rafmacalaba/datause-encoder-data huggingface dataset:
leaf sizes (number of leaves) and the models do not improve.<sup>21</sup> Finally, we check the relationship between the number of iterations (trees) and the out-of-bag error. Figure 6 shows that the out-of-bag error reduces with the number of iterations and stabilizes for both female and male samples from approximately 100 iterations. Therefore, implementing more iterations could not lead to lower outof-bag errors since we already implement 1,000 iterations in our base specification.

Overall, the logistic regression models have a stronger predictive power than the Random Forest model. This implies that if provided with the right list of predictors, one could use simple logistic regression framework to predict aspirations to migrate. Moreover, the method presented in this study may be better suited for explanatory than predictive purposes and further research on the best suited predictive method should be conducted. Nevertheless, it is possible that migration interest is structurally hard to predict and that no large predictive gains will be possible even with alternative models.

# 5.2. Number of predictors

In this study, we choose to retain only the top 30 predictors from Random Forest and LASSO Stability Selection. Considering that we have 301 variables, more top predictors could be analyzed. However, each additional predictor has a lower relevance on the margin. Moreover, since we use these predictors in a regression framework, we mitigate the chance of bias from multicollinearity by limiting the number of predictors. Hence, we argue that 30 is a reasonable number.

**Figure 7. Explanatory power of models with different number of predictors**

Source: Authors’ calculations from DESMIGRATIF (2018) Figure 7 shows the pseudo R-squared and adjusted pseudo R-squared from logistic regressions of interest in migration where independent variables are sequentially included based on their 21 Results available upon request.

32
from the existing literature based on advanced (Graetz and Michaels (2018); Acemoglu et al. (2020); Acemoglu and Restrepo (2019); Koch et al. (2019); Dauth et al. (2017)) and emerging economies (Giuntella and Wang (2019); Artuc et al. (2019)), which document a negative impact of robots on employment. Unlike this literature, our results suggest that for labor the productivity-enhancing effect of robot adoption prevails over the replacement effect. As a further departure from the evidence in advanced economies, we also find that automation generates positive employment spillovers from down-stream robots-adopting plants, which help extend the benefits of automation to non-adopting plants as well. We discuss potential underlying reasons for the difference in findings between Indonesia and other more advanced economies in the concluding section.

By increasing revenues and the demand for domestic inputs, robots generate positive employment spillovers from downstream automating industries. Such spillovers are not limited to manufacturing, but extend to services and construction, thus boosting the demand for labor across sectors. This finding is in line with the increasingly important role of services inputs in manufacturing production, including in Indonesia (HallwardDriemeier and Nayyar (2017); Duggan et al. (2013)).

Our second contribution is to examine in depth the plant level impacts of robot adoption. We provide evidence that plants more exposed to robots lowered real marginal costs, but they also increased markups, exports, and import. This suggests that robot penetration in Indonesia allowed firms to produce higher quality products and upgrade in global value chains (GVCs).<sup>2</sup> We focus the analysis on the impact of robots at two levels of aggregation: plant and local labor market. The local labor market analysis exploits labor force survey data to calculate the shares of workers at high risk of automation in each industry of a local market in the base year. The shares are then used to aggregate robot import by industry and year within the local labor market.<sup>3</sup> We study long-run trends and account for the

> 2Rodrik (2018) argues that GVC integration requires strict production requirements, which are hard to satisfy using manual work. Similarly, World Bank (2020b) and De Backer et al. (2018) suggest that by adopting technologies used in advanced economies, developing countries could boost trade relationships. Maloney and Molina (2019) discuss how automation in developing countries can improve the quality of inputs demanded by advanced economies.

> 3The methodology in this part of the analysis is similar to Acemoglu and Restrepo (2017) and Dauth

3
**Table 3: Fiscal Policy under Coalition Governments:**

**Measured by Number of Political Parties in Government**<sup>_a_</sup>

||(1)|(2)|(3)|
|---|---|---|---|
||Total<br>Expenditure|Total<br>Tax<br>Revenues|Deficit|
|Number of Parties<sup>_a_</sup>|-0.0004<br>(0.0002)*|-0.0004<br>(0.0001)***|-0.0002<br>(0.0001)*|
|Majoritarian System|0.014|-0.009|-0.003|
|(=1, 0 if Proportional)|(0.019)|(0.012)|(0.006)|
|Presidential System<br>(=1, 0 if Parliamentary)|-0.051<br>(0.020)**|-0.034<br>(0.013)**|-0.010<br>(0.009)|
|Regular|-0.028|-0.006|-0.014|
|Elections<br>(=1, 0 otherwise)|(0.017)|(0.010)|(0.008)*|
|Observations|222|221|257|
|R-squared|0.31|0.45|0.19|

a. Count of the number of political parties listed as belonging to the executive government in the Database of Political Institutions, The World Bank Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in the sample are listed in the Appendix 5.

29
**Table 2:** Firm Descriptive statistics

|Variables|Mean<br>Annual Inve|SD<br>Observations<br>stment Allowance|Sample coverage|
|---|---|---|---|
|AIA dummy|0.095|0.294<br>25,117|(2007-2013)|
|AIA available allowance|0.007|0.030<br>25,117|(2007-2013)|
||Firm inv|estments (logs)||
|Total investment|6.598|2.334<br>24,467|(2007-2013)|
|Software investment|2.412|2.592<br>24,467|(2007-2013)|
|Hardware investment|3.764|2.592<br>17,985|(2007-2013)|
|Plant-Machinery investment|5.866|2.409<br>22,976|(2007-2013)|
|Vehicles investment|3.358|2.195<br>19,097|(2007-2013)|
|Land-Building investment|1.198|2.498<br>22,587|(2007-2013)|
||Firm tech|nology adoption||
|Cloud|0.281|0.449<br>2,206|(2007 & 2013)|
|Cloud Storage|0.157|0.364<br>2,206|(2007 & 2013)|
|Cloud Data|0.126|0.364<br>2,206|(2007 & 2013)|
|Cloud CRM|0.09|0.286<br>5,084|(2007 & 2013)|
|Cloud Finance software|0.055|0.228<br>2,206|(2007 & 2013)|
|Cloud Ofce software|0.068|0.252<br>2,206|(2007 & 2013)|
|Cloud Email|0.12|0.325<br>2,206|(2007 & 2013)|
|Cloud Own Software|0.072|0.403<br>2,206|(2007 & 2013)|
|Big data analytics|0.205|0.404<br>2,264|(2007 & 2015)|
|Big data analytics|0.259|0.44<br>1,748|(2007 & 2019)|
|Artifcial Intelligence|0.017|0.13<br>1,748|(2007 & 2019)|
||Contr|ol variables||
|Multi-establishment|0.751|0.433<br>24,467|(2007-2019)|
|Foreign owned|0.330|0.470<br>24,467|(2007-2019)|
|Age (log)|3.304|0.400<br>24,467|(2007-2019)|
|Employment (log)|6.132|1.578<br>23,467|(2007-2019)|

Note: All investment variables are in log thousands of UK pounds, deflated using 4 digit (2007 SIC codes) PPI deflators provided by the ONS. The AIA available allowance is in (nominal) millions of UK pounds.

14
state of the world<sup>10</sup> . Of course, n will be a very large number; it is conceivable that n is infinity.

In its minimal form, the determinist claim says that if at two points of time, t and k, the world is in state x ε X, and at time t+1 it is in state y ε X, then at k+1 it must also be in state y. In other words, being at x causes the world to be at y in the next instant.

This simple axiom is untestable but has many implications. It means, for instance, that if at two consecutive points of time the world is in the same state, then onwards it will forever remain in that state. It follows that, since the world thus far has been changing, it must have always been changing. This in itself does not mean that the world never comes back to the same state. Put simply, it is not that history never repeats itself but either history never repeats itself or it repeats itself endlessly. The determinist axiom also has large implications for our attitude to crime, responsibility and punishment, and much has been written by philosophers on this. But that is not my concern here.

To see what randomized control trials show, let me define the Cartesian product of X2, X3, ... , Xn by Z. What RCTs show is that there exists some z ε Z, such that if we have the world in state (x, z) ε X instead of (y, z) ε X, the world in the next period will be in some a ε X instead of b ε X. This is like saying, other things being the same (that is, z) if you vaccinate people, in the next period, there will be no influenza. But if you did not vaccinate them, there would be influenza. If, we accept the determinist axiom, as many do<sup>11</sup> , then this demonstration means that whenever we switch from (y, z) to (x, z), the world will in the next period switch from b to a. It is the ‘whenever’ that makes this a causal claim. And, this is what I am referring to as ‘circumstantial causality’. Given a certain set of circumstances, changing y to x, has a predictable consequence.

The discovery of circumstantial causal connections, as has happened with the rise of RCT studies, is extremely valuable and, at the same time, of limited consequence, more so than the proponents believe. As such, the RCTs have given

> 10 It should be clarified that the time when a state exists is not a feature of the state and so there cannot be a reference to the temporal position of the state.

> 11 I personally do believe in the determinist axiom, while remaining prepared to be corrected (Basu, 2000).

10
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we conduct a micro-level analysis based on firm-level export data and propose a mechanism through which the productive structure can fuel greater per capita GDP growth. Second, we analyze China to shed light on the country’s export performance and rapid upgrading. Our work helps us understand the increasing specialization of China’s exports (Amiti and Freund 2010) and highlights the role of product consistency with the local productive structure. Our empirical results suggest that products that are closer to the local export basket have an advantage in firm export bundles. This finding is consistent with economies of scale and scope and knowledge spillovers from product-level relatedness. Product spillovers produce export upgrading as producers move through the product space by reallocating their activity toward these connected goods.

Third, we consider potential heterogeneity in the impact of product-level connections with the local productive structure according to firm ownership (foreign or domestic)<sup>1</sup> , trade type (processing or ordinary), and firm productivity. We thus contribute to the recent literature on the particularities of processing trade (Manova and Yu 2012; Dai et al. 2011). A number of studies have emphasized the lack of connection between ordinary activities and those based on imported technology and foreign affiliates (Lemoine and Unal-Kesenci 2004; Hale and Long 2011; Blonigen and Ma 2010). This lack of connection may explain the disappointing results obtained in terms of technological diffusion from processing and foreign activities in China. Fewer spillover gains may emanate from processing and foreign activities because they are less embedded in the local economy. In addition, the distinct functioning of foreign firms, which are mainly engaged in export-platform activities using imported inputs, may limit the spillovers that they generate and from which they can benefit. We further investigate whether the gains from product consistency with the local structure 4
7% of value added.<sup>6</sup> For some estimations, we further partition different samples based on their banking status. In particular, we split the selection and leverage model sample, credit program eligible firms sample, and the credit program users sample into two sub-samples of banked and unbanked

**4 Government Programs and the Expansion of Crisis Credit** The economic crisis and crisis credit episode we study originates with the COVID-19 pandemic. With the health system under severe stress due to the pandemic’s early spread, the Chilean government imposes mandatory lockdowns in several municipalities across the country, starting on March 12, 2020.<sup>7</sup> Consequently, many businesses are forced to close temporarily, either because their demand collapses or because of the impossibility of operating under lockdown. Firms’ cash flows collapse. By November 2020, Chile is the country with the fourth most infections per million people in Latin America and the Caribbean (Engel et al., 2020), and the economy suffers a sizable 5.8% GDP contraction in 2020 (down from a 0.9% expansion in 2019).

In response to the crisis, the Chilean government implements large programs to help firms and to avoid inefficient bankruptcies. It first significantly expands the size and scope of an existing public credit guarantee program, providing financing to firms during the pandemic and sharing the firm credit risk with banks. The existing program, called FOGAPE, is a public fund that guarantees a fraction of loans provided by banks to small firms, those with annual sales less than US$800,000.<sup>8</sup> In the event of default, resources are withdrawn from the fund to pay the guaranteed fraction of the loan to the bank. The program has similarities with the other public credit guarantee programs, mentioned in the introduction, used around the world.

On April 24, 2020, the National Congress of Chile approves a bill (called FOGAPE COVID-19) proposed by the Ministry of Finance that injects US$3 billion into the public credit guarantee fund (1.2% of Chile’s GDP). The law also extends access to the program to medium and large firms, those with annual sales up to US$35 million.<sup>9</sup> Mega firms (those with

> 6Further detailed summary statistics as of December 2019 are described in Appendix Table 1, showing the main variables used in our paper for the matched firms in our sample.

> 7Chile is divided into 16 regions. Each region is divided into municipalities, which constitute the country’s smallest administrative division. There are 345 municipalities in Chile.

> 8FOGAPE is an acronym for Fondo de Garant ́ıa para Peque ̃nos Empresarios or Guarantee Fund for Small Entrepreneurs.

> 9In practice, the different sales limits are defined in Unidades de Fomento (UF), Chile’s unit of account. We transform the values from UF to US dollars using the average value of UF in pesos during 2019 and the dollar-peso exchange rate during 2020. Banks could use different sales indicators to determine each firm’s sales.

9
Policy Research Working Paper 6751

# **Abstract**

Transactional sex is believed to be an important riskcoping mechanism for women in Sub-Saharan Africa and a leading contributor to the HIV/AIDS epidemic. This paper uses data from a panel of women in rural Tanzania whose primary occupation is agriculture. The analysis finds that following a negative shock (such as food insecurity), unmarried women are about three times more likely to have been paid for sex. Regardless of marital status, after a shock women have more unprotected sex and are 36 percent more likely to have a sexually transmitted infection. These empirical findings support the claims that transactional sex is not confined to commercial sex workers and that frequently experienced shocks, such as food insecurity, may lead women to engage in transactional sex as a risk-coping behavior.

This paper is a product of the Human Development and Public Services Team, Development Research 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 ddewalque@worldbank.org, wdow@berkeley.edu, and egong@middlebury.edu.

_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
8 outputs. They find that the stronger the level of corruption, the more important the effect of having a regulator in place to oversee renegotiations.

Measuring the impact of corruption on performance and the interaction between corruption and reform policies allows us to test the extent to which reform policies have managed to reinforce or offset the impact of corruption and, vice versa, the extent to which corruption reinforces or offsets the impact of the policies. This is the rationale for introducing the interaction between the _CORR_ and _POL_ variables in the regression. However, caution is needed when drawing conclusions from the coefficients associated with these interactions. It is also important to account for other economic dimensions likely to drive infrastructure performance which could influence our inference on corruption if we failed to take them into account. Countries with poorer governance generally have lower income, and poor governance cannot be simply attributed to the effects of income. The two most obvious, as recognized in most of the literature, are incomes levels and the degree of urbanization to capture the ability to pay and the size of the market on the demand side. This is why we introduce in vector _X_ , the GDP per capita in 2000 constant U.S. dollars and the urbanization rate, which captures the size of the market from the demand side.

The measure of corruption used here is a corruption index published by the International Country Risk Guide. The index offers comparable figures for about 100 developing countries over a reasonably long period of time. Defined as an assessment of corruption within the political system, the index includes financial corruption (such as bribes connected with trade, taxes, or protection), but it concentrates on corruption in the form of excessive patronage, nepotism, job reservations, “favor-for-favors,” secret party funding, and suspiciously close ties between politics and business. According to the ICRG measure, average corruption levels increased 29 percent in developing countries between 1990 and 2005 (Table 2). Given the empirical evidence about the important role of corruption for economic outcomes, the observed increase in the corruption index may be an important determinant of outputs in infrastructure sectors.

**<u>Table 2 Evolution of average corruption between 1990 and 2004</u>**

||**Average**|**Standard**<br>**deviation**|**Number of**<br>**observations**|
|---|---|---|---|
|**Corruption index(0=low 1=high)**||||
|1990|0.52|0.19|93|
|1995|0.49|0.16|94|
|2000|0.57|0.16|104|
|2005|0.67|0.11|104|

Source: Authors’ calculations using data from ICRG.

From a more technical viewpoint, it may be worth mentioning that our model assumes that the adoption of a certain reform policy is _conditionally_ exogenous to the error term in the model. Because we control for country fixed effects, a linear trend, and GDP per capita, we completely absorb differences in the outcome variables due to intrinsic characteristics of each country that do not vary over time (their history, for example), changes in economic performance, and the generalized trend in IRA and
**Table 1: Descriptive Statistics on Household Participation in associations**

||_Any_<br>_Productive_<br>_Association_<br>|_Professional_<br>_Associations_<br>|_Credit_<br>_Unions_<br>|_Local_<br>_Committees_<br>|_Other non-_<br>_productive_<br>_association_<br>|
|---|---|---|---|---|---|
||_%_|_%_|_%_|_%_|_%_|
|||**HOUSEH**|**OLD PARTIC**|**IPATION**||
|**Total**|6.37|2.30|1.76|4.47|12.50|
|**Area**||||||
|Rural|10.50|3.07|1.19|6.91|13.20|
|Urban|6.01|1.75|2.16|2.72|12.00|
|**Region**||||||
|Managua|7.03|1.83|2.95|2.80|16.46|
|Pacific|6.69|1.85|1.38|4.21|8.08|
|Central|9.98|3.33|1.48|5.90|12.76|
|Atlantic|7.15|1.75|0.88|4.89|14.02|
|**Quintile**||||||
|Quintile 1|6.86|2.29|0.13|4.68|10.18|
|Quintile 3|8.35|1.77|1.58|5.35|13.41|
|Quintile 5|8.78|3.16|2.40|4.37|14.12|
|**Poverty**||||||
|Non poor|8.66|2.29|0.13|4.68|13.66|
|Poor|6.57|1.77|1.58|5.35|10.53|
|**Number of**||6861|6861|6861||
|**Observations**|6861||||6861|

Source: Authors using the 2005 Nicaragua EMNV.
Table 2: Selected provisions in Mercosur and share of selected economies including each provision

||Latin America<br>42 agreements|BRICS<br>39 agreements|
|---|---|---|
|Provision|LegallyEnforceable|LegallyEnforceable|
|Tariff on manufacturing goods|100%|95%|
|Tariff on agricultural goods|100%|95%|
|Customs|95%|85%|
|Export taxes|88%|64%|
|TBT|88%|49%|
|SPS|88%|49%|
|Anti‐dumping|71%|62%|
|GATS|88%|44%|
|Countervailing measures|74%|54%|
|State Aid|67%|51%|
|Competition policy|74%|41%|
|TRIPs|74%|33%|
|Investment|71%|26%|
|Movement of capital|67%|28%|
|Public procurement|76%|15%|
|STE|62%|21%|
|TRIMs|55%|26%|
|IPR|55%|26%|
|Visa and asylum|45%|23%|
|Environmental laws|24%|13%|
|Labor market regulation|19%|13%|

Note: Bold provisions are Legally Enforceable in Mercosur Deep integration and GVC related trade The expansion of Global Value Chains is related with the proliferation of deep agreements going beyond traditional market access issues. The key insight of the literature is that the depth of trade agreements is associated with the international fragmentation of production. An increase in trade flows involving the exchange of customized inputs, incomplete contracts and costs associated with the search for suitable foreign input suppliers creates new forms of cross‐border policy effects compared to a situation where goods are produced in a single location. Therefore, in order for cross‐border production to operate efficiently, national policies including disciplines such as competition policy, investment, infrastructure and institutions should be harmonized across countries.<sup>13</sup> Linking deep agreements and GVC‐related trade<sup>14</sup> The relationship between deep agreements and GVC‐related trade is formally estimated using a structural gravity equation. An augmented gravity equation is estimated for 189 countries,<sup>15</sup> using data from 1990 to 2014, to investigate the relationship between the depth of an agreement

> 13 Antras and Staiger (2012), Lawrence (1996).

> 14 This section is based on Osnago, Rocha and Ruta (2017), forthcoming

> 15 See appendix table A2 for the list of countries that are included in the regression.

25