rafmacalaba/datause-encoder-data huggingface dataset:
institutions, particularly the mechanism of reputation that has been well studied in the repeated games literature (Mailath and Samuelson, 2006).

Conventional literature on what drives FDI normally ignores the legal/property rights institutions and looks only at economic factors behind FDI decisions. In contrast, the cross country studies of FDI that take institutional factors into account add property rights institutions in an _ad hoc_ way as one of the explanatory variables along with other conventional variables on FDI. Both treatments are not entirely satisfactory. In a country where there is a total lack of property rights protection, very few companies would want to invest in that country even if it may enjoy large market size, low labor cost and good geography. All these advantages can be taken away by the grabbing hand of the host government. Therefore, what determines FDI inflows in a weak legal environment is not straightforward without rigorous analysis supported by careful empirical analysis. Indeed, if legal institutions are indispensable to FDI, then there should not be as much regional disparity in FDI as we have observed in China (and likely elsewhere).

In this paper, we first construct a simple self-enforcing contract model to analyze the possibility and determinants of FDI in the absence of the rule of law.<sup>3</sup> We then use a large new data set of firm ownership and business environment from a survey conducted by the World Bank and the National Bureau of Statistics in China to study what determines regional variations of FDI in China. Besides the conventional factors that have traditionally been linked to FDI, we will pay particular attention to many less studied factors.

In addition to the literature already mentioned, our paper is closely related to three strands of literature on the determinants of FDI inflow. The first is the surging new literature on

> 3 There is a difference between the lack of order (or chaos) and the absence of rule of law. China does not have the rule of law but clearly has order. We thank Yingyi Qian, the editor, for pointing this out to us.

3
that reward teachers on the basis of their credentials may not be effective in raising teacher effort.

One reason for low teacher effort in MP and UP may be low accountability of teachers in these states. Teachers may not be accountable and motivated in part because the communities do not have the capacity to hold them accountable. A large proportion of committee members in all three states had not received any training regarding their roles and responsibilities. Parent members of VEC and PTA are not actively participating in their oversight capacity and have very low levels of awareness regarding their roles and responsibilities. The headmasters seem to be executing most of the functions of VECs and PTAs. The communities at large are not aware of the existence of these committees. Whereas in Karnataka data show that even though only 20 percent of parent SDMC members stated receiving any training, they are aware and participating to a much greater extent compared to their counterparts in the other two states. SDMC members report more frequent committee meetings and school visits as well as greater knowledge of their oversight roles. The evaluation of the experiment undertaken as part of this study will provide some evidence on how much increase occurs in teacher effort when communities are better informed of their oversight roles.

However, low rates of teacher attendance and teaching activity are only part of the problem of low learning achievement. Even if the level of average teacher attendance and engagement in teaching in MP and UP were to be the same as in Karnataka or even if 100 percent of the teachers were present and engaged in teaching and assuming all other factors influencing learning stayed the same, the associated gain in language or math scores in the two states would be in the range of 10-11 percentage points based on the regression results in the paper. This is roughly the size of gain in performance across grade cohorts observed in the data. It would still keep the average scores 30-40 percentage points below Karnataka which leaves a large fraction of the children without any mastery of basic competencies.

What is going on in classrooms when teachers are present and teaching is obviously quite important. Although our data cannot shed light on any differences in the classroom processes across states, it does suggest that teachers in MP and UP may not be as effective. This could be related to issues of accountability as well as teachers’ professional development. Classroom processes and teacher training need to be researched. Experimental evaluations of innovations in these areas would be helpful in answering what works towards classrooms that actually deliver learning.

20
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observed difference in earnings is not explained by ethnicity, age, educational attainment, sector of employment, or employment in a rural (versus urban) area. Against this backdrop of different labor market characteristics between men and women, does discrimination play a role in the relatively low employment among women?

According to the latest report of the Department of Statistics of Malaysia, during the fourth quarter of 2022 there were 204,420 job vacancies posted by 42,592 companies. This number was slightly higher than the third quarter of 2022 when 190,170 job vacancies were posted by 39,472 companies. Of the vacancies posted in the fourth quarter of 2022, 43% were ads for professionals, and close to 50% of the job ads were located in the states of Kuala Lumpur and Selangor (DOSM, 2022). Job vacancies are commonly advertised online in Malaysia. We will exploit this feature by utilizing an online job platform for our study.

Our study focuses on entry level positions, that are particularly important to understand. For instance, using US data Wachter (2020) finds that early career opportunities are an important determinant of future career employment trajectories and earnings. This suggestive evidence likely holds in different contexts. In Malaysia, 35% of fresh graduates in Malaysia work in semi-skilled or low skilled occupations (DOSM, 2021). This fact suggests a mismatch between the demanded and the supplied skills in the labor market for young professionals.

# **3 Experimental Design**

In this section we describe the research protocol for implementation and data collection, which includes the following steps: profile creation (section 3.1), job search and classification (section 3.2), randomized assignment and job applications (section 3.3), and monitoring applications (section 3.5).

## **3.1 Profile Creation**

We create similar candidate profiles, each including a resume, that differ only by name (signaling both ethnicity and gender), and multiple signals of a particular soft skill.

We focus on two soft skills: leadership and teamwork, which are compared to a relatively neutral counterfactual. Soft skills are signalled via an executive statement (on applications and in the re6
_from October 2008-May 2009?_ Both questions when answered positively involve a reduction in the utilization of health care in the time period t0 to t1.

Table 6 provides estimates of the probability of reducing utilization of curative care and preventive care for the whole sample and for the poorest per capita income quintile for those households that had access to the Green Card and those that did not have access to any insurance, respectively. For the group of household heads who had no access to health insurance, the probability of reducing utilization through this period was 36.6 percent while for Green Card holders the probability of such reduction was 21.7 percent.<sup>11</sup> Similarly, we observe a difference in the probability of utilization of preventive health care (in Panel B of same Table) of 15.96 percentage points between Green Card holders and those without any insurance (statistically significant with p value <0.01). The same Table also shows the results for the poorest per capita income quintile only. Here, the difference in the means is even larger with 18.8 percentage point for curative and 26.4 percentage points for preventive care. Both differences are statistically significant.

Figure 4 plots the non-parametric kernel regression estimates for the probability of reducing utilization of curative and preventive health services during the assessed period. The Figure plots the conditional probabilities for reducing health care utilization for the Green Card group (Gi=1 in equation (1)) and the group of households with no health insurance coverage. The non-parametric kernel estimation results are in line with the simple mean differences across the insurance groups (Table 6). The x-axis in the Figure is log per capita income and the analyses is conducted at the household level. The difference in utilization probabilities is starkest among the poorer quintiles where the protective impact of the Green Card program was largest.

# **6.2 Parametric Estimation**

By imposing a structure on the relationship between Green Card access ( ) and the change in utilization through the crisis period ( , we can estimate the average treatment effect (ATE) by using a parametric regression. The empirical specification for the parametric estimation in time t is :

> 11 The difference of 14.8 percentage points between the two groups is statistically significant (with a p-value< 0.01), using a Pearson Chi-2 test comparing two binomial distributions.

13
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_15_ The number of paper and electronic non-cash transactions for these developed countries is illustrated in Figure 6. Paper-based non-cash payments (checks and paper giro) are shown in the open portion of each bar while electronic payments (credit and debit cards, electronic giro) are indicated by the solid portion. The number of electronic payments per person has risen in all countries over 198793. Indeed, electronic payments are the only source of growth of non-cash payments in 11 countries and the primary source of growth in two of the remaining three Japan and France, not the U.S.). As a result, the prime mover behind the rise in non-cash transactions for all countries but the U.S. has been the rise in electronic payments. But even the U.S. is forecasted to follow this trend by the turn of the century since the annual number of check payments per person is forecasted to start falling within the next five years. When this occurs electronic payments will be the main reason behind the growth of non-cash transactions in the U.S. as well.

**Figure 6. Annual number of non-cash, paper and electronic transactions per person (14 developed countries)**

<!-- Start of picture text --> 300 -<br>* Electronic transaction<br>t. 250 - 0 Paper-based transaction<br>200<br>150<br>A<br>.~50<br>0<br><!-- End of picture text -->

**_Each_** _country:_ **_Yearly_** _figures 1987 to 1993_

Without neglecting significant cross-country differences in payment evolution, certain universal trends can be identified. First, cash is losing ground to non-cash payments, mostly to electronic cardbased payments that are close substitutes for cash at the point-of-sale. Prepaid cards and smart cards also play a role here but their use is still at an early stage. Second, within the category of non-cash payments, checks are being replaced both by POS card-based payments and, to a lesser extent, electronic credit transfers through giro or ACH networks. And finally, all paper-based payment instruments are being replaced by lower cost electronic transactions. These trends in payment instrument substitution have been affected by a variety of country-specific institutional, economic, and demographic differences across countries, which is the reason why some countries are further ahead in this process than others. These differences are taken up in Section VI, below, where the historical evolution of payments in Europe, Japan, and the U.S. are discussed in some detail.
# **Table** **<u>9. Endline household animals and assets</u>**

||All pr<br>|ojects<br>||Livestock<br>|projects<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Cattle|All assets|Cattle|All assets|Cattle|All assets|
|Training|0.753|0.275***|0.310**|0.152*|0.128|0.089|
||[0.515]|[0.080]|[0.157]|[0.081]|[0.208]|[0.120]|
|Scorecard|||||0.097|0.025|
||||||[0.223]|[0.135]|
|Training x scorecard|||||0.382|0.155|
||||||[0.349]|[0.172]|
|Total interaction effect|||||0.607**|<br>0.306**|
|Training + Scorecard + interaction=0|||||0.017|0.030|
|Training + interaction=0|||||0.058|0.039|
|Scorecard + interaction=0|||||0.055|0.093|
|Training=Scorecard|||||0.877|0.576|
|Control means|2.262|-0.181|2.14|-0.219|2.237|-0.060|
|N|5,909|5,831|4,214|4,149|3,851|3,791|
|R-squared|0.044|0.249|0.091|0.209|0.085|0.134|

Notes: This table reports the OLS regression results for the treatment effect on household-level animal and total asset outcomes at the final endline survey. Columns 1 and 2 are for the full sample. Columns 3 and 4 are for all the livestock projects in the sample. Columns 5 and 6 is only communities in the scorecard sample. All analysis includes only direct program beneficiaries and the top 0.5% of responses have been trimmed. Standard errors are reported in brackets below the coefficients. Regressions include sub-county controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

45
primary education and equal enrollments among girls and boys, but say nothing about learning and competency. Similarly, a number of educational interventions focus on getting children into schools (midday meals, free textbooks, stipends for girls are all examples from very recent history) but do not monitor what these children learn and what effect these interventions have on their learning.

This may be a mistake. Although all children in Pakistan may be enrolled in primary schools by 2015, many may complete their education functionally illiterate and innumerate. International assessments of learning show that many low-income nations perform far below the mean for OECD countries (Mullis and others 2004). Furthermore, improving the quality of schooling may in itself be one of the best ways of getting children into school in the first place—after all, the returns on parental investment depend both on how much a child learns and the number of years she stays in school.

This paper reports on an independent survey of primary schools in rural Pakistan with a focus on the achievement of enrolled children, as measured through test scores. The first part of the paper looks at levels of learning. It gives a sense not only of “average learning” (as in reports from worldwide comparisons), but also points to the _specific_ knowledge that children acquire in school. That is, we look at whether children can add 5+14 and whether they can make a sentence with the word “ball”. Usually, tests administered in Pakistan take the curriculum as their starting point for test design. When children perform far below the curriculum level, the results are reported as “not achieving the desired competency”, leaving the question of what these children actually know unanswered. In contrast, the tests presented here start from the basics (counting and addition in numeracy, alphabets in language) to provide some data on where children are on a knowledge-scale.<sup>1</sup> The second part of the paper presents associations between children’s learning levels and their attributes. We first construct an overall test score from the individual test questions. That is, we “norm” the test by weighting test questions according to difficulty using Item Response Theory, the established procedure in the international testing literature; this is discussed further below.

> 1 There are several reasons for using knowledge-specific tests rather than other secondary data. In a number of tests available to us, the data for these three exercises are not available: we do not have access to the original questions used for a number of tests (and in some cases these are proprietary), the secondary data are presented as test-score “totals” rather than question-by-question responses, which does not allow us to construct the item response scores.

- 3 -
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE As of 2008, Cameroon and the **Figure 24. Expansion of mobile and land-line markets in Cameroon** Democratic Republic of Congo were the top two leading markets for mobile 3,000,000 telephony in Central Africa, with around 2,500,000 4.5 million subscribers in each country, 2,000,000 followed by Chad with 1.1 million 1,500,000 1,000,000 subscribers and Gabon with 1 million 500,000 (figure 24).Since 2006 fixed-line penetration has grown rapidly with the - 2000 2001 2002 2003 2004 2005 2006 2007 2008 expansion of limited-mobility portable phones offered by the incumbent operator, Orange MTN Fixed line CAMTEL.<sup>12</sup> Such phones are a fixed-line Source: Balancing Act 2008. solution that offers mobility of up to 40 Note: In 2008, there were 125,000 fixed lined and 150,000 fixed/mobile lines. kilometers and both mobile and fixed handsets. In 2008, two years after the launch, users of this technology increased from 28,000 to 150,000, surpassing the number of traditional fixed lines in the country (125,000). The number of landline subscriptions per 100 people grew from 0.6 in 2000 to 2.2 in 2009.

**Figure 24. Expansion of mobile and land-line markets in Cameroon**

# **Challenges**

Cameroon has lagged in reforming its telecommunication sector. Even though the sector regulator, ART (Agence de Régulation des Télécommunications), was established by the Telecommunications Act in 1998, no further legislation has been enacted and the regulatory landscape remains unclear. The market is still not fully liberalized. South Africa’s MTN and Orange of France dominate the mobile market, which is one of the highest in Cameroon’s peer group, as measured by the Herfindahl-Hirschmann Index (figure 25).<sup>13</sup> There are numerous Internet service

**Figure 25. Market concentration in Cameroon and selected African peers**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2005 2008<br>Source: World Bank 2008<br>Herfindahl-Hirschmann Index<br><!-- End of picture text --> providers, but the market is led by CAMNET, a CAMTEL subsidiary offering ADLS connections, and the two mobile operators.

> 12 Cameroon Telecommunications.

> 13 The Herfindahl-Hirschmann index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. An HHI of 100 indicates the market is a monopoly, while a lower the HHI the more diluted is the market power as exerted by one company/agent.

37
Table 3: The results for each series using the ETS smoothed data and the elastic net estimator. Relative MSE (1) is the MSE for the expanding window ARMA model results versus the benchmark model given in columns 6. Relative MSE (2) is the MSE for the elastic net model versus the benchmark in column 6. A relative MSE less than 1 indicates that the proposed model beat the benchmark.

|Country|Series|Relative MSE (1)<br>Relative|MSE (2)|N|Benchmark|
|---|---|---|---|---|---|
|hn|food03|1.02453|1.22845|56|ar|
|hn|food04|1.05794|1.02891|56|ar|
|hn|food05|1.50041|1.41338|56|median|
|hn|food06|1.06789|1.34548|56|ar|
|hn|food07|1.02272|1.19456|56|ar|
|hn|food08|1.02289|1.02308|56|ar|
|hn|inf01|1.14475|1.07673|94|ar|
|hn|inf02|0.954699|1.13202|94|ar|
|sv|food01|1.32029|1.34929|69|ar|
|sv|food02|0.981255|1.17397|69|ar|
|sv|food03|0.999156|1.4412|69|ar|
|sv|food04|1.00938|1.37656|69|ar|
|sv|food05|1.0587|1.22251|69|median|
|sv|food06|1.01563|1.2812|69|ar|
|sv|food07|1.14023|1.42201|69|median|
|sv|food08|1.26369|1.31265|69|median|
|sv|food09|1.08291|1.07086|69|median|
|sv|food10|1.05501|1.22104|69|ar|
|sv|food11|1|0.998684|69|median|
|sv|food12|1.24149|1.24152|69|median|
|sv|inf01|1.44499|1.33092|34|median|
|sv|inf02|0.986749|0.834845|34|ar|
|sv|inf03|1.10362|1.51751|34|median|
|sv|inf04|0.97742|0.97742|34|median|
|sv|inf05|1.11776|1.15664|34|ar|
|sv|inf06|1|0.999088|34|mean|
||Conti|nued on next page||||

27
- 32 -

with more detailed data, the estimates could be refined further. The difference may result from different industrial structures among countries. As discussed below, the price elasticity differs across industries. The difference in elasticities may also reflect each economy’s tendency toward new energy-efficient technology. Some countries may have better access to advanced knowledge, and others may be faced with financial difficulties in adopting new technology. Ownership may also matter. Typically, state-owned enterprises or other public entities, which may be covered in the sample, are often irresponsive to price signals. They may continue to use as much energy, labor and other input as they need.

<!-- Start of picture text --> Figure 14. Price elasticity of energy demand by country<br>-1.0<br>-0.8 -0.77 -0.76<br>-0.6<br>-0.40<br>-0.4 -0.33 -0.37 -0.37<br>-0.26<br>-0.21<br>-0.2<br>0.0<br>Estimated energy price elasticity of demand<br>Albania Bosnia and Herzegovina Bulgaria Croatia Macedonia, FYR Romania Serbia 7 countries<br><!-- End of picture text --> The difference in price elasticities across countries remains consistent even if some firmlevel unobservable characteristics are partially taken into account. With the 2005 data merged with the 2009 BEEPS, the fixed-effect SUR models are estimated; Albania and Macedonia are estimated to have the relatively elastic demand for energy, though the levels of elasticities are different from the cross-section case (Table 7).<sup>34 35</sup> Note that the sample data are highly unbalanced; the samples have an overlap of only about 10 percent, as far as the observations have sufficient data items to estimate the cost function under the current approach. Therefore, the models do not eliminate all the heterogeneity in firms but partially

> 34 In addition, the panel analysis incorporated more detailed sectoral classification; the manufacturing sector can be disaggregated. As the result, 18 industrial dummy variables are included.

> 35 The estimated cost functions are presented in Appendix Tables A9.