rafmacalaba/datause-encoder-data huggingface dataset:
certain degree of autonomy in decision-making, especially for the allocation of its current resources” (EUROSTAT [1998]). Generally, this will be above the establishment level.

**Size threshold** : While some registers include even single-person businesses (firms without employees), others omit firms smaller than a certain size, usually in terms of the number of employees (businesses without employees), but sometimes in terms of other measures such as sales (as is the case in the data for France). Data used in this study exclude single-person businesses. However, because smaller firms tend to have more volatile firm dynamics, remaining differences in the threshold across different country datasets should be taken into account in the international comparison.

**Industry coverage** : Special efforts have been made to organize the data along a common industry classification (ISIC Rev.3) that matches the OECDStructural database (STAN). In the panel datasets constructed to generate the tabulations, firms were allocated to the single STAN industry that most closely fit their operations over the complete time-span.

The firm-level and job flows data come from business registers (Finland, the United Kingdom and the United States, Estonia, Latvia, Slovenia), social security databases (Germany, Italy, Mexico) or corporate tax rolls (Argentina, France, Hungary) (Table 1). Annual industry surveys are generally not the best source for firm demographics, due to sampling and reporting issues, but have been used nonetheless for Brazil, Chile, and Colombia. Data for Portugal are drawn from an employment-based register containing information on both establishments and firms. All these databases allow firms and jobs to be tracked over time because addition or removal of firms from the registers reflects the actual entry and exit of 6
# **8 References**

- Ababsa, Myriam and Abu Hussein Ahmad. 2020. “Metropolitan Amman. Comprehensive Climate Plan.”

- Ahlfeldt, Gabriel M, Stephen J Redding, and DM Sturm. 2016. “A Quantitative Framework for Evaluating the Impact of Urban Transport Improvements.” Tech. rep., Working Paper.

- Bird, Julia and Anthony J Venables. 2019. “Growing a developing city: A computable spatial general equilibrium model applied to Dhaka.” _World Bank Policy Research Working Paper_ (8762).

- Monte, Ferdinando, Stephen J Redding, and Esteban Rossi-Hansberg. 2018. “Commuting, migration, and local employment elasticities.” _American Economic Review_ 108 (12):3855– 90.

- Ngai, L Rachel and Christopher A Pissarides. 2008. “Trends in hours and economic growth.” _Review of Economic dynamics_ 11 (2):239–256.

- Redding, Stephen J and Esteban Rossi-Hansberg. 2017. “Quantitative spatial economics.” _Annual Review of Economics_ 9:21–58.

- Redding, Stephen J, Daniel Sturm, and Stephan Heblich. 2018. “The Making of the Modern Metropolis: Evidence from London.” .

- Tsivanidis, Nick. 2019. “Evaluating the impact of urban transit infrastructure: Evidence from bogotaâs transmilenio.”

- Zárate, Román D. 2022. “Spatial Misallocation, Informality, and Transit Improvements: Evidence from Mexico City.”

18
similar in the two SSPs. Given current mitigation efforts we choose to work with SSP 5 (“Taking the Highway”). With these scenario choices, we obtain Table 4.

Table 4: Agricultural losses in South Asia

||Net<br>impact|**RCP 2.6**<br>Direct<br>impact|Private<br>Adaptation<br>effect|Net<br>impact|**RCP 8.5**<br>Direct<br>impact|Private<br>Adaptation<br>effect|
|---|---|---|---|---|---|---|
|**2000**|0.00%|0.00%|0.00%|0.00%|0.00%|0.00%|
|**2010**|-1.26%|-2.29%|1.03%|-0.77%|-1.46%|0.69%|
|**2030**|-1.73%|-3.68%|1.95%|-2.29%|-5.25%|2.96%|
|**2050**|-1.17%|-2.30%|1.13%|-4.93%|-10.08%|5.15%|
|**2080**|-1.74%|-3.35%|1.61%|-14.73%|-22.66%|7.93%|

Source: (Havlík et al. 2015) In the model, we will work with the numbers for the net impact, because the private adaptation is reported in the table is effectively the kind of optimization behavior embedded in the model. For the intermediate years, we interpolate linearly.

_Model modifications_ In order to account for climate damages ) to agricultural output (note that it has a negative sign), we include it explicitly in the calculation of value added in the agricultural sector: tt (DD K K K K 1 ) This equation derives value added for the crop sector, a sub-sector of agriculture, from the value in the Δ log (YYVVGGDD,tt) = cc −θθ<sup>VVGGDD</sup> ∗log (YYVVGGDD,tt−1) −log ((1 + DDtt−1) ∗YY<sup>�</sup> VVGGDD,tt−1)) + Δ log ((1 + DDtt ) ∗YY<sup>�</sup> VVGGDD,tt social accounting matrix (SAM). reflects the damages caused by climate change in year . Potential GDP is not modeled bottom up from sectoral values. Instead potential GDP is calculated from DDtt SS the aggregate production factors. The agricultural damage has the effect of reducing TFP, because the same amount of capital and labor produces a reduced amount of output compared to previous years. The modification to potential GDP ∗ is tt ∗ ∗<sup>αα</sup> K YY YY ββ γγ where is the weight of agriculture in total value-added. It would have been ideal to map damages YYtt = (1 + DD∗ωωVVGGDD)AAttCCtt KKtt−1YYEE,tt ∗ ∗ K where is the weight of agriculture in total value-added. It would have been ideal to map damages in the agricultural sector to changes in informal employment and wages, since they are most likely to be YY VVGGDD affected. ωω

# 3.8.5 Heat impacts on labor productivity

There is extensive evidence that labor productivity is sensitive to temperature and that most countries are beyond the optimum, so that further temperature increases will reduce labor productivity (Burke, Hsiang, and Miguel 2015). According to Sherwood and Huber (2010 and Buzan and Huber (2020), there is an upper limit for humans to adapt to higher temperatures and at strong climate change many world regions would become uninhabitable. Firm level studies in India (Somanathan et al. 2018; Adhvaryuy, Kalaz, and Nyshadham 2019) and China (Zhang et al. 2018) show that heat reduces worker productivity.

48
Table A6: Using the inverse hyperbolic sine to account for zeros

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_sinh_<sup>_−_1</sup>(_imp_)|_sinh_<sup>_−_1</sup>(_exp_)|_sinh_<sup>_−_1</sup>(_imp_)_−_<br>_sinh_<sup>_−_1</sup>(_exp_)|_sinh_<sup>_−_1</sup>(_imp_)|_sinh_<sup>_−_1</sup>(_exp_)|_sinh_<sup>_−_1</sup>(_imp_)_−_<br>_sinh_<sup>_−_1</sup>(_exp_)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_sinh_<sup>_−_1</sup>(<sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1431**|0.3479***|-0.2048***|0.5236***|0.9625***|-0.4389***|
||(0.0621)|(0.1044)|(0.0560)|(0.1827)|(0.2513)|(0.1236)|
|First stage :|||||||
|_Replaceability_ * _init. GDPpc_ * _global robot stock_||||0.0035***|0.0035***|0.0035***|
|||||(0.0003)|(0.0003)|(0.0003)|
|Observations|888,813|888,813|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6382|0.7753|0.3868|0.6349|0.7683|0.3850|
|Kleibergen-Paap rk Wald-F-stat||||109.622|109.622|109.622|
|importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the inverse hyperbolic sine to account for zeros in the regression variables. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
<!-- Start of picture text --> 10 Real GDP Growth (%)<br>9<br>Plan Target<br>8<br>7<br>High Growth II<br>6<br>E + P High Growth I<br>5<br>4<br>3 Baseline<br>2<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text -->

**Average Annual Real GDP Growth (%)**

|**Scenario**|**2016 – 2020**|**2021 – 2025**|**2026 – 2030**|
|---|---|---|---|
|Baseline|4.79|4.60|3.95|
|Efficiency|5.00|5.04|4.26|
|Participation|5.79|4.78|4.07|
|Efficiency+ Participation|6.01|5.23|4.39|
|High Growth I|7.17|6.61|5.88|
|High Growth II|7.75|7.30|6.64|
|**7**<sup>**th**</sup>**Five Year Plan Target**|**7.44**|‐|‐|

It is interesting to note that the difference in average growth rates between High Growth Scenarios I and II, and Efficiency + Participation scenario increases with time. This happens because the gap in capital‐to‐output ratios between the former and the latter increases with time as additional growth achieved via TFP increases does not add to capital accumulation.

In summary, sustaining a high growth rate requires a sustained increase in TFP growth. An investment‐led strategy coupled with improvements in efficiency of public capital will deliver growth of 5 percent for the next decade. However, maintaining an investment share of GDP beyond 2025 will deliver lower than 5 percent growth as capital‐to‐output ratios keep growing, making investment less productive.

17
# <u>Table 3b: HRS on Ability and Effort</u>

||_Agree/Strongly_<br>_Agree_<br>No Controls<br>(1)|_Agree/Strongly_<br>_Agree_<br>Including Control<br>(2)|_Strongly_<br>_Agree_<br>Including<br>Control<br>(3)|_Including_<br>_Neutral_<br>Including<br>Control<br>(4)|
|---|---|---|---|---|
|Able to teach all|-0.049|-0.04|-0.09**|-0.01|
|topics to even the<br>most problematic<br>students|(0.041)|(0.04)|(0.04)|(0.03)|
|Confident in teaching<br>all topics in subject|-0.073***<br>(0.020)|-0.07***<br>(0.02)|-0.18***<br>(0.06)|-0.01<br>(0.01)|
|Changes teaching<br>method at least once a<br>month|-0.102**|-0.09*|-0.11*|-0.01|
||(0.046)|(0.05)|(0.06)|(0.05)|
|Maintain composure<br>when student|-0.087|-0.09*|0.05|-0.06|
|becomes disruptive|(0.052)|(0.05)|(0.05)|(0.04)|
|Believes absence<br>from school|-0.169***|-0.17***|-0.03*|-0.24***|
|acceptable|||||
||(0.049)|(0.05)|(0.02)|(0.05)|
|Student's learning<br>achievement|-0.070*|-0.07**|-0.02|-0.03|
|motivates teacher|||||
||(0.034)|(0.04)|(0.05)|(0.02)|
|Domain 2 Index|-0.387***|-0.38**|-0.43***|-0.13|
||(0.139)|(0.14)|(0.14)|(0.16)|
|Controls|No|Yes|Yes|Yes|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01

19
# **Table A4: Ghana 2013 Management Survey**

|**Management Practice**|**Topic**|**Ghana 2013 questions**|**Score 1 (2013)**|**Score 3 (2013)**|**Score 5 (2013)**|
|---|---|---|---|---|---|
|**CS-Autonomy**|**Roles**|Examines how well procedures<br>for routine work are structured|<br>Procedures for undertaking<br>routine operations are<br>undefined or are rarely<br>followed in practice. The<br>organization relies almost<br>entirely on top-down<br>assignment to get work done,<br>so execution depends on the<br>individuals involved. Main<br>rationale for current practice is<br>“that’s the way it’s always been<br>done”.|Procedures for routine<br>operations are defined and<br>generally followed, but may be<br>inefficient or arbitrary. Where<br>responsibility for work is<br>assigned to individuals, follow-<br>up and checks sometimes<br>occur but are inconsistent.<br>There is some consideration of<br>how best to undertake day-to-<br>day operations but this is<br>mainly on an_ad hoc_ basis.|Routine operations procedures<br>have been consciously<br>structured to improve quality<br>and efficiency, and staff<br>consistently implement these<br>procedures.|
||**Flexibility**|Examines processes for and<br>attitudes towards continuous<br>improvement<br>Examines whether practice<br>improvements are effectively<br>disseminated throughout the<br>organization|Problems are only detected<br>after they have affected work.<br>No process improvements are<br>usually made when problems<br>occur. Frontline staff do not<br>contribute to improving<br>processes.<br>There is no effort to<br>incorporate new ideas or<br>practices. When practice<br>improvements do happen,<br>there is no effort to<br>disseminate them through the<br>organization.|Problems are sometimes<br>detected before they have<br>negative impacts, but there is<br>no structured, proactive<br>process to do so.<br>Improvements are made on an<br>_ad hoc_ basis, but depend<br>entirely on individuals taking<br>initiative. Resolution of<br>problems involves some, but<br>not all, of the appropriate<br>individuals and staff groups, or<br>includes non-relevant<br>individuals or groups.<br>New ideas or practices are<br>sometimes adopted but in an<br>_ad hoc_ way. These are<br>sometimes shared informally<br>or in a limited way, but the<br>organization does not actively<br>encourage this or monitor their<br>adoption.|Exposing problems in a<br>structured way is integral to<br>individuals’ responsibilities and<br>resolution occurs as a part of<br>normal business procedures<br>rather than by extraordinary<br>individual effort or_ad hoc_<br>teams. Resolution of problems<br>involves all appropriate<br>individuals and staff groups.<br>Seeking out and adopting<br>practice improvements is an<br>integral part of the<br>organization’s regular work.<br>Improvements are<br>systematically disseminated<br>throughout the organization<br>and monitored for adoption.|
Claessens, S., M. A. Kose, and M. E. Terrones. 2012. “How do Business and Financial Cycles Interact?” Journal of International Economics 87 (1): 178-190.

Debrun, X., and T. Kinda. 2016. “That Squeezing Feeling: The Interest Burden and Public Debt Stabilization.” International Finance 19 (2): 147-178.

Égert, B., T. J. Kozluk, and D. Sutherland. 2009. “Infrastructure and Growth: Empirical Evidence.” CESifo Working Paper 2700, CESifo, Munich.

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19
# **_Table 4: Forest area coverage in 2019_**

||**Dependent vari**<br>**Forest area(sq. km**|**able:**<br>**) in 2019**|
|---|---|---|
||(1)|(2)|
|Income|-0.203|-0.152**|
||(0.172)|-(0.059)|
|I(income<sup>2</sup>)|0.007|0.006**|
||(0.007)|(0.002)|
|Population density|-0.042|0.027*|
||(0.056)|(0.016)|
|Annual precipitation|0.0005***|0.00003|
||(0.0001)|(0.00003)|
|Annual temperature|-0.012*|-0.011***|
||(0.007)|(0.002)|
|Land area|1.339***|0.134***|
||(0.066)|(0.037)|
|Agricultural land|-0.284***|-0.056***|
||(0.082)|(0.021)|
|Forest area in 1990||0.896***|
|||(0.025)|
|Constant|0.907|1.051***|
||(1.093)|(0.369)|
|Observations|130|130|
|R2|0.94|0.993|
|Adjusted R2|0.937|0.992|
|Residual Std. Error|0.489 (df = 122)|0.170 (df = 121)|
|Note: Standard errors are robust (White);|*p<0.1; **p<0.05; ***p<0.01||

18
# 2.1. Stability

Ratha (2003) was the first to point out the recent remarkable ascending path of global remittances compared to the evolution of other sources of external financing, namely foreign direct investment (FDI), capital non-FDI flows and official development aid (ODA). Moreover, the study found remittances to be more stable than ODA and FDI and much more so than the procyclical non-FDI capital inflows. This latter finding was confirmed by subsequent research including Buch and Kuckulenz (2004) and IMF (2005).

While previous analyses of stability typically focused on global external inflows, our paper examines the issue at the country-specific level, relying on as many individual countries as we could find data for.

# 2.2. Cyclicality

The claims of large size and relative stability of remittances flows elicited the interest of researchers and policy makers alike who became interested in examining remittances’ potential to reduce output volatility and absorb macroeconomic shocks. To that goal, many studies attempted to determine the behavior of remittances in relation to macroeconomic indicators, more particularly whether the former move counter- or pro-cyclically with the latter.

The discussion about cyclicality found a theoretical justification in the literature studying the determinants of remittances. There are several theories explaining why migrants remit. The first gives prevalence to family ties, considered to favor the sending of remittances through various channels, of which an important one is the altruistic motive. The altruistic motive of remitting hypothesizes that migrants help relatives and friends back home out of care considerations, without envisioning a pecuniary or alternative material interest. In other words, the welfare of distant relatives and friends is a component of migrants’ own utility function (Rapoport and Docquier, 2006, Niimi et al, 2008). Under this assumption, remittances are expected to behave counter-cyclically, with migrants remitting more during times of economic hardship in the origin countries. The second, more recent theory holds that migrants optimize placement of their savings between origin and destination countries. Hence, remitting money is a form of investment. This theory is broadly called “the portfolio” approach and its prediction is that remittances display a 4
In an earlier paper, the authors suggested that the dual labor market model may be relevant for Israel:7 / while human capital variables were shown to enhance male earn:ngs within the designated primary labor market, they failed to do so in the secondary sector. In a similar vein, and following Benham, wife's education would be expected to exert a positive influence on husband's earnings in primary labor markets. However, for secondary labor markets, where own human capital attributes generally do not result in enhanced earnings, it might be surmised that husband's earnings would not show any relationship to wife's educational level. To test this proposition, we returned to the original data set used in the previous paper; the male sample was, as before, decomposed into primary and secoodary labor market sectors. Employing a method used by McNabb and Psacharopoulos8/, the allocation to market sector was based on occupational prestige scores, workers in low prestege occupations (ccomr.ising some 29 per cent of the sample) being assigned to the secondary laboi market sector.9/ Earnings functions of the Mincer type were estimated for both primary and secondary labor markets using a specification that included variables relating to spouse's education and to the number of years marr.ed. Our results (reported below) while supporting the noti3n of a positive crossproductivity effect of wife's education on husband's earnings in the primary sector, found no such effect for the secondary sector. Some readers may be sceptical about the validity of the dual labor market model, which remains the subject of some controversy in the literature. Even so, it is hoped that our findings, showing that wife's human capital augments husband's earnings in some occupational categories (of higher prestege) but not in others (with low 3
# **Tracking Economic Fluctuations in Bangladesh with Electricity Consumption***

Selvia Arshad<sup>†</sup> Robert C. M. Beyer<sup>‡</sup>

**JEL Classification :** E01, E65, O53, Q43, R11

**Keywords:** electricity consumption, Bangladesh, economic fluctuations, COVID-19, natural disasters

## **_______________________________________**

> ***** The views expressed here are those of the authors and do not necessarily represent the views of the World Bank, its Executive Board, or its management. The authors gratefully acknowledge very helpful comments from Zahid Hussain, Yutaka Yoshino, Bernard Haven, and Nazmus Sadat Khan, as well as from participants of the 2nd SANEM International Development Conference (SIDC) 2021.

> † World Bank, sarshad2@worldbank.org.

> ‡ Word Bank, rcmbeyer@worldbank.org.
# **Table 4: Results with Total Public Expenditures (Dynamic Panel - GMM)**

## **_Dependent variable: Growth rate of GDP per capita_**

||Fast growing<br>countries<br>**~~(~~1)**|Comparison<br>group<br>**~~(~~2)**|Whole sample<br>pooled<br>**~~(~~3)**|Whole<br>sample with<br>dummies<br>**~~(~~4)**|
|---|---|---|---|---|
|Openness|0.062|-0.006|0.005|-0.004|
|(% of GDP)|(6.032)***|(-0.073)|~~(~~0.141)|(-0.123)|
|Total fiscal revenue|-0.207|-0.136|0.686|1.773|
|(% of GDP)|(-0.662)|(-0.146)|~~(~~0.94)|~~(~~1.523)|
|Total fiscal expenditures (-1)|0.318|-0.326|-0.019|...|
|(% of GDP)|(1.572)*|(-1.091)|(-0.117)||
|DFAST*(Total fiscal expenditures (-1))<br>(% of GDP)|...|...|...|1.698<br>(1.856)*|
|DCOMP*(Total fiscal expenditures (-1))<br>(% of GDP)|...|...|...|-0.918<br>(-1.72)*|
|Budget surplus<br>(% of GDP)|-0.020<br>(-0.097)|0.281<br>~~(~~0.275)|-0.164<br>(-0.321)|-0.946<br>(-1.479)|
|Inflation - consumer price index|-0.492<br>(-6.579)***|-0.056<br>(-4.782)***|-0.086<br>(-6.756)***|-0.088<br>(-3.671)***|
|Growth rate of GDP per capita (-1)|0.125<br>~~(~~0.906)|-0.092<br>(-1.675)*|-0.089<br>(-0.807)|-0.006<br>(-0.031)|
|No. of observations|94|169|264|264|
|Overidentification statistics|7.542|2.461|2.001|1.301|

Note: The estimation method is a dynamic GMM. Annual data are used. (-1) indicates variables lagged one period. DFAST is the dummy variable which is 1 for fast growing countries and 0 otherwise. DCOMP is the dummy variable which is 1 for the comparison group and 0 otherwise. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. Overidentification statistics is for instrumental variables where H0: there is no overidentification problem. We fail to reject in each case.

35