rafmacalaba/datause-encoder-data huggingface dataset:
# Table 35: EARNINGS FUNCTIONS (lnW2) FOR GOVERNMENT AND PRIVATE SECTOR EMPLOYEES, BY LOCATION

|||Abidj|an|||Other|Cities||
|---|---|---|---|---|---|---|---|---|
||Gover|nment|Privat|e Sector|<br>Gover|nment|Privat|e Sector|
|Intercept|10.730|(49.7)|9.886|(53.2)|9.676|(46.7)|9.800|(29.5)|
|Years of Primary Schooling|.170|(2.3)|<br>.015|(0.4)|<br>.085|(0.9)|-.024|(0.3)|
|Years of Secondary Schooling|.165|(4.8)|<br>.113|(2.5)|<br>.180|(6.1)|<br>.043|(0.4)|
|Years of Higher Schooling|.126|(2.6)|<br>.356|(3.2)|<br>.085|(1.5)|<br>.101|(0.7)|
|Years of Current Job Experience<br>|.0068|(0.3)|<br>.141|(6.2)|<br>.074|(3.6)|<br>.079|(1.9)|
|(Years of Current Job Experience)<br>2|.0007|(0.8)|-.0031|<br>(4.2)|-.0008|<br>(1.3)|-.0022|<br>(1.4)|
|Years of Previous Job Experience<br>|.047|(1.0)|<br>.097|(1.7)|<br>.109|(3.0)|<br>.229|(1.5)|
|(Years of Previous Job Experience)<br>2|-.0042|<br>(1.1)|-.0074|<br>(1.6)|-.0054|<br>(2.5)|-.025|(1.6)|
|Years of Apprenticeship|-.007|(0.1)|<br>.023|(0.8)|<br>.030|(0.6)|-.001|(0.1)|
|Years of VTE|-.014|(0.4)|<br>.124|(2.5)|<br>.006|(0.1)|<br>.061|(0.5)|
|CEPE|-.804|(1.7)|-.014|(0.1)|<br>.427|(0.8)|<br>.582|(1.2)|
|BEPC|-.128|(2.4)|-.152|(0.3)|<br>.139|(0.2)|1.199|<br>(1.7)|
|VTE Diploma|-.478|(1.0)|<br>.029|(0.1)|<br>.295|(0.5)|2.119|<br>(2.8)|
|Other Diploma|-.928|(1.8)|-.066|(0.1)|<br>.078|(0.1)|2.364|<br>(2.7)|
|Sex (1-female)|-.431|(3.9)|-.0038|(0.1)|-.315|(2.5)|-.179|(0.5)|
|Non-Ivorian|_||-.202|(1.3)|-.571|(1.2)|<br>.019|<br>(0.1)|
|Seasonal Job|_|_-||_|-||.175|(0.3)|
|R2|.73||.49||.71||.65||
|F|15.8||9.9||13.8||6.8||
|n|98||172||100||76||

Note: Absolute t - values in parentheses.
processing is arguably to facilitate the financing of customs dues payment, as it is believed that in many cases finding the money to pay customs due is the first reason for delaying this step. Savings in opportunity costs and financial charges associated to delayed clearance are most probably underestimated in section 3 because this severe cash availability issue and very high opportunity cost would sometimes offset high demurrage charges after an extended stay in the terminal. Shippers facing extreme cash availability issues have no choice sometimes but to abandon cargo in the port because of the incapacity to pay customs dues and clearance charges, which does explain in part the large number of abnormal delays, or to wait until part of the shipment is sold to pay customs dues.

Finally **maritime transport operation patterns** may be one of the main determinants of inefficient cargo clearance patterns. We have observed in table 10 the very high concentration of import flows on a few routes. Two transshipment hubs (Algeciras and Las Palmas) account for more than 55% of total import volume and the top 6 origins are all transshipment hubs that account together for 87% of total volume. The top two routes (Algeciras Douala and Las Palmas Douala) are also the only two routes that run with a fixed arrival day (Friday and Saturday respectively). As a consequence Fridays and Saturdays are the busiest days in the week and account for more than half of the total traffic as can be seen below. This generates discretionary patterns in the performance of clearance operations and actually encourages shippers to deal with clearance operations on weekly schedules as has been confirmed by local interviews (weekly transport meetings with C&F agents).<sup>28</sup>

||**Monday**|**Tuesday **|**Wednesday **|**Thursday **|**Friday**|**Saturday **|**Sunday**|
|---|---|---|---|---|---|---|---|
|**Container discharge**|8%|7%|7%|9%|39%|16%|15%|
|**Declaration lodging**|25%|20%|18%|19%|18%|0%|0%|
|**Payment of dues**|17%|23%|20%|20%|19%|0%|0%|
|**Issuance of exit bill**|15%|20%|21%|21%|20%|4%|0%|
|**Exit fromyard**|15%|18%|20%|19%|21%|6%|0%|

**Table 11 – Daily distribution of clearance operations** (Source: CAMEROONIAN CUSTOMS, 2009)

> 28 The two routes run on weekly schedules. The two companies (Maersk Line and MSC) have implemented such relay networks to their main routes to reach economies of scale but have different strategies: while Maersk is rather a premium operator with substantially higher rates, MSC opts for low-cost competition. And as a result Maersk schedules are much more reliable than other companies which do not hesitate to delay deliveries for a later week in case of mismatch in the transshipment hub as has been observed elsewhere (Notteboom, 2006). Data collected confirms this observation since delay between container discharge and Customs declaration lodging is 1.5 days lesser in average for cargo shipped on Maersk Line than cargo shipped on MSC.

22
Where the subscripts n, s and t refer to the firm, sector and time period respectively (t=1 when the firm first enters the market, and t=t* when the firm expects to leave the market, with t* endogenous to expected future discounted profits);  refers to profits; r is the discount rate (assumed to be specific to each firm); and  is the sunk cost of entry (which is assumed to vary by sector s).

The exit decision differs in two ways from the entry decision. First, there is no additional sunk cost to consider in the decision-making process given that the firm will have already incurred this cost upon entering the industry.<sup>3</sup> However, having incurred the cost and to avoid having to repay it should it re-enter, it could be optimal to delay responses to negative shocks. Second, the firm has current-period profitability information to inform its forecast of future profitability. Thus, the exit decision will depend on:

- _2.2 The Role of Agglomeration Externalities on Entry and Exit_

A key variable of interest in both the firm entry and exit decision-making process is the expected future firm profits.<sup>4</sup> We assume that firms base their estimation of these future profits upon the information available the period before entry or exit takes place. Obviously some of this information will contain transitory elements (e.g. random

> 3 Firms could face some sunk costs if they shifted locations or sectors.  In this dataset, if firms do relocate, it is counted as an exit and entry, with the sunk cost of the new facility considered for the new plant.  If firms shift sectors, that is not counted as an exit.  With multiple product production facilities, the change in reported sector could simply reflect a relative shift across production rather than a larger change in production facilities.

> 4 There is also evidence on developing countries suggests that other elements of the business environment can affect the feasibility of entry, possibly by affecting future profits but also by restricting which entrepreneurs have access to which markets (e.g. World Bank 2004, Bigsten and Soderbom, 2005, Hallward-Driemeier 2009).

6
**Table 8:  Selected Parameters on the Quality of the Business Environment, 2006**

||**Min. capital**<br>**to start a firm**|**Recovery rate in the**<br>**case of bankruptcy**<br>|**Documents**<br>**for export**|**Time for**<br>**export**|**Cost to**<br>**export**<br>|
|---|---|---|---|---|---|
||**(% of GNI**<br>**per capita)**|**(cents on the $)**|**Number**|**Days**|**(US$ per**<br>**container)**|
|Mongolia|140.2|17.0|11|66|3007|
|Cambodia|66.2|0.0|8|36|736|
|Vietnam|0.0|18.0|6|35|701|
|Azerbaijan|0.0|32.5|7|69|2275|
|Kazakhstan|26.6|20.0|14|24|720|
|Kyrgyz republic|0.6|19.8|..|..|..|
|Uzbekistan|24.7|18.7|10|44|2550|
|Ghana|23.2|24.7|5|21|822|
|Uruguay|183.3|43.2|9|22|552|
|East Asia & Pacific|109.2|24.0|6.9|23.9|885|
|Europe & Central<br>|49.1|29.8|7.4|29.2|1450|
|Asia||||||
|L. America &|24.1|28.2|7.3|22.2|1068|
|Caribbean||||||
|Middle East & N.|859.3|28.8|7.1|27.1|923|
|Africa||||||
|OECD|41.0|73.8|4.8|10.5|811|
|South Asia|0.8|19.7|8.1|34.4|1236|
|Sub-Saharan Africa|297.2|16.1|8.2|40.0|1561|

_Source: Doing Business Survey, World Bank (2006a)_

# **3.3 Are returns to private sector investment in non-mining-related activities low?**

One way to assess the rate of return to economic activity in Mongolia is to estimate total factor productivity growth (TFP) focusing on the period since transition.<sup>30,31</sup> We assume that aggregate output can be expressed as a function of physical and human capital _Y = AF(K,H)_ , where _Y_ is gross domestic product in constant 2000 purchasing power parity (PPP) prices; _A_ is an index of total factor productivity; _K_ is gross domestic capital stock in constant 2000 PPP prices; _H_ is human-capital-adjusted labor input, defined as _H = LDPe_<sup>_(фS)_</sup> , where _L_ is population; _D_ is share of population age 15-64; _P_ is labor force participation rate; _S_ is number of years of education per worker; _ф_ is a parameter that measures the returns to education.

We consider two types of production functions. The first one is a Cobb-Douglas production function with possibly non-constant returns to scale _F(K,H) = [K_<sup>_α_</sup> _x H_<sup>_(1 - α)_</sup> _]_<sup>_γ_</sup> _,_

> 30 PICS (2004) does not include data that allow direct estimation of rates of return to capital for individual firms.

> 31 Notice that TFP growth is difficult to estimate. Small differences in assumptions can lead to very different estimate of TFP growth and growth in TFP reflect factors other than pure technical change such as increasing returns to scale, markups due to imperfect competition, and sectoral reallocations. The growth accounting method for estimating TFP growth is discussed in Ghosh and Kraay (2000). The method relies on data from the Government of Mongolia, the World Bank and IMF.

17
The program was first introduced in some communities in early 1998, but as noted above, local capacity constraints likely led to differences in the timing of program roll out. Program coverage, as indicated in Table 1, was low at the beginning of the period (early 1998) because the crisis had not been anticipated in the 1997-1998 fiscal budget.<sup>11</sup> It was only during the following fiscal year (1998-1999) that coverage of the program expanded above 70 percent of communities. Program coverage then expanded to over 89 percent of communities in 1999-2000 before declining to 80 percent during the first half of fiscal year 2000-2001.<sup>12</sup> During the 1998-2000 period most of the sampled communities (95.4 percent) were exposed to the program for some period of time.

# **4. Evaluation of the PMT Using the Indonesian Family Life Survey**

The data used in this study come from the 1997 and 2000 rounds of the Indonesia Family Life Survey (IFLS), which covers periods before and after the 1997-1998 Asian Financial Crisis and implementation of the child supplementary feeding program. The IFLS is a panel survey that enumerates information on various aspects of individual, household and family livelihoods. In addition, information on characteristics of communities is also collected, including features of the socio-economic environment, physical infrastructure, health and education facilities, and programs administered through these facilities.<sup>13</sup> In this study we link community-level data on implementation

> 11 The national budget plan (fiscal year) in Indonesia prior to 2001 ran from April 1 to March 31.  After 2001, the Government of Indonesia changed the fiscal year to correspond to the calendar year.

> 12 Pritchett et al (2002) note that the initial fiscal year 1998-1999 did not provide for broad emergency programs to cope with negative impacts of the crisis. In July 1998 the current budget was revised to include an explicit Social Safety Net Program.

> 13One author of this paper was a member of the IFLS3 survey team. A discussion of sampling and survey methods for IFLS2 and IFLS3 can be found in Frankenberg, Beegle and Thomas (1999) and Strauss et al (2004b), respectively.

9
# 1. Introduction

A country’s human capital is critical for its economic success. A recent report found that human capital comprises the largest component of a nation’s wealth, at 64 percent (World Bank 2018). In this paper, we focus on one measure of human capital: learning outcomes. Fifteen years of literature demonstrates that learning outcomes, as well as years of schooling, matter substantially for growth (World Bank, 2018; Hanushek and Kimko, 2000; Pritchett, 2001; Hanushek and Woessmann, 2008, 2012). This insight comes at a time when the availability of international student achievement tests (ISATs) is growing. These tests are psychometrically designed, standardized assessments of cognitive skills and enable credible cross-country and over-time comparisons of learning. However, traditional ISATs often exclude developing countries – the countries that have the most potential to gain from human capital accumulation.

In this paper, we address this gap by including the largest number of developing countries in a globally comparable database of learning outcomes to date. We build on a literature producing comparable measures of learning across countries and over time (Barro and Lee, 1996; Hanushek and Kimko, 2000; Barro and Lee, 2001, 2015; Altinok and Murseli, 2007; Hanushek and Woessmann, 2012, 2016; Angrist, Patrinos and Schlotter, 2013; Altinok, Diebolt and de Meulemeester, 2014; UIS, 2017; Altinok, Angrist and Patrinos, 2018). Our database covers 164 countries/territories from 2000-2017.

The central intuition behind the methodology we use is the production of a conversion factor between international standardized achievement tests (ISATs) such as PISA, TIMSS, and PIRLS and their regional counterparts (RSATs) such as SACMEQ, LLECE, and PASEC. This conversion factor is derived by comparing scores for countries that participate in both an RSAT and an ISAT in a given time period, schooling level (primary and secondary), and subject. Following Altinok, Angrist and Patrinos (2018), we call these countries “doubloon countries.” A detailed description of each assessment is provided in Altinok, Angrist and Patrinos (2018). This approach puts all tests on a common scale enabling credible comparison across countries. These tests have been designed and scaled to be comparable over time since the late 1990s. Thus, there is no need for an intertemporal adjustment over the 2000-2017 interval.

This paper outlines a series of improvements over past databases. First, we extend our methodology to enable the first inclusion of the Early Grade Reading Assessment (EGRA). EGRA is a basic literacy assessment that has been conducted in over 65 countries since 2006. This enables us to extend our database substantially. We include 48 countries with data in the last ten years (2007-2017). Many of these countries are large developing economies previously excluded from globally comparable databases such as Ethiopia, Bangladesh, and Nepal. This methodological update paves the way to include a series of assessments that are increasingly common in developing countries and are often excluded from large international assessments.

2
# **Table 5. Uptake of Larger Loan**

OLS

|Sampl|e:|All member|s|Al|l Business|Owners|M|atched Business|Owners|
|---|---|---|---|---|---|---|---|---|---|
||All<br>(1)|Males<br>(2)|Females<br>(3)|All<br>(4)|Males<br>(5)|Females<br>(6)|All<br>(7)|Males<br>(8)|Females<br>(9)|
|Female Brochure|-0.026|-0.033|-0.015|-0.029|-0.017|-0.051**|-0.029|-0.007|-0.043|
||(0.021)|(0.034)|(0.019)|(0.027)|(0.044)|(0.023)|(0.035)|(0.058)|(0.039)|
|Offered Business Training|0.048**|0.046|0.046**|0.058**|0.067|0.037|0.071**|0.056|0.095**|
||(0.021)|(0.035)|(0.019)|(0.027)|(0.044)|(0.024)|(0.035)|(0.055)|(0.047)|
|Female|-0.016|||-0.034|||-0.029|||
||(0.021)|||(0.031)|||(0.046)|||
|Age|0.006***|0.008**|0.000|0.006|0.009|-0.003|0.009|0.011|-0.003|
||(0.002)|(0.004)|(0.004)|(0.004)|(0.006)|(0.006)|(0.006)|(0.008)|(0.011)|
|Age^2|-0.000**|-0.000**|0.000|0.000|0.000|0.000|-0.000*|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|High Decision-making power (1=yes)|0.001|-0.003|-0.022|0.002|0.009|-0.016|-0.002|-0.037|-0.039|
||(0.002)|(0.020)|(0.017)|(0.003)|(0.025)|(0.022)|(0.005)|(0.051)|(0.036)|
|Own Education|0.002|0.005|0.000|0.003|0.004|0.001|0.004|0.004|0.007|
||(0.002)|(0.003)|(0.003)|(0.003)|(0.004)|(0.004)|(0.005)|(0.007)|(0.006)|
|Digit Span Recall|0.003|0.002|0.001|-0.001|-0.002|-0.002|0.002|-0.001|-0.001|
||(0.003)|(0.005)|(0.004)|(0.005)|(0.008)|(0.005)|(0.008)|(0.013)|(0.011)|
|Risk Tolerance|0.001|-0.001|0.002|0.001|-0.001|0.001|0.004|-0.002|0.006|
||(0.002)|(0.003)|(0.003)|(0.003)|(0.004)|(0.003)|(0.005)|(0.008)|(0.006)|
|Yrs. Of Education of Spouse|0.000|-0.005|0.002|-0.001|-0.003|0.000|-0.004|-0.011*|-0.001|
||(0.002)|(0.003)|(0.002)|(0.002)|(0.004)|(0.003)|(0.003)|(0.006)|(0.004)|
|Number of children under 9|0.003|0.003|0|0.007|0.007|0.006|0.01|0.017|0.001|
||(0.003)|(0.004)|(0.004)|(0.004)|(0.006)|(0.007)|(0.008)|(0.011)|(0.011)|
|Business Owner (Yes=1)|0.028**|0.040*|0.01|||||||
||(0.014)|(0.022)|(0.018)|||||||
|Land|0.000|0.001|0.000|0.000|0.001|0.000|-0.001|0.000|-0.002|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|
|Member of a Mixed Group (Yes=1)|-0.092***|-0.086*|-0.105**|-0.150***|-0.121*|-0.196***|-0.166**|0.021|-0.272***|
||(0.032)|(0.047)|(0.048)|(0.043)|(0.066)|(0.070)|(0.073)|(0.236)|(0.100)|
|Eligibility|0.188***|0.246***|0.130***|0.203***|0.299***|0.111***|0.219***|0.304***|0.148***|
||(0.015)|(0.026)|(0.016)|(0.019)|(0.033)|(0.018)|(0.024)|(0.040)|(0.032)|
|Mean Dependent Variable|0.15|0.20|0.08|0.15|0.20|0.08|0.18|0.25|0.11|
|N. Observations|3,451|1,880|1,571|2,065|1,149|916|726|363|363|
|R-squared|0.17|0.18|0.11|0.18|0.17|0.13|0.17|0.17|0.16|

Note: The dependent variable takes value 1 if the member applied for a larger loan. All regressions include branch fixed effects and are estimated using OLS methods. Standard errors are clustered at the borrower group level.The following symbols *, * * and ** * denote significance at the 10, 5 , and 1 percent level, respectively. See Appendix B for definition of variables.
International Law (1); Engineering services (4); and Architecture services (5). Table 2 summarizes this information for all the sectors, including the average restrictiveness score for each sector.

**Table 2: Count of countries for which average restrictiveness in sector is at least 0.5**

|**Sector **|**Score (avg.)**|**Number of countries**|
|---|---|---|
|Accounting and auditing|0.40|9|
|Accounting|0.24|10|
|Air transport|0.41|12|
|Architecture|0.33|5|
|Audiovisual - Broadcasting|0.39|16|
|Audiovisual - Motion pictures|0.43|12|
|Audiovisual - Sound recording|0.34|7|
|Auditing|0.39|10|
|Commercial banking|0.41|13|
|Computer|0.34|8|
|Construction - Engineering|0.34|11|
|Construction|0.41|12|
|Courier|0.34|9|
|Distribution|0.41|12|
|Engineering|0.31|4|
|Insurance|0.38|15|
|Insurance - Actuaries|0.42|16|
|Insurance - Broking and agency|0.47|33|
|Legal|0.40|13|
|Legal - Domestic law|0.45|18|
|Legal - International law|0.16|1|
|Logistics cargo-handling|0.41|12|
|Logistics customs brokerage|0.38|14|
|Logistics freight forwarding|0.41|12|
|Logistics storage and warehouse|0.41|12|
|Maritime transport|0.46|14|
|Rail freight transport|0.49|19|
|Road freight transport|0.46|21|
|Telecommunication|0.42|11|

Source: OECD STRI; own calculations The average restrictiveness of policies towards MNP by STRI measure in the year 2017 for the 45 countries covered in this database is reported in Annex Table 2. Our analysis reveals the following STRI measures to be the most restrictive, where the numbers in parentheses indicate the number of countries for which the average restrictiveness of the concerned STRI measure is at least 0.5: A temporary licensing system is in place (23); At least one engineer must be licensed for the issuance of construction permits (30); Foreign construction engineers are 8
official PEP decree declared otherwise. This pattern is likely due to administrative and bureaucratic loopholes that may have let non-RAMV migrants apply for PEP. Importantly, we know these discrepancies were not due to recall error regarding the migration date because we compared the reported arrival dates in our survey with those reported on PEP applications to migration authorities; in 98.2 percent of cases, they were the same. Moreover, results from the qualitative survey suggest that the arrival date was extremely salient for migrants, marking as it did the end of one life and the start of another. Finally, these discrepancies are also not due to misinformation or misreporting by migrants without PEP since we requested proof of PEP registration for anyone who reported applying for PEP.

For completeness, our main results include the full sample depicted in Figure 3. Robustness tests show the results are remarkably robust (both in magnitude and statistical significance) when the observations of these “defiers” are dropped (see Figure F.1 and Tables F.1– F.3).

Figure 3 also plots gray bars that illustrate the density of migrants who arrived in Colombia each week. Visual inspection of the figure indicates no discontinuity in the number of individuals who arrived in Colombia before or after June 8, 2018. Further, the McCrary test rejects the existence of any discontinuity in the density of the sample or manipulation by individuals (p-value=0.96). This is expected because when RAMV opened, it was not intended to regularize migrants, and there were no public discussions, announcements, or expectations in this regard. Moreover, the data from the survey indicates that only 0.5 percent of respondents reported migrating to register in RAMV.

Panel B in the figure illustrates the discontinuity in the probability of treatment, estimated as the average treatment take-up in each bin. This figure illustrates the discontinuity using a linear polynomial to confirm the existence of a large, robust discontinuity in the probability of treatment around June 8, 2018. At each point, the figure illustrates the mean probability of treatment in each bin and its 95 percent confidence intervals. Figure G illustrates the discontinuity fitting a quadratic polynomial. Both figures illustrate the existence of a large discontinuity in the likelihood of applying for PEP around June 8, 2018.

20
6 column of Table 2) and the GM field trials in India have been boosting yields by as much as 60 percent. More-recent commercial planting data suggest yield per hectare gains in India of more than one-third from adopting GM cotton varieties, and higher net profits despite the GM seed costing three times as much as non-GM seed (Qaim et al. 2006, Bennett et al. 2006).<sup>5</sup> Three GM cotton adoption simulations are presented below. The first one aims to measure the market and welfare effects of adoption that had already taken place by 2001 in the United States, China, Australia, and South Africa. In China’s case it was only about halfway through its adoption process as of 2001, so only a 2.5 percent TFP shock is applied in this case. The simulation is a negative one, in the sense that we examine how the world would have been had that 5 percent shock (2.5 percent in China’s case) not taken place.

That first simulation is then compared with two other shocks: one in which all other countries except the rest of Sub-Saharan Africa adopt GM cotton (and China completes its adoption process), and the other in which Sub-Saharan Africa also adopts. The reason it is worth examining separately the impact of adoption by the rest of SubSaharan Africa is that the region has a history of very slow adoption of new agricultural technologies in the 1970s and 1980s, and during the 1990s its investments in agricultural R&D grew only 1 percent per year and spending actually fell in about half the countries for which data exist (Science Council 2005). To reiterate, the TFP shock in these latter two simulations is also 5 percent except for India and Sub-Saharan Africa (excluding South Africa) where it is 15 percent and for China where it is 2.5 percent. The potential

> 5 There are also benefits from insect-resistant Bt cotton in terms of improved health for farmers (see Hossain et al. 2004), and also less pesticide damage to soil and water, but these benefits are ignored in what follows.