rafmacalaba/datause-encoder-data huggingface dataset:
# What Determines the Rate of Growth and Technological Change?*

# by Paul M. Romer

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Theoretical Framework|4|
|3.|Description of the Model|7|
|4.|Empirical Specification|19|
|5.|Results from Estimation|23|
|6.|Comparison with Other Results|30|
|'.|Conclusions|33|
|Re|ferences|37|
|Ta|bles|38|

* This work was supported by the Macroeconomic Adjustment and Growth Division of the World Bank and by NSF Frant #SES-8821943. The author is grateful for comments that were made by seminar participants at the World Bank.
On the demand side, a major potentially contractionary influence of a devaluation is its *ffect on **the** real value of wealth denominated in domeatic currency. As we eWphasidzd in Section 2 below, UTCS scheme (or devaluations of the coo ercial rte lin a dual rate **system)** have **oven** larger potential real balance effects since they reduce the real value of A1U financial wealth on impact, regardless of currency of dnonmination. On the supply side, devaluations exert contractionary **pressure** by raising the price of imported intermediate goods.

Since these contractionary effects are particularly important in **the** short run (see, e.g., Obstfeld (1966)), they are serious potential liabilities for a policy designed to reduce transitional unemploymnt. At **the least, the** existence of **these** effects liplies that **the** adjustment path under a devaluation or UTCS **may** not be sonotonically superior over time to th* adjustment path ln the absenco of such a pollcy.

# **6.** Conclusions

This paper has examined the similarities and dlfferences between unifom trade **taxes** and exchange rate changes in **a** representative consumer setting. We reached the following concluaions, In the absence of illegal trade and under perfect capital mobility: (1) A UTCS is equivalent in all real respects to a devaluation of the coimercial exchange rate in a dual rate system. (2) In terms of theIr effect on the real value of liitial wealth, both policies mentioned in (1) are more contractionary than an across-the-board noinal wage.

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# **Table 1a. Coefficient of variation of real weekly wages across sub-districts in Bangladesh Bangladesh**

|||All sector<br>|s<br>||
|---|---|---|---|---|
||2005|2010|2013|2016|
|All workers|0.27|0.24|0.09|0.14|
|Males|0.27|0.27|0.10|0.16|
|Females|0.38|0.17|0.21|0.18|
|Young|0.25|0.22|0.09|0.11|
|Old|0.30|0.29|0.13|0.20|
|High-skilled|0.19|0.26|0.13|0.16|
|Low-skilled|0.28|0.21|0.07|0.11|

|||Textiles & A<br>|pparel<br>||
|---|---|---|---|---|
||2005|2010|2013|2016|
|All workers|0.37|0.27|0.15|0.18|
|Males|0.40|0.34|0.21|0.23|
|Females|0.75|0.36|0.16|0.28|
|Young|0.38|0.33|0.14|0.19|
|Old|0.80|0.41|0.26|0.31|
|High-skilled|0.43|0.37|0.24|0.33|
|Low-skilled|0.95|0.30|0.16|0.17|

Note: Real weekly wages in USD and prices of 2011. Author's estimates using labor force survey data from Bangladesh.

**Table 1b. Coefficient of variation of real weekly wages across sub-districts in India India**

|||A<br>|ll sectors<br>|||
|---|---|---|---|---|---|
||1999|2004|2007|2009|2011|
|All workers|0.64|0.59|0.55|0.62|0.54|
|Males|0.60|0.54|0.50|0.61|0.52|
|Females|0.96|0.81|1.32|0.78|0.78|
|Young|0.64|0.65|0.53|0.65|0.50|
|Old|0.65|0.56|0.57|0.63|0.60|
|High-skilled|0.47|0.47|0.46|0.59|0.47|
|Low-skilled|0.87|0.59|0.62|0.62|0.43|

|||Textil<br>|es & Apparel<br>|<br>||
|---|---|---|---|---|---|
||1999|2004|2007|2009|2011|
|All workers|1.01|0.83|0.93|0.92|1.36|
|Males|0.95|0.77|0.90|0.93|1.28|
|Females|0.77|1.23|0.65|0.76|0.68|
|Young|0.60|0.69|0.69|0.87|0.69|
|Old|0.92|0.79|1.05|0.87|1.33|
|High-skilled|0.89|0.77|0.95|0.87|1.36|

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**Table 4: Probit estimates of labor force participation model.**

|Variables|1987|1988|1991|
|---|---|---|---|
|Intercept|-6.9|-6.64|-6.67|
||(-24.75)|(-26.5)|(-13.81)|
|age|0.388|0.40|0.34|
||(32.4)|(21.95)|(17.4)|
|age-squared|-0.004|-0.004|-0.004|
||(-35.97)|(-20.01)|(-17.86)|
|exp|0.047|_0.05_|0.012|
||(5.4)|(3.61)|(0.72)|
|primary|0.84|0.32|-0.295|
||_(4.5)_|(2.13)|(0.86)|
|middle|1.00|0.41|0.004|
||(5.8)|(2.51)|(0.2)|
|secondary|0.395|0.09|-0.28|
||(2.14)|(0.45)|(-1.1 i1)|
|higher|0.299|0.64|0.31|
||(1.08)|(2.26)|(2.01)|
|income|-0.0001|0.0003|-0.003|
||(-2.53)|(-1.43)|(-1.24)|
|hhsize|-0.009|-0.002|-0.03|
||(-1.6)|(-0.44)|(-5.34)|
|rural/urban|0.57|0.56|1.02|
||(12.3)|(11.89)|(22.16)|
|N|7357|7293|8428|
|Log Likelihood|-2225.65|-2160.73|-2522.45|
|Pseudo-R<sup>2</sup>|0.56|0.56|0.57|

is not likely to participate, while the probability of a person with higher level education to participate should be high. In 1987 and 1988, participation was higher for primary and secondary school leavers, while in 1992, participation was higher for those with higher than primary and secondary levels of education, which indicates that labor market is becoming more responsive to education and skilled employment. The household size variable could have positive or negative result. If there are a large number of children under 10 years of age then participation rates could be low, since the parent may have to stay home and look after the child; however if there are a number of adult children in the household then older children can reduce childcare costs of labor force participation by taking care of their siblings while the parents are at work. In our analysis, we find that the household size has a negative effect, but is significant only in 1992. Similarly, the income variable as expected, have negative effects on participation, but is only significant in 1987. Being in a rural area significantly increases the probability of participation.

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

**Acknowledgments.** We thank Horeja Cham, Lamin Dibba, Kristen Himelein, Kai Kaiser, Johan Mistiaen, Ryosuke Shibasaki, Matarr Touray, Tara Vishwanath, and participants at the UN 2020 conference on Big Data, World Bank Poverty & Equity Brown Bang Lunch, and anonymous reviewers for helpful comments.

**Funding statement.** This study received support from the World Bank Trust Fund for Statistical Capacity Building III (TFSCB-III) which is supported by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea.

**Competing interests.** We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

**Data availability statement.** The program codes and the aggregated statistics (if possible and currently under negotiations) will be made available through the GitHub platform (link <u>here : https://github.com/worldbank/covid-mobiledata/tree/1b9f114abc9231964d9109f62df29a146912b4a2/cdr-aggregation#summary-of-indicators).</u>

**Ethical standards.** The research meets all ethical guidelines, including adherence to the legal requirements of The Gambia. In particular, the team only accessed de-identified, anonymized and aggregate CDR data.

**Author contributions.** AA and AW processed the aggregate CDR data to produce key statistics, prepared a first draft of the data section, and approved the final version of the manuscript. EK and MM contributed to the analysis, revised the first draft and approved the final version.

**Supplementary materials.** No supplementary material intended for publication has been provided with the submission.

**Abbreviations:** COVID-19, Coronavirus disease 2019; CDR, Call Detail Records; FTP, file transfer protocol; GBoS, Gambia Bureau of Statistics; GNI, Gross National Income; IMEI, International Mobile Equipment Identity, ISMI, International Mobile Subscriber Identity, IT, Information Technology; LGA, Local Government Area; MNO, mobile network operator; OD, Origin-Destination, PURA, Public Utilities Regulatory Authority; SIM, subscriber identification module; SMS, short message service; VPN, virtual private network;

**20** | P a g e
poverty lines found amongst low-income countries (Ravallion et al., 1991). The same principle was applied by Chen and Ravallion (2001) in up-dating the poverty line using the new PPPs for 1993. As we showed in that paper, simply adjusting for inflation in the US between 1985 and 1993 to update the international poverty line gives a line that is well above those found in lowincome countries.

However, the set of national poverty lines used in all our previous papers are now rather old, being essentially the same set of poverty lines used by Ravallion et al. (1991).<sup>14</sup> RCS have compiled an entirely new set of 75 national poverty lines from the Bank’s country-specific Poverty Assessments. In each case the national poverty line was converted to 2005 international $’s using the individual consumption PPP.

Figure 2 plots the Chen et al. poverty lines against mean consumption per person (from national accounts data) also using the same 2005 PPP’s. The same pattern found by Ravallion et al. (1991) and Ravallion (1998) using the older compilations of national poverty lines is evident in Figure 2, with the poverty line rising with mean consumption, but with a low elasticity initially. Thus absolute poverty appears to be the dominant concern in poor countries, with relative poverty emerging at higher consumption levels; for further discussion of why this happens see Ravallion (2008a).

There are a number of ways one might set a new international poverty line consistent with the original idea of using a line that is typical of the poverty lines found in the poorest countries. Although the relationship in Figure 2 is quite flat at low consumption levels, there is still a sizable variance. The lowest poverty line in the data set is $19.05, though this is found at well above the lowest consumption level; the corresponding consumption level is slightly under $80 per month. The poverty line corresponding to the country with lowest mean consumption (Malawi) is $26.11.

However, these estimates are undoubtedly sensitive to measurement errors. Idiosyncratic differences in the data and methods used for setting national poverty lines are also likely to be playing a role. Some averaging is clearly needed. Chen and Ravallion (2001) used a semi-log parametric model for this purpose, in which the log of the national poverty line is regressed on a quadratic function of the level of consumption per capita at PPP. Using the same method on the

> 14 The only prior update was done by Ravallion (1998), who included a number of new observations for Africa (which was clearly under-represented in the Ravallion et al., 1991, data set).  The results were reasonably similar to the earlier study.

8
construction phases and are constrained by land availability, particularly in the city core and high density areas. Moreover, supply-side approaches are not enough by themselves. In fact, expanding the road network may be partly self-defeating if it creates ever greater demand for travel.<sup>2</sup> And an expansion of road networks in the city periphery does not reduce environmental emissions (Anas and Timilsina, 2009a).

<!-- Start of picture text --> Figure 1. Air Quality and Congestion in<br>Selected Megacities in 2000<br>700<br>600<br>Cairo<br>500<br>400 Delhi<br>Beijing<br>300<br>Mumbai<br>(Bombay) Jakarta<br>Shanghai<br>200 Mexico City<br>Buenos Aires<br>Moscow<br>Rio de Janeiro<br>100<br>Tokyo<br>New York São Paulo<br>Los Angeles Osaka<br>0<br>1.00 2.00 3.00 4.00 5.00 6.00<br>minutes required to travel a mile by car<br>meters<br>atmospheric concentration of total suspended particulates, micro grams /thousand cubic<br><!-- End of picture text --> Note. Suspended Particulates include organic and inorganic particles (e.g., dust, sand, metals, wood particles, smoke), PM-10 (coarse particulates less than 10 micro-meters (μm) in diameter), and PM-2.5 (fine particulates less than 2.5 micro-meters in diameter).

Source. Gurjar et al. (2008) and IAPT (2007).

> 2 In the United States, for example, Duranton and Turner (2009) find that urban road expansion has had minimal impact on alleviating traffic congestion.

3
# Figures

# Figure 1. Illustration of typical dynamics of the Basic SIR model and our model

## A. Basic SIR Dynamics

B. Dynamics of our model C. Illustrative impact of vaccine <!-- Start of picture text --> Number of individuals (standardized) Number of individuals (standardized) Number of individuals (standardized)<br>1.2 1.0 1.0<br>Susceptible Infectious Recovered<br>1.0 0.8 0.8<br>Susceptible (no vaccination)<br>0.8 0.6 Susceptible 0.6 Infectious (no vaccination)<br>0.6 Undocumented infectious Recovered (no vaccination)<br>0.4 Documented infectious 0.4<br>0.4 Undocumented recovered Infectious (with the<br>vaccination from day 25)<br>0.2 0.2 Documented recovered 0.2<br>0.0<br>0.0 0.0<br>1 51 101 151 201<br>Days Days Days<br>1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193<br>1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193<br><!-- End of picture text --> Source: World Bank.

Note: All panel assumes infection rate β= 0.15, removed rate α= 0.02, total population N=1, initial number of susceptible individuals, S0 = 0.95, and initial reported daily confirmed cases B0 = 0.002. The other assumptions of each panel are as follows.

A. The initial number of infected individuals I0 = 0.024, R0 = 0.024.

B. The initial number of documented infected individuals DI0 = 0.048, UI0 = 0.0192, DR0 = 0.048, UR0 = 0.0192,. C. The speed of vaccination is 0.01 per day (It requires 100 day to complete the full vaccination). The initial number of infected people, recovered people, and the other parameters are same as panel A and B.

# Figure 2. Epidemiological assumptions and outcomes

A. Assumed share of effectively vaccinated population: advanced economies and major EMDEs B. Impact of vaccine assumptions on number of COVID-19 cases in major economies C. Impact of alternative pandemic assumptions on social distancing <!-- Start of picture text --> Percent Upside Baseline Downside Index points Upside deviation<br>8070 Per 100,000 people Upside scenarioDownside scenario 65 Downside deviation<br>4 4<br>60 3 3<br>50 2 2<br>1<br>40 1 0<br>30 0 -1<br>20 -1 -2-3<br>10 -2 -4<br>-3 -5<br>0<br>-4<br>Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4<br>-5<br>2021 2022 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4<br>2021 2022<br>2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4<br><!-- End of picture text --> Source: Oxford Economics; World Bank.

A. Solid lines are vaccine distribution assumptions for advanced economies and major EMDEs (including China, India, and Russia).

B. Blue (red) areas show the difference of new daily confirmed COVID-19 cases per 100,000 individuals between the upside (downside) scenario and the baseline pandemic scenario.

C. Blue (red) areas show the difference of an index of mandatory social distancing between the upside (downside) scenario and the baseline pandemic scenario.

14
15 perhaps the single most important indicator of sustained, inclusive growth—is actually taking place or not.

Official definitions of unemployment and employment allow for comparative analysis. However, these concepts do not reflect labor market realities of low-income countries that often exhibit dual economies, and are not operationally useful to policy makers there.<sup>12</sup> In Sub-Saharan Africa, 70-90 percent of the labor force is engaged in non-wage employment. About 80 percent of these non-wage jobs are in agriculture, 10 to 30 percent are in household or microenterprises (this primary employment only). About one-third of those outside the wage and salary sector typically report multiple economic activities over the year - combining agriculture and nonagricultural enterprises. Almost all of the labor force participants in low income households are engaged in household-based activities – family farming, and very small non-farm enterprises, commonly called “informal enterprises” (Figure 2).

**Figure 2: Distribution of primary employment in Sub-Saharan Africa (Percent)**

<!-- Start of picture text --> 10090 8 5 2 12 3 2 8 4 10 2 4 4 7 3 9 3 9 4 3 11 11 12 12 3 6 4 6 9 4<br>80 0 1 9 9 16 16 13 23 15<br>70 17 6 0 1 5 25 13 26 26 31 23 3<br>60 2 1 2<br>50 9 1 1 Wage Public<br>40 Wage Private<br>67 69<br>30 Household enterprise<br>83 79 72 75 73 70 60 52 59 56 52 58<br>20 Wage Agriculture<br>10<br>Family farming<br>0<br>Distribution (%)<br><!-- End of picture text --> Source: Fox and Sohnesen 2012 12 The conceptual challenge here also hinges on issues of interpersonal welfare: who is to decide what is really an acceptable employment opportunity? Large segments of the labor force in developing countries tend to define themselves as unemployed even though they hold some type of provisional employment, which they sometimes hold purely for psychological reasons—to keep their mind busy—and not even for subsistence. While it still makes analytical sense to classify such people as “employed”, the reality is that they still perceive themselves as “unemployed” and would often behave as if they were completely. Many of them are young and educated, often with college degrees, and are at the forefront of sociopolitical uprising for jobs.
# 3.4 Within Sector Variation

In the previous section, I established that the increase in lobbying activity observed during the 2007-2009 nancial crisis was driven by rms in sectors that depend more on external nance. These sectors are therefore more likely to obtain tax benets targeted to them.

In order to understand which rms are behind the increase in lobbying activity, I use a triple dierence approach to show the dierential eect of the credit shock across sectors with dierent degrees of external dependence, accounting for dierences in size. The econometric specication is the following: where _lobbysit_ is the log of lobbying for taxation of a rm _i_ in sector _s_ at period _t_ (lobbying intensity).

The variable denoted by _ωst_ is a set of industry-state xed eects that controls for industry-state time invariant observable and unobservable factors aecting the lobbying decision of rms. On the other hand, _Xsit_ is a vector of rm level characteristics measured in _t −_ 1 . This vector includes assets, sales, and capital.

In the proposed regression, the three key variables are _Tt, SMEt−_ 1 and _Fdepsi_ . Following the recommendation of the Trade Commission, I assign the label _SME_ to those rms with fewer than 500 employees. According to this denition, I construct the dummy variable _SMEit−_ 1 that takes a value of 1 if the rm in the previous period was considered a small-medium rm. This variable captures the fact that lobbying intensity is dierent in the cross section depending on size. The variable _Tt_ is an indicator that takes a value of 1 in the years 2008-2010 and 0 between 2005-2007. This allows me to focus on a 3 year window around the crisis. Finally, _Fdepsi_ is an indicator variable that takes a value of 1 for rms in sectors that depend more on external nance and 0 otherwise. This variable allows me to account for the dierences in lobbying observed across sectors based on nancial needs.

To study the eect of the nancial crisis on the incentives of corporations to lobby , I also include all the interaction terms between the main three variables. The coecient _δ_ 6 captures the eect of the crisis on lobbying for small rms relative to large rms in industries with low external nancial dependence (this is the dierence-in-dierence coecient). On the other hand, the coecient _δ_ 7 measures how much small rms relative to large rms are aected in sectors that depend more on external nance on top of the eect found in sectors with low external nancial dependence. This estimate uses variations in three margins: time (before and after the crisis), rm size (small vs large), and external nancial dependence (low and high).

I estimate equation (1) using an ordinary least squares regression on a balanced panel of 3,402 parent companies and 20,412 rm-year observations. To evaluate the signicance of the coecient, 12
Learning from Developing Country Power Market Experiences: The Case of Peru Renewable generation has had modest but increasing contributions to power supply in Peru and is expected to continue growing in the future. Wind, solar and biomass power plants jointly contributed 2.8% of total net generation in Peru in 2017. Installed renewable generation capacity (other than hydro) increased up to 408 MW in 2017, with 243 MW of wind power, 96 MW of solar power and 69 MW of biomass power (see Figure 5-7). The share of renewable generation in Peru is expected to increase further, following the commissioning of new renewable generation facilities.

These and other expected renewable capacity additions have been driven by renewable auctions, which have resulted in total capacity additions by winning bidders of 537 MW of hydro power plants, 394 MW of wind power plants and 227 MW of solar power plants (see Table 5-2). Note that capacity additions awarded through auctions are yet to be fully materialized.

<!-- Start of picture text --> MW<br> 450<br> 400<br> 350<br> 300<br>243 243<br> 250<br>146 146<br> 200<br> 150 - -<br> 100 84 84 96 96 96 96<br> 50<br> - - 23 - 23 - 61 61 61 61 69 69<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Biomass Solar Wind<br><!-- End of picture text --> Figure 5-7 Evolution of renewable generation capacity in Peru (other than hydro).

Source: COES Table 5-2 Capacity additions (MW) per technology in renewable auctions in Peru.

|**Auction**|**Solar**|**Wind**|**Hydro**|**Biomass**|
|---|---|---|---|---|
|**2009/10**|27|142|180|14|
|**2011**|16|90|102|2|
|**2013**|||211||
|**2015/16**|184|162|80|4|
|**Total**|**227**|**394**|**573**|**20**|

Source: Worldbank Renewable energy (REG) auctions have successfully lowered energy prices for these projects in Peru (see Figure 5-8). The first REG auction was carried out in 2010 in two rounds, in February and August. In March 2011, before the second REG auction, the government approved upgraded regulations through DS No 0122011-EM. Between 2011 and 2014, the second and third REG auctions were carried out. Finally, at the 22
**Figure A18 – Heterogenous treatment effects on willingness to get vaccinated**

<!-- Start of picture text --> a) Sex of respondents b) Location of respondents<br>.4 .6<br>.3 .4<br>.2 .2<br>.1<br>0<br>Female Male Highlands Islands Momase Southern<br>Control Treatment 1 Treatment 2 Treatment 3 Control Treatment 1 Treatment 2 Treatment 3<br>c) Age of respondents d) Education level of respondents<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>18-39 years old 40 years old or more Tertiary education Secondary education or less<br>Control Treatment 1 Treatment 2 Treatment 3 Control Treatment 1 Treatment 2 Treatment 3<br>Note: This chart is based on answers to question 34 in Appendix B.<br>Figure A19 – Treatment effects on people’s willingness to get vaccinated<br>a) Share of respondents willing to get vaccinated b) Share of respondents unwilling to get vaccinated<br>.35 .35<br>.3 .3<br>.25 .25<br>.2 .2<br>.15 .15<br>Control Health experts treatment Social norms treatment Relative safety treatment Control Health experts treatment Social norms treatment Relative safety treatment<br>Share of respondents willing to get vaccinated Share of respondents willing to get vaccinated<br>Share of respondents willing to get vaccinated Share of respondents willing to get vaccinated<br>Share of respondents willing to get vaccinated<br>Share of respondents not willing to get vaccinated<br><!-- End of picture text --> Note: This chart is based on answers to question 34 in Appendix B.

39