rafmacalaba/data-use-mentions-extended huggingface dataset:
15 of the quality of the legal system and a negative effect of bureaucratic costs on the level of use of host economy suppliers. Related to this, Perez-Villar and Seric (2015a) and Amendolagine et al. (2019) find that the level of institutional proximity between home and host economies exercises a positive effect on the extent of backward linkages. Using firm-level data for 28 transition economies, Monastiriotis (2016) estimates productivity spillovers from MNC affiliates from different home economies and finds that positive effects materialize only from affiliates originating in European Union (EU) member countries, indicating that institutional proximity fosters productivity spillovers.

_Adequate financial markets_ . The presence of financial markets that function well is also important. Javorcik and Spatareanu (2009) find for a sample of domestic firms in the Czech Republic that, whereas in general domestic firms experience financial constraints, domestic firms that operate as supplier to MNC affiliates do not. Furthermore, MNC affiliates that provide support to improve their suppliers expect domestic firms to make investments themselves to enhance the impact of supportive linkages (Alfaro-Urano, Manelici, and Vasquez 2019; Jordaan 2011b, 2013). To be able to make such investments, which can facilitate the effects of technology dissemination, domestic firms need access to finance.

_Trade policy_ . Several studies present findings that _trade policy_ generates positive productivity effects. For instance, Farole and Winkler (2014) report an estimated positive effect of an interaction variable between a host economy’s level of trade and the presence of MNC affiliates. For China, Du, Harrison, and Jefferson (2014) report a significant positive effect of an interaction variable between the level of inter-industry MNC participation and a dummy variable capturing the year that China joined the World Trade Organization (WTO). They also find a negative effect of an interaction variable between inter-industry MNC and tariff levels. These effects of trade openness are likely to capture the presence of pecuniary externalities. Access to international markets makes it easier for MNC affiliates to source inputs from international markets, placing local suppliers in direct
that of Italian students. These numbers are in line with observations from previous academic years, when the dropout rate of foreigners was three times higher than that of Italians (30.1 percent vs. 9.8 percent) (ISTAT, 2023). No discernible gender or regional disparities are observed in these dropout rates.<sup>21</sup>

> 15 The number of Ukrainian refugees aged 11-18 who requested temporary protection as of June 30, 2022 was elaborated by ISTAT based on data from the Ministry of Interior.

16 This ratio is estimated by dividing the number of refugees enrolled in the Italian system in the academic year 2022-2023 (i.e., 8,012 students) by the number of Ukrainians aged 11-18 who requested temporary protection as of March 31, 2023 (i.e., 25,345 Ukrainian refugees, based on ISTAT elaborations on data from the Ministry of the Interior).

17 This ratio is estimated by dividing the number of refugees enrolled in grades 7 to 13 in the Italian system in the academic year 2023-2024 (i.e., 7,767 students) by the number of Ukrainians aged 12-18 who requested temporary protection as of January 2024 (i.e., 21,742 Ukrainian refugees, based on ISTAT elaborations on data from the Ministry of the Interior). The data on enrollment provided by the Ministry of Education do not cover Grade 6 for academic year 2023-24. Data from the Ministry of Interior on temporary protection does not capture the actual residence and, hence, does not allow to monitor whether bene�iciaries of temporary protection are still in Italy.

18 Data from the MoE does not include students enrolled in the vocational training centers (Centri per la formazione professionale), which are the responsibility of the regions or provinces and whose data on enrollment are therefore not systematically available. These courses are for students who have completed the lower secondary schools, they usually last three years and do not give access to university.

19 It is not
a categorization of employment as either formal or informal based on registration with the social security system. It also captures individuals who are not employed. Responses to the questions asked allow us to distinguish between workers who are informally employed and those who are unemployed.

To shed light on whether the decline in formal employment was accompanied by an increase in informal employment, unemployment or inactivity, we estimate the effects of the minimum-wage increase using data from seven annual waves of the Turkish Household Labor Force Survey, from 2010 to 2016. To estimate the causal effects of the minimum wage using the HLFS data, we use a grouping estimator in a similar way as Blundell et al. (1998) and Harasztozi and Lindner (2019). We assign working-age individuals in the survey to mutually exclusive groups formed from combinations of the 26 regions (NUTS 2), age in five categories (16-19, 20-24, 25-34, 45-54, 5560), gender, and education (low, medium and high). We measure group-level exposure to the 2015 minimum-wage increase in the same way as we did previously with the EIS registry data; we define exposure as the percentage change in the wage bill that is necessary to bring all workers in a given group to the new minimum wage, relative to the 2015 wage bill.<sup>26</sup> Formally, the grouplevel difference-in-difference equation that we are estimating is the following: P (5) ggii gg gg gg ii gg ii gg ggii yy = αα+ ββ P × EE + φφEE + γγXX + PP + RR + PP × RR + εε where i is the percentage change in the labor-market outcome of interest of group g between year t-1 and year t.<sup>27</sup> is the measure of minimum-wage exposure to the 2016 minimum-wage yy increase at the group level, measured in 2015. gg _POST_ is a dummy variable that takes the value one EE if the group is observed after the minimum-wage increase (i.e., after 2015)
jobs abroad and received job offers from abroad. Participants displaying “home bias” in the lab were less likely to look for jobs abroad by 29p.p. and 17p.p. in the Lisbon and Nairobi samples, respectively. These differences were strongly statistically significant - at the 1% and 2% levels. The same pattern holds for receiving job offers from abroad: participants who exhibited “home bias” in the lab were 2.8p.p. less likely to receive job offers from abroad in the Lisbon sample (not significant) and 4p.p. in the Nairobi sample (significant at the 5% level).

When we look at the probability of living abroad, we find that participants exhibiting “home bias” in the lab were 8 p.p. less likely to be living abroad in the Lisbon sample (significant at the 5% 23
CONSUMPTION SUB-AGGREGATES 15 The goods included in the consumption aggregates follow the guidelines for consumption aggregates found in Deaton and Zaidi (2002).<sup>6</sup> In all three countries, FGT measures rely on consumption data collected by recall questions with probing for each consumption good. The number of consumption goods probed for in the questionnaire varies across countries, with 112 in Tanzania, 126 in Uganda, and 284 in Rwanda.<sup>7</sup> Beegle et al. (2012) review recall-based consumption surveys and report ranges from 37 to 305 goods, with a mean of 137 goods in total. Thus, the sample of countries seems to represent typical recall consumption surveys, even if the total number of goods doesn’t actually encompass all non-durable consumption. In each country, consumption goods are valued in year one’s prices corrected by the same price adjustment as used to evaluate the trend in poverty in the surveys. In each year values are spatially and temporally deflated.

The three countries are chosen to display variation in level and trend in poverty headcount. Headcount poverty stood at 15 percent in Tanzania in year one, compared to 57 percent in Rwanda, and poverty fell substantially in Uganda and Rwanda, while it increased in Tanzania (Development Research Group of the World Bank, Downloaded 2017). The five year time span between surveys in Rwanda can also be seen as an upper bound for the approach, as most countries implement full consumption surveys every five years or less.

4.2. **Looking for Linearity.** Moving from theory to practice, we would like to gauge the consumption patterns of households, as some goods might be more suitable than others. Figure 2 illustrates how a sum of linear goods will always lead to a linear aggregate, but Figure 5 also shows that a sum of expenditures on non-linear goods can be linear for a particular set of prices. To gauge properties of goods in relation to the total aggregate, we run an OLS regression
|`Foreign double counting`|`5458.62`|_`0.23`_|
|---|---|---|
|`GVC-related trade (GVC)`|`781287.59`|_`32.21`_|
|`GVC-backward (GVCB)`|`408693.30`|_`16.85`_|
|`GVC-forward (GVCF)`|`372594.29`|_`15.36`_|

The detailed decomposition can be also computed for a particular sector of export, e.g., “Manufacture of computer, electronic and optical products” (sector code 17 for the loaded ICIO table) by using<sup>15</sup> ``` .icio,exporter(chn,17) ``` ``` Decompositionofgrossexports: Perspective:exporter Exporter:CHN Importer:totalCHNexports Sectorofexport:17 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`560552.89`|_`100.00`_|
|`Domestic content (DC)`|`417041.87`|_`74.40`_|
|`Domestic Value-Added (DVA)`|`404306.15`|_`72.13`_|
|`VAX -> DVA absorbed abroad`|`386215.71`|_`68.90`_|
|`DAVAX`|`315342.79`|_`56.26`_|
|`Reflection`|`18090.44`|_`3.23`_|
|`Domestic double counting`|`12735.72`|_`2.27`_|
|`Foreign content (FC)`|`143511.01`|_`25.60
**Figure 9 Migration to the United States becomes more skill-intensive as origin countries develop**

<!-- Start of picture text --> a. The education level of migrants increases<br><!-- End of picture text -->

- **b. The emigration probability of the highly educated increases**

_Sources_ : Original estimation based on data of migrants in the United States from harmonized US Census microdata for years 1960 (5% sample), 1970 (1% state form 1 sample), 1980 (5% state sample), 1990 (5% state sample), and 2000 (5% sample), and American Community 19
children younger than 5 years in the household, and height-for-age Z-score. The effects on child cognition of demographic controls in the table are in the expected direction. Z-scores of children living in urban areas are 0.33 standard deviation higher than those of children living in urban areas. In addition, children’s nutritional status also affects performance in the PPVT. The coefficients for wealth are positive, statistically significant, and among the highest, which is in line with research that points to socio-economic status gradients of cognition as measured by vocabulary (Schady 2011; Paxson and Schady 2007; Fernald et al. 2011; Naudeau et al. 2011; Schady et al 2015).

We next explore whether these negative impacts hold three years after the first measure of vocabulary. Using data of child vocabulary at the age of 8, we estimate the model presented in equations (2) and (3). The main results are presented in Table IV. Our estimations show that maternal depression has no effect on child vocabulary at the age of 8. Not only the estimated coefficients are not statistically significant, but also their sizes are very small – that is, the vocabulary of children whose mothers’ experienced mental health problems when they were 1 year old caught up with the vocabulary of children whose mothers did not suffer mental health problems. These results suggest that the effect of exposure to maternal depression during early childhood need not undermine language development permanently and exposure to rich vocabulary environments later on during childhood can compensate earlier developmental gaps. For our sample, it is possible that the convergence in vocabulary development is explained by the fact that by the time they reached age 8, all children had had exposure to formal education opportunities (99.9% of children in our sample), which may have a compensatory effect on children’s vocabulary development. Still, given that early vocabulary constitutes a foundational skill that facilitates the development of other cognitive skills (and potentially socio-emotional skills as well), based on our results we cannot rule out
status ─ wives.

The 2006 DHS also contains information on a number of individual welfare indicators for women as well as for their children. The first of these I examine is women‘s body mass index (BMI), a measure of health. Low BMI may reflect heightened stress and undernourishment.<sup>19</sup> In addition to the level of BMI, one can look at the share of women whose BMI is greater than the underweight cut-off of 1900.<sup>20</sup> Other indicators available from the DHS relate to whether women have dependent children ─ defined as children for whom they have primary responsibility ─ and whether a mother has dependent children living without her in a different

> 18  Table 5 presents the population weighted percentages which turn out not to be significantly different from the sample percentages quoted in the text.

> 19  BMI is equal to weight/(height/100)2. An individual is widely considered overweight if his or her BMI is larger than 25; and underweight if it is less than 18.5.  (Note that the DHS multiplies BMI by 100 to ensure precision, and I follow their practice.)

> 20  I use 1900 rather than the more common 1850 cutoff benchmark because there are few data points at higher ages for some groups of women resulting in high volatility in the non-parametric regression lines used later.

17
# **Exporter Dynamics Database**<sup>*</sup>

## **Tolga Cebeci**<sup>**1**</sup> **Ana M. Fernandes**<sup>**2**</sup> **Caroline Freund**<sup>**3**</sup> **Martha Denisse Pierola**<sup>**4**</sup>

## **JEL Classification codes** : C81, F14.

**Keywords:** exporter dynamics, exporter growth, firm-level data.

* This database was conceived and initiated in 2009 by Caroline Freund and Martha Denisse Pierola with support of the governments of Norway, Sweden and the United Kingdom through the Multi-Donor Trust Fund for Trade and Development. We also acknowledge the generous financial support from the World Bank research support budget and the Knowledge for Change Program (KCP), a trust funded partnership in support of research and data collection on poverty reduction and sustainable development housed in the office of the Chief Economist of the World Bank (www.worldbank.org/kcp). We are grateful to Mario Gutierrez-Rocha and Jamal Haidar for their contribution to this project, and to Aaditya Mattoo for useful comments and discussions. We thank Alberto Behar and Madina Kukenova for collaboration with the project early on and to Francis Aidoo, Paul Brenton, Maurizio Bussolo, Huot Chea, Julian Clarke, Alain D’Hoore, Pablo Fajnzylber, Elisa Gamberoni, Sidiki Guindo, Javier Illescas, Mehar Khan, Sanjay Kathuria, Josaphat Kweka, Erjon Luci, Mariem Malouche, Eric Manes, Yira Mascaro, Mamadou Ndione, Zeinab Partow, Stefano Paternostro, Jorge Rachid, Richard Record, Jose Guilherme Reis, Jose Daniel Reyes, Nadeem Rizwan, Liviu Stirbat, Dimitri Stoelinga, Daria Taglioni, Maria Nicolas, Erik von Uexkull, Cristian Ugarte, and Ravindra Yatawara for facilitating the access to the data for developing countries. We thank Lina Ahlin, Martin
public spending from administrative services to infrastructure and human development, while improving or at least maintaining current government efficiency.

- It is important that the government balance spending on infrastructure and human development as they complement each other in efforts to transform and diversify Liberia’s economy. Spending on infrastructure tends to have relative strong and immediate growth and poverty reduction effects, whereas spending more directly on human development has a stronger positive impact on non-poverty MDG indicators at the cost of lowered economic growth. Both infrastructure and human development policies can be used as tools toward accelerating poverty reduction.

- Strong growth driven by rapid mining expansion entails drawbacks and risks, including the persistence of an enclave economy that will not benefit the majority of the population and increased vulnerability to fluctuations in iron ore world prices. The development of the oil industry (not considered in these simulations) would bring similar opportunities and challenges.

The paper is organized into five sections including this introduction. Section 2 presents the basic features of MAMS.<sup>5</sup> The simulation analysis, which is the focus of the paper, is covered in the next two sections: the base scenario in Section 3 and a set of alternative scenarios, which are contrasted with the base scenario, in Section 4. The final section summarizes the main findings and conclusion. Appendices 1 and 2 include a set of figures with selected simulation results and a brief discussion of the Liberian database for MAMS, respectively.

# **2. STRUCTURE OF MAMS**

MAMS includes three core institutions: households, government, and the rest of the world. Payment flows of any given year are summarized in Figure 2.1 (see Appendix).

<u>Activities produce, selling their output at home or abroad, and use their revenues to</u> cover their costs (of intermediate inputs, factor hiring, and taxes). Their decisions to pursue particular activities with certain levels of factor use are driven by profit maximization. The shares exported and sold domestically depend
, it means that she does not have power to influence the decision. In addition, many studies focus on the decision-making of married women with respect to their husbands. Yet, in many contexts, intergenerational or extended family households are common—and different dynamics may be at play when decisions are made with a mother-in-law or son, rather than a spouse.

In this paper, we use unique mixed methods data on decision-making within rural households in Kilifi County, Kenya, collected between October 2022 and March 2023, to better understand when not participating in decision-making might be a form of agency. Our data is unique in that it allows us to examine decision-making across various family relationships and roles within the household, and to interrogate the way household composition relates with decision-making power, or authority or influence in decisions made in the household, and forms of agency.

Our intrahousehold survey was administered to all adults in the household, going beyond the usual focus of just the husband and wife. Questions are asked about the decisions made about the household’s water, including water collection, allocation, and expenditures. Because water collection and allocation are often within women’s domain, these provide new insights into intrahousehold decision-making. In addition, we ask more traditional decision-making questions on health care for oneself, visits to one’s family or relatives, and on how the money one earns is used. We also draw on qualitative data on intrahousehold decisionmaking processes and norms, with a particular focus on securing water for the household.

Kilifi County, which is on the eastern coast of Kenya just north of Mombasa City, is predominately inhabited by the Giriama people, one of the ethnic groups that make up the Mijikenda peoples. Households traditionally consist of a large homestead with several small huts built on the compound. Extended families often live together on the compound and share daily tasks. Sons’ and nephews’ homes are often situated alongside their father’s or uncle’s homes. The Giriama are traditionally polygamous and in some homesteads,
# **5. Results**

## **Selection model**

As indicated in the previous section, the first step of the semi-parametric methodology employed is estimating the probability of a household diversifying. The probability is modeled through a logit model (Maddala, 1983). Figures 2 (2010/11 round) and 3 (2012/13 round) present the propensity scores for diversifying and non-diversifying households. As is evident from the graphs there is almost full common support in our sample. The results for this model are presented in Table 5, with columns labeled accordingly for each round of the GHS-Panel survey. The values reported are the marginal effects from the logit model.

The characteristics of the head of the household, such as marital status and years of completed education, are significantly related to the diversification choice. The head’s years of education is positive and significantly related to the probability of a household diversifying in both years. This is possibly related to education allowing households to overcome barriers to entry, and possibly have access to a broader range of options on which to diversify. The gender of the head is significant (1%) only for 2012/13, while the head’s age does not appear to be significant in both models.

As expected, higher infrastructural constraints limit the opportunities to diversify for farming households (e.g. if the household is very distant from a bus stop or a market center, the opportunity cost of diversifying may be too high for them to do so). Thus, it seems to be the case that improved infrastructure may “pull” households towards diversification, or at least provide more options. The excluded instrument used for the participation equation is statistically significant. An increase in the average of the mean yearly precipitation during the rainy season from 2001 up to 3 years before the survey interview for each household is negative and significantly related to diversification. Households who experienced higher than average rainfall during the rainy season in 2001-2007 (round 1) and
|0.114|0.467|0.005|0.017|-0.007|0.222|1,824|
||||(0.017)|(0.021)|(0.018)|||
|Sewing machine|0.015|0.129|0.014*|0.016|0.012|0.739|1,824|
||||(0.008)|(0.012)|(0.008)|||
|TV|0.840|0.385|0.042**|0.029|0.054***|0.272|1,824|
||||(0.017)|(0.022)|(0.019)|||

Notes: This table reports the intent-to-treat effects of the interventions on a household asset index, disaggregating the results from Table 3. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

56