rafmacalaba/datause-encoder-data huggingface dataset:
|**R&D, dummy**|324413|0.1|0.3|0.0|0.0|0.0|1.0|
|---|---|---|---|---|---|---|---|
|**Fixed invest**|242997|8.9|1.8|-4.5|7.8|10.2|17.8|
|**Employment (log**<br>**number of full-time**<br>**workers)**|324413|3.4|1.2|1.6|2.5|4.2|10.3|
|**Firm age**|324413|13.9|8.2|0.0|8.0|17.0|100.0|
|**Human capital**|324413|10.2|3.2|0.0|9.0|12.0|100.0|
|**Part of a group, dummy**|324413|0.1|0.3|0.0|0.0|0.0|1.0|
|**FDI**|324413|0.1|0.2|0.0|0.0|0.0|1.0|
|**Diversification index**|324413|93.2|13.6|3.0|90.0|100.0|100.0|
|**Manager experience**|324413|15.8|7.4|1.0|10.0|20.0|35.0|
|**Cooperation**|324413|46.8|16.3|0.0|45.9|54.9|100.0|
|**Large city**|324413|0.9|0.3|0.0|1.0|1.0|1.0|
|**Licenses**|324413|0.1|0.3|0.0|0.0|0.0|1.0|
|**Competitors 1**|324413|0.0|0.1|0.0|0.0|0.0|1.0|
|**Competitors 2**|324413|0.0|0.0|0.0|0.0|0.0|1.0|
|**Competitors 3**|324413|0.1|0.3|0.0|0.0|0.0|1.0|
|**Competitors 4**|324413|0.9|0.3|0.0|1.0|1.0|1.0|
|**International market**|324413|7.9|5.7|0.0|4.6|10.0|75.0|
|**Materials**|302761|8.9|1.6|-7.3|8.0|9.9|16.0|

# 3. Empirical results

The CDM model allows us to explore the relationships between innovation efforts, innovation outputs, and productivity while dealing with issues related to selectivity bias (only a few firms report positive investment in R&D at any given time) and endogeneity (due to measurement error or unobservable factors that affect both productivity and the choice of inputs/innovation inputs). This approach has been widely used in the literature (Lööf et al., 2017) as it allows to unpack the relationship between innovation input and productivity by considering the innovation output.

Our work is closely related to Crespi et al. (2016). Both studies rely on data from World Bank Enterprise Survey: ours focuses on East Asia, while theirs on Latin America.

## 3.1 What are the determinants of innovation investments?

The first stage of the CDM model seeks to estimate how firms decide whether and how much to invest in R&D. The firm’s innovative effort is assumed to be a latent variable that could be (imperfectly) observed when firms report positive R&D expenditures. We thus apply a generalized Tobit model estimated by maximum likelihood, considering the following potential determinants:

- **Internal capabilities.** Proxies include (i) the firm age which captures the tacit knowledge accumulated through experience and learning by doing; (ii) human capital which reflects the cognitive skills needed to absorb new knowledge and develop new technologies (Acemoglu et al., 2006); (iii) a group dummy which identifies whether the firm is part of a large group or a subsidiary of a multinational which in turn may imply easier access to more sophisticated knowledge (Girma and Gorg, 2007) and/or human capital (Kumar and Aggarwal, 2005); (iv) FDI; (v) sales diversification is an indicator of the scope of the firm’s productive capabilities and the extent to which the firm’s knowledge base is focused on narrow/specialized sectors or can apply to different sectors; (vi) manager experience is associated with

6
of Table I, respectively.

# TABLE I

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|∆ ̄_y_|-0.206***|-0.213***|-0.00296|||||
||(0.007)|(0.006)|(0.020)|||||
|∆_σ_||0.133***|-0.222***|||||
|||(0.013)|(0.042)|||||
|∆ ̄_y × σ_|||-0.199***<br>(0.025)|||||
|∆ ̄_y ×_ <sup>_z_</sup><br> ̄_y_|||-0.135***<br>(0.009)|||||
|∆_σ × σ_|||0.544***<br>(0.062)|||||
|∆_σ ×_ <sup>_z_</sup><br> ̄_y_|||-0.0598*<br>(0.035)|||||
|∆ ̄_y × κ_ ̄_y_||||0.942***<br>(0.009)||1||
|∆_σ × κσ_||||0.750***<br>(0.022)||1||
|Growth Component|||||1.081***<br>(0.007)||1|
|Distribution Component|||||0.882***<br>(0.015)||1|
|Constant|0.103|0.156*|0.203***|0.165***|0.0129|0|0|
||(0.087)|(0.083)|(0.075)|(0.036)|(0.024)|||
|E[_dH_]|-0.826|-0.794|-0.794|-0.794|-0.794|-0.794|-0.794|
|E[∆ ̄_y_]|4.499|4.430|4.430|4.430|4.430|4.430|4.430|
|E[∆_σ_]||-0.0626|-0.0626|-0.0626|-0.0626|-0.0626|-0.0626|
|Observations|1310|1307|1307|1307|1307|1307|1307|
|R-squared|0.425|0.463|0.584|0.893|0.951|0.876|0.942|

_Note:_ *,**,*** denote significance at the 1,5, and 10 percent level, respectively. Standard errors presented in parentheses. Dependent variable: percentage point change in the headcount poverty rate in country _i_ from year _t − n_ to _t_ , where the headcount poverty rate is calculated as the percent of the population under the $1.90 poverty line.

It is clear from looking at the _R_<sup>2</sup> for each specification, that Equation (10) and (13) (Columns 4 and 5, respectively) do a far superior job at explaining changes in poverty in our sample. Moreover, it appears that our log-normality assumption provides us with a very reasonable approximation to the arithmetic identity that exists between changes in 13
- 11 -

Table 2. Performance of Phnom Penh Water Supply Authority

||1993|2003|
|---|---|---|
|Customers|26,881|105,771|
|Billing rate (%)|28|83|
|Collection rate (%)|50|101|
|Unaccounted for water (%)|72|17|
|Total revenue (Billion riels)|0.7|38.6|
|Totaloperating expense (Billion riels)|1.4|10.1|

Source: ADB _et al_ . (2005).

There are several shortcomings in the above property rights models. First, ownership and provision are designed to be tied together. This may not hold in reality, particularly in the recent infrastructure sector where there are a variety of arrangements for private sector participation. This problem can be addressed by the infrastructure bundling models in the following section. Second, the models do not take into account the government’s informational losses from contracting out to the outside (e.g., Schmidt, 1996).<sup>7</sup> Third, it is assumed that there is no rent-seeking lobbyist and self-interested politician or government (e.g., Boyco _et al_ ., 1996). Fourth, there may be a number of potential contractors that can undertake a public contract in question. The property rights approach does not provide any guidance on how to select the best contractor. This can be answered by auction theory. Finally, since contracts are assumed to be incomplete, it is likely that the original contracts would be adjusted in due course, especially in the context of infrastructure development, which usually takes a considerably long time to accomplish. Some recent models incorporate these ex post contractual adjustments in an explicit manner.

> 7 Schmidt (1996) emphasizes the government’s informational losses from contracting. The government cares about the social benefit and can acquire a producing firm through nationalization, in which case it can directly control production. If the government already owns the production facility, the government can privatize it. Only managers can observe the unverifiable true production cost before deciding on the level of production. Therefore, if the government privatizes the firm, it loses such important information. The right to have access to inside information about the firm translates into a residual right of control. Under nationalization, however, the manager does not take into account the social benefit and do not make an effort to the optimal level. Hence, the government prefers privatization to nationalization if and only if the social welfare gain through more investment outweighs the negative effect of information losses under privatization.
# Acknowledgments

The research for this monograph was in large part funded by grant no. 674-03 of the World Bank Research Committee. We are especially indebted to Guy Pfeffermann for conceiving the idea and assisting us in launching and implementing the project. Without his guidanice and supervision the study would not have been realized. We are also grateful to a number of other individ-ials and organizations that provided invaluable help during the research project: Mark Leiserson contributed enormously to the project'r formation, and the World Bank Senegal Division, the Industrial S.rategy and Policy Division, and the World Bank Resident Mission in Dakar assisted in our fieldworlk We are particularly grateful to Kemal Dervis, Christian Duvigneau, Nicolas Gorestani Frans Kaps, Christopher Redfern, and Eugene Scanteie.

We extend special thanks to Mine. Thiongane, the Director of Statistics at the Ministry of Finance in Senegal, for providing us with the data; to Prospere Youm, the Director of Forecasting at the Ministry of Finance, for providing data on the effective rate of protection; and to Alhousseney Sy, the Chief of the Census Bureau, for collaborating with us on the sample survey. The trade union representatives and numerous enterprise managers who provided us with their- perspectives have made valuable contnbutions to qpruinderstanding of Senegal's industrial sector and its labor market institutions.

Finally, we thank Bela Balassa, Guy Standing, Gerald Starr, Erik Thorbecke, and the participants at the World Bank seminar for their many helpful comments, Sathi Pandian and Seng-Lee Wong for their excellent computational assistance, and Lauree Graham for invaluable help in the preparation of the manuscript. All remaining errors are our own.

## _Kadwrine Temrel Jan Sveinar_
<!-- Start of picture text --> Figure B7 Returns to Urban Residence<br>.40<br>.36<br>.32<br>.28<br>2007<br>.24<br>.20<br>.16 2001<br>.12<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile<br>Coefficient<br><!-- End of picture text --> 52
# **Table 4.1 2000 GDP and Employment by Enterprise Type (%)**

||**GDP**|**Employment**|
|---|---|---|
|Public Sector|40.6|8.8|
|_- State Owned Enterprises (SOEs)_|_31.6_|_5.2_|
|_- State Administration_|_9_|_3.6_|
|Collectives|8.5|0.6|
|Domestic Private Sector|40.2|90.0|
|_- Households & Farmers_|_32.6_|_87.9_|
|_* Non-Agricultural HH_||_8.4_|
|**_- Private Companies_**|**_7.6_**|**_2.1_**|
|Foreign-Invested Companies|10.7|0.6|
|**Total**|**100.0**|**100.0**|

Source: GSO, 2001 21. Average firm size varies based on enterprise form. The smallest form sole proprietorship employs on average just over 10 employees per firm. Limited liability companies average 43 employees and shareholding companies are the biggest with an average of 170 workers. Firms in manufacturing are relatively larger, with average employment per shareholding company reaching 269 workers. Numbers on partnerships are not available.<sup>18</sup>

**Table 4.2 Average Firm Size by Enterprise Form**

||**%of Total Firms**|**Ave. Employees,**<br>**Private Firms**|**Ave. Employees,**<br>**Private Manufacturers**|
|---|---|---|---|
|Sole Proprietorship|61|10|23|
|Limited Liability|37|43|132|
|Shareholding|2|170|269|

Source: GSO, 2001 22. It is clearer with employment than it is with GDP that official statistics fail to capture the full private sector picture. Surveyed manufacturing firms had a mean level of employment of 144 full-time workers per firm—26 percent higher than the GSO average of 114 for the same six provinces.<sup>19</sup> Applying this mark-up to the entire country, domestic formal private sector employment rises to over a million workers and 2.6 percent of total employment. The survey results indicate that the private sector also creates temporary and seasonal jobs that total an additional 20 percent on top of this estimate, allowing for a rough estimate of three percent for the actual private sector share of total employment.

23. Employment in the formal private sector is underestimated because private companies: i) under-report employment to officials to avoid various fees including worker and health insurance; ii) rely on seasonal or temporary workers, who often are officially categorized under agricultural employment; and iii) outsource work to households because of fluctuations in demand and in order to avoid official labor fees. One of the means by which employers appear to be able to avoid reporting some workers

> 18 Only five partnerships were set up in the first two years of the Enterprise Law.  The survey made a specific effort to include Partnerships, but still only succeeded in capturing one.

> 19 Given that interviews were conducted by people attached to the Ministry of Labor, there is reason to believe that employment numbers from the survey are still underestimated.

11
36 supervision missions spend relatively less time in the capitol city and on "hardware* questions and more time in the field on "softwareo issues.

112. The need for intensive supervision and technical expertise might be met in one or a combination of the following ways: (a) using more specialized, technical personnel -- usually local consultants -- on missions, with specific responsibility for particular technical components, (b) placing more Bank personnel in the field or increasing the responsibility of field offices for supervision, (c) building monitoring into the projects and providing requisite training to local personnel, and (d) setting project sulpervision coefficients at an appropriate level for PHN projects -- perhaps 15 staff weeks -- based on the need for intensive oversight.

113. PHN and structural adjustment. Because of the health and nutrition consequences common to many adjustment efforts -- decreased spending for social services, for example, and the dismantling of consumer food subsidies -- the health and nutrition sectors should figure more prominently in policy-based lending operations to protect vulnerable groups affected by the adjustment process. This will require careful sector work. We must also encourage greater collaboration among personnel in the Country Departments responsible for structural adjustment projects, personnel responsible for PHN lending, and technical PHN personnel who can help develop components to address specific health and nutrition needs within the structural adjustment process.

# B. Sector and PolicX and Research Work

# Sector work

114. PHN sector work remains critical to Bank dialogue with governments and to project identification and design. Paradoxically, although more staff time has been used in the preparation of reports, fewer are being processed to green cover and beyond -- reportedly because of inadequate staff time to devote to sector work and the demands of lending.

115. Clearly, sector work is crucial to sound lending operations. To safeguard its role, prevent a decline in the number of studies being completed and supplement some of the skills lacking in-house, the Bank should increase country contributions to sector work. This would increase the impact of sector work and reduce the burden on staff time but different staff skills and input may be needed to manage the collaboration. Our experience with such efforts -- including two sector studies completed this year in Brazil (to which local consultants contributed substantially) and Chile (where the entire study was done by a local consultant) -- offers a possible model for future collaboration.
24 Figure 19 <!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>LKSTPRPC<br>0.08<br>0.06<br>0.04<br> LGDPPC<br>0.02<br>0.00<br>-0.02<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br><!-- End of picture text --> Another interesting result refers to the impact of a tax shock. A permanent increase of the tax ratio (of 1.5 percent of GDP) is associated with a lower GDP per capita of close to 1 percent (Figure 20), similar to the ARDL result. The same shock is associated with a lower private capital stock (Figure 21) Figure 20 <!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>TOTTAXGD<br>-0.006<br>-0.008<br>-0.010 LGDPPC<br>-0.012<br>-0.014<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br>Figure 21<br><!-- End of picture text --> <!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>TOTTAXGD<br>0.002<br>0.001<br>0.000<br>-0.001<br> LKSTPRPC<br>-0.002<br>-0.003<br>-0.004<br>-0.005<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br><!-- End of picture text --> A shock that leads to a permanent rise of government consumption expenditure (of 7 percent in real terms) is associated with a fall in per capita GDP (Figure 22). This shock is associated with a higher tax ratio (Figure 23), lower private capital stock (Fig 24) and lower public capital stock as well (Figure 25).
correct for biases due to non-random selection of respondents within the household (Brubaker et al, 2021; Kugler et al., 2021). Therefore, while household level outcomes such as self-reported income declines are plausibly representative, individual level outcomes such as employment mainly apply to household heads. See World Bank (2023) for more information on the HFPS.

The outcome variables taken from the HFPS are listed in Table 1 and include income change, employment, food security, and prevention measures such as social distancing and quitting schools. Work stoppage is measured in the first wave of the phone survey in each country, and is constructed based on the questions “Was the respondent working at the time the survey was conducted?” and “Was the respondent working before the pandemic?”. In particular, work stoppage is set equal to 1 if a respondent reported that they were working prior to the pandemic but not currently working at the time of the survey, 0 if they were working prior to the pandemic and were currently working at the time of the survey, and missing if they were not working prior to the pandemic.

Income change is measured by asking respondents whether their household’s income has increased, decreased, or stayed the same since the start of the pandemic (first wave only) or since the last survey wave (all subsequent waves). When looking at the evolution of the crisis over time, we combined these two questions into a single measure, in order to include all waves. Income change is further asked separately for each income source including farm, nonfarm, wages, and pensions. The question for total income change is “Has your Total Household Income changed since the pandemic started?” There are 5 categories of answers: 1 = Increased, 2 = Stayed the same, 3 = Decreased, 4 = Not received, 9 = Do not know. We create a variable on income loss, where “Decreased” is recoded as 1, “Do not know” and “Not received” as missing, and “Stayed the same” or “Increased” as 0. We also create a variable on income gain, where “Increased” is recoded as 1, “Do not know” and “Not received” as missing, and “Stayed the same” or “Decreased” as 0. The creation of variables for different income sources, farm income, non-farm income and wage income follows the same rule. Not all income questions were asked in each survey, so the sample of available regions varies by indicator.

Food security is another key metric of household well-being, which is measured using a series of questions on household constraints accessing food. The analysis uses two questions: (1) “in 7
# Acknowledgement and Disclaimer

Authors are thankful to all participants of the joint seminar organized by the World Bank and the Ministry of Economic Development and Trade of the Republic of Tajikistan in Dushanbe in June 2013 as well as participants of the seminar in Washington, DC in October 2013. We are particularly thankful to Marsha McGraw Olive, Ambar Narayan, Carolina Sanchez-Paramo and Luis-Felipe Lopez-Calva for their useful comments and suggestions. The authors also thank Kimberly Bolch for editing the report. The first draft of paper was circulated in December, 2013.

All PPP figures used in this paper is based in the 2008 ICP round. All Poverty rates based on the dollarper-day measures are also based on the PPP conversations factors from the 2008 ICP round. As ICP estimates are revised, they are of five data sources that are used to compute the incidence of poverty using the “dollar a day” poverty line methodology. The use of the 2011 PPPs in the calculation of global or regional poverty estimates requires additional analysis. One issue that warrants consideration is that people who live at or below the poverty line likely have different patterns of consumption than the total population (which is the basis for the PPPs). The representation of price data in rural areas (where many of the poor live), from local markets (where the poor shop) and of poorer quality goods also warrants closer examination. DEC economists engaged in the global poverty calculations will obtain detailed data and metadata from the ICP _Global Office_ , regional coordinating agencies, and participating economies in order to assess whether the new PPP estimates can be used for poverty measurement purposes, or whether adjustments will be needed. These data will take some time to acquire and assess and so DEC will not incorporate the new PPPs into the poverty measure calculations immediately. This is consistent with past practice: when the 2005 PPPs were released in 2007, DEC analyzed the PPPs in detail and made additional adjustments before incorporating them into the poverty measures two years later.” The support of UK-DFID is gratefully acknowledged.

2
unbanked—households in the middle and lower parts of the income distribution. Thus, banks acting as remittance paying agents are well-positioned to offer other services to unbanked households receiving remittances. Fourth, processing remittance flows provides banks with information on the income of recipient households. This information may make banks better able to extend loans to otherwise opaque borrowers. On the other hand, since remittances might help relax households’ financing constraints, the demand for and the overall level of credit might fall as remittances increase. Regardless of remittance recipients’ demand for credit, overall credit levels might still increase in remittance receiving areas if banks are able to finance previously unfunded or underfunded projects as a result of the increase in liquidity due to the intermediation of remittances.<sup>3</sup> Mexico is an interesting case to study the link between remittances and banking sector breadth and depth because the country is among the top recipients of remittances worldwide, with more than $27 billion in flows in 2007. Remittances also flow disproportionately to rural and semi-urban areas in Mexico, which have been traditionally unbanked. Rural households are more than three times as likely to receive remittances: 12.3 percent versus 3.6 percent of households in larger communities. Furthermore, remittance flows are geographically concentrated within the Mexican territory. Of the 1.4 million Mexican households receiving remittances, almost half (680,000) are located in the Central Western states of Aguascalientes, Colima, Guanajuato, Jalisco, Michoacán, Nayarit, San Luis Potosí, and Zacatecas. In those states, approximately 13% of all households receive remittances, more than twice the national average.4 The geographical concentration of remittance flows, which is tied to early 20th century migration patterns, is important for identifying the impact of remittances on the banking sector. Also, while most remittances are channeled through money transfer companies such as Western Union or Moneygram, Orozco (2004) reports that 55 percent of remittance collection points in Mexico are commercial bank branches.<sup>5</sup> This implies that banks play a key role in the distribution of remittances and are well-positioned to offer other financial services to individuals that visit banks to collect remittances. Finally, while remittance transaction costs have declined

> 3 In other words, even if remittance recipients do not have a need to borrow, the increase in loanable funds from the intermediation of remittance might allow banks to increase credit to other households.

> 4 Calculations based on _Encuesta Nacional Ingreso-Gasto de los Hogares_ , 2004. We are indebted to Pavel Luengas at the Inter-American Development Bank for help in processing this information.

> 5 Electronic transfers accounted for almost 93 percent of all remittance transactions in 2006, up from 51 percent in 1995.

3