rafmacalaba/datause-encoder-data huggingface dataset:
that the most important determinants of the speed at which a country adopts a broad range of technologies are its human capital endowment, government type, degree of openness to trade, and its adoption of predecessor technologies. More recently, Parente and Prescott (2005) argue that countries have widely divergent living standards not because they have access to different stocks of knowledge, but rather because they differ in the severity of the constraints imposed on the technology choices of their citizenry. Drawing on this literature, a recent study by the World Bank (2008) concludes that the diffusion of international knowledge and technology to a developing country depends on two main factors: (i) the extent of its access to international knowledge and technology (via trade, FDI, contacts with its diaspora, and other communications channels) and (ii) its ability to absorb and adapt international knowledge and technology. This absorptive capacity is determined by the technological literacy and skills of the country’s labor force, the availability of financing for innovative firms, sound governance, and a good business environment. Moreover, given the various market imperfections associated with technology and knowledge diffusion, including increasing returns to scale and technological spillovers, that study also finds scope for carefully targeted proactive policies aimed specifically and enhancing technology transfer.

Empirical evidence on the relative impact of various determinants of technology and knowledge diffusion in developing countries at the micro level is still scarce, in part due to the limited availability of data. This paper aims to contribute to filling this gap by investigating how the “investment climate”, namely the incentive regime faced by a firm (related to competition, ownership, and governance), access to complementary inputs (skills, credit, and infrastructure), and access to international knowledge affect firms’ technology choices. Our analysis exploits a unique dataset - the Business Enterprise and Economic Performance Surveys (BEEPS) of firms in the Eastern Europe and Central Asia (ECA) region in 2002 and 2005 - which combines information on the investment climate with measures of technological update at the firm level. Interestingly, according to the 2005 BEEPS data, three quarters of the firms in the ECA region consider the acquisition of machinery and equipment to be the most relevant source of technological update. Thus, our proxies for technological update are ISO certification and web-use by firms.

Our results show that access to complementary inputs - skilled labor, managerial capacity, R&D, finance and to a lesser extent, good infrastructure - are positively associated with technology adoption. Results for the relationship between market incentives and ISO certification or web-use are more nuanced: (i) while consumer pressure is related to more technology use, competitor pressure is not, (ii) privatized firms exhibit better technology adoption outcomes only in presence of a clear private owner

- 2 -
Our preferred model of only using growth in real GDP per capita does not suffer from the first issue but it does suffer from the last issue. The nowcasted growth rates have not yet been realized and are likely to deviate from the growth rates that eventually will be estimated by national authorities. Hence, they will likely be less predictive of welfare changes observed in household surveys. Since our preferred method for nearcasting, in that sense, only suffers from one of the two extra challenges that the other models suffer from when used for nowcasting, we think its advantages stand out even more when nowcasting. In fact, when running all our methods earlier in the calendar year (recall that earlier in the calendar year the informational space available for nearcasting approximates that of nowcasting), just using GDP growth becomes relatively more advantageous.

Using growth in real GDP per capita also comes with risks. If it is the case that nowcasted estimates of GDP are much more off target than nowcasted estimates of other variables, then it is possible that using only GDP growth will be relatively less attractive for nowcasting than nearcasting. Some evidence suggests that nowcasted growth rates by IMF might be too optimistic (Sandefur and Subramanian, 2020).

Another concern arises if GDP numbers are subject to quality concerns and are not deemed credible. If this is the case, then they may likewise be less relevant for both nearcasting and nowcasting. The fact that using growth rates in real GDP per capita worked well when trained on historical data suggests that historically this has not been frequently the case (or at least not more so than quality concerns with other indicators). Nonetheless, Figure 8 did show a large discrepancy for India when using growth in real GDP per capita and using more explanatory variables. Though this may simply be because true growth in real GDP per capita is less connected with household consumption in India than in other countries, evidence suggests that GDP growth rates in India in recent years are non-credibly high (Subramanian, 2019). If this is indeed the case, then this serves as another argument against only relying on growth in real GDP per capita.

Despite of these arguments against just using national accounts we think the arguments in favor – particularly the relatively high accuracy, simplicity, ability to work for both nearcasting and nowcasting, ability to work with any poverty line, and ability to avoid large shifts when moving from survey estimates to extrapolations – create a compelling case.

25
# <u>References:</u>

Allen, Franklin, Jun Qian, and Meijaun Quian, 2005. Law, Finance and Economic Growth in China. _Journal of Financial Economics_ 77, 57-116.

Bates, Thomas, Kathleen Kahle, and Rene Stulz, 2008, Why do U.S. Firms Hold so Much More Cash than They Used to?, working paper.

Beck, Thorsten , Asli Demirguc-Kunt and Vojislav Maksimovic 2006. Financial and Legal Constraints to Growth: Does Firm Size Matter. _Journal of Finance_ 1, 137-177.

Berkowitz Jeremy and Michelle J. White, 2004. Bankruptcy and Small Firms’ Access to Credit. _Rand Journal of Economics_ 35, 69-84.

Biais, Bruno and Christian Gollier, 1997. Trade Credit and Credit Rationing. _Review of Financial Studies_ 10, 903-937.

Boissay, Frederic and Reint Gropp, 2007. Trade Credit Defaults and Liquidity Provision by Firms, European Central Bank Working Paper n. 753.

Brennan, Michael, Vojislav Maksimovic and Josef Zechner, 1988. Vendor Financing. _Journal of Finance_ 43, 1127-1141.

Burkart, Mike and Tore Ellingsen, 2004. In-Kind Finance: A Theory of Trade Credit. _American Economic Review_ 94, 569-590.

Burkart, Mike, Tore Ellingsen, Mariassunta Giannetti, 2006. What You Sell is What You Lend? Explaining Trade Credit Contracts. ECGI Finance Research Paper No. 71/2005.

Calomiris, Charles., Charles. Himmelberg, and Paul Wachtel, 1995. Commercial Paper, Corporate Finance and the Business Cycle: A Microeconomic Perspective, CarnegieRochester Series on Public Policy 42, 203-50.

Cull, Robert, Lixin Colin Xu, and Tian Zhu, 2007. Formal Finance and Trade Credit During China’s Transition. _Journal of Financial Intermediation,_ forthcoming.

Cunat, Vicente, 2006. “Trade Credit: Suppliers and Debt Collectors as Insurance Providers,” _Review of Financial Studies_ , 20.

Demirguc-Kunt, Asli, and Vojislav Maksimovic, 1998. Law, Finance, and Firm Growth. _Journal of Finance_ 6, 2107-2137.

Demirguc-Kunt, Asli, and Vojislav Maksimovic, 1999. Institutions, Financial Markets, and Firms Debt Maturity. _Journal of Financial Economics_ 54, 295-336.

33
excluded on the basis that it was too urban and distinct from the rest of the country, and Region 5, because it was used extensively to pilot the WSD prior to the large randomized experiment. Of the 276 schools, one school was excluded from the sample because it was very small and had only a few students in grades 1 and 2. Another school was closed but still appeared on the official list of schools. Figure 3 summarizes the sampling procedure.

Of the 273 remaining schools, 90 schools were randomly assigned to the WSD treatment, 94 schools to the Grant-only treatment, and 89 schools served as the control group. The schools were clustered in groups of 2 or 3 schools on the basis of geographic proximity to limit contamination while allowing useful exchange and cooperation between nearby schools.<sup>7</sup> Because this represents the universe of schools meeting the inclusion criteria, rather than a sample, clustering of groups of schools is unnecessary in the subsequent analysis.<sup>8</sup> The randomization was further stratified by school size and accessibility.<sup>9</sup> Each group proved to be similar at baseline, as discussed in detail in Section 5.1. As all schools remained in the study between baseline and endline, there is zero attrition.

# **4. Data**

The Gambia Bureau of Statistics, under the supervision of the research team, collected the data for this study. The baseline data were collected in 2008 at the onset of the study, the first round of follow-up data were collected in 2009, the second round of follow-up data were collected in 2010, and the end-line data were collected in 2011 (Table 2).

In the 2009 follow-up, data were collected in the WSD and Control schools only. The Grantonly schools were not visited at that time because grant disbursement was delayed in one region during the first year, and many schools that had received their grant had not yet used it.<sup>10</sup> This problem of slow disbursement of education grants by local committees was also observed in Kenya (Conn et al. 2008).

At each round, teams of enumerators arrived unannounced (in order to avoid strategic attendance by teachers and students) at each school and collected information about the school and the students, conducted classroom observation, and gave a literacy and 7 At the regional level, schools that are close to one another are assigned a “cluster monitor” who serves as a liaison between the regional directorate and those schools. The cluster monitor is encouraged to promote good practices among the schools she is assigned to.

> 8 Furthermore, the intracluster correlation for test scores and absenteeism is much higher (5580% higher) at the school level than at the level of school clusters.

9 The Ministry defines accessibility through “hardship status”. Schools that are most remote receive an allowance from the Government, as discussed in Pugatch & Schroeder (2014).

> 10 This information was obtained from the regional directorates who were the key intermediaries for the grant disbursement process.

9
# **Table 5. Instrumental Variable Estimation of Investor Protection and the Effect of Governance on Short-Term Debt**

This table presents the estimates of the following regression.

_stditjk = β0 + β1 x govit + β3 x govit x investorjt + β2 x investorjt + Ω x controlsi,t-1 + αj +_ λt _+ θk + εitjk._ We use two-stage least square (2SLS) approach to instrument _investorjt_ by common law dummy variable which is equal to 1 if the country has a common law and 0 otherwise. The dependent variable is short-term debt and leverage is instrumented by lagged short-term debt. Each column reports the results for a different governance measure. The definition of the variables is in the Appendix. We winsorize all variables at the 1% level except the governance variables. We use one-year lagged values of firm and country-level control variables. All specifications include country, year and industry fixed effects. z-values from 2SLS approach are reported in the parenthesis. Robust standard errors are estimated by clustering at the firm level.<sup>***</sup> ,<sup>**</sup> , and<sup>*</sup> indicate significance at 1%, 5%, and 10% levels, <u>respectively.</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||D|ependent variable|:_std_||
|governance measure(gov):|_gov44_|_gov_board_|_gov_indep_|_size_eff_|_ceo_chair_|
|gov|0.294|0.958|0.544*|0.446***|-0.106|
||(0.336)|(1.302)|(1.681)|(3.303)|(-1.245)|
|gov x_investor_|-0.175|-0.226**|-0.128***|-0.067***|0.009|
||(-1.501)|(-2.310)|(-2.884)|(-3.741)|(0.829)|
|_investor_|0.071|0.088|0.036|0.124**|-0.147***|
||(0.930)|(1.416)|(0.854)|(2.370)|(-2.691)|
|_leverage_|-4.820***|-4.799***|-4.778***|-4.742***|-4.738***|
||(-17.134)|(-17.219)|(-17.296)|(-17.251)|(-16.305)|
|_size_|0.041***|0.044***|0.037***|0.033***|0.020***|
||(4.926)|(5.232)|(4.612)|(4.183)|(2.621)|
|_asset_tangibility_|0.454***|0.450***|0.443***|0.452***|0.403***|
||(5.173)|(5.155)|(5.091)|(5.213)|(4.487)|
|_asset_turnover_|0.015|0.015|0.011|0.013|0.016|
||(0.746)|(0.763)|(0.581)|(0.668)|(0.805)|
|_roa_|-1.185***|-1.179***|-1.173***|-1.167***|-1.145***|
||(-13.359)|(-13.397)|(-13.394)|(-13.400)|(-12.677)|
|_mb_|0.034***|0.032***|0.031***|0.030***|0.027**|
||(2.982)|(2.869)|(2.739)|(2.639)|(2.352)|
|_gdp_growth_|0.002|0.004|0.001|0.009|0.010|
||(0.205)|(0.448)|(0.149)|(1.069)|(1.167)|
|_inflation_|0.035***|0.039***|0.039***|0.032***|-0.009|
||(2.630)|(3.073)|(3.123)|(2.659)|(-0.688)|
|_deposits_|0.005***|0.005***|0.005***|0.005***|0.005***|
||(3.048)|(3.104)|(3.292)|(2.889)|(3.329)|
|_bonds_|-0.002|-0.002|-0.002|-0.001|-0.003|
||(-0.928)|(-0.772)|(-0.996)|(-0.305)|(-1.389)|
|_stock_market_cap_|-0.004***|-0.003***|-0.004***|-0.003***|0.002*|
||(-3.996)|(-3.644)|(-4.300)|(-3.170)|(1.771)|
|_stock_market_turnover_|-0.000|-0.000|-0.000|-0.001|0.001|
||(-0.131)|(-0.407)|(-0.500)|(-1.075)|(0.948)|
|Constant|<br>1.929***|<br>1.458**|<br>1.922***|<br>0.841*|<br>2.408***|
||(2.692)|(2.493)|(4.213)|(1.674)|(4.463)|
|Observations|24,409|24,409|24,409|24,388|21,715|
|R-squared|-7.407|-7.335|-7.270|-7.196|-7.361|
|Industry, Country and Year FE|Yes|Yes|Yes|Yes|Yes|

25
# Quitting and Labor Turnover:

# Microeconomic Evidence and Macroeconomic Consequences*

Tom Krebs** Department of Economics University of Illinois, Urbana-Champaign William F. Maloney*** The World Bank _Keywords:_ Efficiency Wages, Employment, Labor Turnover, Macroeconomic Policy, Self-Employment..

_JEL Classification Numbers:_ E24, E60, J41, J63.

We would like to thank Omar Arias, Patricio Aroca, Roger Koenker, Pravin Krishna, Guillermo Perry and seminar participants at the University of Illinois for helpful comments. We also thank the Mexican National Institute of Statistics, Geography, and Information (INEGI) for the use of the data. INEGI is in no way responsible for the incorrect manipulation of the data or erroneous conclusions drawn from it.

Currently visiting Brown University, Department of Economics, Brown University, Robinson Hall 302D, 64 Waterman Street, Providence, RI 02912, tkrebs@uiuc.edu.

Poverty Reduction and Economic Management Division, Latin America and the Caribbean, The World Bank,1818 H St. N.W., Washington, D.C. 20433, wmaloney@worldbank.org.
HOUSING MARKETS AND RENT CONTROL IN GHANA 39 his employer, build a large house, let it to the university for thousands of cedis per month, and continue to occupy subsidi university housing. This is now forbidden (by the Public Servants (Control of Provision of Bungalows) Decree, 1979) but undoubtedly existed until then.

# Cost of Housing built by the Government

There has inevitably been substantial increases in the cost of houses built by government agencies. Data are only available from a few documents on a less than regular periodicity. In 1948 when the Asawasi estate was built using stabilized mud bricks, single roomed laborer's quarters with shared utilities cost £140 (C280), while the two roomed self-contained cottages cost £285 (C570) (Gold Coast, 1948). At that time minimum wage was about C7 per month. Ofei (1975) reported that similar houses in North Suntresu were being sold to their tenants for C700 and C1700 respectively (when rents were C6.5 per room per month in the private sector and minimum wage was C25).

In 1967, when the minimum wage was C17.5 per month, the government- built one bedroom house cost C1,600, the two bedroom house cost C6,000 (Nierstrasz and Hunnik, 1967). Table 2.6 shows how house prices in the government sector rose from 1974 to 1980. Thus, in 1980, the cheapest government-built house would cost 2,500 months' (208 years') rent for one room or 167 months' (14 years') minimum wage. Unless the prospective owner was committed to sub-letting for many times the legal rent, there could be little rationality in exchanging renting for homeownership under such circumstances.

Table 2.6: Prices of Selected S.H.C. House Types 1974 and 1980.

|Price|Mornths'Minimum|Wage|
|---|---|---|
|House Type<br>1974<br>1980|1974|1980|
|**SH.1**<br>5,500<br>50,000|110|170|
|SH.2<br>7,400<br>59,000|150|500|
|**SH.3**<br>7,700<br>74,000|150|250|
|SH.4<br>14,600<br>103,000|290|340|
|SH.5<br>18,300<br>146,000|370|490|
|--------------------------------------------------------|||
|Sources: 1974, State Housing Corpo<br>1980, Kwakye-Safo (1981)|ration (1975),<br>quoting SHC reco|rds.|

# Recent Changes

In the 1980s, there has been a hiatus in government activity in the housing field while pressing structural issues were engaged followed by a renewed emphasis on what is seen to be a serious housing problem. Except for the tenfold increase in the nominal level of rentals in January, 1986, which was quickly eroded in real terms, little attention has been paid to encouraging rental housing. The draft Housing Plan concentrates heavily on encouraging the building of new single household dwellings. The landlords who are willing to build rooms in compounds (or their equivalent) have been largely ignored except for vilification by the press after reports of evictions.

It cannot be doubted that, since our data collection in 1986, at a time when landlords appear to have been happy with the rew rental leve'ls (personal communication, Kumasi Houseowners Association), Lents have crept up and advances
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22
<!-- Start of picture text --> Model Data 95% CI Model Data 95% CI<br>300 0.4<br>0.35<br>250<br>0.3<br>200<br>0.25<br>150 0.2<br>0.15<br>100<br>0.1<br>50<br>0.05<br>0 0<br>06-07 07-08 08-09 09-10 10-11 11-12 2006 2007 2008 2009 2010 2011 2012<br>Number of Attacks Success Rate<br>(a) Total number of attacks (b) Success rate<br>Model Data 95% CI Model Data 95% CI<br>5 4<br>3.5<br>4<br>3<br>2.5<br>3<br>2<br>2<br>1.5<br>1<br>1<br>0.5<br>0 0<br>2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012<br>Average Size Average Conditional Size<br>(c) Size of attacked ships (d) Size of hijacked ships<br>Model Data 95% CI Model Data 95% CI<br>6 0.7<br>5 0.6<br>4 0.5<br>3 0.4<br>2 0.3<br>1 0.2<br>0 0.1<br>-1 0<br>2006 2007 2008 2009 2010 2006 2007 2008 2009 2010<br>Average Ransom Average Delay<br>(e) Ransoms (f) Length of captivity<br><!-- End of picture text --> Figure 6: Model fit of first moments Note: The figures plot the average number of attacks, success rates, size of attacked and hijacked ships, ransoms and length of captivity in the data v.s. the model simulated at the benchmark parameters listed in Table 1. The confidence intervals are 1-standard-deviation bands. Data sources: IMB (2012), UNODC-WB, and authors’ calculations.

27
Song (2011), the CO2 intensity of production declined by nearly 60% between 1985 and 2008, the last year for which we have data (figure 9)<sup>96</sup> . This resulted in CO2 savings of about 170 million metric tons in 2008 (figure 10). Nearly all of the savings came from closing outdated facilities (77%) and materials substitution<sup>97</sup> (27%) (Rock and Song 2013).

<!-- Start of picture text --> Figure 9<br>China<br>CO2 Intentsity of Pulp and Paper<br>(Metric Tons of CO2 per Metric Ton of Paper)<br>4.0<br>3.6<br>3.2<br>2.8<br>2.4<br>2.0<br>1.6<br>1.2<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>Source: Rock and Song (2011)<br>Figure 10<br>China<br>CO2 Emissions from Pulp and Paper<br>(Million Metric Tons)<br>300<br>250<br>200<br>150 CO2 After Technological Upgrading<br>CO2 Business as Usual<br>100<br>50<br>0<br>86 88 90 92 94 96 98 00 02 04 06 08<br>Source: Rock and Song (2011)<br>CO2 Intensity<br>CO2 Emitted in MMT<br><!-- End of picture text --> 96 At the same time energy efficiency in paper production improved from 1.39 metric tons of coal equivalent per metric ton of paper in 1985 to .5 metric tons of coal equivalent per metric ton of paper in 2008 (Rock and Song 2013: Annex Table 2).

97 The biggest materials substitution involved used waste-paper instead of raw materials (wood, reeds, and straw) for pulping.

25