rafmacalaba/datause-encoder-data huggingface dataset:
Chile’s commitment to the innovation agenda, and its previous far reaching microreforms, we have chosen this country to serve as illustration of the analysis that we propose. Thus, the paper can be seen as both as a general exploration of ways to identify innovation shortfalls, and as an analysis of whether Chile suffers from one.

We begin in the next section by exploring the relationship between R&D investment rates and specialization patterns. Based on data at the OECD level we show that R&D investment rates vary tremendously across sectors, going from 0.2% of value added in “publishing, printing, and reproduction of recorded media” and “construction” to 25% in “pharmaceuticals” and 27% in “office, accounting and computing machinery.” Clearly, if for exogenous reasons a country is specialized in sectors characterized by low innovation, then one should not be surprised or concerned to find that it spends relatively little on R&D. Ideally, one would directly compare R&D investment rates in the same sector across countries, but unfortunately such data is not available for LDCs. Thus, we perform an indirect analysis, asking how much would R&D investment rates fall in OECD countries if they had the economic structure of a resource rich Latin America country, using Chile as an example. The results suggest that compositional matters are relevant, but do not explain a large part of the country’s innovation shortfall.

Section 3 turns to the question of whether low R&D investment rates should be seen as “innovation shortfalls” caused by environments that adversely affect innovation, or as part of a broader problem of low accumulation in all types of capital. Put differently, the question is whether a country’s low R&D investment results from problems common to accumulation overall, or whether in fact the activity of innovation itself is somehow especially impeded. Clearly, the appropriate policy depends critically on the answer to this question.

Consider, again, the case of Chile, which had an R&D investment rate of 0.4% of GDP in 1995, a rate that is well below the comparable number for the U.S. (2.5% in 1995). It is

> 3 Ricardo Lagos, Discourse on the State of the Nation, May 21st, 2005, (see

2
<!-- Start of picture text --> WPS 1209<br><!-- End of picture text --> This paper-- a product of the'rade Policy Division, Policy Research Depamnent-is part of the Regional Integration Initiative Study of the Bank's East AsiaRegion and ofthe research project funded bytbe Bank's ResearchSupport Budget, "Underatanding Blatee alTradeMFows: An Application to East Asia"(RPO 67786). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Dawn Ballantyne, room N 10-023, extension 37947 (October 1993, 57 pages).

Panagariya studies the case for three different approaches to regionalism in East Asia.

First, he examines closely the only serious - attempt at preferential trading in the region the Association of Southeast Asian Nations (ASEAN), which has recently announced plans to form the ASEAN Free Trade Area (AFTA). Conclusion: the costs of such subregional schemes far outweigh their expected benefits.

Second, he evaluates the case for a formal East Asian trading bloc along the lines of the European Community, and concludes that although the _threat_ of such a bloc mnay serve some purpose, its actual execution might be difficult, given the divcrse levels of protection across different countries in the region, and the possibility of retaliation from the United States through increased protection against East Asian goods.

Third, he examines the cage for _siunltaneous,_ most favored nation-styie, nondiscriminatory regionwide liberalizatiot:. Panagariya argues that that although such a regional approach "may" be feasible, the case for it is far from airtight. On the one hand, this approach will face less resistance from the United States, and is likely to promote an open world trading system in the long run. On the other hand, in the short run it is likely to be resisted because of the adverse effect on terms of trade in the participeting countries.

The Policy Research Working Paper Series dissemi.nates the findings of work under way in the Rank. An objective of the series ir to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Resea'ch Dissemination Center
while if they are substitutes, the impact of water on health may well fall with income. All _this_ applies equally to other non-parental inputs, such as sanitation.

If the _/gs_ differ across the poor and nonpoor at any point in time, it seems likely they may also change differently over time in the two groups, and hence _D3_ in eqn (6) may be nonzero. We explored this possibility, but the model we estimated in which the fl's were allowed to change differently across the two consumption groups produced predicted values of the infant and under-five mortality rates that were a long way from the actual values (see Table 4 below). We therefore settled on a model that allows _Di, D2_ and _D4_ to be nonzero, but in which _D3_ is constrained to be zero. We allow, in other words, for poor and nonpoor children to experience dif'ferent changes in the means of the their health determinants, and for them to have differenit values of and different impacts of these determinants at the relevant point in time. But we do not allow for _differential_ changes in the impacts of the determinants. In our Weibull model, we thus include the x's, a 1998 dummy, a vector of interactions between thte x's and the 1998 dummy, a dummy indicating whether the child is poor, and a vector of interactions between the x's and the "poor" dummy. In so doing, we constrain the parameterp to be constant over time. Tests revealed this to a be a reasonable assumption.

# **_Data and variable definitions_**

We include three sets of variables in our survival demand equation-child-level, household-level, and community-level variables (see T'able 2 for variable definitions) . At the level of the child, we include the child's gender and age. The hazard is likely to decreasing with age, at least up a point. From previous research, we would expect the coefficient on "boy" to be negative. Among our household-level variables, we include the number of years of schooling of the mother, the logariithm of equivalent household consumption, and dummies indicating whether the household had safe drinking water and satisfactory sanitation facilities."' The equivalence scale we used was simply the square root of the number of household members, which allows for economies of scale in household production but not for differences in food and other requirements across people of different ages and genders. Consumption was measured in the same way in each year and the 1993 data were expressed in 1998 prices using the Vietnam CPI. For the drinking water and sanitation dummies, we aimed to get as close as possible to the UNICEF definitions (Government of Vietnam 2000), though in the case of sanitation our figures are probably somewhat conservative.12

> " We also included an ethr.ic minority dummy, but dropped it as ii:s coefficient was never significant. This is probably due to the concentration of the ethnic minorities in specific regions, for which we control.

> 12 Safe drinking water was defined here as: tap or standpipe; deep dug well with pump; or rain water. Satisfactory sanitation was defined as flush toilet or latrine. Both differ slightly from the definitions used by UNICEF in its analysis

_15_
Finally, section 6 offers concluding remarks, suggests directions for future research, and acknowledges the limitations of our approach.

# 2. Sources of discrepancies in international trade reporting

A growing body of literature has studied the relative contributions of several factors to the observed discrepancies in international merchandise trade reporting (Ferrantino & Zhi Wang 2008; Carrère & Grigoriou, 2015; Demir & Javorcik, 2020; Kee & Nicita, 2022). These studies have identified both structural and non-logistic reasons for the lack of dual recording and observed inconsistencies in successfully mirrored records. In this section, we outline the causes of discrepancies and explore their effect on the quality of the obtained data.

Discrepancies in international trade reporting are structural, i.e., they are driven by logistic reasons related to the valuation methods and reporting guidelines for international merchandise trade. These guidelines are detailed in the International Merchandise Trade Statistics: Concepts and Definitions 2010 (IMTS2010), published by the United Nations Statistical Division (UNSD). The UN Comtrade databases, the primary source of official merchandise trade data collected and disseminated by UNSD https://comtradeplus.un.org/, strive to align with the IMTS2010 guidelines in the empirical data that they provide. As a result, data obtained through UN Comtrade (or any source that adheres to these recommendations) inherently possess several structural discrepancies. An example of this structural discrepancy occurs in valuation methods that typically consider freight and insurance costs for imports but not for exports. Therefore, structural discrepancies arise because the reported import value exceeds that of the corresponding mirror. Many have noted that extreme (and even negative) empirical measurements of difference between reported imports and mirror records suggest discrepancies that cannot entirely be explained by differences in valuation methods (Carrère & Grigoriou, 2015).

Moreover, IMTS2010 recommends listing the country of origin for imports, but the last known destination for exports (United Nations, International Merchandise Trade Statistics). This will structurally create transactions without a corresponding mirror in cases where exports pass through intermediate countries before reaching their final destination. In such cases, reported exports from the intermediate countries will not have a mirror as the partner that they list in their export transaction lists the original source country (and not the intermediate country) in their 5
in Equation (1)), and the other is another non-consumption survey that offers only the variables (xij). One major difference is that both surveys should provide nationally representative data.

We can divide this case into two subcategories depending on whether the non-consumption survey is of the same design or a different design from the consumption survey. The former subcategory—hereafter referred to as within-survey imputation—includes situations where the household consumption data collected in the two surveys are not comparable over time due to changes to the consumption items (while the xij remain similar). This situation occurs more often than one might think; for example, national statistical agencies regularly update the list of consumption items from time to time to reflect changes in household consumption patterns regarding high-technology goods such as smart phones or smart televisions. The latter subcategory—hereafter referred to as across-survey imputation—includes situations where the consumption survey was implemented a few years back, and a newer round is yet to be fielded. At the same time, there exists another more recent non-consumption survey such as a labor force survey (LFS) that can be combined with the consumption survey to provide more recent poverty estimates. Both of these sub-categories of within-survey imputation and across-survey imputation are also commonly known as survey-to-survey imputation (which is different from survey-tocensus imputation or the poverty mapping technique discussed in the next section).

We discuss next the techniques and challenges for both these subcategories.

# _Method Description_

Similar to proxy means testing, survey-to-survey imputation methods consist of two main steps. The first step is to estimate the household consumption model using the nationally representative household (or larger) survey, as expressed in Equation (1). Once this is done, the second step is to combine the predicted coefficients β̂ and the distribution of the error term 14
# **Reflections of Employers’ Gender Preferences in Job Ads in India: An Analysis of Online Job Portal Data**<sup>**1**</sup>

Afra R Chowdhury<sup>2</sup> , Ana C Areias, Saori Imaizumi, Shinsaku Nomura, Futoshi Yamauchi JEL Classifications: J16, J23, J31, J71 Key words: Employers’ gender preference, Gender targeting, Salary gap, Recruitment advertisement, Job portal, India 1 We would like to thank John Gibbons, Sean Blagsvedt, and Vir Kashyap (formerly Babajob, which was merged into QuikrJobs in June 2017) for their continuing collaborations and generous data sharing, and Pankhuri Shrivastava (QuikrJobs) for continued support to this study. Financial supports are received from the World Bank’s Jobs Umbrella Trust Fund, SRP, and South Asia Gender Innovation Lab. We would also like to thank Chunmei Gao for conducting text analysis and Sharanya Vasudevan for research support. We are grateful to Aphichoke Kotikula and Jennifer Solotaroff for technical discussions, and Keiko Miwa and Tazeen Fasih for valuable comments. The opinions and conclusions expressed in this paper are ours and do not necessarily reflect positions of the World Bank and its member governments. Any remaining errors are ours.

2 Corresponding author. Education Global Practice, South Asia, The World Bank, 1818 H Street, NW, Washington DC 20433; Email: achowdhury5@worldbank.org, afra.rchowdhury@gmail.com.
# **8 Appendixes**

## **Appendix 1: Data Sources**

|**Data Type **|**Source**|
|---|---|
|Baseline population by age and sex, 2010|World Population Prospects|
|Baseline age-specific fertility rates, 2010-2100|World Population Prospects|
|Years of education by 5 year age-sex groups, 2010|Barro & Lee, 2013|
|Adult height by 5 year age-sex groups,|Liberia DHS, 2013|
|Labor force participation by 5 year age-sex groups, 2010|ILO|
|Sector- and gender-disaggregated earnings|HIES 2014|
|Hours devoted to domestic chores and housework|UN Women, 2019|
|Hours devoted to collecting firewood or fetching water|HIES 2014|
|Output, 2005|WDI|
|Output, 2010|WDI|
|Oil Output, 2010|WDI|
|Capital stock, 2010|WDI|
|Agricultural land, 2010|WDI|
|Proportion of GDP between sectors, 2010|Abid, 2016|
|Proportion of labor between sectors, 2010|ILO|

## **Appendix 2:  Departures from CKW 2017**

### **Non-Tradable Production**

It is assumed that there is no overlap between (1) hours devoted to domestic chores and housework and (2) hours devoted to collecting firewood (or charcoal) or fetching water.

The total daily female contribution through non-tradable production (NTPFt) is given by: Where POPft = female population aged 15-100 at time t hf1 = share involved in fetching water/firewood and housework and domestic chores ρf1f = hours devoted to fetching water/firewood by POPF1t × hF1 ρF1h = hours devoted to housework and domestic chores by POPF1t × hF1 W. infft = hourly wage at time t in the traditional sector.

### **Labor Allocation across Sectors by Gender**

Our gender-specific labor model requires that the modern sector and traditional sector wages, which endogenously adjust by gender G= {m, f} and within their respective labor markets, will in turn determine equilibrium male and female labor supply allocations across the two sectors that employ workers. Total male labor supply Lmt and total female labor supply Lft are shared across the modern M and traditional A sectors such that In addition, we have the following labor market-clearing conditions: 31
exogenous variation in total income net of transfers/taxes. This justifies a straightforward instrumental variables estimator for the mean benefit withdrawal rate.

We have also pointed to another distinct source of bias in the standard practice, stemming from correlated differences in the program’s idiosyncratic benefit withdrawal rates. Correlated incidence heterogeneity casts doubt on the exclusion restriction for our instrumental variables estimator. Nonetheless, we have argued that, as long as this source of heterogeneity reflects latent pro-poor targeting (consistent with the observed data) then we can interpret our preferred estimate of the mean benefit withdrawal rate as an upper bound to the true value.

To illustrate our approach, we have used a specially designed and commissioned survey to study what is probably the largest cash transfer programs in the world (in terms of coverage), namely China’s _Dibao_ program. Our results suggest a sizeable bias in the benefit incidence picture that is implied by either the formal rules or the usual statistical practice of calculating conditional means at different net incomes. In the present application, our estimated mean benefit withdrawal rate is much lower than the formal rules suggest, yet about double that implied by the standard statistical approach.

By focusing on the key parameter for policy design, we can also offer some insights for policy reform in the light of the literature on optimal taxation. The central government’s design for _Dibao_ aims to use means-tested transfers to assure that no registered urban resident has an income below a stipulated “ _Dibao_ poverty line.” In theory this is ideal for protection but bad for promotion given that it imposes a 100% marginal tax rate on poor participants—a poverty trap. However, we find no sign of this in the data. Indeed, if anything the benefit withdrawal rate is too low, implying that the scheme is relatively unresponsive to income changes. Incentives for “promotion” are strong, but performance in “protection” is weak. The reason is found in local implementation practices. Local agents implicitly put a far higher weight on promotion than implied by the central government’s design for the scheme (though we find that cities with higher average income tend to put a higher weight on protection).

Our key policy conclusion is that the _Dibao_ program is unlikely to provide a strong disincentive for earning extra income among participants. Incentive effects appear to be more serious than presumed by standard non-behavioral incidence analysis but still much less severe than basic incentive theory would suggest given the program’s design on paper. Indeed, our 20
67. (e) <u>Relevance of Government Sponsored Institutions:</u> It is clear from the above that labor market institutions are still developing in TL. The Government is keen to strengthen these and to improve mechanisms for training, facilitation of employment, tribunals and other institutions. The main point of interface between government-run institutions and employers is the Ministry of Labor and Community Reinsertion and its training and recruitment arm. The survey asked employers about the Employment Centers for Skills Development operated by the then Secretariat (now Ministry) and its relevance to them for training and recruitment of their employees. Only 30 percent of the formal enterprises knew about the public Employment Centers, and only half of those that were aware had actually had any contact with an employment center (see Figure 23). Even more notably, only eight firms (i.e. less than 2 percent of all formal enterprises) had undertaken any recruitment through the centre. Such low awareness and usage is in part a product of the young age of the centre. However, the results suggest a need to monitor closely if the situation improves over time and the implications for an employment centre-based approach to employment services.

<!-- Start of picture text --> Figure 23: Awareness of employers of public<br>employment centres<br>Not aware<br>Aware - no contact<br>Aware - contact<br>Aware - used<br>Source: UES, 2004<br><!-- End of picture text -->

# **3.   Conclusions and Policy Implications**

68. Several observations on the environment for employment creation in Timor-Leste emerge from the survey and other evidence. The major points can be summarized as follows:

- Timor-Leste has undergone major macroeconomic fluctuations in recent years which would have challenged even the most resilient economy to sustain job growth.  Despite prudent fiscal management, the demand shocks of 2002 and 2003 are likely to be important drivers of employment outcomes.  In addition to shocks, TL’s low state of economic development also creates a range of challenges for job creation, particularly in the formal sector.

- In the face of such macro and structural challenges, the business environment is less conducive than it could be to creation of good quality jobs in TL. The most negative feature is the high monetary and time costs of contract enforcement, but other elements such as the time and costs of business start-up and the difficulty of hiring workers in labour regulations are also likely to retard job growth.

31
11 USD 50 million after accounting for fluctuations in commodity prices. The hydrocarbons and the mineral datasets themselves were constructed from many underlying sources. Minex constructed the data from company public reports (Annual Reports, press releases, NR 43-101 studies, etcetera), technical and trade journals (such as Economic Geology, Northern Miner and Mining Journal) and Government Files (from the various Geological Surveys). The data was up to date as of August 2013. Minex defines the discovery date of a mineral discovery as the moment when it was realized that the deposit has significant value, usually the date of the first economic drill intersection. Some deposits might have had small-scale operations in place prior to the discovery date – if there is an order-of-magnitude increase in the known size of the deposits, the date of the increase in the size of the discovery is taken as the discovery date.

Overall, we believe our data set to have the most comprehensive list of giant or big natural resource deposits for the period since 1950. Nevertheless, there are a number of constraints. First, while only deposits above a certain threshold size are even considered for inclusion in the dataset (see the list in Appendix B), there is likely to still be a certain measurement bias towards larger deposits given better documentation for larger deposits. Secondly, data for certain countries such as Russia and China might be under-measured given less accessible data. Last, our dataset excludes iron ore and bauxite. Since they are more abundant than other metals, there are not always well-timed discoveries to speak of and Minex excluded them from the dataset. Exploitation decisions tend to be based more on proximity to port facilities for iron ore and substantial energy availability for bauxite than other factors.

Figure 2 shows a map with all natural resource discoveries included in our dataset. Figure 3 plots the total number of worldwide discoveries (split between minerals and hydrocarbons) by year. Since the early 1980s the average number of discoveries has been fairly stable at the global level. In our empirical work we use discoveries disaggregated by 128 countries and 33 types of resources.