rafmacalaba/datause-encoder-data huggingface dataset:
description of our research methods, see the technical annex on the Skills Demand Model.) The following four specifications are used in the model:

- (1) In the baseline specification, which is the production function typically estimated from establishment census data, log labor productivity is regressed on the log of capital per employee, measures of education and skill composition of workers, and sector dummy variables. The estimated coefficients of capital per worker and skill shares provide estimates of the returns to capital and to different skill investments.

- (2) A second specification adds a set of measures to capture the productivity differences across firms arising from skill deficits. Informed by the previous analyses of skill deficits, several drivers of skills demand are included: a set of firm attributes – education of top managers, foreign and female ownership, multi-firm organization, and young firms – and indicator variables for high-skill firms – their exporter and innovator status.

- (3) The third specification replaces the set of firm attributes and demand-drivers in (2) with employers’ responses to these skill deficits. Skill-deficit mitigating strategies include firm provision of in-house training, external training, employment of expatriate MPT workers, outsourcing of professional services, and hiring of new workers. Their estimated coefficients provide insights into the productivity impacts of these different skill mitigation strategies.

- (4) The final specification adds back the firm attributes and demand-drivers to (3). This specification provides insights into how much of the demand-driven productivity differences across firms are explained by skill-deficit mitigating strategies and whether different firm attributes and demand-drivers have residual productivity effects independent of their roles in employer decisions about skills and skill strategies.

The results of this analysis are summarized in Table 4. Before turning to the findings on skill deficits and skill strategies, consider the two estimated results of capital and skills composition in the baseline production function:

- _Capital intensity is positive and statistically significant in most specifications_ , suggesting that workers are more productive (by about 6.6 to 7.5 percent) the higher the amount of fixed assets (equipment, machinery and buildings) that each worker gets to work with.

- _The share of tertiary-educated workers is significantly associated with higher labor productivity (0.8 to 0.4 percent) but not those of the other skill variables_ (upper secondary, TVET qualified and MPT shares). The lack of any statistical significance either points to the possibility of measurement error or suggests that the productivity effects of the latter are not different from that of lower-educated workers (with primary or lower secondary education) or those working in lower-skill jobs (sales, plant operators and other unspecified occupations). This absence of a productivity impact may reflect well-known deficiencies in the formal education and training of these skill groups documented in Section II.

35
# _Pathways through which public spending improves child well-being_

The results reinforce the findings of Valdivia (2004) that the expansion in health infrastructure in Peru during the 1990s had a significant causal impact on children’s nutrition only in urban areas. Worse health outcomes for the rural population may thus be a consequence of the lack of effectiveness of public services in these areas.

_Differences in public services availability between urban and rural areas._ The lack of effectiveness of public spending may be related to the differences in the availability of public services between urban and rural areas. The availability of health professionals, health facilities, and programs in a given locality may be pathways through which public spending improves child well-being. To assess these pathways, I estimate IV panel models in which the dependent variable is the change in an access measure at the community level. The community level measures include the change in the probability of accessing a general physician, a pediatrician or gynecologist, a midwife, a child growth control program, and a public health center<sup>24</sup> . The results are presented in Table 4. Changes in public spending only increase access to local health professionals and child growth controls in urban areas. However, in rural areas, public spending does not modify access to local health professionals, but it decreases access to child growth controls while increasing the probability of accessing public health centers. This is not a surprising result because, according to Table 2, public health centers increased dramatically in rural areas during the period of analysis.

24 These community level measures were obtained from the community survey, so the access to health professionals and facilities is not conditional on health. All of these estimations include regional and time fixed effects. As controls, I use community characteristics (the average household wealth score in the community, population size, a dummy for living in the highlands and a poverty index) and regional variables (average level of education in the region and debt payments). I cluster the standard errors at the region-year level.

17
# Fiscal Policy and Economic Activity in South Asia<sup>*</sup>

**Robert C. M. Beyer** a **Lazar Milivojevic** The World Bank

Goethe University Frankfurt

**Keywords:** fiscal policy, South Asia, fiscal multipliers, government spending, tax buoyancy

**JEL Classification:** E32, E62, H50, H62

__________________________________________________________________________________

> * The authors thank Milagros Chocce and Rucheta Singh for excellent research assistance and Martin Rama, Ila Patnaik, Guillermo Javier Vuletin, Hans Timmer, as well as Aart C. Kraay and the participants at the _South Asia Workshop on Fiscal Policy_ in Kathmandu and the _5th International Conference on South Asian Economic Development_ in New Delhi for comments and suggestions. This paper builds on work initiated for the _South Asia Economic Focus_ Fall 2018 (World Bank 2018) _._ a The corresponding author is Robert C. M. Beyer: rcmbeyer@worldbank.org, +1 (202) 6297845, 1818 H Street NW, Washington DC.
Thailnd Morocco Malaysia CSte d'Ivoire

||Bangladesh|CSte d'Ivoire|Malaysia|Morocco|Thailnd|
|---|---|---|---|---|---|
|Post-,<br>iday<br>Tax Provisions||||||
|Corporate Tax<br>Rate|401 (Public)<br>41 (Private)|4011+OX (NIF)|43Xleam5X<br>abatement|49*.X|3OX(Public)<br>351 (Privat)|
|Doprociatlon<br>Rat"|Same as above<br>Recapture rulae<br>apply|Same as above|Sa as abov<br>Recapture<br>rulesapply|Same as aove|Same so above<br>Recapture rues<br>Wly|
|Other Tax<br>Incentives||||||
|- avallablo<br>after<br>holiday|Inveetmet<br>allowance<br>251.<br>Depreciation<br>bee<br>notadjuste|No|No|lb|No|
|- notavallablo<br>forholiday|Accelorated<br>at1005<br>ors0os<br>201|Accelerated at twice<br>theer_s rate|Acceleratedat401<br>IvestmStt<br>Tax<br>Allowanc<br>of 1001|lIveetmeet<br>11_e"'<br>- 101<br>of profItsa'*I<br>up to OX of<br>I Westmont|_None_|

Source: International Bureu of Fiscal<sup>Documentation 1907 and 16.</sup>
_25_ The behavior of stock prices immediately after the announcement of the program can also be seen as an indicator of optimism or pessimismn. Figure 12 shows a very different reaction in real share prices in Denmark as compared to France and Ireland. In Denmark, the real value of the Danish Real Industrial Share Price Index almost doubled in one year after October 1982.

But agents' expectations also operated through a second channel, which had important implications for aggregate supply. French and Irish agents seem to have had _doubts about the pace of the disinflation._ It was not, as we have already emphasized that agents had doubts about the final success of disinflation. Instead, the cases of France and Ireland show that economic agents thought the inflation targets of the policymaker to be too optimistic.

Figure 10. France and Ireland: Consumer **Confidence Index.** Source: European Economy Supplement B.

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-4-

ethanol production Polici is favor the consumption of ethanol over other fuels and regular increases in supply have been necessary to meet the energy demands of the ever-enlarging fleet of ethanol-powered cars.

Until recently the retail price of ethanol was set at a substantial discount relative to the blended gasoline/ethanol price, and many cars are fully reliant on pure ethanol. It is very costly to convert ethanol-powered engines to gasoline. The ethanol fuel program has reduced Brazil's dependence on imported oil, but has made its economy highly dependent on the use of sugarcane for ethanol production. Each year the stock of cars running on pure ethanol is increasing. With total production controlled by government policies, large volumes of the 1989/90 cane crop have been diverted from sugar production to meet the rising ethanol demand. This has occurred despite the higher sugLr prices and has caused Brazil's sugar exports to decline to a 25year low--in fact they have been declining since 1984/85 (see Figure 2). The empirical evidence (see Wong, Sturgiss and Borrell, 1989) is that Brazil's capacity to switch cane between sugar and ethanol in response to even larger changes in crude oil and sugar prices is greatly limited under current policies.
Table B9: Mechanism at play, excluding China and India as destination countries

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>HHI|(6)<br>log(# MNEs)|(7)<br>log(K)|
|---|---|---|---|---|---|---|---|
|Uncertainty (standardized)|0.891*|-0.316|1.437***|1.507***|0.051**|-0.056**|0.019|
||(0.451)|(0.603)|(0.536)|(0.514)|(0.025)|(0.027)|(0.032)|
|Uncertainty _×_ Developing||1.861***<br>(0.694)||||||
|Observations|2,309|2,309|1,449|1,432|1,432|1,432|1,432|
|R-squared|0.823|0.824|0.791|0.797|0.691|0.875|0.389|
|Controls|No|No|No|Yes|Yes|Yes|Yes|
|Data Coverage|All co|untries|||Developin|g||

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

34
in the context of transportation infrastructure, because such investments might also affect locations further away. The market access measure captures such spillovers. As with our previous empirical approaches, we rely on Kebeles as our primary unit; however, to test the sensitivity of results, we also repeat the analysis using Woredas (N = 780). The 1x1km pixels are less suitable for market access analysis.

The measure of market access is constructed using the following equation (Donaldson and Hornbeck, 2016): where population of the other markets ( _popj_ ) is held constant at 2000 levels and where we measure the travel time _ttij,t_ between the origin unit _i_ and the destination _j_ at time _t_ (see Appendix S11 for details on estimating travel time using the RSDP road network data). We use the most populous pixel within each unit as the location for estimating travel times. In our primary models, we use an elasticity ( _θ_ ) of 3.8, following Donaldson and Hornbeck (2016); however, we test the robustness of our results to alternate measures.

We aim to understand whether market access growth is associated with changes in economic activity, urbanization, or cropland. As our primary model, we use a long-difference framework adopted from Alder (2017), who estimates how changes in market access are associated with changes in nighttime lights in India. We regress changes in our outcome variables on changes in market access, as specified in the following equation: where ∆ _IHS_ ( _yi_ ) represents changes in outcome variables as specified in the above section, and we take the inverse hyperbolic sine transformation before differencing; _MAi_ is the measure of market access; _Ni_ is a vector of indicator variables for different levels of baseline nighttime lights; _Ci_ is a vector of controls, including initial market access, average nighttime lights at baseline, pre-trends in luminosity, and pre-trends in the number of urban pixels; and _γz_ are Zone fixed effects (where these represent Zone-year fixed effects in a panel framework). In computing the long difference, baseline values are from 1996 and endline values are from 2016. In all models, we cluster standard errors on Woredas (when estimating the model using Woredas as the unit of analysis, we cluster using Zones, Ethiopia’s second-level administrative division, of which there are 64). We explore heterogeneity in the impact of market access by interacting market access with indicator variables ( _Ni_ ) for different baseline levels of nighttime lights.

An identification challenge is that market access may be endogenous. For example, road upgrades may 18
Similar to the case of enterprise data, the Global Findex data also allow us to explore the reasons why households do not have accounts with formal financial institutions. In the median African country, 68 percent of the population cites lack of money as a reason for not having an account, while only 52 percent in non-African countries do. 25 percent of the population in the median African country cites high costs or lack of the necessary documentation, while 22 percent point to geographic barriers. Outside Africa, 18 percent of the population in the median developing country point to prohibitive costs, 13 percent to geographic barriers and 11 percent to lack of necessary documentation. Almost the same share of population cites lack of trust for not having an account (12 percent in Africa, 11 percent outside), while religious reasons are less prevalent in Africa (2 percent) than outside (4 percent). Finally, a smaller share of the population cites no need for an account in Africa (5 percent) than outside (9 percent). Overall, this points to similar barriers in Africa and other developing countries, though some of these barriers seem stronger in Sub-Saharan Africa.

# **2.6. The role of microfinance in Africa**

Like other developing regions, microfinance has grown rapidly in Sub-Saharan Africa. The number of borrowers served by the African microfinance institutions (MFIs) that report to the Microfinance Information eXchange (the MIX) increased from 1.6 million in 2003 to 8.5 million in 2009.<sup>viii</sup> A part of this increase is attributable to the MIX’s efforts to expand the number of MFIs that report to them, but even among the 48 African MFIs that reported information to the MIX in all years between 2005 and 2009, the number of active borrowers increased from 2.3 million to 4.8 million.

The institutional presentation is quite similar in Africa and the rest of the developing world. For example, as of 2009, MFIs organized as banks represented 7 percent of the 22
<!-- Start of picture text --> ves} &o ‘2 & - SsPm* ksFo ce@ &GG &@ ie—@M &CO &Ww &oSe s‘eeee &2<br>eM LS& ° § Oo£ &S&FPe YSys ¢ yw oo<br>1 1<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>60 ~———-eme <a eee Se -<<=<< -——<br>40 — es = =< =<br>o-<br>Brazil Uruguay Costa Rica Chile<br>mQi 9 Q2 8Q3 g8Q4 8Q5<br><!-- End of picture text -->
Doh, J. P. and Pearce, J. A. (2004). ‘Corporate entrepreneurship and real options in transitional policyenvironments: theory development’.Journal of Management Studies,41, 645–64 El-Anis, I. (2018): Jordan in 2018: Too Stable to Fail, Too Small to Flourish, Mediterranean Yearbook 2018, IE Med.

Fatás, M., & Mihov, I. (2013): Policy Volatility, Institutions, and Economic Growth, The Review of Economics and Statistics, Vol. 95(2), 362-376.

Fazekas, M; Benoit, C; & Brenner, D. (2024) An Introduction to LegDat: The global dataset of legislative processes, outputs and outcomes. Manuscript under peer review.

Gratton, G.; Guiso, L.; Michelacci, C.; & Morelli, M. (2021): From Weber to Kafka: Political Instability and the Overproduction of Laws, American Economic Review, Vol. 111(9), 2964-3003.

Gulen, H. & Ion, M. (2016): Policy Uncertainty and Corporate Investment, The Review of Financial Studies, Vol. 29(3), 523-564.

Henisz, W.J. (2000): The Institutional Environment for Economic Growth, Economics and Politics, Vol. 12(1), 1-31.

Hoffmann, V.H.; Trautmann, T.; & Hamprecht, J. (2009): Regulatory Uncertainty: A Reason to Postpone Investments? Not Necessarily, Journal of Management Studies, Vol. 46 (7), 1227-1253.

IMF (2016): IMF Executive Board Approves US$723 million Extended Arrangement Under the Extended Fund Facility for Jordan, https://www.imf.org/en/News/Articles/2016/08/25/PR16381Jordan-IMF-Executive-Board-Approves-US-723-million-Extended-Arrangement (last accessed: 16.06.2024) IMF (2019): Jordan: Second Review Under the Extended Arrangement Under the Extended Fund Faciltiy, Requests for a Waiver of Nonobservance of Performance Criterion, an Extension of the Arrangement and Rephasing of Access, accessible under: <u>https://www.elibrary.imf.org/configurable/content/journals$002f002$002f2019$002f127$002farticl e-A001-en.xml?t:ac=journals%24002f002%24002f2019%24002f127%24002farticle-A001-en.xml</u> (last access: 16.06.2024).

Iqbal, U.; Gan, C. & Nadeem, M. (2020): Economic Policy Uncertainty and Firm Performance, Applied Economics Letters, Vol. 27(10), 765-770.

Kelly, B.; Pastor, L. & Veronesi, P. (2016): The Price of Political Uncertainty: Theory and Evidence from the Option Market, The Journal of Finance, Vol. 71(5) , 2417-2480.

24
during 2003-2009. Labor market gains, namely labor earnings, accounted for 21 percentage points of this total, while creation of new employment opportunities contributed minimally to poverty reduction=. Among other income sources, remittances played a crucial role accounting for 8 percentage points of poverty reduction. These results are not surprising given importance of labor earnings in total income of households and the real growth of labor earnings, pensions and remittances during 2003-2009 (tables A1 and A2). Other factors such as the demographic dividend<sup>8</sup> , pensions and social assistance were also poverty decreasing, but their role was rather minor.

18. **Remittances and labor earnings are more important for the welfare improvement of the middle class, while employment and pensions are more important for the poor/vulnerable and the bottom 40 percent of the population.** Figures 4.3 and 4.4 demonstrate the role each factor plays in welfare changes across different classes and the bottom 40 percent of the population. The middle class benefited more from the labor earnings and remittances in relative terms than the poor/vulnerable and the bottom 40. In contrast, employment and pensions were more important for the poor and the vulnerable as well as for the bottom 40 percent of the population.

19. **Employment played the most important role for inequality reduction, while remittances had a neutral or even a dis-equalizing effect.** Decomposing changes in income per capita Gini shows that all factors, except remittances, were associated with lower inequality (figure 4.2). Interestingly, employment played a minimal role in poverty reduction, but had the strongest equalizing impact which is in line with the higher role of employment in improving the wellbeing of the poor and vulnerable. Remittances had a dis-equalizing effect and this is consistent with their high role in welfare improvement of the middle class. This may be a result of costs international migrants have to incur and which make international migration a difficult option for the poorest households.

**Figure 4.1: Income poverty decomposition, Figure 4.2: Income Gini decomposition, 20032003-2009 2009**

<!-- Start of picture text --> 0<br>0.01<br>-4<br>-0.01<br>-8<br>-0.02<br>-12<br>-0.03<br>-16<br>-20 -0.04<br>-24 -0.05<br>Gini<br>share of adults share of employed labor earnings pensions social assistance remittances other income<br>points<br>drop in poverty, percentage share of pensions<br>share of adults employed labor earnings social assistance remittances other income<br><!-- End of picture text --> Source: Authors’ calculation based on ECAPOV data. Notes: Poverty line is 2.5 USD/PPP per day. Welfare aggregate is income per capita.

Source: Authors’ calculation based on ECAPOV data. Notes: Poverty line is 2.5 USD/PPP per day. Welfare aggregate is income per capita.

> 8 Demographic dividend is reflected by the share of adults in income decomposition.

14
**Table 14. Explaining cross-country variation in efficiency, Multiple Inputs-Multiple Outputs**

|Independent<br>Variable|EDU2-2|EDU2-2n|EDU3-2|EDU3-2n|EDU3-3|EDU3-3bl|HEA2-2|HEA3-2|HEA3-2m|HEA3-3|
|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|-.00212**|-.00767*|-.00219**|-.00425|-.001000|-.00340***|.00126*|.00205*|.00203***|.00203***|
|GOVEXP|-.00321*|-.00365|-.00203***|.00099|-.00123***|-.00316***|-.0012***|-.00273*|-.0009|-.00090|
|PUBTOT|--|--|--|--|--|--|-.00151*|-.00142*|-.00159***|-.00151***|
|GDPPC|-.00001**|-6.6e-7|-.00001***|-.00003|-4.2e-6|1.98e-6|-2.7e-6|4.2e-6*|-7.1e-7|-9.3e-7|
|URBAN|.00138***|-.00045|.00191**|.001997|.00127*|.00091|-.00095*|-.00148*|-.00106|-.00105|
|AIDS|-.03295|-.05843|-.00956|-.14763|.01797|.06022|-.04815*|-.033147**|-.07162|-.06999|
|GINI|-.06485|.43602|-.14717|.27058|-.17237**|-.15697|-.03997|-.07958***|-.01015|-.01387|
|EXTAID|.00010|-.00622|.00152|-.00274|-.00066|.00123|.00087|.00128***|-.00095|-.00106|
|CONS|1.0655*|1.0223|1.0642*|1.0124*|1.06570*|1.1218*|1.0098|1.0117*|.98891*|.98787*|
|# of Obs|76|34|69|32|69|63|97|98|98|98|
|(# ofCountrs)|(49)|(20)|(44)|(19)|(44)|(40)|(55)|(56)|(56)|(56)|
|Wald Chi2(6)|24.48|11.69|20.84|7.44|18.72|9.18|185.21|229.98|19.25|18.62|
|(Prob>Chi2)|(.00)|(.11)|(.00)|(.38)|(.01)|(.24)|(.00)|(.00)|(.01)|(.02)|

# Note:

- 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise EDU2-2: Inputs: orthogonalized public spending on education per capita, teachers per pupil Outputs: gross primary and secondary enrollments

- EDU2-2n: same inputs as EDU2-2, outputs: net primary and secondary enrollment EDU3-2: literacy of adult is added to EDU2-2 as input

- EDU3-2n: literacy of adult is added to EDU2-2n as input EDU3-3: literacy of youth is added to EDU3-2 as output EDU3-3bl: same inputs as in EDU3-2,

outputs: average years of school, first level complete, and second level complete (Barro-Lee education indicators)

- HEA2-2: Inputs: orthogonalized public spending on health per capita, literacy of adult

- Outputs: life expectancy at birth, and immunization DPT

- HEA3-2: orthogonalized private spending on health per capita is added to HEA2-2 as input HEA3-2m: Immunization Measles is in place of DPT in HEA3-2 as output HEA3-3: Immunization Measles is added to HEA3-2 as output

34