rafmacalaba/datause-encoder-data huggingface dataset:
The most recent year for which data is available is 2011, which is used to arrive at the labor share of 0.51 for Bangladesh. The figure below compares the labor share of income of Bangladesh to the mean/median labor shares of lower‐middle income countries obtained from PWT 8.1 for the year 2011. The labor share of the country lies close to the mean/median of the lower‐middle income countries though somewhat lower. In the robustness exercises, I show how alternative values of � affect the results of the analysis.

- **Depreciation Rate:** ���. ��� **.** The annual depreciation rate of capital stock is sourced from the PWT 8.1. The PWT 8.1 classifies capital stock into six different categories with each category having a different rate of depreciation. A somewhat lower depreciation rate for Bangladesh is rooted in the fact that the country has larger share of capital stock in assets that depreciate slowly relative to assets that have a much higher rate of depreciation such as computers, software etc. The aggregate depreciation rate for the country is likely to inch upward as the capital mix shifts towards assets that have a higher depreciation rate. The robustness exercises discuss the sensitivity of the findings to the choice of higher depreciation rates.

- ��

- **Initial capital‐to‐output ratio:** ��. ��. The initial capital‐to‐output ratio is calculated ��

- using the capital stock and GDP data from the PWT 8.1. The most recent year for which data is available from the PWT 8.1 is 2011 which is used to calculate the specified value of capital‐to‐output ratio. The figure below compares the capital‐to‐output ratio of the country with some of its neighbors. The capital‐to‐output ratio of the country is lower compared to China driven by the fact that China has made massive investments in capital stock over the last few decades. Though still in range with other neighbors except Nepal, the capital‐to‐output ratio is somewhat higher for Bangladesh. As seen in

<!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Bangladesh China India Nepal Pakistan Sri Lanka<br>Capital‐to‐Output Ratio<br><!-- End of picture text -->

**Source: PWT 8.1**

equation (8), a higher value of the capital‐to‐output ratio puts downward pressure on the growth impact from increasing investment.

8
## 32 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# **Table 7.** Estimates of the relationship between urban form and productivity (OLS). With outliers.

(Dependent variable: ldr)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|1996 norm. exchange|0.424***<br>(0.0601)|||0.394***<br>(0.0643)||0.392***<br>(0.0563)|||0.353***<br>(0.0611)||
|1996 norm. perimeter||0.432**|||0.404**||0.414**|||0.373**|
|||(0.1765)|||(0.1654)||(0.1801)|||(0.1725)|
|1990 fullness|||0.710***|0.604**|0.594**|||0.695**|0.600**|0.588**|
||||(0.2487)|(0.2454)|(0.2560)|||(0.2680)|(0.2621)|(0.2779)|
|1990 fullness<sup>2</sup>|||-0.601***|-0.466**|-0.446**|||-0.652***|-0.527**|-0.506**|
||||(0.1997)|(0.2035)|(0.2058)|||(0.2107)|(0.2122)|(0.2206)|
|1996 circuity|0.157|0.138|0.009|0.127|0.106|0.321|0.316|0.230|0.316|0.306|
||(0.8991)|(0.8638)|(0.8912)|(0.8763)|(0.8435)|(0.9297)|(0.8954)|(0.9235)|(0.9057)|(0.8753)|
|1996 intersection density|<br>0.461***|<br>0.474***|<br>0.507***|<br>0.485***|<br>0.495***||||||
||(0.0804)|(0.0784)|(0.0860)|(0.0907)|(0.0887)||||||
|1996 street density||||||0.040***|0.041***|0.044***|0.042***|0.042***|
|||||||(0.0065)|(0.0064)|(0.0070)|(0.0074)|(0.0072)|
|1990 sprawl|-0.128*|-0.134*||||-0.051|-0.056||||
||(0.0648)|(0.0776)||||(0.0604)|(0.0733)||||
|1990 population|-0.010|-0.000|-0.010|-0.007|0.002|-0.012|-0.002|-0.012|-0.008|-0.000|
||(0.0110)|(0.0068)|(0.0086)|(0.0094)|(0.0060)|(0.0106)|(0.0063)|(0.0085)|(0.0091)|(0.0055)|
|1990 population<sup>2</sup>|0.000|-0.000|0.000|0.000|-0.000|0.000|0.000|0.000|0.000|-0.000|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Constant|5.566***|5.605***|5.730***|5.292***|5.322***|5.242***|5.250***|5.423***|5.056***|5.063***|
||(0.8586)|(0.7410)|(0.9005)|(0.8773)|(0.7344)|(0.8952)|(0.7775)|(0.9390)|(0.9136)|(0.7696)|
|Country dummies|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|N|919|919|919|919|919|919|919|919|919|919|
|R-squared|0.287|0.286|0.281|0.288|0.288|0.293|0.293|0.289|0.295|0.295|

Note: Robust standard errors clustered at the country level in parentheses. *** p<0.01, ** p<0.05, * p<0.1; All models include geographical characteristics as measured of natural amenities, namely, distance in km to international border, temperature, precipitation and coast indicator. Intersection_density has been divided by 100; street_density has been divided by 1,000; population has been divided by 100,000; and the control dist_border_kms has been divided by 1,000.
(Fryer, 2013; Fryer Jr, 2011). Given these mixed results, it is clear that context and the details of design matter greatly for determining the impacts of performance-based rewards (Bruns et al., 2011). In some contexts teachers may lack the skills and knowledge to respond effectively to incentives, even if they wished to.

Our contribution is both to the design and to the discussion of context specificity of these program. First, we document the contrast in the impacts between in-kind and recognition rewards, and explore heterogeneity in how these effects are mediated by teacher and student gender. To the best of our knowledge, this is the first paper with a clear identification strategy to look at the contrasts between financial and recognition rewards for government civil servants. A similar study experimentally compared financial incentives with providing recognition for pro-social behavior, although the agents were private-sector hairdressers (Ashraf et al., 2014). Second, we document impacts on learning outcomes even in an environment where teacher skills are relatively low.

# **2 Intervention, Sample, and Evaluation Design**

Guinea, located on the west coast of Africa, is one of the poorest countries in the world. It is ranked 174/189 on the Human Development Index and, as of 2012, 36 percent of the population lived in extreme poverty.<sup>1,2</sup> School enrollment at all education levels have been increasing in recent years, but serious challenges remain both in access and quality. For example, an assessment administered in 2017 revealed that only 25 percent of grade two and three children could read a simple text,<sup>3</sup> and according to the Human Capital Index a child born today can expect to complete 7 years of schooling, but adjusting for quality suggests that amounts to only 4.5 years of high-quality schooling (World Bank, 2018).

These apparent problems induced a newly elected government in 2011 to seek innovative approaches in personnel and human resource management reforms. Within this context, the Ministry of Pre-University and Civic Education (MEPU-EC) initiated a donor-funded individual performance-based reward program for teachers. The program started in the 2012-2013 academic year and was implemented relatively well (see discussion below), but faced serious challenges in its last year of implementation (2013-14 school year) due to the outbreak of Ebola in December 2013, and especially as the epidemic spread in 2014.<sup>4</sup>

> 1 `http://hdr.undp.org/en/data`

> 2 `https://data.worldbank.org/country/guinea`

> 3 `https://resenguinee.org/`

> 4Source: `https://www.cdc.gov/vhf/ebola/history/2014-2016-outbreak` Accessed September 5th, 2020

4
|Internet|X<br>IT|intensity|||||||||1|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Number|of<br>FDI|projects||||||||1|0.1599*|
|verage|arket|cess|ri¤|||||||.1171*|.1404*|
|A|m|ac|ta||||||1|-0|-0|
|verage|put|ri¤|||||||2561*|.1765*|.1875*|
|A|in|ta||||||1|0.|-0|-0|
|erage|put|¤||||||965*|014*|366*|109*|
|Av|out|tari|||||1|0.8|0.3|-0.1|-0.2|
|of|X|ct|ity||||3*|7*|1*|*|*|
|e||tra|ns||||11|35|67|628|585|
|Rul|law|con|inte|||1|-0.4|-0.4|-0.1|0.1|0.3|
|.|ource|ow-|nt<br>nat.<br>ource<br>nsity|||182*|917*|915*|265*|745*|586*|
|Nat|res|end|me<br>X<br>res<br>inte||1|-0.0|-0.1|-0.0|-0.1|-0.0|-0.1|
|pital|ow-|nt<br>X|ital<br>nsity||496*|925*|446*|104*|962*|205*|441*|
|Ca|end|me|cap<br>inte|1|0.6|0.0|-0.2|-0.2|-0.0|0.1|0.2|
|h-|l|ow-|nt<br>skill<br>nsity|715*|648*|177*|490*|596*|697*|217*|326*|
|Hig|skil|end|me<br>X<br>inte|1<br>0.2|0.0|0.6|-0.4|-0.4|-0.1|0.2|0.3|
|||||ill intensity<br>al intensity|s. intensity|ct intensity|utput tari¤|input tari¤|access tari¤|DI projects|IT intensity|
|||||X sk<br>capit|t. re|ntra|ge o|rage|ket|of F|t X|
|||||t <br>|a|co|ra|e|ar|r|e|
|||||High-skill endowmen<br>Capital endowment X|Nat. res. endow. X n|Rule of law X|Ave|Av|Average m|Numbe|Intern|

9
13 It is interesting to note, nevertheless, that basic education is not generally Included In the mix of services offered displaced workers. An exception to this statement is Sweden, which makes basic education part of is comprehensive adult training program. Another exception is Canada, and one of the few basic education programs for which evaluation evidence is available is the Canadian Basic Training for Skill Development (BTSD) program. Fortunately, the limited evidence available for the BTSD program can be buttressed with evaluation results recently gathered from an experimental welfare-to-work program In the U.S.

The Canadian BTSD Drogram. Created in 1982, the Canadian National Institutional Training Program (NITP) established seven component programs which provided funding for classroom training purchased from public and private vocatiqnal centers, technical institutes, and communiy colleses.a Among the seven programs, the report by Robinson and others (1985) provides an evaluation of the Skill Training and BTSD programs. Skill Training courses were designed either to provide entrylevel skills in a particular occupation or to update occupation-specffic skills. In contrast, BTSD courses were Intended to improve basic skills in mathematics and communications so that participants could meet the academic requirements for entry into Skill Training or proceed on to employment.

The NITP evaluation compares labor market outcomes for samples of program participants who finished training courses in 1983-84 with the outcomes observed for a matched comparison sample of nonparticipants. About 30% of Skili Training participants were women, while nearly half of BTSD trainees were female. For both programs, more than three-quarters of all trainees were unemployed prior to training, and most trainees were in the 20-44 age bracket.

Results for skill training may be characterized as, at best, mbied; while distinctly negative resuits were obtained for basic education. In particular, the regression analysis suggests that BTSD training signh%,antly decreased weekly and annual eamings, as well as both of the employment measures examined. Two caveats, however, should be noted. First, wages and annual eamings of nonparticipants are on average much higher those of BTSD trainees. Despite the attempt to match members of the comparison group to sample members, It appears that the small comparison group contains relatively
# **Table IA2 – Impact of Debt Ceilings on States’ GDP and Employment (Sample of States with** **_Sustainable_ and** **_Under-Watch_ Debt)**

<mark>This table reports OLS estimates of the impact of the FD Law on GDP and employment for the sample of ten states classified in 2016Q1 as with</mark> “Sustainable” and “Under-Watch” public indebtedness. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary and tertiary sectors of state _s_ in quarter _qUnderWatchs,16Q1_ is an indicator variable that equals one for the five states that had an indebtedness classification of “Under-Watch” in 2016Q1 and equals zero for the remaining five states with an indebtedness classification of “Sustainable”. _Postq_ is an indicator variable that equals one from 2016Q2 onwards. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent <u>levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||GD|Ps,q|Emplo<br>Tot|yment<br>als,q|Emplo<br>Prima|yment<br>rys,q|Employ<br>Second|ment<br>arys,q|Empl<br>Ter|oyment<br>tiarys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Postq|-0.006||-0.011*||-0.047***||-0.018*||-0.007||
||(0.008)||(0.006)||(0.014)||(0.008)||(0.012)||
|Postq*UnderWatchs,16Q1|0.007*|0.007*|0.016***|0.016***|0.078**|0.072***|0.020*|0.02|0.014*|0.015*|
||(0.004)|(0.004)|(0.003)|(0.004)|(0.029)|(0.023)|(0.02)|(0.021)|(0.007)|(0.009)|
|Observations|192|192|144|144|144|144|144|144|144|144|
|R-squared|0.316|0.608|0.281|0.411|0.153|0.259|0.128|0.216|0.251|0.366|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

75
Table C5: Robustness on GDP Growth: Drivers of Total Debt Inflows, by Sector - Advanced Economies (Quarterly AHKS data, missing filled from Public Sources)

||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|
|---|---|---|---|---|
|log(VIX_t−_1)|-10.48<sup>_∗∗∗_</sup>|0.614|-8.600<sup>_∗∗∗_</sup>|-2.495<sup>_∗∗_</sup>|
||(2.986)|(1.418)|(2.301)|(1.064)|
|GDP Growth_it−_1|0.566<sup>_∗∗_</sup>|0.00402|0.446<sup>_∗∗_</sup>|0.116|
||(0.241)|(0.0688)|(0.211)|(0.0737)|
|Observations|1127|1127|1127|1127|
|_R_<sup>2</sup>|0.047|0.000|0.046|0.025|
|CountryFE|Yes|Yes|Yes|Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Total Debt is the sum of Portfolio Debt and Other Investment Debt inflow data, constructed by AHKS as described in Section 2. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as country year-on-year percentage GDP growth minus aggregate advanced economy year-on-year GDP growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 99
# **2. Data**

# **2.1. Data sources**

Since we are interested in the phenomenon of firm informality, for this study we focus on a universe of private non-agricultural firms with fewer than 25 workers, but situate this universe within employment in Egypt. This focus is because public enterprises and government establishments are not at risk of being informal.<sup>7</sup> Formality is typically not relevant for most agricultural activities, and informality among firms of 25 workers or more is rare (Assaad, AlSharawy, & Salemi, 2019). Given this universe, we use data sets from household surveys with a household enterprise module and establishment surveys or censuses that represent both formal and informal firms of all sizes.

The first data source we use is the Egypt Labor Market Panel Survey (ELMPS) carried out by the Economic Research Forum in collaboration with Egypt’s Central Agency for Public Mobilization and Statistics (CAPMAS). Four waves of this survey were carried out in 1998, 2006, 2012, and 2018.<sup>8</sup> We primarily use the 2012 and 2018 waves for contemporaneity with our other data sources, but use the 1998 and 2006 waves when exploring firm dynamics. The second data source is the 2012/13 Economic Census (EcC), which is an in-depth survey of establishments carried out by CAPMAS every five to six years. The third is the Egypt 2017 Establishment Census (EsC), which is a full census of establishments, also carried out by CAPMAS in conjunction with the decennial population and housing census. These data sources have different coverage and depth of detail, as we discuss below.

# **2.1.1. Egypt Labor Market Panel Survey**

The ELMPS is a nationally-representative household survey that has tracked a panel of households (continually adding refresher samples) since 1998.<sup>9</sup> The ELMPS has, since its inception, included a nonagricultural household enterprise module. Information on agricultural activities is collected in other modules that are not relevant for our purpose. Any household which reports a member who is selfemployed, an employer, or unpaid family worker in either their primary or secondary jobs must respond to either the non-agricultural or agricultural enterprise module.<sup>10</sup> Households can report on multiple enterprises. There were 2,315 enterprises in 2,195 households in the 2012 wave of the ELMPS and 2,228 7 The public sector does not have informal firms, but it does, increasingly, have some informal employment (Selwaness & Ehab, 2019).

8 For more information on the data, see Krafft, Assaad, and Rahman (2019). The data are publicly available at <u>www.erfdataportal.com.</u> 9 The ELMPS does not cover the Frontier governorates of Matrouh, New Valley, Red Sea and North and South Sinai, which together comprise about 2% of Egypt’s population.

10 Households that did not initially report members as employed in these statuses may also report non-agricultural enterprises; indeed, allowing this structure identifies substantially more employment among women (Krafft, Keo, & Fedi, 2019).

4
exported product falls into (or change) either a different chapter (CC at the 2-digit level under the HS); or heading (CH at the four-digit level); or subheading (CS at the six-digit level), or item (CI at the eight-digit level), than any of its imported inputs. Changes of classification expressed at broader levels of aggregation are, in principle, more constraining.

The HS schedule was not designed specifically as a system used to confer origin. Indeed, in some cases it can be argued that a given level of transformation (however measured) can be identified by a change of heading for some products, whereas it cannot be identified for others. As a result, schemes using the CTC can provide a rationale for a wide range of exceptions generally prohibiting the use of non-originating materials from a certain chapter, heading, sub-heading, or item **.** Allowances, on the contrary, allow the use of non-originating materials from certain classifications.

**ii) The domestic content rule, or regional value content (VC)** entails a minimum

percentage of local value added in the originating country, or a maximum percentage of value originating in non-member countries.

**iii) Technical Requirement (TECH)** requires the product to undergo specific manufacturing or processing operations in the originating country or prohibits the use of certain inputs.

Appendix A3 provides examples of the definition of RoO under NAFTA, which show that product-specific rules of origin (PSRO) are complex and difficult to measure because of the combinations of criteria within and across regimes. In the case of NAFTA, the first column of table 1 shows the number of tariff lines with positive exports for every combination of RoO defined under the agreement.

Table 1 here: Description of RoO under NAFTA.

Estevadeordal (2000) was the first to propose a discrete RoO index based on an “observation rule” to measure the restrictiveness of the resulting complex system of PSRO. The rule was based on two assumptions. The first one, a change at the level of chapter (CC) is more restrictive than a

> Nomenclature comprises 20 sections further subdivided into 96 chapters (commodity group identified by 2-digit code). Chapters are subdivided into headings (4-digit codes) and subheadings (6-digit codes, about 5000 of them), where the harmonization stops. Some administrations such as Eurostat use finer classifications (up to 10 digits).

8
increases in the municipal transfers will require further national government outlays, it is clear that the growth in the national government deficit in the 1990s was mainly due to its own spending, not to higher transfers to territorial governments (see also Bird and Fiszbein 1998).

Table 4. Government Fiscal Accounts at Three Levels, 1987-97

|(percentage of GDP)|||||||||
|---|---|---|---|---|---|---|---|---|
|_Level ofgovernment_|_1987-90_|_1991_|_1992_|_1993_|_1994_|_1995_|_1996_|_1997_|
|_National central government_|||||||||
|Total revenue|10.9|12.1|12.7|13.5|15.2|13.2|13.5|13.9|
|Total spending|11.7|11.5|15.4|14.5|16.3|16.6|18.5|18.8|
|Primary spending'|_5.1_|4.8|6.4|7.0|8.3|8.5|10.1|10.5|
|<br>Transfers to subnational governments and|5.5|5.4|7.7|5.9|6.8|6.5|6.3|6.0|
|enterprises|||||||||
|Of which, current to subnational central<br>Administrations|0.7|1.2|1.1|1.4|1.9|2.0|||
|Interest|1.0|1.2|1.3|1.5|1.2|1.6|2.1|2.3|
|Total surplus (deficit)b|-1.0|-0.7|-2.8|-1.3|0.6|-2.8|-4.9|-4.9|
|Primary balance|0.1|0.5|_-1.5_|0.3|1.7|-1.2|-2.8|-2.6|
|<br>Domestic financing|1.4|0.8|3.6|_0.5_|-0.5|3.1|||
|External financing|-0.4|-0.1|-0.8|0.7|0.0|-0.3|||
|_Departmental central administration_|||||||||
|<br>Total revenue|1.8|1.8|1.8|1.9|1.8|2.0|||
|Total transfers to central administration|0.4|0.4|0.4|_0.5_|0.5|0.6|||
|Own revenue|1.4|1.4|1.4|1.4|1.4|1.4|||
|Total spending|1.9|2.0|1.9|2.2|2.1|2.1|||
|<br>Primary spending'|1.8|1.8|1.8|2.1|1.9|1.9|||
|Interest|0.1|0.1|0.1|0.1|0.2|0.2|||
|Total surplus (deficit)b|-0.2|-0.2|-0.1|-0.4|-0.3|-0.1|||
|Primary balance|-0.1|-0.1|0.0|-0.3|-0.1|0.1|||
|<br>Domestic financing|0.2|0.2|0.1|0.4|0.3|0.1|||
|_Municipal central administration_|||||||||
|<br>Total revenue|2.2|2.6|3.0|3.4|3.9|4.4|||
|Transfers (from national and departments)|0.9|1.2|1.5|1.8|1.8|2.1|||
|Own revenue|1.3|1.4|1.5|1.6|2.1|2.3|||
|Total spending|2.3|2.9|3.0|3.7|4.4|4.6|||
|Primary spendinga|2.2|2.7|2.8|3.5|4.1|4.2|||
|Interest|0.1|0.2|0.2|0.2|0.3|0.4|||
|Total surplus (deficit)b|-0.1|-0.2|-0.1|-0.3|-0.5|-0.2|||
|Primnary balance|0.0|0.0|0.1|0.0|-0.2|0.2|||
|Domestic financing|0.2|0.3|0.1|0.2|0.5|0.2|||

- a. Primary spending is net of interest and transfers to territories.

b. Balance after adjustment of transfers and inclusion of net lending.

_Source:_ Central bank; Clavijo (1998); World Bank and International Monetary Fund staff estimates.

13 Fiscal data from various sources all show the same broad trends described here, although they differ in detail. The central bank's assemblage of data for 1987-95 is the best long-term series.

32