rafmacalaba/datause-encoder-data huggingface dataset:
Protection Discussion Paper Series No. 0402.

Biesebroeck, Van, J. (2005) “Firm Size Matters: Growth and Productivity Growth in African Manufacturing”, _Economic Development and Cultural Change_ , Vol. 53(3).

Biggs, T. (2002) "Is Small and Beautiful Worthy of Subsidy?", IFC Discussion Paper.

Bigsten, A & Gebreeyesus, M. (2007) “The Small, the Young and the Productive: Determinants of Manufacturing Firm Growth in Ethiopia” _Economic Development and Cultural Change_ , Vol 55(4).

Blundell, R.& Costa Dias, M. (2002) "Alternative Approaches to Evaluation in Empirical Microeconomics" CEMMAP Working Paper, CWP 10/02, Institute for Fiscal Studies Bourguignon, F., Fournier, M. & Gurgand, M. (2007) "Selection Bias Corrections Based on the Multinomial Logit Model: Monte Carlo Comparisons" _Journal of Economic Surveys_ , Vol 21, No. 1.

Card, D. (2001) "Estimating the Returns to Schooling: Evidence on Some Persistent Econometric Problems.", _Econometrica_ , Vol. 69(5).

Dar, A. & Tzannatos, Z.P (1999) “Active Labor Market Programs: A Review of the Evidence from Evaluations” World Bank Social Protection Discussion Paper No. 9901.

Fretwell, D., Benus, J. & O’Leary, C. (1999) “Evaluating the Impact of Active Labor Market Programs: Results of Cross Country Studies in Europe and Asia” World Bank Social Protection Discussion Paper No. 9915.

Global Development Solutions (2006) "Integrated Value Chain Analysis for the Housing Construction Sector in Ethiopia" Halberg, K. (2001) "A market-oriented strategy for small and medium-scale enterprises." Discussion Paper No. 40. International Finance Corporation.

HDPO (2004) "Brief Project Profile", mimeo.

Lee, J. (1983) "Generalized Econometric Models with Selectivity" _Econometrica_ , Vol. 51, No 3.

Söderbom, M., Teal, F., Wambugu, A. & Kahyarara, G. (2005) "The Dynamics of Returns to Education in Kenyan and Tanzanian Manufacturing" GPRG Discussion Paper GPRG-WPS-017. World Bank (2004) "Land and Housing in Addis", mimeo.

World Bank (2007) "The Addis Ababa Integrated Housing Program - Background Paper to a survey of firms and workers in the construction sector in Addis", mimeo.

(no author) (2004) "Addis Ababa Integrated Housing Development Program", mimeo.

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(no author) (2007) "Mugher Cement Enterprise Increases Cement Price" Addis Fortune: accessed January 5, 2007 via: http://www.docengines.com

-10-
|spending<br>evenue)<br>=0.30)<br>(10)<br>-0.003<br>(0.000)**<br>-0.004<br>(0.001)**<br>-0.012<br>(0.003)**<br>-0.088<br>(0.021)**<br>0.092<br>(0.026)**<br>58827<br>4837<br>0.005<br>(0.001)**<br>-0.016<br>(0.002)**<br>-0.002<br>(0.002)<br>-0.061<br>(0.020)**<br>0.052<br>(0.018)**<br>58726<br>4736<br>d clustered<br>form, 1997<br>* indicates<br>n 100,000s.|
|---|
|Education<br>(% of r<br>(mean<br>(9)<br>-0.004<br>(0.000)**<br>58827<br>4837<br>0.005<br>(0.001)**<br>58726<br>4736<br>arentheses, an<br>ith the pre-re<br>cates p_<_.01; <br> is measured i|
|n spending<br> capita<br>n=2.62)<br>(8)<br>0.167<br>(0.003)**<br>0.027<br>(0.021)<br>-0.247<br>(0.122)*<br>-1.212<br>(0.311)**<br>1.487<br>(0.564)**<br>58749<br>4837<br>0.372<br>(0.018)**<br>-0.274<br>(0.039)**<br>0.004<br>(0.060)<br>-0.532<br>(0.319)+<br>0.498<br>(0.354)<br>58648<br>4736<br>rrors are in p<br>d interacted w<br>ared. ** indi<br>4. Population|
|Educatio<br>per <br>(mea<br>(7)<br>0.169<br>(0.003)**<br>58749<br>4837<br>0.380<br>(0.019)**<br>58648<br>4736<br>ust standard e<br>near time tren<br>enrollment squ<br> income is 0.6|
|ucture<br> index<br>=0)<br>(6)<br>0.002<br>(0.002)<br>0.183<br>(0.019)**<br>0.103<br>(0.055)+<br>0.446<br>(0.175)*<br>-0.411<br>(0.226)+<br>56900<br>4818<br>-0.121<br>(0.017)**<br>0.367<br>(0.034)**<br>-0.040<br>(0.037)<br>0.075<br>(0.213)<br>0.156<br>(0.236)<br>56798<br>4716<br>venue. Rob<br>well as a li<br>e-primary <br>e per capita|
|Infrastr<br>quality <br>(mean<br>(5)<br>0.005<br>(0.002)**<br>56900<br>4818<br>-0.134<br>(0.019)**<br>56798<br>4716<br>g simulated re<br>ed efects, as <br>lment, and pr<br>ean of averag<br>ro Nacional.|
|Teacher<br>quality index<br>(mean=0)<br>(3)<br>(4)<br>0.014<br>0.010<br>(0.003)**<br>(0.003)**<br>0.194<br>(0.027)**<br>-0.087<br>(0.049)+<br>-0.202<br>(0.310)<br>0.581<br>(0.377)<br>51841<br>51841<br>4797<br>4797<br>-0.379<br>-0.363<br>(0.039)**<br>(0.036)**<br>0.755<br>(0.071)**<br>-0.533<br>(0.141)**<br>-1.307<br>(0.594)*<br>2.182<br>(0.743)**<br>51732<br>51732<br>4688<br>4688<br>instrument for revenue usin<br>de municipality and year fx<br> revenue, pre-primary enrol<br>ta revenue is 9.61 and the m<br>om IBGE, INEP, and Tesou|
|Pre-primary<br>enrollment<br>(mean=0.23)<br>(1)<br>(2)<br>0.002<br>0.002<br>(0.000)**<br>(0.000)**<br>0.008<br>(0.003)*<br>-0.032<br>(0.010)**<br>-0.089<br>(0.023)**<br>0.031<br>(0.036)<br>59248<br>59248<br>4842<br>4842<br>0.009<br>0.008<br>(0.002)**<br>(0.002)**<br>-0.002<br>(0.005)<br>-0.024<br>(0.009)**<br>-0.067<br>(0.023)**<br>-0.001<br>(0.037)<br>59154<br>59154<br>4748<br>4748<br>is a municipality - year. I<br>l. All specifcations inclu<br>enue, per capita transfer <br>10. The mean of per capi<br>lations based on data fr|
|Dependent Variable:<br>_Panel A: OLS Results_<br>Revenue<br>Average income<br>Population<br>0-6 population<br>7-15 population<br>Observations<br>Municipalities<br>_Panel B: IV Results_<br>Revenue<br>Average income<br>Population<br>0-6 population<br>7-15 population<br>Observations<br>Municipalities<br>_Notes:_ An observation<br>at the municipality leve<br>levels of per capita rev<br>p_<_.05; + indicates p_<_.<br>_Sources:_ Author’s calcu|

45
values (Go, 2015). In Norway, altruism plays a role at the local government level but mostly by increasing support for education among the middle‐aged who have children, rather than through stronger support for old‐age care among the middle aged who have elderly parents (Rattsø and Sørensen, 2010).

Our paper is closer in nature to a second strand of literature that focuses on subjective preferences for the allocation of public spending, rather than on actual outcomes.<sup>5</sup> Busemeyer, Goerres and Weschle (2009) provide a thorough review of the literature and report mixed empirical findings on the effects of ageing on overall welfare spending, probably as a result of aggregation effects. The authors focus on OECD countries and contribute to the literature by analyzing different components of welfare spending and by focusing on the total population, rather than just the labor force. They find significant differences in preferences for redistribution across age groups, particularly for education and pensions, although the strength of these age‐related differences varies across countries.

The literature also highlights the importance of cohort, in addition to time, effects. Fullerton and Dixon (2010) use data for the United States and look at three spending categories (health care, education and social security) over a long time span (1984–2008). They find evidence in support of the “grey peril” hypothesis for education, although their results are mixed for pensions and health care. Our paper is closely related to Sorensen (2013), who uses repeated cross‐sections for 22 countries and finds that when time and cohort effects are taken into account, elderly people prefer lower (higher) spending on education (health care and pensions). These life‐cycle effects vary considerably across countries, but they are generally quite small. One limitation of the data used by Sorensen (2013) is that the alternatives available in the questionnaire for additional government expenditure are not mutually exclusive. In other words, individuals can choose to increase or decrease expenditure in all categories. In contrast, in a more realistic scenario individuals would be faced with trade‐offs, so that increasing expenditure in one category would require offsets in others.

Against this background, our paper makes three contributions to the literature. First, it tests the “grey peril” hypothesis for a larger set of countries. Using data from LITS II, we use comparable data for 34 economies, mostly developing ones. This is important, as most papers in the literature focus on advanced economies. Second, even though Sorensen (2013) allows for age and cohort effects, we use

> 5 There is of course a large literature showing how individual preferences do not necessarily translate into actual policy. See for example Gilens and Page (2015) for a recent example.

6
COVID-19 months in 2020. Because demand for services in each country could be correlated across time, standard errors are clustered at the country level.

Observed changes in demand for services during the COVID-19 period could be due to: (i) lower consumer spending due to income losses, precautionary savings and reduced shopping due to fear of contracting the virus, and (ii) government restrictions on movement, business closures, and lockdowns. Government responses to the pandemic, therefore, may compound the economic fallout and lead to a greater decline in demand. To isolate the impact of government measures on demand, we construct two indicator variables for the introduction of social distancing and lockdown measures. These variables reflect spatial and temporal variations in the implementation of government measures and take the value one for weeks in which government measures were in place and zero otherwise. We estimate the following fixed effects specification to quantify the compounding impacts of government measures on demand for services: c 1C C c C c c , (2) cc cc DD = αα + γγ CC100 × mmC CCm + δδ + εε Where C C c is a dummy variable that switches from zero to one in the week in which the number of confirmed cases surpassed 100, and C c is a dummy variable for the CC100 introduction of social distancing or lockdown measures. By combining data on the spread of the mmC CCm pandemic and government measures, we can assess the compounding effect of social distancing and lockdown measures on consumer demand for services. This estimation can inform the extent to which impacts are driven by government-imposed (supply) restrictions and/or demand-driven contractions in economic activities.

# **4. Results and Discussion**

We present two sets of evidence on the effects of COVID-19 on demand for various services. Our first results use a continuous measure of COVID-19 confirmed cases/deaths to gauge countries’ exposure to the pandemic. For the second set of results, we use dummy variables for the first, tenth, and hundredth confirmed cases/deaths to reflect potential non-linearity in the impacts of the spread of the pandemic.

Table 1 provides regression results for weekly confirmed cases by service type and associated search terms. Given the sudden outbreak and rapid growth in the number of COVID16
property holds. Figure 1 presents a normal quantile plot of this rainfall measure, based on 75 observations. The plot suggests some conformity to the normal, with perhaps fatter tails. The Kolmogorov-Smirnov test statistic is .119, which is only statistically significant at the 20 percent level of significance. We thus cannot reject the assumption that the monsoon onset date has a normal distribution.10 Of the other test statistics, the results indicate the absence of technological scale effects on profits. Differences in investment portfolios across wealth classes evidently do not arise from technical scale economies. The results also indicate that schooling has no effects on profits, but reject the hypothesis that the age of the farmer does not affect profits (the marginal return is positive at sample mean values). These latter results are consistent with the hypothesis that in environment **s** ubject to risk but characterized by stationarity in weather and very slow technological change, experience, but not formal schooling, has real payoffs, as found in Rosenzweig and Wolpin (1985)." The tests also reject the hypothesis that the size of the family labor force does not influence profits. This result appears to contradict the findings by Pitt and Rosenzweig (1986) and by Benjamin (1992), based on Indonesian data, indicating the perfect substitutability of hired and family labor.

A necessary and sufficient condition for the global quasi-concavity of the profit function (8) in the a is that the Hessian matrix of second partials with respect to the ai, consisting solely of the **O6jb.** be negative semi-definite. This in turn implies that all of the eigenvalues of the Hessian be non-positive. Based on our set of point estimates of the 61j, six of the eight eigenvalues were negative. To test if the non-negativ **e** igenvalues indicate rejection of quasi-concavity, we implemented the test proposed by Lau (1978). We reestimated (8) replacing the matrix of<sup>6</sup> ijs by parameters corresponding to the Cholesky 18
domestic banks between 1994 and 1999. Finally, examining the behavior of U.S. bank claims on a broad set of countries since the mid-1980s, Goldberg (2001) finds that U.S. banks did not retrench their lending significantly following period of crises.

Aside from increasing competition and improving stability, there is evidence that foreign bank entry might also benefit borrowers in developing countries by improving banking sector efficiency. In contrast to studies of foreign bank entry in the United States, several papers on developing countries have found that foreign banks, which are often from developed countries, are more efficient than their domestic competitors.<sup>2</sup> Furthermore, studies have found that foreign banks tend to expand into areas where local profit opportunities are greatest, consistent with the hypothesis that foreign investors utilize their know-how and human resources to restructure inefficient banks.<sup>3</sup> Despite the benefits associated with greater competition, stability, and efficiency, opponents of foreign bank entry argue that this process might still harm small and medium-sized enterprises. One reason for this is that foreign entrants tend to be large (Focarelli and Pozzolo, 2000). Evidence from the United States indicates that large and organizationally complex institutions find it difficult to lend to informationally opaque small and medium-sized enterprises.<sup>4</sup> These organizational diseconomies might explain why a number of studies have found that foreign banks appear to allocate greater shares of their lending portfolios to commercial and industrial loans, providing indirect evidence that foreign banks might be more important in the market for loans to large companies. Goldberg (1992) notes that foreign banks operating in the U.S. held 28.5% of all commercial and industrial loans, but only 22.6 % of all banking assets.<sup>5</sup> In addition, in a survey of 271 foreign banks operating in the U.S., Cho _et al._ (1987) find that 56% pointed to trade finance as a major area of specialization; 44% listed corporate banking; and 31% mentioned foreign exchange 3
# 6. Empirical Results

## 6.1. OLS Results

We start with results from OLS estimation of the wage equation which controls for urban and estate dummies and indicators of sectoral composition along with land endowment, but no human capital or productivity controls are used.<sup>23</sup> The results are presented in column 1 of Table 2. Consistent with the theoretical model in section (2) above, the coe¢ cient of percentage of area under LDO restrictions is negative, and the coe¢ cient of its interaction with travel time to urban center is positive. Both of the coe¢ cients are signi...cant at 1 percent level. All standard errors reported in this paper are corrected for clustered sampling design of the HIES 2002 survey.

The second column in Table 2 reports the estimation results when we include a set of measures of human capital both at the individual and DSD levels. The DSD level variables are percentage of labor force with primary or more schooling, and a measure of current malaria parasite infection rate (Plasmodium Vivax, the most common malaria parasite for human infections in Asia). The individual level controls include education, age (as a measure of experience and cohort e¤ect), gender dummy, and etnicity/religion dummies.<sup>24</sup> The gender dummy may capture possible gender based division of economic activities, ethnicity/religion is used as a control for cultural norms regarding work ethic, and marital status is used as a proxy for di¤erences in motivations and preferences. The estimated coe¢ cient on LDO restrictions become numerically smaller (from -0.49 to -0.37) which is consistent with the idea that the estimate in column (1) of Table 2 might have partially captured the negative correlation between human capital and LDO restrictions. The interaction e¤ect has the right sign. Both of the coe¢ cients are statistically highly signi...cant (at 1 percent or lower level). As discussed before, a negative e¤ect of LDO restrictions in this speci...cation can be interpreted as strong evidence, as the estimated e¤ect is likely to be significantly biased towards zero. The evidence from this speci...cation that both the direct e¤ect of LDO restrictions, and its interaction with travel time are statistically signi...cant at 1 percent level

> 23 Note that we do not control for population in a DSD in any of the regressions. Because historical data on DSD level population are not available from the period before LDO restrictions were imposed. Since the main channel through which the land restrictions a¤ect wage in our general equilibrium model is migration and e¤ective labor endowment, any variable capturing e¤ective labor endowment is thus a ‘bad control’in the terminology of Angrist and Pischke (2009).

> 24 The omitted category for the ethnicity (religion) dummies is Sinhalese. About 84 percent of Sri Lanka’s population are Sinhalese.

21
As equation (6) incorporates an unobserved individual effect, _μz_ , the issue of dealing with unobserved heterogeneity looms large. Time-invariant product level effects might include comparative advantage characteristics or trade policy devices.<sup>9</sup> In particular, Broda et al. (2007) have shown that a (large) country’s trade policy for a given product is a function of its market power, thus linking the EC’s and US’s tariff profile to their commodity specific buyer power. In order to accommodate arbitrary correlation between the unobserved product effect and the regressors, estimation of equation (6) proceeds with a fixed effects panel estimator and robust standard errors that allow for intra-group correlation. Data will be pooled across seven GSP countries so that a cross-sectional unit corresponds to a (country _×_ tariff line) pair.

# **4 Empirical Assessment of Trade Diversion**

## **4.1 Data Description**

Trade data to estimate equation (6) is sampled at the HS 6-digit level, the most detailed yet internationally comparable one, for the time period 1996–2001. EC tariff and trade data is obtained from the International Trade Centre (ITC), Geneva, and additional trade data on EC imports as well as trade conversion factors are from OECD (2004). Tariff data consists of _ad valorem_ tariff equivalents at the HS 8-digit level which are based on applied, rather than bound, rates and also encompass specific duties and quantitative measures (if any). At this level of disaggregation, it is thus one of the most accurate tariff data bases currently available. US imports and duties collected are obtained from the United States International Trade Commission (USITC), and US tariffs are calculated as implicit rates

> 9For instance, the main factor that influences the scope of preference utilization, as identified by a number of empirical studies, is most likely the complexity of the ‘rules of origin’ regime in place (see UNCTAD, 2003, 2001 _b_ ).

10
# **<u>Table 5.1: Anecdotal evidence on time use from qualitative poverty study</u>**

_‘A los niños los ponen a trabajar, a traer leña, a acarrear agua.’_ School Teacher, Ladino Community, Qualitative Poverty Study.

In the ENCOVI, households who collect water on a regular basis report that on average they travel around nine minutes to reach their nearest water source (Table 5.2). The equivalent distance for fuel wood collection was a 50 minute walk in urban areas and a 70 minute in rural areas. The average number of persons per household involved in such a trip is around 1.50 in urban areas and 1.85 in rural areas. Moreover, the survey demonstrates clear gender specialization in collection activities, with men and boys accounting for 65% of the labor devoted to the collection of fuel wood, and women and girls accounting for 74% of the labor devoted to the collection of water (Figure 5.1).

**Table 5.2: Distance to source of wood and water**

<u>(Among those who collect water and buy or collect wood)</u>

||National|Bya|rea||Byconsu|mption|quintile||
|---|---|---|---|---|---|---|---|---|
|||Urban|Rural|1|2|3|4|5|
|Water collection|||||||||
|Minutes|12.5|13.2|12.3|14.5|12.8|10.6|12.6|7.0|
|Meters|242|111|267|351|216|222|136|72|
|Wood collection or purchase|||||||||
|Minutes|63.4|51.0|67.2|66.9|67.2|63.3|54.8|47.9|
|Meters|1,336|1,032|1,448|1,611|1,511|1,236|961|917|

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala

**Figure 5.1: Intra-Household Allocation of Fuel Wood and Water Collection Tasks** (individuals who collected wood and water on the day before the survey)

<!-- Start of picture text --> (a) Fuel wood (b) Water<br>11%<br>13%<br>41% 13%<br>Men Women<br>24%<br>Boys Girls<br>50%<br>Women Men<br>Girls Boys<br>24%<br>24%<br><!-- End of picture text --> _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala 37
Table 5 ( _Continued_ )

||1999|2000|W<br>2001|orking<br>2002|2005|2006|2007|
|---|---|---|---|---|---|---|---|
|Age|.044**<br>(1.97)|.085***<br>(5.44)|.050***<br>(9.51)|.062***<br>(13.47)|.056***<br>(2.87)|.058***<br>(6.97)|.048***<br>(5.56)|
|Male|-.020<br>(-0.47)|.104***<br>(2.55)|.089***<br>(4.18)|.100***<br>(4.59)|.137***<br>(3.31)|.048*<br>(1.37)|.074***<br>(2.47)|
|Indigenous|.101**|.058*|.058***|.068***|.170***|.033|.003|
||(1.95)|(1.56)|(2.72)|(3.00)|(4.11)|(1.06)|(.12)|
|Urban|-.406**<br>(-6.29)|* -.202***<br>(-3.18)|-.244***<br>(-7.81)|-.291***<br>(-9.96)|-.275***<br>(-3.53)|-.293***<br>(-5.21)|-.228***<br>(-4.33)|
|Spanish|-.299*<br>(-1.67)|-.200***<br>(-2.10)|-.116***<br>(-2.65)|-.205***<br>(-5.90)|-.138<br>(-1.05)|-.178**<br>(-1.31)|-.048<br>(-.87)|
|EdMHead|dropped|dropped|-.026<br>(-1.18)|-.059**<br>(-1.72)|-.099**<br>(-2.06)|-.041<br>(-.79)|-.070**<br>(-2.26)|
|EdFHead|dropped|dropped|-.054<br>(-1.08)|-.016<br>(-0.31)|-.175**<br>(-1.96)|-.196**<br>(-1.92)|-.081<br>(-1.21)|
|Extr. poor|.055<br>(1.03)|.041<br>(1.24)|.054***<br>(2.56)|.062***<br>(2.62)|.056*<br>(1.41)|.186***<br>(5.43)|.083***<br>(2.61)|
|BJP|.|.|.|.|.|.|.035<br>(1.09)|

Source: Authors’ estimations based on MECOVI. In 1999 and 2000 the variables EdFhead and EdMhead have been dropped because there are very few educated heads.

Notes: The number in parenthesis report z-statistics. Estimations performed using the expansion factor. *** Significant at 1%, **significant at 5%, * significant at 10%.

26