rafmacalaba/datause-encoder-data huggingface dataset:
that of Acemoglu & Restrepo (2017) by constructing a measure of exposure to automation in Europe. While automation patterns in Europe are highly correlated with those of the US, trade between Mexico and Europe represents less than 5 percent of Mexico’s total trade. Thereby, it is unlikely that patterns of automation in Europe respond to changes in Mexico’s tradable sectors.

However, there could still be a concern about the exclusion restriction not being satisfied if unobservable factors such as secular sectoral trends or trade links with other large economies could influence both Mexico’s exports and automation in Europe. We address these concerns using several alternative specifications and robustness checks. We show that the results are robust to controlling for exposure to domestic automation, the exclusion of the automotive sector, controlling for the initial share of manufacturing jobs in the area and eliminating outliers, among other tests. They are also not affected by exposure to ICT adoption in the US, exposure to Chinese imports and the initial degree of job offshoreability in the local area.

In addition, we investigate the labor market impacts of exposure to US automation. We first focus on employment in the tradable sector (agriculture, mining and manufacturing) as this would capture the first-order impacts. The results show that the ratio of employment in the tradable sector to population is not affected by exposure to US automation, or by the decline in exports caused by US automation. However, this average effect hides substantial heterogeneity across LLMs. Areas that initially had a relatively higher share of manufacturing jobs susceptible to being replaced by automation did experience a decline in the ratio of manufacturing employment to population. This could be driven by areas intensive in unskilled jobs that were initially offshored from the US but that are more susceptible to being reshored due to automation, since jobs susceptible to being offshored also tend to be susceptible to being automated. In contrast, areas where the fraction of jobs susceptible of being automated was low, experienced an increase in the manufacturing employment to population ratio. We argue that this latter effect could be explained by the fact that since jobs 3
|Sector|Total|1993-98|1999-2003|
|---|---|---|---|
|Agriculture|6|2|4|
|Iron and Steel|10|4|6|
|Tires|3|2|1|
|Textile|6|3|3|
|Meters|8|5|3|
|Shoes|3|1|2|
|Chemicals|5|2|3|
|Others|16|7|9|
|Total|57|26|31|

Source: INDECOPI Agricultural cases are only six. This scarce use of defense mechanisms must not be seen as an absence of unfair practices in agriculture but as a result of the composition of the sector in Peru. It is mostly composed of small and medium size farms which lack the organizational or management capacity to prepare an antidumping case. A group of cotton producers, for instance, expressed their interest in presenting a dumping case but they did not submit an application requesting that the Commission initiate an investigation on its own account, and arguing that they lacked the necessary management capacity. However, Commission policy is that affected producers must provide proof of damage themselves and be involved in the process.

# _(d) Incidence by Country_

|**Tab**|**le 6**<br>**.**Ant<br>|idumping Inves<br>|tigations by Cou<br>|ntry<br>|
|---|---|---|---|---|
||Total|Dumping|Subsidies|Safeguards|
|China|20|19||1|
|Other Asian<br>Countries|7|7|||
|Chile|11|11|||
|Argentina|9|7|2||
|Brazil|6|5|1||
|Colombia-<br>Venezuela-Bolivia|4|4|||
|Mexico|3|3|||
|Europe|2|1|1||
|Non-WTO|7|6||1|
|Others|2|1|1||

Source: INDECOPI Note: some cases are recorded in more than one country and therefore, sums differ from other tables.

23
Several recent papers suggest refinements to the Shift-share model (e.g. Borusyak et al. 2022) and provide a new framework for shift-share instrumental variable regressions following a quasi-random assignment of shocks allowing exposure shares to be endogenous. Their analysis claims that identification and consistency may arise from the exogeneity of shocks while providing a new guidance for shift share instrumental variable (SSIV) estimations. Further versions of this study might include these new methodologies.

# **_Data_**

The shift-share Bartik analysis draws on labor market indicators from the _Enquête Nationale sur l’Emploi_ (Labor Force Survey, LFS) and on trade flows from the United Nations (UN) COMTRADE database. The LFS, a nationally representative survey conducted by the _Haut Commisariat au Plan_ , includes detailed information on the active population’s main demographic and professional characteristics, enabling the study of Moroccan labor market trends. It provides employment, formality, and participation data from 2000 to 2018.

The lack of homogeneous regional variables over time limits our LFS analysis. Only 10 regions can be homogenized across the entire 2000-2018 period, a low number of observations for econometric analysis. LFS’s “province” variable for the period between 2000 and 2009 contains 60 consistent observations, and so our analysis relies on these (excluding 2009 because of post-crisis shocks). The LFS also lacks industry codes for 20152017 and has a different classification system in 2018.

Annual bilateral trade flow data from 2000 to 2018 come from the UN Comtrade database. This analysis focuses on Moroccan exports, or its analog (world imports of the rest of the world from Morocco). We merge these trade data with labor market indicators using the concordance between ISIC rev 3.1 (from the LFS) and HS0 – 1988/92 trade classification (used by UN COMTRADE).

# **_Results_**

# <u>Informality</u>

An increase in exports per worker in Morocco is associated with falling informality **.** To the extent that our instrument controls for endogeneity, our results are consistent with the hypothesis that increasing exports reduced informality rates from 2000 to 2004. The correlation dissipates somewhat from 2000 to 2008. An increase of US$100 in exports per worker led to a decrease of 0.9 percentage points in informality in the first period and 0.6 percentage points in the second period in provinces with higher exposure to trade. These results are statistically significant and apply for all kind of workers (Table 4.1).

**Table 4.1: Estimated effect on informality rate from a US$ 100 increase in exports per worker in Morocco**

|Type of worker|Exports|
|---|---|
||2000-2004<br>2000-2008|

18 | P a g e
**Table 3 Performance Indices for Selected Occupations**

|**Occupation**|**Prestige Index**|**Education Index 1**<br>**(Census 2000)**|**Education Index 2**<br>**(Census 2000)**|
|---|---|---|---|
|Accountants and auditors|65.4|92.8|16.1|
|Bank tellers|43.3|54.8|13.7|
|Cashiers|29.5|31.8|12.3|
|Civil engineers|68.8|95.8|16.4|
|Clergyand religious workers|63.3|89.8|15.8|
|Computer software developers|60.5|95.0|16.2|
|Farm workers|24.5|19.6|10.0|
|Judges|71.5|93.3|16.2|
|Laundryworkers|31.8|20.1|11.2|
|Lawyers|74.8|99.9|17.0|
|Mathematicians and mathematical scientists|63.5|99.4|16.6|
|Physicians|86.1|99.7|17.0|
|Police,detectives,andprivate investigators|59.7|84.1|15.1|
|Registered nurses|66.5|98.1|15.9|
|Secondaryschool teachers|66.4|99.5|16.8|
|Secretaries|46.1|64.0|14.1|
|Taxi cab drivers and chauffeurs|28.1|42.5|12.7|

**Table 4. Estimates Based on Equation (1)**

|**EDUCATION INDEX 1**||**DECADE: 1970-19**|**79**|**DECADE**|**: 1980-1989**|
|---|---|---|---|---|---|
|**VARIABLES**|**CENSUS 1980**|**CENSUS 1990**|**CENSUS 2000**|**CENSUS 1990**|**CENSUS 2000**|
|**Age**|0.00316<br>[0.00410]|0.00684<br>[0.00482]|-0.0069<br>[0.00624]|0.00884***<br>[0.00163]|0.00568***<br>[0.00207]|
|**Age squared**|0.0000214<br>[0.0000700]|-0.000106*<br>[0.0000610]|0.0000401<br>[6.32e-05]|-0.000113***<br>[2.38e-05]|-9.29e-05***<br>[2.37e-05]|
|**Years since arrival (dummy with value 1**<br>**for second half of the decade)**|-0.0596***<br>[0.00573]|-0.0300***<br>[0.00490]|-0.0352***<br>[0.00502]|-0.0413***<br>[0.00344]|-0.0317***<br>[0.00332]|
|**EDUCATION DUMMIES**||||||
|**five to nine years of education**<br>**ten to twelve years of education**<br>**high-school level**<br>**some college**<br>**Bachelor's Degree or higher**|0.00444<br>[0.00705]<br>0.0566***<br>[0.00933]<br>0.168***<br>[0.00974]<br>0.451***<br>[0.0134]<br>1.011***<br>[0.0123]|0.0231***<br>[0.00646]<br>0.0646***<br>[0.00874]<br>0.173***<br>[0.00836]<br>0.419***<br>[0.0105]<br>0.825***<br>[0.0101]|0.0382***<br>[0.00697]<br>0.0866***<br>[0.00969]<br>0.191***<br>[0.00860]<br>0.430***<br>[0.0107]<br>0.826***<br>[0.0103]|-0.000464<br>[0.00491]<br>0.0195***<br>[0.00571]<br>0.109***<br>[0.00564]<br>0.317***<br>[0.00735]<br>0.700***<br>[0.00740]|0.0201***<br>[0.00531]<br>0.0606***<br>[0.00604]<br>0.146***<br>[0.00576]<br>0.380***<br>[0.00712]<br>0.746***<br>[0.00702]|
|**Constant**|5.610***<br>[0.189]|5.654***<br>[0.181]|6.056***<br>[0.221]|5.950***<br>[0.0785]|6.015***<br>[0.0828]|
|**Observations**|41234|39445|35880|75851|78745|
|**Country fixed effects**|yes|yes|yes|yes|yes|
|**R-squared**|0.51|0.51|0.51|0.49|0.48|
|*** p<0.01, ** p<0.05, * p<0.1<br>Robust standard errors in brackets||||||

29
6

||MAJOR DIRECT L|OAN PROG|RAMS OF TH|E U.S. GOVE|RNMENT, 198|9|Milionsof$|
|---|---|---|---|---|---|---|---|
|Program|Description|New Loam|% Subsidy|Subsidy cost|Outstandig<br>loans|% write-offs|Losses|
|Housing andUrban<br>Development|Housing fortheelderly or<br>**hwidicapped**|350<br>(2%)|21.7%|73.0|7,560<br>(4%)||l|
|Small Business<br>Administration|Small businessesandt<br>**who**s(ffer bs1 from<br>physical disasters|265<br>1%)|14.2%|37.6|5,822<br>(3%)|8.5%|494.9|
|Tennessee Valley<br>Authority|Provides finacing for<br>resoure development in the<br>Tennessee Vally area|250<br>(1%)|5.0%|12.5|2,150<br>(1%)|||
|**Rural**<br>Electrification<br>Administration|Provides financing for numl<br>electricity and telephone|177<br>**(1**%)|15.2%|26.9|33,452<br>(16%)<br>_yst_m__l|||
|TOTAL All**Federal**<br>**Programs'**||20,005<br>(100%)|22.5%|884.7<br>_|207,402<br>(100%)|0.8%|1,760<br>l|

SOURCESpecial Analysis F," in S§mcial Analvses: Budget of the United States Goverment. Fiscal Year 1989, Executive Office of the President, Office of Management and Budget (washington: U.S. Govemnment Printing Office, 1988), pp. F-64-78.

5 **The** average subsidy is calculated only for the programs where the subsidy amount is available.
- [17] Morduch, J. and Sicular, T. (2002) ‘Rethinking inequality decomposition, with evidence from rural China’, _The Economic Journal_ , vol. 112, pp. 93-106.

- [18] Redmond, G. and Kattuman, P. (2001) ‘Employment polarisation and inequality in the UK and Hungary’, _Cambridge Journal of Economics_ , vol. 25, pp. 467-480.

- [19] Revenga, A. (1997) ‘Employment and the wage effect of trade liberalization: The case of Mexican manufacturing’, _Journal of Labor Economics_ , vol. 15, pp. S20-S43.

- [20] Shorrocks, A.F. (1982) ‘Inequality decomposition by factor components’, _Econometrica_ 50, pp. 193-211.

- [21] Shorrocks, A.F. (1983) ‘The impact of income components on the distribution of family incomes’, _The Quarterly Journal of Economics_ , vol. 98, pp. 311-26.

- [22] Shorrocks, A.F. (1999) ‘Decomposition procedures for distributional analysis: A unified framework base on the Shapley value’, Department of Economics, University of Essex, mimeo.

- [23] Sz ́ekely, M. (1995) ‘Apectos de la desigualdad en Mexico’, _El Trimestre Economico_ , Fondo de Cultura Economica.

- [24] Wood, A., (1997) ‘Openness and wage inequality in developing countries: The Latin American challenge to East Asian conventional wisdom’, _The World Bank Economic Review_ 11, pp. 33-57.

27
Policy Research Working Paper 10699

# **Abstract**

This paper examines the socioeconomic factors correlated with vulnerability to natural hazards, using unique data from the High-Frequency Phone Surveys conducted in Haiti in 2021, 2022, and 2023. The results indicate a high overall exposure to hazards, with a significant number of individuals living in households facing the threat of multiple hazards. The analysis finds that disaster preparedness is generally low, with the poorest households experiencing the most significant challenges. Households in the bottom two wealth quintiles are less likely to have the necessary supplies to prepare adequately for and respond to disasters compared to those in the upper quintiles. Moreover, the level of education of the household head and access to the internet are found to be correlated with the likelihood of having better disaster preparedness. This suggests that higher levels of education and internet access play a significant role in improving preparedness levels among households. Overall, these findings highlight the importance of addressing socioeconomic factors when developing strategies to enhance resilience to natural hazards. By focusing on improving disaster preparedness among the most vulnerable households and promoting education and internet access, policy makers can mitigate the negative impacts of natural disasters on affected communities.

This paper is a product of the Poverty and Equity Global Practice and the Urban, Disaster Risk Management, Resilience and Land Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gcanavire@worldbank.org; ncarrasco@worldbank.org; mcardonabotero@worldbank.org; and onsababera@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
To assess the potential impact which changes in property rights may have on investment incentives and thus productivity of land use, we estimate a net farm income function containing the investments in question. Use of the predicted levels of investment with and without higher levels of tenure security suggests that changes in the property rights regime could have a quantitatively large impact on rural productivity. Credible elimination of the risk of redistribution and resolution is estimated to result in a 1.5% increase in net farm income and full transferabilit of land rights would greatly increase the extent of benefits, by an additional 4.4%. Compared to an average rate of growth of agricultural value added of 3.34% over the last decade of economic liberalization (as compared to growth of 0.84% for the decade of the 1980s),1 these are large figures that could considerably enhance the value of the asset endowment of the poor. Moreover, as we considering only investments in terracing and abstract from gains in allocative efficiency likely to come about with more secure and transferable land rights, implying that our estimate is a lower bound of the true impact of tenure security. This illustrates the continuing high levels of tenure insecurity and the scope for land policy to make a significant contribution to poverty reduction in this country. Methodologically, this suggests that some of the conclusions derived in the literature on the subject may have to be revisited.

The paper is structured as follows. Section two provides a brief review of the literature, a discussion of land tenure arrangements in Ethiopia, the conceptual model and the estimation strategy. Section three discusses data source and descriptive statistics. The econometric results, both for the determinants of land related investment and the simulation of the impact of greater tenure security, via investment, on net farn income are presented in section four. Section five closes with a number of policy implications.

# **2. Conceptual Model and Econometric Approach**

To provide the conceptual framework for our empirical approach, we put investment decisions within the context of a standard two-period household model. Regarding the impact of tenure security on agricultural production, we distinguish between security and transferability of land rights. We note that the standard result of higher tenure security leading to higher investment will hold if investment is to increase productivity but that the impact becomes ambiguous if investment is also undertaken to enhance future tenure security. By contrast, the impact of greater transferability of land rights, at any given level of tenure security, is unambiguously positive as it allows farmers to capitalize on their investment even if they are not able to self-cultivate in the second period.

> Data on agncultural value added are from **the** World Bank's **World Development Indicators.**

3
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importance when PFAs set their strategies.

## [Insert Table 1 about here]

Data on Colombian pension funds was provided by ASOFONDOS (Colombian Association of Pension Fund Administrators). The database includes the detailed security allocations for the funds managed by each of the six PFAs, on a monthly basis for the period 2004:1 to 2010:12. Summary statistics for this data set are presented in Table 1 at two-year intervals. As of June 2010, total assets under management were US$44.1 billion (equal to 17% of Colombian GDP). At that time, 32% of these funds were invested in Colombian stocks, which amounted to 7.1% of the total domestic market capitalization. Throughout the sample period, net flows to these funds were positive, which reflects the fact that most of the workers contributing to these funds were still young (more than 70% were younger than 40 years old).

In addition to the pension funds, PFAs manage voluntary retirement funds in separate accounts. These voluntary accounts supplement the compulsory retirement savings in the pension funds. Contrary to pension funds, these accounts are subject to very few regulations. In particular, they are not subject to the MRG and do not have limits on individual securities or asset classes.<sup>7</sup> Moreover, workers are typically directly involved in the asset allocation of their voluntary portfolios. Panels D and E in Table 1 present summary statistics of the voluntary funds.

In the following sections I present two empirical exercises suggesting that relative performance concerns are important for the portfolio dynamics of PFAs. For this, I introduce two measures that describe trading activity by PFAs. The first is an aggregate fund measure that describes how each manager rebalances her portfolio relative to the peer portfolio. The second focuses on fund trades of individual stocks. For both empirical exercises, I focus on the trading behavior across domestic stocks. While these represent only a fraction of the total portfolio, correlated behavior among managers is likely to be more pronounced for these securities, which display higher dispersion of returns than other assets in PFAs portfolios.

# **3.2. Trading Strategies and Relative Performance**

In this section I introduce a measure of the direction of a PFA’s trades relative to its peers. The objective is to summarize the trading behavior and strategies of Colombian pension fund managers in a parsimonious way.

> MRG. Even in the turmoil of October 2008, the PFA with the lowest returns managed to have returns 118bps above the MRG (this is the closest any PFA was to the MRG in the sample period)

> 7The only major restriction that these funds share is the short selling ban.

7
- 5 -

Figure 2. GDP growth and infrastructure stock <!-- Start of picture text --> 20<br>15<br>ΔGDP = 0.0032 Road + 9.507<br>10<br> (0.0017) (1.198)<br>R 2 = 0.142<br>5<br>0<br>-5<br>0 200 400 600 800 1000 1200 1400<br>Road density (km per 100 sq. km of land area)<br>Source: Author's estimation.<br>Figure 3. Agricultural growth and infrastructure stock<br>25<br>20<br>15<br>10<br>5<br>0 ΔGDP = -0.0091 Road + 7.060<br> (0.0039) (1.563)<br>-5 R 2 = 0.172<br>-10<br>0 100 200 300 400 500 600<br>Road density (km per 100 sq. km of land area)<br>Source: Author's estimation.<br>Unexplained GDP growth rate (%)<br>rate (%)<br>Unexplained Agricultural growth<br><!-- End of picture text --> <u>Table 1. Impact of general infrastructure stocks on overall and agricultural growth, 2001-05</u>

|||Avg. G|DPgrowth200|1-05|||Agricultu|ral GDPgrow|th2001-05||
|---|---|---|---|---|---|---|---|---|---|---|
|ln GDP2000|-0.710 <sup>***</sup>|-0.804 <sup>***</sup>|-0.967 <sup>***</sup>|-1.068 <sup>***</sup>|-0.481 <sup>**</sup>|-0.522 <sup>**</sup>|-0.786 <sup>***</sup>|-1.089 <sup>***</sup>|-0.475|-0.731 <sup>***</sup>|
||(0.159)|(0.199)|(0.210)|(0.289)|(0.192)|(0.217)|(0.259)|(0.296)|(0.371)|(0.222)|
|Road density|0.0032 <sup>*</sup>|||||-0.009 <sup>**</sup>|||||
||(0.0017)|||||(0.004)|||||
|Share of paved roads||0.018 <sup>**</sup>|||||0.003||||
|||(0.010)|||||(0.012)||||
|Electricity consumption|||0.00014 <sup>**</sup>|||||0.00004|||
||||(0.00007)|||||(0.00009)|||
|Teledensity||||0.0021 <sup>**</sup>|||||-0.0009||
|||||(0.0008)|||||(0.0011)||
|Water access|||||-0.006|||||-0.009|
||||||(0.016)|||||(0.018)|
|Constant|9.507 <sup>***</sup>|9.578 <sup>***</sup>|11.337 <sup>***</sup>|10.823 <sup>***</sup>|7.982 <sup>***</sup>|7.060 <sup>***</sup>|8.365 <sup>***</sup>|10.941 <sup>***</sup>|6.543 <sup>***</sup>|8.676 <sup>***</sup>|
||(1.198)|(1.290)|(1.490)|(1.762)|(1.163)|(1.563)|(1.685)|(2.097)|(2.242)|(1.378)|
|Obs.|124|114|125|177|161|116|105|116|160|148|
|R-squared|0.142|0.133|0.174|0.087|0.076|0.172|0.117|0.129|0.116|0.130|
|_F_ statistics|9.97|8.51|12.86|8.29|6.51|11.75|6.74|17.17|10.26|10.83|

Source: Author's estimations. Note that the dependent variables are the five-year average GDP growth rate and agriculture sector growth rate, respectively. The standard errors are shown in parentheses.

- *, **, and *** indicate the 10%, 5% and 1% significance levels, respectively.