rafmacalaba/datause-encoder-data huggingface dataset:
# **II. Price Behavior**

One of the important developments recently is the gradual increase in agricultural prices, especially after the mid 2000s (Table 1).

**Table 1: Changes in Agricultural Price Indices and Manufacturing Unit Value, 1990/91-2007/08**

|||In% Change||
|---|---|---|---|
|Price Index|1990-91/2000-01|2000-01/2006-07|2000-01/2007-08|
|Manufacturing Unit Value (MUV)|-5.2|15.8|22.0|
|Raw Commodities (world trade weights)|-13.9|61.4|99.0|
|Raw Commodities (developing country trade weights)|-14.6|67.7|108.0|

Source: World Bank Commodity Price database.

While agricultural prices were declining during the 1990s, they increased very rapidly during the 2000s. This is a period of low inflation and price increases, outside basic commodities, was very limited. MUV index which measures the traded prices of manufactured goods do not increase at the same rate. Within agriculture, the difference between the weights for developing country exports and world exports is now very small, showing greater decline in 1990s and faster increase in the 2000s.<sup>4</sup>

**Figure 1: Agricultural and Manufactured Good Price Indices**

<!-- Start of picture text --> Agricultural and Manufactured Goods Prices<br>200.00<br>180.00<br>160.00<br>140.00<br>120.00<br>100.00<br>80.00<br>60.00<br>40.00<br>20.00<br>0.00<br>Agricultural Price Index (WW) MUV<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br>2000=100<br><!-- End of picture text --> Figure 1 shows the price series for agricultural prices with world weights and the MUV going back to 1980. There is declining prices until 2001 with cyclical fluctuations in the mid 1990s. Agricultural prices came down 23 percent during the 1980s and 15 percent during the 1990s. After 2002, there is a steady rate of price increase until 2008 of more than 60 percent. The price increases since 2002 are quite unusual and during the postwar period price spikes of similar proportions is seen during the Korean War around 1953 and during the oil price shock around 1974. Finally, 2000/01 which is used as a

> 4 These updated and revised data is somewhat different than the series used in Aksoy (2004), large differences between agricultural prices with developing country weight and world weights have disappeared with the updated data series.

3
_Politics_ ’) in Mexico, we employ a close election regression discontinuity (RD) approach. Using nearly 10,000 direct legislative elections in over 2,000 municipalities between 1993 and 2012, we compare municipalities where the presidential party barely won the legislative elections with municipalities where it barely lost. We find a striking pattern between presidential party status and new federal highway construction. In municipalities where the president’s party secured the majority in closely contested legislative races, the length of federal highway built more than doubled—relative to locations lost to the opposition—in the subsequent 4 to 5-year period. Our RD results are stable across a variety of specifications and robustness checks. As expected, for roads under the authority of local governments, including state and rural roads, we find that party alignment between the president and the legislature has no effect. We also test and rule out the possibility that presidential election outcomes induce a disproportionate distribution of highways.<sup>3</sup> Together, these findings suggest that it is only the interaction between the executive and the legislature that is systematically related to variation in federal highway construction across Mexican constituencies.

We further examine a this interaction ex- potential explanation underpinning by ploring the idea that _divided government_ —the division of the executive and legislative branches between different parties or coalitions—can stimulate distributive spending and fiscal pork (McCubbins, 1991; McCarty, 2000). McCarty (2000) formalizes this theory in a model of presidential pork and predicts that when “the size of the president’s party in the legislature... is small (such as divided government), spending should be more distributive than when it is stronger (unified government)” (see pages 123-124). Consistent with McCarty’s model, we find a striking pattern of political bias in the geographic distribution of federal highways when the size of the president’s party in the legislature is relatively smaller than the size of the opposition (divided government). Since

> 3 We collect data on presidential election results at the municipality-level and compare constituencies barely won by the president with constituencies he narrowly lost to the runner-up contestant. We find no evidence that more highways were constructed in municipalities that voted for the president.

2
on export spillovers, in which proximity to other exporters is assumed to reduce the fixed export cost due to the endogenous formation of informational networks between exporting firms. Most of the literature on this topic is empirical. In a pioneering work, Aitken et al. (1997) show that the export decisions of local firms in Mexico is positively influenced by their proximity to multinational exporters. This result has been confirmed by Kneller and Pisu (2007) for UK data and by Kemme et al. (2009) for India. In contrast, Barrios et al. (2003) do not find clear evidence for these export spillovers from foreign firms in Spain, whereas Ruane and Sutherland (2005) find that the export intensity of foreign-owned enterprises is negatively correlated with the export decision and export intensity of domestic firms in Irish manufacturing, suggesting that no (and even negative) export spillovers derive from third-country export-platform FDI. This prediction bodes ill for China, where foreign firms are primarily engaged in the processing trade (i.e., the assembly of imported inputs, which are then re-exported as a final product). In the context of China, three studies investigate export spillovers emanating from foreign firms (Ma 2006; Swenson 2008; Chen and Swenson (Forthcoming)). These papers relate the probability of exporting (or the number of new export transactions at the city or province level) to the presence of multinational firms. They find evidence of positive foreign export spillovers at the two-digit industry level (approximately 100 SITC or HS sectors).

This paper goes further in developing an understanding of the mechanisms at play in foreign export spillovers in China by exploiting data at a finer level, both in terms of the geography of exports and in terms of activities. In particular, Krautheim (2012) argues in his theoretical paper that the relevant information might be destination specific. For example, technical regulations or specific consumer tastes vary across countries. Koenig (2009) finds evidence of export spillovers from French data only when the destination dimension is taken into account. Export spillovers might also occur at a fine product category level that is more detailed than the HS2 categories, which might be highly heterogeneous. For example, in the French case, Koenig et 5
Over the period July 1981-December 2009, the NDVI in Gash Barka ranged between 0.073-0.714 and varies widely across sub-zones<sup>10</sup> . The vegetation level recorded by satellites remained fairly stable (Figure B1), suggesting that policies of the NMCP may have been crucial to fight malaria since the environment remains hospitable for the vector. The weekly average value of NDVI in the 13 surveyed sub-zones of Gash Barka is represented in Figure B1. Vegetation starts increasing in July, following the inception of the rainy season, peaks in September and declines sharply by the end of October. The dashed vertical lines show the period when the survey was conducted, i.e., the second week of October.

For each sub-zone we counted the number of 2-week periods in which NDVI exceeded 0.361 during the period 2000-2009 (Table A in Figure B2). We also tried a lower threshold of 0.3 to allow for a possibly lower threshold (Table B in Figure B2). Cells are coloured from red (arid) to green or blue (more vegetaion). We then assigned a value _ndvi 2 {_ 0 _,_ 1 _,_ 2 _}_ to each sub-zone, where 0 hints to “very limited vegetation” (sub-zones Akurdet, Dighe, Forto and Mensura), 1 stands for “some vegetation” (sub-zones Barentu, Gogne, Haykota, Mogolo and Tesseney) and 2 means “with significant vegetation” (sub-zones Goluj, Laelay-Gash, Mulki and Shambko).

# **B.2 Wealth**

Finally, we checked whether treatment effects varied depending on households’ wealth. Following the Filmer-Pritchett (FP) procedure (Filmer and Pritchett, 2001), we split all categorical variables into sets of dummy variables (we exploit information on households’ main water source, toilet type, fuel used for cooking, wall and roof material, presence and type of any windows, access to electricity, ownership of electronics and any vehicles, the number of persons per room of the dwelling and ownership of any livestock) and we use Principal Components Analysis (PCA) to assign the indicator weights. We use only the first factors produced by PCA to represent our wealth index, as suggested by McKenzie find that _P. falciparum_ Malaria is most prevalent in regions with NDVI _>_ 0.4 for at least 6 consecutive months.

> 10Vegetation data was retrieved from the website of the International Research Institute for Climate and Society of Columbia University (http://iridl.ldeo.columbia.edu/maproom/Health/Regional/Africa/Malaria/NDVI/). For zone Gash Barka only, we downloaded the available time series for each sub-zone over the entire available period.

7
from Planet – which features spatial pixel resolution of 3 to 5 meters – and use those predictions as inputs into the small area estimation model. We make use of two different CNNs: one to estimate the poverty rate (defined as extreme or moderate headcount poverty according to the CONEVAL definition) and one to recognize the land classification type of an image. We train the former using the 2014 MCS-ENIGH. Land classification subdivides images into coverage by building, road, water, grassland, forest, and background (all other classes). Because CNNs require square tiles of imagery for prediction, we train the CNN to estimate poverty or land type at the tile level, which are roughly 750 square meters each. The CNN predicts poverty or land type at this tile, and we use an area-weighted sum to aggregate the predictions to the AGEB – or sub-area – level. We then aggregate these AGEB-level predictions to municipalities, weighting by the 2010 census population.<sup>14</sup> The auxiliary information that augments the small area estimation models is therefore AGEB-level and municipal-level CNN predictions of poverty or land type. Summary statistics for the CNN generated auxiliary spatial data are shown in Table 2, and more technical details of the CNN are presented in Appendix A.

Intuitively, the poverty CNN uses all available visual characteristics in an image to predict extreme or moderate poverty as defined by the 2014 MCS-ENIGH. If these visual characteristics reliably predict poverty, then CNN model predictions can be extrapolated to areas not covered by the MCSENIGH and used to estimate poverty predictions for these areas. Adding this additional information should allow a small estimation model to have lower variance and higher predictive performance. While Babenko et al. (2017) demonstrate the performance of the CNN poverty predictions against a benchmark, incorporating this information into a small area estimation model improves the predictions in three ways. First, it enables the estimates to also benefit from auxiliary data on land classification. Second, it conditions the predictions on sample information through the empirical best predictor model, making the predictions more accurate. Finally, it enables the appropriate estimation of uncertainty, in the form of mean squared error estimates, from which one can construct reasonably accurate confidence intervals.

> 14 An alternative when there is no census available is to use estimated population from WorldPop

> (https://www.worldpop.org/), which seems to perform well in other applications (e.g., Merfeld et al., 2022).

14
# **Table 7. The Effect of Drought Relief Compensation on the Share**

# **of Votes for other Parties**

||Dependent variable:<br>Share of votes for PRI,<br>2006|<br>Share of votes for<br>PRD,2006|Share of votes for<br>Other Parties,2006|
|---|---|---|---|
||(1)|(2)|(3)|
|Below Cutoff|-2.884|-3.876|-2.041|
||(1.196)**|(0.965)***|(0.610)***|
|Rain Deviation|-0.08|0.047|-0.013|
||(0.012)***|(0.018)***|(0.007)**|
|Constant|20.962|65.546|21.014|
||(9.658)**|(7.626)***|(4.040)***|
|Observations|1038|1038|1038|
|R-squared|0.6561|0.6719|0.3918|
|State controls|Yes|Yes|Yes|
|Controls at electoral section level|Yes|Yes|Yes|
|Controls at municipal level|Yes|Yes|Yes|
|Mean of dependent variable|27.934|20.933|6.03|
|Robust standard errors in parentheses||||

State controls are dummy variables for each state.. Controls at the electoral sections include altitude, distance from the weather station, distance to the nearest river and distance to the "cabecera". Controls at the municipal level include municipal income per capita for the year 2000.

* significant at 10%; ** significant at 5%; *** significant at 1%

Page **29** of **29**
Policy Research Working Paper 7966

# **Abstract**

Economists and other social scientists are increasingly using big data analytics to address longstanding economic questions and complement existing information sources. Big data produced by online platforms can yield a wealth of diverse, highly granular, multidimensional information with a variety of potential applications. This paper examines how online job-portal data can be used as a basis for policy-relevant research in the fields of labor economics and workforce skills development, through an empirical analysis of information generated by Babajob, an online Indian job portal. The analysis highlights five key areas where online job-portal data can contribute to the development of labor market policies and analytical knowledge: (i) labor market monitoring and analysis; (ii) assessing demand for workforce skills; (iii) observing job-search behavior and improving skills matching; (iv) predictive analysis of skills demand; and (v) experimental studies. The unique nature of the data produced by online job-search portals allows for the application of diverse analytical methodologies, including descriptive data analysis, time-series analysis, text analysis, predictive analysis, and transactional data analysis. This paper is intended to contribute to the academic literature and the development of public policies. It contributes to the literature on labor economics through application of big data analytics to real-world data. The analysis also provides a unique case study on labor market data analytics in a developing-country context in South Asia. Finally, the report examines the potential for using big data to improve the design and implementation of labor market policies and promote demand-driven skills development.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at snomura@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
ing a level of schooling of at least _s_ ̄ - referred to as the enrollment or completion rate hereafter - and the gender ratio among those choosing at least _s_ ̄. The reversal occurs when the ratio _GGzmzf_ ( ̄( ̄ _zz_ )) reaches values above 1.

For the reversal to occur under the tail hypothesis, a decrease in the scholastic performance threshold ̄ _z_ or, equivalently, an increase in the net benefits of education _b_ which translates into an increase in _P_ , is required. Our distributional framework of investment in schooling also implies that the mean scholastic performance of individuals that attain a given level of schooling _s_ ̄ decreases as the share of population that attains _s_ ̄ increases. Using US data, Appendix A1.2 shows that the average scholastic performance of individuals attending tertiary education has indeed decreased over time.

Under the assumption that _σm_<sup>2</sup><sup>_>σ_</sup> _f_<sup>2,itcanbeshownthattherelationshipbetweenthefemale-</sup> to-male ratio among individuals choosing a level of schooling of at least _s_ ̄, denoted _R_<sup>_TH_</sup> , and the share of individuals that achieve a level of schooling of at least _s_ ̄, denoted _P_<sup>_TH_</sup> , exhibit three key properties:

**Proposition 1.** _R_<sup>_TH_</sup> _tends to zero when P_<sup>_TH_</sup> _tends to zero._

**Proposition 2.** _R_<sup>_TH_</sup> _tends to one when P_<sup>_TH_</sup> _tends to one._

**Proposition 3.** _There exists a value of P_<sup>_TH_</sup> _∈_ [0 _,_ 1[ _such that R_<sup>_TH_</sup> = 1 _. This value is unique and always exists._

**Proof.** See Appendix A3.

The distributional assumption _σm_<sup>2</sup><sup>_>σ_</sup> _f_<sup>2isanecessaryandsufficientconditionforProposition1</sup> to 3 to hold, without any condition imposed on the relative values of _μm_ and _μf_ . In addition, Proposition 1 to 3 also hold when _z_ is assumed to follow two-parameter probability distribution functions other than the normal distribution.<sup>17</sup>

> 17Proofs of Propositions 1 to 3 for distribution functions other than the normal distribution are available upon request.

16
complexity penalties. In particular, larger _n_ means a larger penalty for lack of fit and this will tend to favour larger models (since they fit at least as well as the models nested in them). Indeed, we notice in Table 3 that the median model size is larger throughout for _n_ = 100. Furthermore, including irrelevant regressors will not change the fit much, so it is understandable that the inclusion probabilities of irrelevant regressors are about the same for both sample sizes. However, the real difference lies in the fact that with the larger sample size we move less frequently to models that lack one of the “true” regressors as this normally implies a substantial lack-of-fit penalty.

**Table 2.** Simulated Data, Models 1 and 2 (null model)—results for model size and inclusion probability of the true model. Number of regressors _k_ = 15.

||||Mod|el 1|||||Mod|el 2||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||True model size||5||5||0||0||0||0|
|P|rior mean model size||7.5||7.5||7.5||7.5||7.5||7.5|
||_n_||50||100||50||100||1000|10|,000|
|||||me<br>media|dian a<br>n post|verag<br>erior|e post<br> prob.|erior <br> true|mode<br> mode|l size<br>l (in|: _η_<br> %):|_γ_||
||Prior on _g_|_η_|_γ_|_η_|_γ_|_η_|_γ_|_η_|_γ_|_η_|_γ_|_η_|_γ_|
|1|Benchmark _c_= 1|4.2|0.4|5.2|3.1|0.1|88|0.1|87|0.1|94|0.0|98|
|2|Benchmark _c_= 0_._1|5.3|0.5|5.9|2.5|0.1|90|0.1|89|0.0|95|0.0|99|
|3|Benchmark _c_= 0_._01|6.2|0.5|6.1|2.3|1.1|56|0.8|45|0.2|89|0.0|97|
|4|Maruyama-George|5.5|0.5|5.7|2.5|0.3|81|0.1|89|0.0|97|0.0|99|
|5|Bottolo-Richardson|6.4|0.5|6.2|2.2|4.8|18|4.2|20|3.9|28|3.2|31|
|6|Hyper-_g_, _a_= 3|7.4|0.3|6.9|1.7|6.6|9|6.4|9|6.4|9|6.3|9|
|7|Hyper-_g_, _a_= 4|7.7|0.3|7.2|1.5|6.8|8|6.6|8|6.7|8|6.6|8|
|8|F-Z, _a_= 2 + 2_/k_<sup>2</sup>|6.3|0.4|6.1|2.2|2.2|41|2.0|40|2.0|40|2.2|44|
|9|F-Z, _a_= 2 + 2_/n_|6.3|0.4|6.2|2.3|2.5|40|2.1|37|2.0|42|2.1|43|
|10|Horseshoe|6.8|0.4|6.5|2.1|6.5|10|6.2|11|6.3|11|6.3|11|
|11|Forte _et al._|6.0|0.5|6.0|2.4|0.3|78|0.2|86|0.0|96|0.0|99|
|12|Hyper-_g/n_|5.8|0.5|5.8|2.4|1.2|44|0.7|53|0.1|91|0.0|98|
|13|Horseshoe/_n_|6.1|0.4|6.0|2.4|3.9|29|3.1|35|1.7|54|0.6|66|
|14|Zellner-Siow|5.6|0.5|5.9|2.4|0.4|73|0.2|82|0.1|94|0.0|98|
|15|_g_ =_n_|4.8|0.6|5.1|3.1|0.4|71|0.2|80|0.1|94|0.0|98|
|16|_g_ =_k_<sup>2</sup>|3.6|0.3|4.6|3.6|0.2|87|0.2|86|0.2|86|0.1|87|

Combining the results on consistency with those of the posterior probability assigned to the true model, we conclude from this section that the best performing priors seem to be the benchmark, Maruyama-George, Forte _et al._ and the Zellner-Siow priors.

# **8. Applications to Real Data**

In the context of real applications, we will further examine all the different priors, and will also investigate the predictive performance as well as Bayes factors. For each application, we will randomly partition the sample in an estimation sample and a prediction sample, of a fixed size. We do this 50 times and this allows us to assess predictive performance based 21
Table 8. C umulative Contributions to the Change in Poverty Head Count Ratio

||Banglades|h,2000-10|Peru,200|4 - 2010|Thailand,2|000-2009|
|---|---|---|---|---|---|---|
|Contribution to national|||||||
|moderate poverty|Percent|Share of|Percent|Share of|Percent|Share of|
|reduction on account of<br>cumulative changes in:|point<br>change|total<br>change|point<br>change|total<br>change|point<br>change|total<br>change|
|Population|-4.41|25%|-0.88|5%|-2.51|18%|
|Education|-0.88|5%|-0.31|2%|-1.94|14%|
|Occupation|-1.38|8%|-3.90|24%|0.40|-3%|
|Sector|-0.51|3%|0.02|0%|-1.40|10%|
|Returns Non-Farm|-2.93|17%|-1.95|12%|-1.34|10%|
|Returns Farm|-8.18|47%|-5.06|31%|-3.54|25%|
|Residuals|1.33|-8%|0.05|0%|0.25|-2%|
|Non-Labor income|-1.93|11%|-0.13|1%|-2.87|20%|
|Others|1.55|-9%|-3.96|25%|-1.15|8%|
|Total|-17.34|100%|-16.13|100%|-14.11|100%|

35
4

# GATS. Section VII concludes.

# **II.** Synopsis of Negotiating Positions<sup>5</sup>

Before the 1986 Ministerial meeting at Punta del Est_ establishing tne agenda of the Uruguay round, a group of ten developing countries defended the view that GATT negotiations should not address services.' While these countries did not manage to block the inclusion of services on the round's agenda, they did succeed in putting services on a separate track in an attempt to establish the principle that no cross-issue linkages be possible between traditional GATT issues and services. Moreover, at their insistence economic development and growth were agreed to be an objective of any agreement.<sup>7</sup> Many developing countries continued to express doubts about the value of a multilateral agreement liberalizing trade in services during the first year of discussions. Submissions by Argentina, Brazil and India to the Group of Negotiations on Services (GNS) in 1987 and the first half of 1988 are representative of the initial negotiating positions of the major developing countries.' The primary criterion for these countries was that any agreement further the goal of economic development and growth.' Argentina and India argued that this objective could be best met through market sharing arrangements, so as to ensure that developing countries increased their share of world trade in services. An Argentinean submission was also representative in emphasizing that developing countries remain unconstrained with respect to regulatory regimes pertaining to service industries, including possible export promotion schemes. Many dt;veloping countries emphasized that lack of statistical in'formation made it impossible to determine how trade should be defined for purposes of an agreement. Data limitations were used as one justification for seeking to exclude service transactions involving establisliment by foreign providers (foreign direct investment -- FDI).'° Similarly, it was argued that agreement on a definition of trade in