rafmacalaba/datause-encoder-data huggingface dataset:
where _Y_ is the real net value added, _A_ is the productivity level, _K_ is the amount of real capital and _L_ is the labor used in the production. Let _s=1,...,S_ index states, _i=1,..,I_ index industries and _t=1,...,T_ index time in years. Our estimating equation can be written as: ln _Yist_ = ln _Aist_ + α ln _Kist_ + β ln _List i_ = 1... _I_ ; _s_ = 1,..., _S_ ; _t_ = 1,..., _T_ (2) with _i_ denoting industries, _s_ denoting states and _t_ denoting time. The _i_ and _s_ subscripts denote the cross section dimension and _t_ denotes time series dimension of panel data. Productivity _Aist_ depends on the policy variables: and where<sup>μ</sup> _i_<sup>denotes industry specific andλ</sup> _t_<sup>denotes time specific unobservable characteristics.</sup> ε _ist_ is the remainder disturbance with _IID(0,_ σε 2 _)._ As can be seen, this is a three-dimensional panel 13 data model, and we run a fixed effects regression. _FLEX_ is a state-specific dummy variable that takes the value one for states with flexible labor markets and _NRP_ is the industry and time varying tariff rate. We also use non-tariff barriers and import penetration as alternatives to _NRP. DEV_ is defined as the logarithm of real per capita development expenditure and varies by state and time. _GSDP_ is the logarithm of real Gross State Domestic Product and is used to control for the possible existence of economies of scale. We run our regressions with and without the _GSDP_ control.

The impact of policy variables on employment and capital accumulation is estimated using a specification analogous to the one in equation 3. In these regressions the dependent variable is number of workers and real invested capital, respectively.

We extend our analysis of the determinants of productivity to using delicensing (along with its interaction with labor market flexibility) of industries as an additional industry regulation

> 13 We have 270 cross-sectional units (18 industries and 15 states), and thus the number of cross-sectional units far exceeds the number of years, which is only 10. This allows us to ignore cointegration-related issues in our estimation.

15
Ward, Christopher and William Byrd (2004). “Afghanistan’s Opium Drug Economy”. _World Bank South Asia PREM Working Paper Series, Report No. SASPR-5_ (December).

World Bank (2005). _Afghanistan—State Building, Sustaining Growth, and Reducing Poverty (A World Bank Country Study)._ Washington, DC: The World Bank.

World Bank (2006). “Treating the Opium Problem in World Bank Operations in Afghanistan: Guideline Note”.

Zia, M.E., D. Radcliffe, C. Ward, W. Byrd, K. Goeldner, R. Kloeppinger-Todd, S. Maimbo, D. Mansfield, D. Pearce, S. Rasmussen, and E. Zeballos (2005). “Rural Finance in Afghanistan and the Challenge of the Opium Economy”. World Bank _South Asia Region PREM Working Pa per Series,_ Report No. SASPR-9 (July).

30
economy. Until recently, deterrent measures, such as detecting, criminalizing, and penalizing undeclared work, have been prevalent, but EU countries have increasingly adopted incentive measures for inducing voluntary compliance (Williams 2009). According to a survey of policy measures adopted in the EU countries, almost all countries impose administrative or penal sanctions on either purchasers or suppliers of goods and services involved in undeclared work (Dekker et al. 2010). However, despite buyers’ gains from tax evasion, fewer countries impose penalties on purchasers (Jensen and Wőhlbier 2012, 28). The concept of a penalty for the purchase of undeclared work is based on the buyer’s illegal benefit from the low price of undeclared work resulting from tax evasion.

In Italy, for example, under the Decreto Bersani, a law passed in 2006, a retailer that fails to issue a sales receipt three times in a five‐year period can be closed, and construction sites can be shut down if employment irregularities are found. It is also illegal to use cash to pay more than €100 for professional services (A. T. Kearney and Schneider 2009). Buyers of undeclared work must keep receipts to present to tax authorities, if asked, to avoid sanctions. Norway requires purchasers to pay using electronic means or to keep invoices for a specified time to avoid criminal liability for purchases of undeclared services (Jensen and Wőhlbier 2012, 28). Penalizing both providers and buyers of undeclared work can be effective in that it more effectively breaks the collusive relationship between them. However, the process of monitoring a large number of small and micro businesses to determine if they are issuing receipts—and then ensuring that the general public keeps those receipts—is almost impossible, given the tax authorities’ limited resources. Those measures have potential deterrent effects, but they may not be implementable in practice. With this in mind, many scholars have questioned the effectiveness of deterrent measures, in general, including detecting and punishing (Williams and Schneider 2016, 147–48).

Another noticeable deterrent measure for reducing overall cash transactions that can lead to tax evasion and fraud has been rolled out by some of the EU countries. Belgium, Bulgaria, the Czech Republic, Denmark, France, Greece, Hungary, Italy, Portugal, and the Slovak Republic have introduced cash payment limits, mostly since the crisis of 2012, as shown in Table 1 below.

Table 1 Cash Payment Restrictions in the European Union

|Belgium|€3,000|January 1, 2014|
|---|---|---|
|Bulgaria|lev 10,000(≈ €5,112)|July 1, 2011|
|Czech<br>Republic|CZK 350,000 (≈ €12,763)|January 1, 2013|
|Denmark|DKr 10,000 (≈ €1,340)|July 1, 2012|
|France|€3,000 (resident and nonresident traders)|January 1, 2002|

9
Figure 3.3 portrays the time path of a measure of the productivity differential, based on the (log) ratio of nonitradable to tradable prices of Argentina relative to its trading partners. An increase represents an improvement in Argentina's productivity differential, and calls for an appreciation of the equilibrium REER. The figure suggests a clear rise in relative productivity in the early 1990s and hence, given other things, an appreciation of the equilibrium REER between 1990 and 1993. Much of this gain in productivity is likely to reflect efficiency gains derived from the end of hyperinflation achieved in 1991. After 1993, however, there were no additional gains in relative productivity, and in fact a partial reversal appears to have taken place after 1994. This absence of further productivity gains appears consistent with the stalling of Argentina's structural reform process in the second half of the decade.

If the initial productivity gains are largely responsible for the appreciation of the equilibrium REER in the early 1990s, its depreciation in the late 1990s is driven by the changes in Argentina's net foreign asset position, shown in Figure 3.4. In the figure, an increase represents a rise in Argentina's NFA position (relative to GDP) and hence calls for an appreciation of the equilibrium REER, given other things. **Figure 3.4. Net Foreign Assets (Percent of GDP)** <!-- Start of picture text -->
-10%<br>1990  1991  1992  1993  1994  1995  1996  1997  1998  1999  2000  2001<br>-15%<br>-20%<br>-25%<br>-30%<br>-35%<br>-40%<br>Source:  Alberola,  L6pez and Serv6n  (2003)<br><!-- End of picture text -->

18 See Alberola, L6pez and Serven (2003) for more details on the construction of this measure. An alternative measure based on ratios of aggregate labor productivity yields broadly similar qualitative results.

22
Table 7: Elasticities: OECD vs. non-OECD

||**Model**|**Periodicity **|**Total**|**OECD**<br>**Non-OECD**|
|---|---|---|---|---|
|**_Static_**|||||
|Linear|PSM|Annual|0.44 ***|0.82 ***<br>0.14 ***|
||||(0.05)|(0.05)<br>(0.02)|
|Exponential|PSM|Annual|0.78 ***<br>(0.06)|0.83 ***<br>0.22 ***<br>(0.04)<br>(0.03)|
|**_Dynamic_**|||||
|Linear|GMM|Quiquenial|0.96 ***<br>(0.09)|1.04 ***<br>0.89 ***<br>(0.06)<br>(0.11)|
|Exponential|PSM|Annual|0.91 ***<br>(0.06)|0.95 ***<br>0.70 ***<br>(0.04)<br>(0.12)|

# Table 8: Returns to R&D

|**Ha**<br>**(19**|**ll et al**<br>**86)**<br>**Gr**<br>**(20**|**iffit et al**<br>**02)**||**LFM**|||**GMM**||
|---|---|---|---|---|---|---|---|---|
||||Total|OECD|NON-OECD|Total|OECD|NON-OECD|
|Firms|642|407|||||||
|Patents|_35_|35|1132|1975|34|1097|1831|119|
|R&D Millions of Dollars of 1976|21.48|35.00|||||||
|R&D $M of 1995|49.91|_81.30_|9010|12050|722|8320|13700|1110|
|R&D/Patents|1.43|2.32|7.96|6.10|21.24|7.58|7.48|9.33|
|Correction Applied/Granted|0.65|0.65|||||||
|R&D/Patents (Corrected)|2.19|3.57|||||||
|Elasticity|0.3|0.506|0.91|0.95|0.7|0.98|1.04|0.9|
|Implied Returns to R&D|13.68%|14.16%|11.43%|15.57%|3.30%|12.92%|13.90%|9.54%|

30
good health and the means to adapt to a labor market with rapidly changing skills needs – elements that determine the ability to work into old age and influence worker productivity.<sup>16</sup>

# The nature of work among older adults

**Old-age labor force participation is generally lower, the higher a country’s average income level.** In the average LIC, more than half of the 65+ population is active in the labor market (Figure 2). Moving up through country income groups, participation rates fall systematically. In lower middle-income countries (LMICs), 33 percent of the 65+ population is active, and in upper middle-income countries (UMICs) and HICs, this rate falls to 19 and 13 percent, respectively. Across income groups, European countries are outliers, with significantly lower participation rates than other regions. High labor force participation in poor countries largely reflects a need to work. In Thailand, for example, 31 percent of people above age 60 report that labor income is their main source of income, which is much higher than the share reporting pensions (5 percent) or elderly allowance (17 percent).<sup>17</sup>

**Figure 2: Labor force participation for older workers falls with a country’s income**

<!-- Start of picture text --> labor force participation rate (%), age 65+, by<br>country income group<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>LICs LMICs UMICs HICs LMIC UMIC HIC<br>All regions Europe only<br><!-- End of picture text --> Source: Estimates based on Staudinger et al. 2016. Refers to 2013 data.

**The pattern of participation over the life cycle is similar across country income levels, despite very**

**different levels of participation** . Figure 3 presents three cases as an illustration: low-income Liberia, middle-income Morocco, and the high-income Republic of Korea. Across these very different countries, labor force participation falls after age 55, for both women and men. In Morocco, where female labor force participation is very low, the pattern for male participation is nonetheless similar to that of both richer and poorer countries, and even among women, there is an inverse u-shape

> 16 A complete assessment of labor market conditions for mature workers is outside of the scope of this paper. However, several World Bank country-level or regional studies provide more comprehensive overviews of labor market conditions for older workers including sector and occupations analysis, for example for Thailand (World Bank 2021b), Vietnam (World Bank 2021a), East Asia and the Pacific (World Bank 2016), Malaysia (World Bank 2020), and the Russian Federation (Levin 2015).

> 17 World Bank 2021b.

7
which equals zero only if _δ_ = 1, i.e. all spillovers are transitory. This would imply coefficient estimates for entries and exits that are equal but opposite in sign. On the other hand, if FDI spillovers are permanent, then _δ_ = 0 and there should be no significant effect from exits. Finally, if the coefficient is between zero and one, then this would suggest the presence of both transitory and permanent spillovers.

Note that in both of these latter two cases, it should be that the estimated effect of new FDI is greater in absolute value than for exiting FDI if entering investment creates spillovers immediately. Thus, by estimating the impact of increases and decreases in FDI separately, i.e. not assuming that _δ_ = 1, we can test for the presence of transitory versus persistent spillovers. Further, recall that in equation (8), we assumed that firm _i_ ’s firmspecific characteristics and the spillovers were additively separable. If this is not the case, then the impact of spillovers will vary across firms. This too is something we will explore in the data. Finally, just as employment growth depends on FDI, so too does capital growth, with investment responding comparably to changes in FDI.

# **3 Data Description and Empirical Strategy**

Our data come from the firm census for 2006 and 2011 from the Department of Statistics in Jordan. These data represent information of about 100,000 firms in Jordan in each year from both, manufacturing and services. We observe 15,465 firms in both periods.<sup>14</sup> These data provide several pieces of firm level information, including the employment of the firm, the share of foreign ownership, the year of incorporation, revenues, capital investment, the region it is located in, and the sector in which it operates. Together, these firms comprise 53% of employment in Jordan as compared to Labor Force Survey (LFS) data.<sup>15</sup> In studying the spillover effects of FDI, these Jordanian data have several attractive features. First, prior to 2000, FDI inflows were very low (below 1% of GDP). Since then, the government removed restrictions to FDI in manufacturing and several service sectors including banking, telecommunication, and retail trade.<sup>16</sup> As a result, net FDI inflows tripled from US$ 0.9 billion in 2000 to US$ 2.8 billion in 2008. On average, these FDI inflows amounted to 10.1 percent of GDP over the decade, a share which ranks Jordan among

> 14We use sample weights for each firm and year making the subset of firms observed in both periods representative for the entire population of more than 100,000 firms in the 2006 and 2011 firm census. As we focus on changes over this five-year period, we can only use firms for which data are available in both years.

> 15Note that LFS data typically exceed firm census employment data significantly in most countries, since they usually better cover informal employment, illegal foreign workers, family workers, etc.

> 16Note that, transport and professional (accounting and legal) services remain restricted to foreign investors (Schiffbauer et al., 2015). This motivates our sample splits between services and manufacturing.

8
macroeconomic growth, is most likely to happen through formally registered firms. These weaknesses in the informal sector suggest that understanding the drivers of formal firm entry and survival are key to promoting economic growth and employment.

The paper proceeds as follows. Section 2 reviews previous literature related to firm dynamics. Section 3 reviews the relevant economic and political history in Cote d'Ivoire. Section 4 presents the data set and summary statistics. Section 5 presents business dynamics, survival test methodology, and results. Section 6 concludes.

# **2. Related literature**

Empirical studies of firm dynamics overwhelmingly focus on firm behavior in the US, Canada, and other industrialized countries.<sup>5</sup> In general, these studies find that gross entry is substantial in most industries and is significantly higher than net entry due to the high death rate of infant firms (for example, see Haltiwanger, et al. 2008a).<sup>6</sup> Successful entrants grow rapidly, so that an entrant cohort's initial market share falls slowly. For instance, an early study of patterns of firm entry, growth, and exit in US manufacturing industries over the period 1963-1982 shows that there are substantial and persistent differences in entry and exit rates across industries and that industries with higher than average entry rates tend to also have higher than average exit rates (Dunne et al., 1988). The authors also find that most entrants and exiters are substantially smaller than continuing producers.

Recent cross-country studies have examined the impact of country characteristics. For example, a study of entry rates in over 100 countries finds significant relationships between entrepreneurial activity and indicators of economic and financial development and growth, the quality of the legal and regulatory environment, and governance (Klapper et al., 2010; Klapper and Love, 2011a and 2011b). The literature also considers the relationship of the legal and regulatory environment on firm entry and growth. For instance, Klapper et al. (2006) provides a comprehensive picture of entry rates across high- and middle-income countries in Europe (using a firm-level database of registered firms) showing that onerous entry regulations hamper the creation of new firms, especially in industries that naturally should have high entry. Furthermore,

> 5 See Caves (1998) for a thorough survey of the literature on job turnover and firm mobility.

> 6 There is also a substantial literature on entry into an industry (possibly by a firm from another industry) as distinguished from firm creation or entrepreneurship; see Gilbert (1989) for a comprehensive survey.

4
WPS 3276

# **Economic and Welfare Effects of the Abolition of Health User Fees: Evidence from Uganda**

**Klaus Deininger**<sup>*****</sup> **Paul Mpuga**<sup>******</sup>

- *The World Bank, Washington D.C.

- **Johannes Kepler University, Linz-Austria & Makerere University, Kampala-Uganda

## World Bank Policy Research Working Paper 3276, April 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ We thank the Uganda Bureau of Statistics, especially James Muwonge and Tom Emwanu, for making the data available. Support from the African Statistical Capacity Building Initiative and the Norwegian ESSD Trust Fund is gratefully acknowledged.
**Table 7. Incidence of envelope wages**

|||Envelope|wages?||Share of<br>|
|---|---|---|---|---|---|
|Incidence of envelope wages byinformalgroup:|None|Part|All|Part or all|employed|
|Informal type-1: salaried at firm, no contract|8.5%|11.4%|80.0%|91.5%|6.7%|
|Informal type-2: self-employed|34.0%|9.6%|56.4%|66.0%|10.3%|
|Informal: primary|23.9%|10.3%|65.8%|76.1%|17.0%|
|Formal workers|90.4%|9.0%|0.6%|9.6%|83.0%|
|Total|79.8%|9.2%|11.0%|20.2%|100.0%|

<u>Source: Authors’ estimates using RLMS 2016.</u> <u>Note: In the RLMS, informal employment includes workers that are employed at an enterprise without a contract in their primary job and workers employed but not at an enterprise</u> in their primary job. Workers with a secondary job or in no job but in irregular economic activities are not included.

51 Informal Employment and Worker’s Wellbeing in Russia / June 2019
# **Figure 11 Top Argentine Exports: High Productivity Products, Share of Total Exports %**

<!-- Start of picture text --> Top 25 Next in the Top 50<br>8%7% Wheat and meslinMotor cars and other motor vehiclesParts and accessories of the motorMotor vehicles for the transport ofTubes,pipes and hollow profiles,sea 1.0%0.9% Chemical wood pulp, soda or sulphatFish, frozen, (excl. those of 03.04Cyclic hydrocarbons<br>6% Medicaments of mixed or unmixed pro 0.8% Polyethers and epoxide resins; poly<br>Fish fillets and other fish meat, f Malt extract; food preparations of<br>5% Unwrought aluminium 0.7% Accessory parts suitable for engineShaving preparations, personal deod<br>0.6%<br>4% 0.5%<br>3% 0.4%<br>0.3%<br>2%<br>0.2%<br>1%<br>0.1%<br>0% 0.0%<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 75 Next in the Top 100<br>0.8%<br>Cheese and curd Other plates..., of plastics, not r<br>Fibreboard of wood or other ligneou Chocolate and other food preparatio Wool, not carded or combed<br>0.5% Compression-ignition,combustion pis Polymers of propylene or of other o 0.7% Other vegetables preserved other thPumps for liquids,with or without m<br>Uncoated paper..., for writing... i Uncoated paper..., for writing... i Automatic regulating,controlling in<br>Tapes,valves,for pipes pressure red 0.6% Tractors(other than tractors of hea<br>0.4% 0.5% Food preparations not elsewhere speEdible offal of bovine animals...,Hormones; derivatives thereof used<br>Paper, paperboard, etc, coated...,<br>0.3% 0.4% Halogenated derivatives of hydrocar<br>0.3%<br>0.2%<br>0.2%<br>0.1%<br>0.1%<br>0.0% 0.0%<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 125<br>0.4%<br>Nonwovens Photographic film in rolls, instant<br>Prepared glues and adhesives, nes; Animal or vegetable fats and oils..<br>Synthetic rubber derived from oils, Polyamides in primary forms<br>All types of electrical ingnition,g Barley<br>Air or vacuum pumps,exhausting and Printed books, brochures, leaflets<br>0.3% Machines,mechanical appliances havi Carboxylic acids with oxygen functi<br>Refrigerators,freezers electric or Mixtures of odoriferous substances<br>0.2%<br>0.1%<br>0.0%<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> 16
# **1. Introduction**

Schools throughout the world closed in response to COVID-19 beginning in 2020. In some cases, they quickly reopened in 2021, but in some countries, they remained closed longer (Patrinos 2023). Globally, schools were closed for an average of 5.5 months (22 weeks) since the onset of the pandemic, equivalent to two-thirds of an academic year, when localized school closures are considered (UNESCO 2023a). The duration varies by region, from just one month in Oceania, to 2.5 months (10 weeks) in Europe, to as many as 5 months (20 weeks) in Latin America and the Caribbean. Early on, the school closures were expected to contribute to what was already described as a learning crisis (Angrist et al. 2021; World Bank 2017).

Country studies show large losses (Alasino et al. 2024; Betthäuser et al. 2023; Carlana and La Ferrara 2023; Donnelly and Patrinos 2021; Engzell et al. 2021; Hammerstein et al. 2021; Jack and Oster 2023; Jack et al 2022; Maldonado and De Witte 2022; Zierer 2021). On average, school closures induced by COVID-19 led to a learning loss of 0.17 of a standard deviation – that is, at least one-third of a year’s worth of learning (Patrinos et al. 2023). Distance learning during the school closures does not seem to have helped very much (Haelermans et al. 2022; Singh et al. 2022); only the duration of school closures led to variations. Most studies observe increases in inequality where certain demographics of students experienced learning losses that were more significant than others. These learning losses could translate to earnings losses and could cost this generation of students trillions of dollars (Psacharopoulos et al. 2021).

Also, global studies – that is, studies using data from international assessments where students in many countries take the same tests and under the same conditions, thus, making them comparable across country and time – document significant learning losses (Jakubowski et al 2023; Kennedy and Strietholt 2023). International reading scores declined an average of 2