2.5%<br>Oilseeds: ‐1.5%, +2%, +8.5%<br>Sugar: ‐4%, ‐1.5%, +5.75%|
|**Baier et al. (2009)**|24 months ending June 2008;<br>historical crop price<br>elasticities from academic<br>literature; bivariate<br>regression estimates of<br>indirect effects|Global biofuel production growth responsible for<br>17%, 14% and 100% of the rises in corn, soybean<br>and sugar prices, respectively, and 12 % of the<br>rise in the IMF’s food price index.|
|**Lazear (2008)**|12 months ending March<br>2008|US ethanol production increase accounted for<br>20% of the rise in cornprices.|
|**IMF (2008)**|Estimated range covers the<br>plausible values for the price<br>elasticityof demand|Range of 25‐45% for the share of the rise in corn<br>prices attributable to ethanol production increase<br>in the US|
|**Collins (2008)**|2006/07‐2008/09; Two<br>scenarios considered: (1)<br>normal and (2) restricted,<br>with price inelastic market<br>demand and supply|Under the normal scenario, the increase in<br>ethanol production accounted for 30% of the rise<br>in corn price; Under the restricted scenario,<br>ethanol could account for 60% of the expected<br>increase in cornprices.|
|**Glauber (2008)**|12 months ending April 2008|Increase in US biofuels accounted for about 25<br>percent of the rise in corn prices;<br>US biofuels production accounts for about 10<br>percent of the rise in global food prices IMF<br>global food commodity price index.|
|**Lipsky (2008) and**<br>**Johnson(2008)**|2005‐2007|Increased demand for world biofuels accounts for<br>70percent of the increase in cornprices.|
|**Mitchell (2008)**|2002‐mid‐2008; ad hoc<br>methodology: impact of<br>movement in dollar and<br>energy prices on food prices<br>estimated, residual allocated<br>to the effect of biofuels.|70‐75 percent of the increase in food<br>commodities prices was due to world biofuels<br>and the related consequences of low grain stocks,<br>large land use shifts, speculative activity and<br>export bans|
|**Abbott et al. (2008)**|Rise in corn price from about<br>$2 to $6 per bushel<br>accompanying the rise in oil<br>price from $40 in 2004 to<br>$120 in 2008|$1 of the $4 increase in corn price (25%) due to<br>the fixed subsidy of 51‐cents per gallon of ethanol|

20
**The data on key variables required to compute firm-level TFP, are obtained from the Amadeus database.**<sup>**4**</sup> The sample covers 78,704 manufacturing firms (NACE 10 – 35 i.e. 25 manufacturing sectors divided into 270 four-digit manufacturing sub-sectors) for which data are available for the Russian Federation, over the period 2003 to 2008. Firms’ output, capital stock, materials, and labor, are proxied by operating revenue (turnover), tangible fixed assets, cost of goods sold, and the number of employees, respectively. This provides an unbalanced panel of about 204,000 observations. This number of observations is the result of data cleaning procedures similar to those used in Alam et al (2008) that excludes: (i) missing observations on any of the key variables for estimated productivity functions; (ii) observations including a positive number of subsidiaries in order to prevent double counting of firms and also to avoid the inclusion of shell companies which are not directly involved in the production process; (iii) observations involving ratios greater than 1 between material and output; and (iv) observations with TFP (post-estimation) greater (less) than one percent standard deviation to minimize the impact of outliers. Full description of the characteristics of the sample is presented in Annex 1.

# **4. Results and Discussion**

# **(i) Productivity Dynamics and Output Growth**

The Levensohn and Petrin (2003) estimates of the Cobb-Douglas production function for Russian manufacturing firms over the period 2003 to 2008 indicate diminishing returns to scale (results are summarized in table 1). All factor inputs are statistically significant. However, the null hypothesis of constant returns to scale is rejected overall, even at the one percent level of statistical significance i.e., increasing all three factor inputs by 100 percent increases output by less than 100 percent (94 percent in this case).

> 4 Amadeus is a database of comparable financial information (in a standardized format) for public and private companies, on over 18 million companies across 43 Western and Eastern European countries. It includes comprehensive information on all main dimensions of firm operations and performance (e.g., the profit and loss account, and the balance sheet). For more detail on this database, please see http://bvdinfo.com/Products/Company-Information/International/AMADEUS.aspx.

7
53

# Table 11: Topical distribution of the most-cited 50 publications of the top four countries

||Canada|Netherlands|United Kingdom|United States|
|---|---|---|---|---|
|Health and its value|7|5|6|4|
|Efficiency and equity|2|16|9|3|
|Determinants of health and ill-health|5|3|4|12|
|Public health|2|||4|
|Health and the economy|6|4|2|7|
|Health statistics and econometrics|2|2|3|7|
|Demand for health and health care|4||1|3|
|Medical insurance|2|3|1|4|
|Supply of health services|2|3|3|1|
|Human resources|2|1||2|
|Markets in health care|2|2|1|1|
|Economic evaluation|10|10|17|1|
|_Total_|_46_|_49_|_47_|_49_|
|Normalized Herfindahl index|0.100|0.163|0.185|0.113|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating. Institutions were then assigned a country. The 50 most-cited publications for each of the listed countries were then assigned either one of the 12 topics listed in the appendix or a ―topic not assigned‖ code. The latter included publications that were too general (some were actually textbooks) some had no economic content despite being in EconLit, and some had no health content despite the JEL code.
BANGLADESH

## - 2. Projections NRR-1 by 2015

|_Year_|Populatio|DW.<br>ratio|p1a-d|g6e-th<br>rate|CIO|cm|TFR|G<br>Nm|SO<br>0<br>Frm-tem|IM|x1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|113134|87.2|1990.94|2.13|34.7|13.1|4.55|52.3|52.0|111|161|
|1995<br>|125869|77.8|1995-99|1.95|31.5|11.9|3.90|53.9|53.8|100|143|
|2000|138736|68.6|2000-04|184|29.3|10.9|3.46|55.6|55.9|87|124|
|2005<br>|152071|63.2|2005-09|1.65|26.6|10.0|3.06|57.3|57.8|79|111|
|2010|165185|58.4|2010-14|1.43|23.6|9.3|2.73|59.1|59.8|71|98|
|2015<br>|177388<br>|53.8|2014-19|1.20|20.7|8.7|2.42|60.9|61.9|63|86|
|2020<br>|188321<br>|48.8|2020-24|1.11|19.5|8.3|2.31|62.7|64.1|55|73|
|2025<br>|199061<br>|45.7|2025-49|0.83|16.8|8.6|2.20|67.4|69.8|3.|44|
|2050<br>|244895<br>|49.4<br>|2050-74|0.38|14.0|10.2|2.10|73.2|76.7|13|16|
|2075<br>|269515<br>|58.6<br>|2075-99<br>|0.21|13.0|10.8|2.08|77.1|81.5|7|9|
|2100<br>|284241<br>|66.1<br>|2100-24<br>|0.14|12.3|10.9|2.06|80.1|85.6|3|4|
|2125<br>2150|294580<br>300001|69.0<br>71.5|2125-49|0.07|12.0|11.3|2.06|81.2|87.2|3|4|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 10 86 4 2024 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>60-64<br>55.59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10.14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

## 4. Comparison of Country with Region and Income Group, 1985-90

TFR **eO** Growth rate Countr-y Region Incom group Country Region Income 2roup Counwtry Region Income group 39