rafmacalaba/datause-encoder-data huggingface dataset:
where coefficient β1 is an estimate of the portion of the change that remains after accounting for the differences in mean characteristics.

To arrive at the proportions that are explained and unexplained: and the components of the explained portion are: While test scores and individual and family information are at the individual level, school resources and other school-related inputs are at the school level. In choosing the estimation method, we recognized that observed test scores can be expected to be correlated at the school level due to clustering effects. Therefore, the assumption that disturbances are independently and identically distributed with fixed conditional variance did not hold. As a result **,** we used the estimation method of OLS by cluster at the school level.

# <u>Data</u>

The PISA is an international assessments initiated by the OECD. It assesses 15 year olds in each participating country in three main subject areas – reading, mathematics, and scientific literacy. We focused on the results for Indonesia in mathematics in the assessments for 2003 and 2006. We did not include information for 2000 even though it was available, because the sample was very different. For instance, dataset for the 2000 survey has much fewer observations regarding parents’ education than the 2003 and 2006 surveys; while there were 8,828 and 9,292 observations in 2003 and 2006, in 2000 the sample contained only 2,777 observations. In short, we do not believe that the 2000 sample is comparable with subsequent rounds.

Instead of testing the knowledge and skills specified in the national curricula of the participating countries, the PISA aims to test the ability of students to apply their acquired knowledge in the three subject areas in real-life situations. The targeted student population falls between the ages of 15 years and 3 months and 16 years and 2 months who are enrolled in the seventh grade or higher. Indonesia uses a two-stage sampling frame with a cluster design. We applied weights to the data at the student level. The PISA standardizes the data for OECD countries with the mean at 500 points and the standard deviation set to 100. Thus, it is the OECD means and standard deviation that are the benchmarks for the other participating countries.

6
# 2. Some methodological issues

We start with the simplest question of all: what is “income” in the calculations of global inequality? As we have seen, most of the early work used national accounts data, that is GDI per capita expressed in the same currency (international or PPP dollars). This is because household survey data for many countries of the world were simply unavailable (and even when they existed, researchers could not get them because the statistical agencies refused to release them).<sup>11</sup> There are currently three main sources of world wide data on GDI per capita across time and across countries. They are World Bank data available in World Bank World Development Indicators (WDI), Penn World Tables, and Angus Maddison’s data. The advantage of using GDI per capita as “income” is that these numbers, even if the three sources do not always agree among themselves, are relatively uncontroversial. We know what is meant by GDI per capita and we know that these values do give some generally accepted mean incomes of all nations.<sup>12</sup> The main drawback of this approach is that GDI per capita is not “income” in any recognizable sense to any individual or household. Gross domestic income includes components such as corporate investment from retained profits, build-up of stocks, government spending on defense etc., which are not part of an even broadly defined household disposable income. In addition, publicly-financed health and education are part of GDI per capita but not included in household per capita disposable income unless one is able to impute—which is quasi impossible in a multi-country context—these values back to individual households based on survey data on their school attendance and use of medical services.

11 This is still the case with many countries that refuse to release micro data to any institution or individual. Such countries are quite diverse, ranging from Japan to Algeria. There is thus a paradoxical situation that a number of expensive instruments like household surveys exist in the world, yet they cannot be used because of misplaced policies of some countries’ statistical offices. The issue of confidentiality with which they sometimes defend their practice is clearly bogus since no researcher can ever identify the participating households. Are we to believe that a researcher at (say) Cornell University in 2005 is going to identify the Japanese households that took part in a survey conducted in 1973?

> 12 It has not always been like that. Consider the problems of converting  Communist countries’ national accounts methodology into NAS system, and of course the issue of deliberate falsification of national accounts.

8
**Table 22: Characteristics most likely source of critical skill workers**

||**Percentage of the firms who answer “yes”**<br>**when being asked “Is the supply of**<br>**knowledge workers constraining the**<br>**development of the firm?”**|
|---|---|
|**_Beijing_**||
|National from local market|61|
|Expat from local market|8|
|National from regional market|38|
|Expat from regional market|8|
|National from N.America/Europe|4|
|Expat from N.America/Europe|-|
|**_Shanghai_**||
|National from local market|65|
|Expat from local market|21|
|National from regional market|31|
|Expat from regional market|3|
|National from N.America/Europe|14|
|Expat from N.America/Europe|10|

_Note_ : percent may not add to 100% as multiple responses are included.

**Table 23: Substitution of skills scarce in local market by using workers from elsewhere**

|Has the firm tried to substitute for the skills<br>that are scarce in local market by using<br>workers availableinothercities?|High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|No|40|50|33|42|
|Yes|60|50|67|58|
|Total|100|100|100|100|
|If yes, success of arrangement is rated as|4.1|3.9|3.8|3.9|
|_Shanghai_|||||
|No|24|33|30|28|
|Yes|76|67|70|72|
|Total|100|100|100|100|
|Ifyes,success of arrangement is rated as|4.1|2.7|4.6|4.2|

Measured on a Likert scale of 1 (not at all) to 5 (very well) 34
larger for women, the impacts are similar (0.11 for men, and 0.09 for women, measured as points on a 5-point Likert scale, or around 0.16 s.d.) after 7 years. Column 9 shows that both types of training program have a lasting impact on business practices, with men and women doing 6 to 9 percentage points more practices after 7 years. Combining these factors into an “A” index, we see a statistically significant long-run increase of 0.28 s.d. for men and 0.18 s.d. for women. Table A8 also examines impacts on two other potential components of “A” that were not measured in the same way or not measured in the short-run: there is a small positive impact on innovation, and on digitalization.

Taken together, these results suggest long-term divergence for men, with the impact on profits growing over time. Men have built up capital, are able to borrow more if they need to, and they may be in a self-reinforcing psychological mindset whereby success increases self-efficacy, further boosting the business. There is a sustained impact on “A”, so that their optimal business size may continue to increase with time. In contrast, the impacts for women are either flat or converging back towards the control group, and they have built up much less capital. This lack of long-term success may then prevent a positive reinforcement cycle.

# **4.3 Why is there this Long-Term Gender Difference?**

We consider several possible explanations for these differences in long-term effects by gender. A first potential explanation is that the impacts arise from gender differences in industry and efficient scale. The women in our sample are running smaller businesses than men on average, and are more likely to be in commerce. De Mel et al. (2009) argue that women see lower returns to capital than men because many of them are operating in industries where the efficient scale is low. Hardy and Kagy (2020) note further that women tend to concentrate in a small number of industries, so that demand is more of a constraint on their growth than access to inputs. Sectors like commerce and hairdressing may have less scope for growth through capital accumulation alone, but face difficulties consolidating into a smaller number of larger firms (Hardy et al, 2024). Even though all our surveys took place at times when markets were open, a further sectorial reason for differences could be if the COVID-19 pandemic hit women-dominated sectors harder, causing 13
<!-- Start of picture text --> 42% 38.9% 39.1% 39.9%<br>36.6% 36.7% 36.8% 36.6%<br>37%<br>32%<br>26.6%<br>. 25.2%<br>- 27% 24.2% 23.6% 23.2% 22.5% 24.1%<br>= 22%<br><!-- End of picture text -->

|42%||38.9%||||39.1%|39.9%|
|---|---|---|---|---|---|---|---|
|37%|36.6%||36.7%|36.8%|36.6%|||
|32%<br>- 27%|24.2%|26.6%<br>.|23.6%|23.2%|22.5%|24.1%|25.2%|
|= 22%<br>8<br>3<br>17%<br>co<br>=<br>12%|10.2%|11.3%|9-2%|7.995|5-34|7.9%|9.5%<br>.|
|7%|3.9%|4.8%|3.4%|2.7%|5 19%|2.3%|3.7%|
|2%||||||||
||2008|2009|2010|2011|2012|2013|2014|
||—@4U|SD<br>—@|55USD|—@8US|D<br>—e1|0USD||

<!-- Start of picture text --> 8<br>3 17%<br>co 11.3%<br>= 10.2% 9.5%<br>12% 9-2% 7.995 5-34 7.9% .<br>7% 3.9% 4.8% 3.4% 2.7% 5 19% 2.3% 3.7%<br>2%<br>2008 2009 2010 2011 2012 2013 2014<br>—@4USD —@55USD —@8USD —e10USD<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>28 ya<br>o ie,<br>o6<br>g<br>3.4<br>=<br>=<br>a<br>oO 2 Z<br>0 aa<br>0 4 5 10 15<br>daily expenditure per capita in $ 2011 PPP<br><!-- End of picture text -->
**2**

# **_1. What this study is about_**

The September 2008 collapse of Lehman Brothers, a mid-size ‘Wall Street’ investment banker, sent a wave of fear around world financial markets. Banks virtually stopped lending to each other. The risk premium on interbank borrowing shot up to 5 per cent, whereas typically it was close to zero. Although authorities scrambled to inject liquidity into financial markets, the damage was done. The risk premium on corporate bonds shot up even more to over 6 per cent. Large CAPEX projects were shelved, the corporate sector virtually stopped borrowing, trade credit was hard to get and, with falling demand, particularly for investment goods and manufacturing durables like cars, trade volumes collapsed.

The result is that the global financial crisis has seen the largest and sharpest drop in global economic activity of the modern era. In 2009, most major developed economies find themselves in a deep recession. The fallout for global trade, both for volumes and the pattern of trade has been dramatic. The OECD predicts world trade volumes could shrink by 13 percent in 2009 from 2008 levels.<sup>1</sup> The contraction in trade has several interrelated causes comprising both price and income effects as global financial flows readjust, real exchange rates realign, terms of trade change and domestic savings rise with a concomitant drop in domestic demand. That is, financial problems have had devastating real effects. Each of these effects reverberates around the world, some compounding and some offsetting each other.

Governments have responded with an easing of monetary and fiscal policy that in turn have their own effects on activity and financial and trade flows. The downturn in activity is causing unemployment to rise sharply and, with it, a political response to protect domestic industries through various combinations of domestic subsidies and border protection. There is potential for protectionism to rise further.

Both the causes of the crisis and the policy responses are reshaping the level and pattern of world trade. The objective of this study is to disentangle the various direct and indirect effects of the crisis on international trade and how events might unravel. To do this, a dynamic, intertemporal general equilibrium model that fully integrates the financial and real sectors of the economy is used to unravel and understand the mechanisms at work. The model incorporates wealth effects, expectations and financial markets for bonds, equities and foreign exchange as well as trade and financial flows. It is a suitable tool to analyse the impact of the crisis and policy responses on global trade and financial flows.

The paper is organised as follows. In the next section, the main linkages and mechanisms by which the financial crisis affects trade is given. This is necessary on two counts: it sets up the modelling approach and it also serves as a basis for developing the

> 1 OECD 2009 http://www.oecd.org/document/12/0,3343,en_2649_37431_42788172_1_1_1_1,00.html
often deemed the gold standard of impact evaluation, because they consistently produce the most accurate results” (http://www.povertyactionlab.org/about-j-pal).<sup>1</sup> To compound matters, the instruments currently used to reach policy makers—the media, dedicated seminars, and policy briefs<sup>2</sup> —are not well designed to ensure timeliness, since they are almost invariably brought into play only after the research has been completed and reviewed. Thus, the main vehicles now used to strengthen the impact of research on policy reinforce, or at least coincide with, the researcher’s commitment to rigor, in that both imply an interaction with the policy-maker that follows research, and therefore raise the question of whether as much as possible is being done to ensure the timely delivery of results.

# _A Way Forward_

The experience of the International Development Research Centre (IDRC), perhaps the leading international organization as far as linking research and policy is concerned, as well as that of other institutions, suggests a way forward. In particular, Carden (2009, 45), drawing on a detailed review of 23 IDRC-funded research projects and other evidence, stresses two points: first, the importance of initiating a dialogue with the policy maker as early as possible in the research process; and second, the value of submitting short papers tightly focused on the policy question under consideration, as opposed to the more traditional, methodology-heavy accounts typically produced at the end of a research project. It is this combination of early interaction and policy-focused submissions that leads to the main proposal of this article—the introduction of what we term provisional

> 1. Similar statements abound: the mission statement of the Coalition for Evidence-Based Policy asserts that “evidence of effectiveness generally cannot be considered definitive without ultimate confirmation in wellconducted randomized controlled trials” (http://coalition4evidence.org/). The Report of the Evaluation Gap Working Group observes that “most questions about the impact of social programs require collecting data over years. Valid evidence of a program’s effectiveness often cannot be produced in less time” (Savedoff et al. 2006, 25) 2. For a useful overview of policy briefs, see Jones and Walsh (2006).
**Children’s Birth Location:** For any household member who are 15 or older, the IFLS has information about the location types of birth: village, small town, or a big city. If the respondents were born in a village, we define the birth location as rural and otherwise, urban. The IFLS collects this information in all waves. For any respondent, we use the birth location in the latest wave in which the respondent reported his/her birth location type.

**Child Cognitive Ability:** We use the Raven test scores and two memory tests to construct the cognitive ability index. An advantage of these measures is that they do not require any knowledge of numeracy or literacy to do well in the tests. To arrive at the index, we first take the first principal component of the raven test scores and two memory tests. Then we regress that on age and age square. A critical issue is that cognition outcomes are positively correlated with age because of the Flynn effect. We, therefore, use residual of the regression as the cognitive ability index.

**Education Expenses:** The IFLS has separate education modules for children (age below 15) and adult (age equal to or above 15). The expense data includes all types of school-related expenses. We aggregate the expenses Using the Panel ID, we match individuals of our children sample (18 to 40 years old in wave 5) with their education expense data in the previous waves. The expenses are then adjusted for inflation taking 1993 as the base year; we obtain the yearly inflation of Indonesia from the World Bank web site.

In the investment sample, we also control for number of school age of children. To calculate the number of school children in wave, we first obtain the fathers’ ID from the roster. We then calculate how many other individuals in the education sample also listed as the same ID as the father. That number provides us with the number of school age children. Finally, in the investment sample, we also include whether the household is located in urban area or 3
There is a degree of subjectivity in all such choices. Each graph in this paper gives the web link (embedded in its title) to the corresponding _Viewer_ page, so readers can readily test the robustness of my claims.

One must consider some possible biases in the _Viewer_ . Is it representative of the views of society at any date? There are two potential sources of bias to note. First, there is the question of whether the Michel et al. sample of 5.2 million books is representative of all books. _Google Books_ now include 15 million digitized books drawn from 40 university libraries around the world, with supplementation from publishers. The Michel et al. sample was chosen according to the quality of both the OCR and the metadata (such as date and place of publication). The notes on the _Viewer_ ‟s website claim that they did “a good job at filtering out books with low OCR quality scores,” though they also acknowledge some errors (such as some pre-1950 references to the “internet”). No other selection criteria are mentioned by Michel et al. Given that _Google Books_ has relied heavily on university libraries, it might be conjectured that the sample is more representative of the scholarly literature. However, popular books do seem to be well represented. The fact that the _Google Books_ site is linked to retail sales options also suggests there will be an incentive to represent the popular literature.

A second possible source of bias is that books need not be representative of current thinking in the population. Books can only written by literate people (by definition). A literature written and read by relatively well-off people need not represent well the views of the population at large. Mass literacy emerged in Britain, the US and much of Western Europe around the turn of the 20th century (Vincent, 2000); around 1800, books could be produced by only about one half the population of England or France (say). And mass literacy is only recently emerging in the bulk (though still not all) of the developing world. With rising overall literacy rates, the literature of the late 20th century is almost certainly coming from a broader cross-section of society than that of the early 18th century. To the extent that this yields more references to poverty there will be a selection bias for inferences about overall public attention to poverty, including among groups in society that contribute little to the literature.

The spread of mass literacy must surely have impacted on the literature. While we don‟t know the counterfactual, there is no sign in Figure 1 that the emergence of mass literacy in the developed world in the early 20th century led to a rise in awareness of poverty; the surge of 6
22 significantly smaller than the forecasted one. This result is similar to that reported in the quarterly GDP exercise in the first section (Table 2), where these two countries defy the model's forecasting ability. In Mexico's case, the omission of US output is definitely related to this outcome and points at the need of further working on this issue.

|**Predictions for 19**|Ta<br>**98-1999 and observed**|ble 8<br>**changes in industrial**|**output growth rates**|
|---|---|---|---|
||**(1)**<br>Predicted total<br>effect of shocks<br>1998-1999|(2)<br>Observed changes<br>in industrial output<br>growth rates|(3)<br>Effects of<br>unidentified shocks|
|Argentina|-28.3|-24.3|+4.0|
|<br>Brazil|-15.1|-7.3|+8.5|
|Chile|-9.5|-8.2|+1.3|
|Colombia|-16.9|-20.5|-3.6|
|Mexico|-9.3|-6.0|+3.3|

(1) Sum of the net effects of tables 7and 8 for each country (2) Difference of the average growth rates for 1999 and 1997 from Table 4. (3) (2)-(1) To evaluate the relative importance of external factors vis-a-vis the domestic ones, we compute the ratio of the net effects of the external variables to the total predicted effect of the shocks. Due to the differences between the predicted change and the observed value, the ratio of the net external effects to the observed change in output growth was also computed. In all the countries, except Colombia, the ratios are well above .5 (Table 9), pointing at the importance of external factors explaining output fluctuations in the subperiod 1998-1999.

Table 9 Effects of external factors on growth as a ratio of the total predicted changes and the observed changes in output growth

||External effects/Total<br>predicted effects|External<br>changes|effects/ Observed|
|---|---|---|---|
|Argentina|.64||.74|
|Brazil|.62||1.29|
|Chile|.66||.77|
|Colombia|.28||.23|
|Mexico|1.3||2.1|

These results coincide with those obtained from the panel exercise with quarterly GDP (Table 2), according to which about 50% of the observed regional slowdown in 19981999 was due to external effects. Colombia is the country where the slowdown is less related to the external factors (Table 9). Brazil and Mexico, in contrast, seem to be the more affected by external shocks. These results support the Joyce-Kamas (1997) study of Mexico and Colombia where they conclude that external factors are the main source of
# **References**

- Abebe, G., S. Caria, M. Fafchamps, P. Falco, S. Franklin, S. Quinn, and F. Shilpi (2017). Job Fairs: Matching Firms and Workers in a Field Experiment in Ethiopia. _Working_ .

- _paper_

- Abebe, G., S. Caria, and E. Ortiz-Ospina (2017). The Selection of Talent: Experimental .

- and Structural Evidence from Ethiopia. _Working paper_

- Adhvaryu, A., N. Kala, and A. Nyshadham (2018). The Skills to Pay the Bills: Returns .

- to On-The-Job Soft Skills Training. _Working paper_

- Ashraf, N., O. Bandiera, S. S. Lee, et al. (2014). Do-Gooders and Go-Getters: Career .

- Incentives, Selection, and Performance in Public Service Delivery. _Working Paper_

- Bandiera, O., I. Barankay, and I. Rasul (2011). Field Experiments with Firms. _The Journal of Economic Perspectives 25_ (3), 63–82.

- Blattman, C. and S. Dercon (2017). The Impacts of Industrial and Entrepreneurial Work on Income and Health: Experimental Evidence from Ethiopia. _Working paper_ .

- Bloom, N., B. Eifert, A. Mahajan, D. McKenzie, and J. Roberts (2013). Does Management Matter? Evidence from India. _The Quarterly Journal of Economics 128_ (1), 1–51.

- Bloom, N., A. Mahajan, D. McKenzie, and J. Roberts (2018). Do Management Interventions Last? Evidence from India. _Working paper_ .

- Butler, M. (2018). Labour Challenges in Hawassa.

- Clark, G. (1994). Factory Discipline. _The Journal of Economic History 54_ (1), 128–163.

- Dean, J. (2018). Noise, Cognitive Function, and Worker Productivity. _Working paper_ .

- Deserranno, E. (2014). Financial Incentives as Signals: Experimental Evidence from the Recruitment of Health Workers. _Working Paper_ .

- Elkan, W. (1971). An African Labour Force. In _Developing the Underdeveloped Countries_ , pp. 240–246. Springer.

- Farber, H. S., D. Herbst, I. Kuziemko, and S. Naidu (2018). Unions and Inequality Over .

- the Twentieth Century: New Evidence from Survey Data. _NBER Working Paper_

13