rafmacalaba/datause-encoder-data huggingface dataset:
access to systematic assessments of labor demand or the supply of workforce skills. Moreover, despite the large size and economic importance of the informal sector in many developing countries and emerging markets, assessments of supply and demand in the informal labor market are often minimal or wholly absent. As a result, strategies to improve skills matching and keep training providers apprised of evolving demand conditions in the labor market remain important challenges.

There is emerging evidence that analyses of online job-search engines and other employment services can be used to address information asymmetry and coordination failures in the labor market. Global online job portals such as LinkedIn, Indeed, Monster and Career Builder are bridging the gap between employers and job seekers in many countries. In areas where the use of such websites is still limited, local online job portals often provide vacancy announcements for both the formal and informal sectors and help expand access to job information for those with internet connectivity. These online platforms, whether global or local, provide a rich and continuous stream of labor market data that has remained largely untapped by policy makers.<sup>2</sup> In the field of labor economics, the prevalence of big data from online job portals is increasing, and various researchers have explored different methodological approaches to analyzing this data. For instance, Kureková et al. (2015) conducted a literature review in an effort to identify the advantages and disadvantages of using online job data and concluded that it represented a rich source of information that should be exploited despite issues with the representativeness of online job-portal data. Labor economics has traditionally relied on sample survey data, such as workforce and household surveys. Such surveys are often infrequently conducted, and their scope and coverage can entail important limitations. By contrast, big data has the potential to generate multidimensional, granular, real-time information, which can be used to provide new insights into longstanding economic questions, enhance economic research and aid public policy development.

# **2. Objectives**

This paper discusses potential applications of online job-portal data for academic research and policy formulation through an empirical study of data produced by Babajob, an online job-matching platform in India. The paper is designed to both contribute to the academic literature and to support the development

> 2 For example, LinkedIn, a social networking service for businesses and professionals launched in 2003, had data on 433 million individuals as of Q1 2016. The site is available in over 200 countries worldwide and in 20 different languages. The data obtained through LinkedIn have been primarily used for business purposes, rather than for policy formulation, though various industry- and skills-focused analyses have been published on LinkedIn’s official blog. Tambe (2014) used skills data from LinkedIn to measure employers’ investment in big-data-related human resources management.

3
corruption index are significantly related to GDP per capita growth.

# **6 Predictions**

**In-sample fit** The in-sample predictions of the growth rates of Nigeria based on the different specifications in Tables 2 and A.5 are graphically represented in Figures A.2 and A.3 in the Appendix. Relying on this graphical analysis, specifications (5) to (7) in Table 2 provide the best in-sample fit of Nigeria’s growth rates. This seems to be mainly driven by the prediction of the spike in growth in the period 2000-2004<sup>15</sup> . Specification (7) is chosen to serve as the base for the out-of-sample growth predictions, because it contains the finest decomposition of the natural resource variable.

**General assumptions** GDP per capita growth rates are predicted for six five-year periods starting in 2015-19, such that the last prediction period is 2040-44. The high degree of uncertainty about the future development of covariates such as investment, trade or government expenditures, is accommodated by the definition of different scenarios. Common to all scenarios is the assumption that the fixed effect of Nigeria is moderately improving over time.<sup>16</sup> Figure A.4 in the Appendix shows the magnitude of the fixed effects of all countries in the sample. Fixed effects can be understood as country-specific deviations from the overall constant of the regression. Nigeria’s fixed effect is strikingly low, and among all the countries in the sample, only three countries have lower fixed effects, among them Liberia as another Sub-Saharan African country. Assumptions concerning changes of fixed effects over time reflect beliefs about the development of countries relative to each other. In other words, if income convergence is assumed, fixed effects are modeled to converge to each other. We assume, that Nigeria’s fixed effect will increase to -3 until 2050, which is a level that is close to Burkina Faso (-3.7), the Senegal (-3.2), Indonesia (-2.9) or Cote d’Ivoire (-2.4) in the current estimation.

In addition, in each scenario GDP per capita is updated, i.e. the GDP per capita growth rate in ( _t −_ 1) is used in combination with the level of GDP per capita in ( _t −_ 1) to calculate GDP per capita in period _t_ . The future development of the remaining covariates differs across scenarios, and the underlying assumptions are explained in detail below.

**Scenario 0:** This baseline scenario is rather pessimistic, and assumes that oil rents in GDP remain at the low level that is predicted for 2015-19 until the end of the prediction period, 204044. Expected oil rents for 2015-19 are based on the World Bank’s Commodity Price Forecasts for

> 15The high average growth rate in this period comes from a growth rate of close to 30% in 2004, as reported in the World Bank’s World Development Indicators, but also by the United Nations (official data) and by the National Bureau of Statistics in Nigeria.

> 16See for example IIASA (2015).

18
# **Table 5(4): Robustness checks, using (t-r)*differentiated interaction**

||(1)|(2)|
|---|---|---|
|(t-r)|0.154|0.176|
||(0.428)|(0.402)|
|diff|-2.064||
||(3.588)||
|(t-r)*diff|1.120*||
||(0.603)||
|Ad-R&D-Sales||-8.488***|
|||(3.067)|
|(t-r)*Ad-R&D-Sales||1.184**|
|||(0.512)|
|prc_sh|-8.645***|-8.813***|
||(1.386)|(1.447)|
|collpriv_sh|-16.060***|-14.176***|
||(2.288)|(2.401)|
|for_sh|-47.938***|-46.444***|
||(1.775)|(1.827)|
|HS4 fixed effects|Yes|Yes|
|F statistics for H0: b1+b3=0|7.34||
|(p-value)|(0.007)||
|Observations|6,719|6,098|
|R-squared|0.519|0.489|

Notes: The regressions are at HS8 level. The dependent variable is the share of indirect exports (conservative measure), ranging from 0 to 100. The export shares by trade regime and firm ownership type variables range from 0 to 1. The omitted trade regime is normal trade. The omitted firm ownership type is SOE. “diff” is a dummy variable which equals one if a product belongs to differentiated product and zero for both homogenous and referenced products. *** p<0.01, ** p<0.05, * p<0.1.

40
supply packets for students in their schools, and how much they wished to keep for themselves. Each packet contained one notebook and pen and was worth 200 RWF. Teachers could decide to allocate any amount, from zero to all 2,000 RWF, which would supply ten randomly chosen students with a packet.

We also asked teachers to undertake a _Grading Task_ which measured their mastery of the curriculum in the main subject that they teach.<sup>21</sup> Teachers were asked to grade a student examination script, and had 5 minutes to determine if a series of student answers were correct or incorrect. They received a fixed payment for participation. Performance on this task was used to estimate a measure of teacher skill based on a two-parameter item response theory (IRT) model.

# **3.3 Student learning**

Student learning was measured in three rounds of assessment: baseline, the end of the 2016 school year, and the end of the 2017 school year (indexed by _r_ = 0 _,_ 1 _,_ 2). These student assessments play a dual role in our study: they provide the primary measure of learning for analysis of program impacts, and they were used in the experienced P4P arm for purposes of performance awards.

Working with the Ministry of Education, we developed comprehensive subjectand grade-specific, competency-based assessments for grades 4, 5 and 6. These assessments were based on the new Rwanda national curriculum and covered the five core subjects: Kinyarwanda, English, Mathematics, Sciences, and Social Studies. There was one assessment per grade-subject, with students at the beginning of the year being assessed on the prior year’s material. Each test aimed to cover the entire curriculum for the corresponding subject and year, with questions becoming progressively more difficult as a student advanced in the test. The questions were a combination of multiple choice and fill-in diagrams. The tests were piloted extensively; they have no ceiling effects while floor effects are limited.<sup>22</sup> In each round, we randomly sampled a subset of students from each grade to take the test. In Year 1, both baseline and endline student samples were drawn from the official school register of enrolled students compiled by the head teacher at the beginning of the year. This ensured that the sampling protocol did not create incentives for strategic

> 21See Bold et al. (2017) who use a similar approach to assess teacher content knowledge.

> 22Test scores are approximately normally distributed with a mean of close to 50 percent of questions answered correctly. A validation exercise of the test at baseline found its scores to be predictive of the national Primary Leaving Exam scores (both measured in school averages).

16
efficient ���1� , this is more than offset by the FDI assumed to flow in each year. With an expanding external debt to GDP ratio, the implied investment share becomes even larger compared to the assumed path of investment share in section 3.1. This implies that the obtained growth path for a particular scenario will be higher compared to the corresponding scenario in which the investment is independent of changes in the external sector. This can also be seen by comparing the five year average rates shown in table below to the corresponding table presented in section 3.1.

In summary, all exercises underline the importance of a sustained high TFP growth in attaining a sustained high GDP growth. An investment‐led strategy by itself will not be able to deliver high growth for many years as without other factors driving growth, a resulting increase in capital‐to‐output ratio will keep chipping at the growth impact of investment. Given the importance of TFP growth, it is essential to identify factors that can generate high TFP growth growing forward. In the next section, I examine the quantitative impact of one such factor – more efficient allocation of existing resources across sectors.<sup>8</sup>

# **4. TFP Growth and Resource Allocation across Sectors**

Average labor productivity in agriculture is less than one‐fourth compared to average labor productivity in both industry and services. While the employment share of agriculture has shrunk by around 6.5 percentage points during the last 15 years, the country still employs 44 percent of its workforce in the relatively unproductive agricultural sector. The huge gaps in productivity across sectors are symptoms of misallocation of resources and economic growth can be achieved simply by moving resources out of agriculture to the other sectors.

While there are large gaps in average productivity, allocative efficiency requires a convergence in marginal productivity across sectors. Assuming Cobb‐Douglas production technologies at sector level together with perfect competition in factor markets, the measurement of marginal productivity reduces to adjusting the average products of a sector with the sector‐specific labor share of income. The table below shows the marginal productivity in industry and services relative to agriculture for Bangladesh which have been computed using labor shares of income at sector level from the GTAP database.

||**Margin**<br>|**al Productivity of La**<br>|**bor Relative to Agr **<br>|**iculture**<br>|
|---|---|---|---|---|
||**2000**|**2005**|**2010**|**2015**|
|Industry|4.38|4.41|4.19|4.32|
|Services|7.93|9.68|11.20|11.56|

> 8 Please see appendix A.2 for a brief discussion on factors that are important in driving TFP growth. This discussion is a summary of the survey on productivity by Syverson (2011). The interested reader should read the paper as the discussion here highlights only some of the important factors that can lead to productivity growth and is in no way provides a comprehensive listing of such factors.

23
Table 4. Cotton Exports by Leading Exporters, 1988/89 to 1990/91 1/.

||I<br>18|-<br>.I.......|.......1989/90..<br>_.. 1990/91._<br>3|
|---|---|---|---|
|Rank' COuntty<br>-..|||C<br>tons)|
|I. SudanI-|162|141|103<br>1M3.3|
|2.Mali.|89|96|113<br>9.3|
|3.C8te k'ilvole.|101|11l|80<br>97.3.|
|4.Zimbabwe|82|63|49<br>64.7|
|5. SBurkina Faso|60|61|72<br>64.3|
|6. Chad|48|48|57<br>_51.0_|
|7. Caeroon|59|38|_45_<br>47.3<br>:|
|8 Tanzania|62|37|37<br>45.3|
||9.:Benin|39|46|_51_<br>_45.3_|
||10. Togo31||3 1|3&<br>33.3.|
|L,. Senegalt<br>.|13|11|i6<br>13.3|
|12.<br>Cen.Af.Rep.'|8|10|.8.7.|
|i3'.':nia.-'|2|4|9<br>'<br>_.5.0..:_|
|.14. Mozambique .|||4<br>4'7:-|
|_15._SouthAfrica|S|3|4<br>4.0|
|16. Uganda-|2|2|4<br>2.7.|
|17.<br>Madagascar'|1|2|3<br>-<br>2.0:'|
|18. Kenya||1|-<br>2<br>1.3|
|19-31.Others|2?<br>'|16|14<br>17.3<br>'.:'|
|Total SSA|784|725|705<br>-<br>738<br>:-|
|Africa Total|852|769|727'<br>783|
|World Total|5,704|5,249|5,006<br>5,320|
|SSA Share (%) of:||||
|Africa|92|94|97<br>94|
|World|14|14|14<br>14|

1/ Ranked on average exports during the 1988/89 to 1990/91 period. Source: ICAC, Cotton: World Statistics, April, 1992.

9
US$1.5m per day [16]. In sum, there was no consensus on the production numbers or revenue they created.

On 15 May 2015, U.S. Army special forces killed a senior Daesh leader known as Abu Sayyaf, who, according to the U.S. Department of Defense “helped direct the terrorist organization’s illicit oil, gas and financial operations.” This raid also yielded significant amounts of intelligence into the Daesh economy, including administrative data providing a retrospective look at Daesh oil production in certain regions. Below, we shall compare our estimates from remote sensing with the administrative data captured during the raid.

# **1.2 Remote sensing estimation of oil production with gas flaring**

Our approach relies on the property that the extraction of oil is associated with the liberation of natural gas, primarily methane, which is initially dissolved in crude oil in constant proportions. The gas is typically collected and flared unless infrastructure exists to either re-inject the gas into the field, utilize the gas on-site, or package and send the gas off to markets [29]. Flaring is the generic method of natural gas disposal in Syria and most of Iraq, as it is in most of the world [47].<sup>3</sup> Remote sensing from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor deployed on the NOAA/NASA Suomi NPP satellite [12] allows the use of multi-spectral methods [14, 12] to estimate a flare’s radiant heat (RH), a measure of the heat released with the combustion of the natural gas. RH is thus an indicator of the volume of gas flared, which in turn is a predictor of the volume of oil extracted.

Figure 2 plots the relationship between yearly oil production and measured RH in 2012-2015 for the 22 significant oil fields that are deemed comparable to the fields Daesh now controls. Each point represents the logarithm of yearly oil production (vertical axis) and the logarithm of yearly RH (horizontal axis) for any given oil field in our estimation sample, with black symbols

> 3Large fields in southern Iraq where Daesh is not present have undertaken flaring reduction projects to collect the natural gas for economic uses.

6
The Presence of Keywords in Academic Articles Evolution of Institutions v. Other Topics in NBER's Abstracts <!-- Start of picture text --> !<br>!<br>i}<br>n !<br>ae] !<br>$ 9.0%-<br>a1<br>xI<br>Dl<br>51<br>5 Keywords<br>is}<br>« 7.0% | -® education<br>3 -® health<br>G \ © institution<br>Ss !<br>oO I<br>Dl<br>£ 1<br>5 5.0% 1<br>° !<br>oO I<br>oO !<br>!<br>!<br>!<br>| 1<br>1995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->

Source: data scraped by the authors from NBER's website. It contains the abstracts from all of the articles published up to November 2019
profitability, while desirable, are simply not available in a standard format across countries.

Caveats aside, it seems likely that our five indicators – M2/GDP, Private Credit/GDP, Cash/M2, interest spreads, and concentration – provide a reasonably complete picture of financial sector development since 1992. Note also that the indicators are differentially suited to capturing short versus long-term financial development. On the one extreme, movements in the ratio of cash/M2 can provide a summary of depositors’ short-term reactions to policy changes. On the other end of the spectrum, the private credit ratio and the concentration ratio, while both admittedly subject to some short-term perturbations, tend to capture long-term financial development. M2/GDP and spreads are, arguably, somewhere in between the two extremes. Taken together, the five indicators are likely to provide a more complete picture of both long-term and short-term banking development.

While the financial outcome variables that we use are standard within the finance and growth literature, our data on World Bank projects to support financial reform are not. The World Bank’s Operations Evaluation Department (OED) has recently compiled a comprehensive database of 556 projects from 1992 to 2003 in which an intention to reform the financial sector was expressed. These projects include loans for adjustment, technical assistance, or lines of credit (LOC).<sup>6</sup> We focus our analysis on adjustment loans because these tended to be larger (in dollar terms), with conditionality attached to specific financial reforms, but aimed at broad financial sector development. By contrast, LOC often focused on channeling funds through specific financial institution(s), while technical assistance loans tended to be much smaller than adjustment loans, and focused on more narrow issues. The Appendix lists the 68 countries that received at least one adjustment loan with conditions tied to financial reform. As noted, these are either pure financial sector adjustment loans or multi-sector adjustment loans with financial sector components. A control group of 38 countries identified as having received no such loans is also listed. Our goal was to include all developing countries that had reasonably

> 6 Lines of credit (LOC) are funds passed through an intermediary, for demand driven purposes, where the final end-user has to repay the loan, usually with interest.

5
Table 8a: Estimations of urban manufacturing <u>production functions and local 1-person establishment shares</u>

||Full sample<br>|Full sample<br>|Full sample<br>|Tradables<br>industries<br>|Col. 4<br>excluding 1-<br>person firms<br>|Non-tradables<br>industries<br>|Col. 6<br>excluding 1-<br>person firms<br>|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||||DV: Log outpu|t in manufactur|ing establishmen|t||
|Log employment in establishment|0.594<br>[0.010]***|0.592<br>[0.009]***|0.591<br>[0.009]***|0.578<br>[0.013]***|0.743<br>[0.013]***|0.601<br>[0.011]***|0.734<br>[0.012]***|
|Log capital in establishment|0.065<br>[0.003]***|0.065<br>[0.003]***|0.065<br>[0.003]***|0.052<br>[0.004]***|0.068<br>[0.004]***|0.086<br>[0.004]***|0.091<br>[0.005]***|
|Log materials in establishment|0.559<br>[0.006]***|0.559<br>[0.006]***|0.558<br>[0.006]***|0.581<br>[0.007]***|0.516<br>[0.007]***|0.528<br>[0.008]***|0.508<br>[0.009]***|
|Informal manufacturing employment||0.145|0.151|0.124|0.154|0.202|0.149|
|share in district||[0.028]***|[0.035]***|[0.049]**|[0.038]***|[0.040]***|[0.041]***|
|Informal manufacturing 1-person firm|||-0.043|0.190|0.054|-0.449|-0.259|
|employment share in district|||[0.142]|[0.175]|[0.162]|[0.162]***|[0.155]*|
|Observations|284,615|284,615|284,615|139,664|102,754|144,951|124,999|
|Adjusted R-squared|0.928|0.928|0.928|0.918|0.924|0.938|0.936|
|State-industry-year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Estimations consider simple production functions for manufacturing establishments in urban areas. Estimations report standard errors clustered by districtindustry, include state-industry-year fixed effects, and weight observations by sample weights. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.
Seasonality in Local Food Markets and Consumption

# – Evidence from Tanzania

Jonathan Kaminski, Luc Christiaensen and Christopher L. Gilbert<sup>1</sup>

**JEL:** D12, D14, D40, D91, I31

**Keywords:** maize, rice, seasonality, Sub‐Saharan Africa, calorie, food consumption

1 Jonathan Kaminski (kaminski.jonathan@gmail.com , corresponding author) is at Noble Agri, Luc Christiaensen (lchristiaensen@worldbank.org ) at the World Bank, and Christopher Gilbert at the SAIS Bologna Center, Johns Hopkins University (christopher.gilbert@jhu.edu ). The initial draft of the paper was presented at the “Agriculture in Africa – Telling Facts from Myths” workshop in November, 2013 at the World Bank in Washington DC, and the joint IMF, CTED, OEP International Conference “Food Price Volatility: Causes and Challenges” in February 2014, in Rabat. The authors would like to thank the referees, John Baffes, Christophe Gouel, Wouter Zant and seminar and conference participants in Accra, Washington, Rabat, Clermont‐Ferrand, Paris, Bologna and Rome II for comments.
lower value of a under the two-step procedure implies _(cetens paribus)_ a significantly lower adverse effect of globalization on poverty.

# **VI.** CONCLUDING REMARKS

> Globalization, or the integration of economies and societies through trade,

> investment, finance, information and labor flows is, in the view of many, an

> inescapable feature of the world today. On the one hand, there is a considerable

> body of opinion arguing that globalization has led to substantial economic progress among rich and poor countries alike and, indeed, may be the principal

> mechanism for the intemational convergence of living standards. On the other,

> many point to the challenges that it poses for many countries as well as for the

> most vulnerable socio-economic groups within countries.

The purpose of this paper has been to examine the extent to which globalization affects the poor. Section II presented various arguments that may explain how trade and financial integration may hurt the poor. Two main points emerged from the discussion. The first is it is usually difficult to draw clear-cut theoretical conclusions regarding the effect of globalization on poverty as a result of conflicting effects, both in the short and the long run. Empirical studies are thus important to assess whether net effects are positive or negative. The second is that it is possible that nonlinearities may be involved in the relationship between globalization and poverty. Accounting for these effects is crucial for empirical testing.

> The second part presented some preliminary evidence, based on linear cross-country regressions linking various measures of real and financial integration to poverty. The regressions (performed over a group of 11 low- and

> middle-income countries and with data covering the late 1980s and the 1990s)

38