rafmacalaba/datause-encoder-data huggingface dataset:
# 5. Diversi fi cation versus Specialization

We have seen that snapshots of China’s export sector taken in 1992 and 2005 look very different, with the increased churning from agriculture and textiles into machinery, electronics and assembly. As a result of this transformation, China’s exports may have become more specialized or more diversi fi ed. Traditional trade theory highlights the combination of increased trade and specialization as a key factor in promoting higher living standards. Imbs and Warziarg (2003), however, fi nd that countries tend to diversify production as they grow from low levels of income, and that they only begin to specialize once they reach a relatively high level of income. This is consistent with countries moving from exploiting natural resources to developing new industrial sectors as they grow. Hausmann and Rodrik (2003) argue that in the early stage of development, more entrepreneurship and potentially greater diversi fi cation may help producers identify the sectors in which it is a competitive producer.

We examine whether China’s exports display increased or decreased specialization in Figure 8, by plotting the inverse cumulative export shares for all products at the HS 6- digit level. A shift to the left of the curve would indicate increased specialization. Looking across all products, it appears from Figure 8 that there is hardly any change in the degree of specialization. Yet, when we magnify the image of Figure 8 in Figure 9, showing the cumulative trade shares when we keep only the largest 500 categories by value, which account for nearly 80 percent of total exports in either of the years, there is a noticeable downward shift in the curve suggesting there has been an increase in specialization. The pattern is very similar, with a slightly greater increase in specialization, if we only include manufacturing exports.

This fi nding is con fi rmed using the Gini coe ffi cient, which is an alternative way to measure changes in specialization, by measuring export equality in each period. It is de fi ned as where there are n products, i is a product’s rank (1 is smallest and n is largest), and csharei 7
# **Figure 7: Citation curves for Google Scholar citations for the World Bank and various universities**

# (a) Full curve

<!-- Start of picture text --> 0 500 1000 1500 2000 2500<br>Harvard<br>Oxford<br>Princeton<br>World Bank<br>Yale<br>4000<br>3000<br>2000<br>citations<br>1000<br>0<br><!-- End of picture text --> 35
1. Introduction

The world is rapidly losing biodiversity. Pimm et al. (2014) find that the current rate of species extinction is at least 1,000 times the background rate. Corroborating evidence is provided by the Living Planet Index (LPI), an indicator of global biodiversity based on population trends for vertebrate species in terrestrial, The LPI informs the Convention on Biological Diversity (CBD) and its Conference of the Parties (COP). In response to such indicators of rapid decline, 188 governments in the COP ratified the Kunming-Montreal Global Biodiversity Framework (GBF) at the COP 15 meeting in December 2022. Among other measures, the Framework commits participants to protecting 30 percent of the planet by 2030.

Effective implementation of the Framework will require addressing two difficult questions: (1) What is the spatial distribution of global biodiversity that should be protected? (2) How can protecting 30% of the This paper attempts to contribute by revisiting the spatial distribution of global biodiversity with species occurrence records from the Global Biodiversity Information Facility (GBIF). During the past 15 years, the GBIF’s global reporting network has expanded to include occurrences for over 2 million species. During of these records include locational coordinates, the GBIF data can enable new estimates of spatial distributions for previously-unmapped species, as well as improved estimates for species with existing Species maps underpin spatial analyses of global biodiversity. If all species are treated equally, then a species; (2) Overlay all chosen maps on a high-resolution global grid; (3) Count the total species incidence in each grid cell. In practice, global biodiversity analyses can modify species counting in three dimensions. life” that describes overall genetic variation in the global biome. Second, species may have different conservation priority weights because their vulnerability to human encroachment differs greatly. Third, species’ distributions may be far from uniform in the spaces enclosed by their maps. If a species’ spatial distribution density is known, its counting weight for each cell can be made proportional to its likelihood The scientific literature features plentiful examples of weighted species counting (e.g., Guo et al. 2022; genetic and environmental data, as well as expert analysis of their roles in the assignment of counting weights. These are inevitably time-consuming and intensive in technical resources that remain in short to 30% global protection by 2030. Global biodiversity could be assessed with expert-mapped, wellresearched species only, but this would ignore the huge population of other species with recorded GBIF occurrences. Unfortunately, policy makers cannot automatically assume that well-documented species adequately represent the larger population.

For more detailed information, see ~~hitps://www.livingplanetindex.org/latest_results~~ https://www.gbif.org/analytics/global
- 2 -

The third group of funds seems to be composed of independent MPFs. The funds sponsored by financial institutions account for about 85 percent of total assets, despite charging higher fees. One of the main challenges faced by Hungarian regulators is to reduce the current degree of market segmentation, taking advantage of the presence of low-cost funds to improve competition and reduce costs and fees for all participants.

6. This paper is structured as follows. Section II analyzes the industrial organization of the Hungarian MPFs by type of sponsor. Section III, IV and V provide an analysis of the performance of the MPF system in terms of asset growth, portfolio composition and investment return, and fees, respectively. Section VI examines the links between pension reform and capital market development. Section VII examines the key regulatory issues in the accumulation and payout phases. Conclusions and policy recommendations follow.

# **II.   THE STRUCTURE OF THE SECOND PILLAR**

7. The second pillar started operating in January 1998, with 38 MPFs initially licensed by the Pension Fund Supervision Agency. The Pension Supervisor was integrated in the year 2000 into the Hungarian Financial Supervisory Authority (HFSA).<sup>3</sup> Table 1 reports summary indicators of the evolution of the MPF system between 1998 and 2004. At the end of 2004, there were 18 MPFs operating in Hungary, managing the accounts of 2.4 million members (more than half of the labor force) and assets of 4 percent of GDP. The second pillar manages a larger portion of assets than the third pillar. It also covers more than twice as many members.

8. The second pillar has always been very concentrated, with the six largest MPFs accounting for more than 80 percent of total assets. Nevertheless, the reduction in the number of MPFs did not contribute to an increase in concentration since the MPFs that left the market (mostly through mergers or acquisitions) were very small. By contrast, the consolidation in the third pillar was accompanied by an increase in concentration. In 2004 there are still 75 voluntary pension funds in operation, from the 315 in 1998.

**Table 1: Summary Data on the Structure of the Second and Third Pillars**

||**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|
|---|---|---|---|---|---|---|---|
|**Second Pillar**||||||||
|Number of funds|38|39|-|18|18|18|18|
|Total Assets (% of GDP)|0.3|0.8|1.3|1.9|2.5|3.0|4.0|
|Number of members (000)|1,347|2,064|2,193|2,253|2,214|2,304|2,402|
|Share of 6 largest funds (%)|83|84|84|86|87|-|83|
|**Third Pillar**||||||||
|Number of funds|315|-|-|-|82|-|75|
|Total Assets (% of GDP)|1.0|1.4|1.7|2.0|2.2|2.4|2.6|
|Number of members (000)|940|1,008|1,081|1,153|1,179|-|-|
|Share of 15 largest funds(%)|47|59|71|74|80|-|-|

Source: HFSA.

9. The relatively small number of funds and the concentrated market structure are not features unique to the Hungarian second pillar. As a matter of fact, in other reforming 3 See Section VII.
for SR eligibility and heterogeneous effects by household characteristics also indicate consistent results of an increase in informal employment.

The rest of the paper is organized as follows: in the next section, we provide background information on the health sector reform. In section II, we review articles that examined the effects of other reforms that occurred in Colombia during the period of our study as well as studies of informal employment in other Latin American countries. In the data section, we describe the datasets used in the analysis. In Section IV, we present the empirical results for informal employment using the two datasets and different identification strategies. We conclude in Section V.

# **I Background**

In 1990, Colombia adopted a new constitution that emphasized social program spending for the poor. Law 100 of 1993 reformed the social security system, including pensions, workplace accidents, complementary social services, and health insurance. The health sector reform aimed to achieve universal health insurance coverage through the creation of two regimes: a Contributive Regime (CR) and a Subsidized Regime (SR).

The main difference between the SR and all other changes to the social security system is that all but the SR were implemented nationally in January 1994. Unlike the SR, the other changes did not require the SISBEN. Using the variation in the implementation dates of the SISBEN across municipalities and year fixed effects as well as a discontinuous eligibility threshold for the SR, we are able to identify the effects that are due to the SR and not due to the other national changes that occurred when Law 100 came into effect.

The CR, which was implemented in the whole country after the passing of the law, made health insurance through employment mandatory regardless of occupation. The SR was de4
contradiction between the LS and GMM results, but the latter are more precise particularly in the cases of earthquakes and storms. Under LS, only droughts and floods carry statistically significant coefficients, with droughts producing a substantial drop in agricultural growth and floods causing an increase in growth of all major sectors and, thus, aggregate GDP.

The second exercise checks the robustness of the findings against the disaster measurement method. In particular, a count (incidence) variable commonly used in the literature (e.g. IMF, 2003; Becker and Mauro, 2006) is taken to measure natural disasters, rather than the continuous (intensity) variable used in the main specification. The count variable used is the average number of events in the corresponding country and five-year window. A natural disaster qualifies as an “event” if the number of people affected times 0.3 plus the number of casualties is greater than 0.01% of the population.<sup>19</sup> The results (Table 4) are remarkably similar to those obtained with the continuous measure of natural disasters. In fact, for droughts and floods the results are the same, in terms of sign and statistical significance. For earthquakes and storms, the count or incidence variable fails to identify a significant effect on industrial growth. As will be seen below, this reflects in a sense, the tension of the industrial growth effects of these two variables, i.e. they are positive only if earthquakes and storms are not severe. The count variable does not contain enough information to discern the positive effects that apply to the majority of these natural disasters.

# _Are the effects of natural disasters linear?_

So far, the analysis has focused on the average effect of a disaster. Yet, their intensity differs substantially, and there is a priori no reason to believe that their effects should be linear. The simple specification used so far may be a good representation of the effects of the majority of natural disasters, but it may also distort the true effects of the most severe ones. To examine this issue, the corresponding natural disaster measure is interacted with a dummy variable that has the value of 1 for the top 10% of natural disasters according to intensity, and 0 for the rest. One interaction term per natural

> 19 The IMF also considers disasters that cause damages of at least half a percent of national GDP.

24
- Okunogbe, O. (2021). Becoming Legible to the State : The Role of Detection and Enforcement Capacity in Tax Compliance. _World Bank Policy Research working paper,no. WPS 9852_ .

- URL `https://documents.worldbank.org/en/publication/documents-reports/ documentdetail` 4

- Okunogbe, O., & Santoro, F. (2021). The Promise and Limitations of Information Technology for Tax Mobilization. _World Bank Policy Research working paper,no. WPS 9848_ . URL `https://documents.worldbank.org/en/publication/documents-reports/ documentdetail` 2

- Pomeranz, D., Marshall, C., & Castellón, P. (2014). Randomized tax enforcement massages : a policy tool for improving audit strategies. _Tax Administration Review_ , (36), 1–21. Number: 36 Publisher: Inter-American Center of Tax Administrators (C I A T). 16

- Slemrod, J., Blumenthal, M., & Christian, C. (2001). Taxpayer response to an increased probability of audit: evidence from a controlled experiment in Minnesota. _Journal of Public Economics_ , _79_ (3), 455–483.

- URL `https://www.sciencedirect.com/science/article/pii/S0047272799001073` 3

- Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. _Journal of the American Statistical Association_ , _113_ (523), 1228–1242. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/01621459.2017.1319839. 3, 17

23
one price shock (Panel A) of 20% on a product that represents 10% of the consumption basket of a household, then the GAP is largely below 5% of income. It can be noted here that, the higher is the expenditure share, the lower is the GAP. This is mainly explained by the low substitution effect (low compensated cross-elasticities) with the high expenditure share. It is natural that the GAP will depend on the expenditure share and will not exceeds it. Panel B in Figure 14 shows similar estimates but when we consider two price changes such that, _α_ 2 = 0 _._ 2 and _dp_ 2 = 0 _._ 5 _dp_ 1.

Table 4: Summary of Welfare Measure, Computation Methods and Functional Requirements

|_Welfare change measurement_|_Equation_|_Requirements_|
|---|---|---|
|**LV**|**12**|Initial quantities/expenditures|
|**PV**|**13**|Marshallian demand function|
|**CS: True value**|**17**|Marshallian demand function|
|CS: Elasticity approximation|27|Initial quantities/expenditures|
|CS: TAYLOR<br>~~(~~order=2)|58|Marshallian demand function|
|CS: TAYLOR<br>~~(~~order=3)|62|Marshallian demand function|
|**EV: True Value**|**20**|Indirect utility function|
|EV: Taylor (order=2)|60|Marshallian demand function|
|EV: Taylor (order=3)|39|Marshallian demand function|
|EV: Vartia (10 iterations)|72|Marshallian demand function|
|EV: Breslaw and Smith (10 iterations)|73|Marshallian demand function/numerical derivatives|
|EV: Euler (10 iterations)|74|1st order derivative of the welfare function|
|EV: RK4 (10 iterations)|75|1st order derivative of the welfare function|
|**CV: True Value**|**22**|Indirect utility function|
|CV: Taylor (order=2)|59|Marshallian demand function|
|CV: Taylor (order=3)|40|Marshallian demand function|
|CV: Vartia (10 iterations)|71|Marshallian demand function|
|CV: Breslaw and Smith (10 iterations)|73|Marshallian demand function/numerical derivatives|
|CV: Euler (10 iterations)|74|1st order derivative of the welfare function|
|CV: RK4 (10 iterations)|75|1st order derivative of the welfare function|

38
<u>Table A.1: Number of non-missing observations in the LiTS sample used in the analysis by selected age cohorts and survey years.</u>

|||<br>1940-1|<br>949|||<br>**Age**<br>1960-|<br>**cohorts**<br>1969|||<br>1990-1|<br>999||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Survey year|2010|2016|2023|Total|2010|2016|2023|Total|2010|2016|2023|Total|
|Country|||||||||||||
|Albania|346|330|163|839|714|823|415|1,952|486|813|443|1,742|
|Armenia|280|366|159|805|553|725|431|1,709|729|668|406|1,803|
|Azerbaijan|192|116|45|353|601|748|364|1,713|747|1,180|538|2,465|
|Bosnia and Herzegovina|267|255|142|664|462|734|429|1,625|421|719|253|1,393|
|Bulgaria|348|441|221|1,010|415|619|339|1,373|250|397|187|834|
|Croatia|340|323|201|864|378|619|384|1,381|302|497|236|1,035|
|Czechia|235|359|97|691|405|446|311|1,162|325|296|343|964|
|Estonia|262|448|170|880|269|427|327|1,023|258|221|174|653|
|Georgia|303|444|159|906|479|746|368|1,593|506|593|332|1,431|
|Germany|233|164|69|466|450|568|342|1,360|238|283|239|760|
|Hungary|344|420|154|918|305|421|340|1,066|263|334|267|864|
|Kazakhstan|177|175|29|381|487|595|224|1,306|510|622|362|1,494|
|Kosovo|323|313|147|783|636|912|555|2,103|956|1,302|616|2,874|
|Kyrgyzstan|201|193|61|455|469|742|365|1,576|849|1,018|493|2,360|
|Latvia|274|397|236|907|282|491|239|1,012|249|304|166|719|
|Lithuania|293|338|204|835|380|535|222|1,137|303|329|329|961|
|Moldova|285|306|161|752|372|524|231|1,127|352|419|176|947|
|Montenegro|231|263|91|585|504|620|295|1,419|516|632|439|1,587|
|North Macedonia|363|408|183|954|653|728|398|1,779|563|729|356|1,648|
|Poland|461|376|132|969|580|404|300|1,284|489|309|285|1,083|
|Romania|321|411|108|840|376|411|296|1,083|265|300|241|806|
|Russian Federation|341|179|53|573|472|427|226|1,125|393|429|213|1,035|
|Serbia|482|404|108|994|617|609|338|1,564|487|410|284|1,181|
|Slovak Republic|141|413|158|712|495|566|355|1,416|443|442|198|1,083|
|Slovenia|243|440|223|906|520|532|442|1,494|327|355|280|962|
|Tajikistan|208|184|70|462|631|799|451|1,881|1,296|1,357|740|3,393|
|<br>Türkiye|134|54|51|239|472|585|335|1,392|642|951|777|2,370|

Note: Column “Total” shows the number of non-missing observations (sum) in the three selected cohorts.
Table I.9. Robustness Analysis for the Transfers from Government Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.131*|0.268**|0.152*|0.102|0.121|0.094|0.131*|0.268**|0.152*|0.102|0.121|0.094|
||(0.074)|(0.119)|(0.082)|(0.064)|(0.080)|(0.067)|(0.074)|(0.119)|(0.082)|(0.064)|(0.080)|(0.067)|
|Obs. left|463|287|434|467|287|448|463|287|434|467|287|448|
|Obs. right|976|530|857|1,584|1,145|1,549|976|530|857|1,584|1,145|1,549|
|Mean values (Non-RAMV)|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.221**|0.252**|0.221**|0.211**|0.184**|0.199**|0.247**|0.250*|0.256**|0.254**|0.172**|0.254**|
||(0.097)|(0.106)|(0.102)|(0.091)|(0.088)|(0.091)|(0.114)|(0.146)|(0.126)|(0.107)|(0.086)|(0.110)|
|Obs. left|539|457|500|500|449|482|539|457|500|500|449|482|
|Obs. right|1,169|962|1,145|1,578|1,895|1,617|1,169|962|1,145|1,578|1,895|1,617|
|Mean values (Non-RAMV)|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.313***|0.285**|0.320**|0.292***|0.257**|0.274**|0.316**|0.346**|0.256**|0.375***|0.389***|0.376***|
||(0.121)|(0.124)|(0.125)|(0.105)|(0.109)|(0.107)|(0.138)|(0.171)|(0.126)|(0.109)|(0.135)|(0.112)|
|Obs. left|596|549|578|531|549|527|596|549|500|531|549|527|
|Obs. right|1,314|1,182|1,241|1,880|1,773|1,988|1,314|1,182|1,145|1,880|1,773|1,988|
|Mean values (Non-RAMV)|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|0.065|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
50m<sup>2</sup> if conditions are ideal and at sizes around 100m<sup>2</sup> under average conditions. They also report that fires are located at the correct location with a spatial margin of error of less than 100m on average. Unfortunately, the resolution of MODIS is such that the data available in FIRMS only measures whether at least one fire exists in a given square kilometer. As a result, MODIS does not provide any information about the size of the detected fire or the total burned area. Further, it does not distinguish between pixels with only one fire and those with multiple fires.

I combine the FIRMS data on fire presence with remotely sensed landcover data from the European Space Agency’s Copernicus system to determine the land uses on which fires occur.<sup>18</sup> Copernicus land cover data assigns each pixel to a land class based on imaging that measures its reflectivity. Classes include water/ice, urban, wetland, irrigated cropland, non-irrigated cropland, various classes of forest, and various classes of dry shrubland. I use the cropland classes to determine which fires occur on cropland and focus my analysis on these agricultural fires. I assign agricultural fires based on land use in 2006. I supplement the land use data from Copernicus with additional polygon layers from the Harvard Center for Geographic Analysis (CGA) that identify roads and urban areas in India. I verify that my agricultural fires do not include any fires that occur in locations that the CGA defines as urban. Finally, I aggregate the assigned agricultural fires to the district by month level.

Figure 1 shows the annual average number of monthly fires per subdistrict prior to MNREGA. Fires are concentrated in the northwest of the country and along the Indo-Gangetic Plain. However, it is clear that burning occurs on cropland throughout India. Both of these facts are confirmed by Figure 2 showing the distribution of states by mean monthly fires in subdistricts. While fires are used in most states in India, their use in Punjab and Haryana is far more widespread than in other states (see notes on Figure 2).

**Weather Controls:** I collect weather re-analysis data from ERA5. ERA5 is a weather re-analysis product produced by the European Commission’s Copernicus Climate Change Service.<sup>19</sup> When working with weather data, there is a trade-off between using re-analysis data, which combines observed data with a physics model to provide data at a fine resolution over long

> 18Copernicus data is available here:https://land.copernicus.eu/imagery-in-situ

> 19Data available here: https://cds.climate.copernicus.eu/cdsapp!/dataset/reanalysis-era5-land?tab=overview

11