rafmacalaba/data-use-mentions-extended huggingface dataset:
Mehta, B. S., Awasthi, I. C., Mehta, B. S., & Awasthi, I. C. (2019). Measurement of Women’s Work. _Women and Labour Market Dynamics: New Insights and Evidences_ , 33-54.

- <mark>Muller, M., & Sousa, L. D. (2020).</mark> _<mark>“She Helps Me All the Time”: Underestimating Women's Economic Engagement in Rural Honduras</mark>_ <mark>. The World Bank.</mark>

- ONEC (2012). Encuesta de Hogares de Propósitos Múltiples - Preliminar 2022 [Data set]. October 2023 - Oficina Nacional de Estadística y Censos. https://onec.bcr.gob.sv/Repositorio_archivos/

<mark>UN Women (2023). Empoderamiento económico de las mujeres. URL:</mark> <u><mark>https://elsalvador.unwomen.org/es/nuestro-trabajo/empoderamiento-economico</mark></u> <mark>Accessed on October 25, 2023.</mark>

- <mark>Urminsky, O., Hansen, C., & Chernozhukov, V. (2016). Using double-lasso regression for principled variable selection.</mark> _<mark>Available at SSRN 2733374</mark>_ <mark>.</mark>

- <mark>World Bank (2011).</mark> _<mark>World Development Report 2012: Gender Equality and Development.</mark>_ <mark>Washington, DC: World Bank.</mark>

<mark>World Bank (2023). Gender Data Portal. URL:</mark> <u><mark>https://genderdata.worldbank.org/indicators/sl-uem-zs/?gender=total. Accessed on</mark></u> <mark>October
investments in African manufacturing have been held back by high risk rather than low returns on investments. We discuss this in detail in Section 3.4.

# _ii) Access to credit_

Bigsten et al. (2003) look at formal credit market participation and credit constraints, based on survey data on firms in Burundi, Cameroon, Côte d’Ivoire, Ghana, Kenya, and Zimbabwe observed between 1991 and 1995. The authors use data on firms’ demand for external funds and whether loan applications were approved or not. Table 1, originally Table 2 in Bigsten et al. (2003), displays summary data on the frequency of loan applications, and, conditional on an application having been made, the outcome. These data suggest the demand for formal loans among African manufacturers is low: less than 20 percent of the firms in the sample had applied for a formal loan in the year prior to the time of the survey. Among those applying, the majority of firms obtain loans. There are large differences across the size distribution: among small firms loan applications are less common, and the success rate lower, than among larger firms. This is another example of considerable heterogeneity across firms.

**Table 1. Formal Credit Market Participation by Firm Size (percentages of firms)**

||Micro|Small|Medium|Large|All|
|---|---|---|---|---|---|
|Did not apply|92|82|80|75|82|
|Applied and did not receive|6|11|9|5|8|
|Applied and received|2|7|11|20|10|

_Source_ : Bigsten et al. (2003) A firm may be credit constrained even if it does not apply for a loan. For instance, a firm may expect that an application will not be successful precisely because there are credit constraints, and may therefore decide not to
subjective perceptions across countries, as there may be systematic reporting biases, including significant cross-country cultural differences in reporting an impact. Within countries, at the household level, we find that richer households felt a decline in their relative income position, which is consistent with evidence from household surveys that the crisis reduced mostly the incomes and consumption of middle- and upper-income groups. But we also find that poorer households report being (subjectively) affected by the crisis more. Differences in the feasibility of coping strategies can help explain variations in the subjective perceptions of crisis impact: the poorest third were forced to reduce their consumption of staple foods and health spending, and tended to depend on public safety nets. By contrast, richer households had more options to cope, pursuing so-called "active strategies" (such as increasing their labor supply), borrowing from friends and family or banks, and cutting spending on non-essentials. Transition countries also differed significantly from the western European comparator countries in that a large proportion of their populations resorted to reducing the consumption of basic necessities during the crisis. Private safety nets and informal insurance mechanisms could not meet the shortfall in income and, in some cases, the reduction of informal transfers reinforced the impact of the crisis.

We find subjective perceptions of the impact of the crisis to be relevant to socio-political outcomes. The severity of the impact influenced life satisfaction and perceptions of government performance: the harder the impact, the lower the satisfaction level and the more negative the assessment of government performance. However, reflecting the complexity of the relationship between economic growth and happiness, satisfaction with life in 2010 was lower in 15 countries (and especially so in Latvia, Lithuania and Romania) and higher in 13 countries compared to 2006 levels. Looking ahead, households are optimistic. Despite the shocks, they expect an improvement in their social position over the next four years. Their experiences during the crisis suggest that more efficient and better-targeted safety nets and social insurance mechanisms could help sustain and spread the gains from anticipated future growth.
also excludes the possibility that some large private sector employers may not provide registered employment contracts or encourage employee contributions to social insurance schemes. Similar objections might apply to domestic and temporary workers who are often entirely classified as informal.

The economic significance attached to whether an employment relationship conforms to state labor contracting regulations depends on the degree to which those regulations impart inflexibility in the extent to which both parties can tailor the terms and conditions of employment to their best advantage. So, for example, if regulations permit flexible hours of work with no effective upper constraint then the economic impact of signing a formal contract compared to working informally may be less, and the rationality for participating in the informal sector reduced. The existence of significant lay-off costs if a formal contract is terminated may be one of the main factors pushing employers towards offering informal employment (see World Bank, 2002 on the case of Brazil).

The argument that both employees and employers may prefer to enter into either unregistered or registered employment relationships will hinge on the balance of the advantages and disadvantages. This is the central argument behind recent work (such as Maloney 2004) which rejects the traditional view that the informal sector is a labor market segment populated by displaced workers queuing for good jobs in the rationed formal sector. Perhaps the most persuasive evidence in support of this is in Maloney (1999). He uses Mexican panel data to examine between-sector and within-sector transitions. The definition of informality used in this research is based on establishment size rather than contract status. However no evidence is found 8
The second factor that determines whether a firm will export is its cost efficiency. Clerides et al. (1998) derive a model where exporting is associated with a fixed cost, incurred in every period of exporting. The authors show that, in this model, firms with marginal costs below some threshold choose to export, while firms with marginal costs above the threshold remain focused on the domestic market. Thus, this model predicts that relatively efficient firms will self-select into the export market. A number of studies have tested for self-selection based on data from both developed and developing countries. On balance, these papers present quite strong evidence that efficient firms do indeed self-select into the export market.

The paper by Bigsten et al. (2004) analyzes whether there is any evidence of self-selection into exports markets. They find this effect to be relatively weak. Van Biesebroeck (2005a) finds a somewhat stronger self-selection effect on a larger sample. Both studies report results suggesting that causality runs in the other direction, i.e. from exporting to efficiency. We discuss this next.

The fact that exporters tend to be more productive than non-exporters is a well-known result that seems to hold both in rich and poor countries. Mengistae and Pattillo (2004) report a positive correlation between productivity and exporting among firms in Ethiopia, Ghana and Kenya. The question of whether this is because exporting actually _causes_ efficiency gains has received a lot of attention in the recent literature. From a policy perspective, whether or not firms in developing countries can “learn” from exporting is an important issue. As we have seen, the domestic markets for manufactures are typically very small in Africa, so industrialization will partly have to be through exports. Under learning-by-exporting the competitiveness gap can be reduced endogenously through increased international trade. As discussed in the previous paragraph, one methodological challenge in testing for learning-by-exporting effects is that causality may run in the other direction, i.e. efficient firms may selfselect into the export market. Therefore, efficiency and exports may
smaller farmers generally over reporting their land relatively more than larger farmers, and with the largest farmers actually under reporting land size.

These findings go in the same direction as the results of De Groote and Traorè (2005) for Southern Mali. As stated above, De Groote and Traorè find that there is an approximate linear negative relationship between measurement error and plot size. Farmers have a tendency to overestimate small plots (which they define as below 1 hectare<sup>4</sup> ) compared to larger ones, which they somewhat underestimate. Also, the GPS and self-reported plot area measures in our sample display a correlation of 0.97 (which drops to a more reasonable, but still respectable, 0.77 when we trim the right end of the distribution of a few very large plots), well above the 0.15 found by Goldstein and Udry (1999) in their Ghana dataset.

One additional issue in our data, that is immediately apparent from the visual inspection of the distributions of the two land measures in Figure 1, is the considerable tendency of respondents (or enumerators) to round their reported plot size to nearest acre or half acre. This „heaping‟ in the response pattern is not uncommon (Roberts and Brewer, 2001) but we suspect it may be particularly important in the case of land measurement since it is bound to matter proportionally more to the left of the distribution, as the same amount of rounding represents a larger percentage of the actual plot size.

Figure 1 also confirms that the two distributions are, overall, quite similar. However, while the means are not much different, at specific points the distributions deviate considerably, in a way that appears to be driven by heaping in the self-reporting distribution as opposed to a smooth curve for the GPS measure. Finally, the comparison of the two distributions appears to support the case for treating the GPS measure as the more accurate of the two.

The systematic patterns in the difference between land measurements we have observed
- Korpershoek, H., Harms, T., de Boer, H., van Kuijk, M., & Doolaard, S. (2016). A metaanalysis of the effects of classroom management strategies and classroom management programs on students’ academic, behavioral, emotional, and motivational outcomes. _Review of Educational Research_ , _86_ (3), 643-680.

- <mark>Kramarski, B., & Zeichner, O. (2001). Using technology to enhance mathematical reasoning: Effects of feedback and self-regulation learning.</mark> _<mark>Educational Media International</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(2-3), 77-82.</mark>

- <mark>Lane, K. L., Wehby, J., Menzies, H. M., Doukas, G. L., Munton, S. M., & Gregg, R. M. (2003). Social skills instruction for students at risk for antisocial behavior: The effects of small-group instruction.</mark> _<mark>Behavioral Disorders</mark>_ <mark>,</mark> _<mark>28</mark>_ <mark>(3), 229-248.</mark>

- Lavy, V. (2010). _Do differences in schools' instruction time explain international achievement gaps? Evidence from developed and developing countries_ (No. w16227). National Bureau of Economic Research, Cambridge, MA.

- Lavy, V. (2015). Do differences in schools' instruction time explain international achievement gaps? Evidence from developed and developing countries. _The Economic Journal_ , _125_ (588), F397-F424.

- Lee, Y., & Kinzie, M. B. (2012). Teacher question and student response with regard to cognition and language use. _Instructional Science: An International Journal of the Learning Sciences_ 40(
that higher repurchase cost of machinery is associated with higher labor productivity, and this relationship is significant at the 1 percent level.

# **6. Conclusion**

Nationally representative firm-level survey data for 22 UMICs and 11 HICs shows that labor productivity in upper-middle-income countries is lower by about 58 percent than in the highincome countries. The lower productivity in the UMICs holds in the full sample and also in the sub-samples of firm-size (small, medium, and large) and sector (manufacturing, retail, and industry). The Oaxaca-Blinder decomposition results reveal that the endowment effect and the structural effect contribute almost equally to the productivity gap. Several firm-level and countrylevel variables are found to contribute significantly to the productivity gap via the endowment effect and the structural effect. These findings provide insights into the likely reasons for why middle-income countries fail to transition to high-income status.

Several issues remain to be explored. We provide a couple of examples to illustrate the point. First, we found that a large part of the structural effect remains unidentified. Future work in this area can help shed light on the likely factors involved here. Second, the decomposition technique used above does not identify the channels or the mechanisms through which the firmlevel and country-level variables impact labor productivity in UMICs and HICs. Further, the decomposition technique does not allow for any causal inferences. These remain important areas for future research.

26
|---|---|---|---|
|1|0 – 0.5|5|4 ‐ 8|
|2|0.5 ‐ 1|6|8 ‐ 16|
|3|1 – 2|7|16 – 32|
|4|2 – 4|8|32+|

A further analysis routine followed. Each facility‐specific cost time raster file was converted to a polygonal vector data set with each travel class forming one polygon. We then imposed provincial and local government unit boundaries onto these polygons. Finally, we aggregated the population per travel class per administrative area based on the WorldPop 2015 UN Adjusted population density data set. To show the percentage population breakdown for each travel class in each area we divided each polygon’s population by the total population for its administrative or physio‐geographic area. Figure 10 illustrates this process in detail. Simple
|Dependent mean<br>3.245|-0.197|5.845|

_Note:_ The table presents results for the main outcomes but under different specifications that help discard the hypothesis that effects were driven by conflict. Panel A shows results when separating the control group between governmentcontrolled areas and areas disputed by guerrillas. Notice that in Panel A, the omitted category concerns segments under pure governmental dominance. Panel B shows results using a doughnut-hole methodology with a hole of 400 m. The unit of observation in all columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

_Recruitment—_ Coercive recruitment has figured prominently in work that ties civil conflict to lower levels of education. Early military experience is a bad substitute for education and labor market experience, and child soldiers lose key formative years of schooling (Blattman and Miguel, 2010). Nonetheless, coercive recruitment is unlikely to be the force behind our results. First, child soldiers were not prominent in the FMLN. Estimates suggest that of the 9,000–12,000 FMLN members, only 2,000 (about 20 percent) were under age 18, while the percentage of underage combatants in 42
_Transfers and social spending ease the tension between financial openness andpolitics_

**Figure** 5.5: **Relationships among civil liberties, capital mobility, and government social expenditures**

<!-- Start of picture text --> Civil Financial<br>Liberties Openness<br>r06y<br>-0.2 r-0.6<br>\ / + r--0-6--<br>r__O.7 0.O2<br>Gov't Social Transfers<br>Expenditures (% GDP)<br>(% GDP)<br><!-- End of picture text --> Note: The cross-country data, with sample size randing from 70 to 140, shows statistically significant results for all three relationships, with the highest correlation between civil liberty and government social expenditures (see Table A5.5 in the Appendix for a summary of the statistics and sources of the main variables used).

20
br><!-- End of picture text --> <!-- Start of picture text --> 4<br><!-- End of picture text --> <!-- Start of picture text --> Public Debt<br><!-- End of picture text --> <!-- Start of picture text --> AX é cia ee<br>fu fe<br>rR<br>. KH<br>\_/<br><!-- End of picture text --> <!-- Start of picture text --> a<br>aw<br>On<br>a° AX é cia ee<br>© fu fe<br>Ze rR<br>°<br>=ao$= . KH<br>o \_/<br><!-- End of picture text --> <!-- Start of picture text -->
-5 -4 -3 -2 -1 0 1 2 3 4 5<br>Timeline<br>Source: Authors' calculations based on the IMF Historical Public Debt Database<br><!-- End of picture text -->

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not give any indication of the effect of these incentives. Over the 1980s, Turkey's average wheat yield grew more slowly than in previous decades and did not even keep pace with increases in world average yield. Part of the explanation for slow growth in wheat yield may be slow growth in fertilizer use, as already discussed.

In the early 1990s, the annual value of the wheat crop in Turkey is roughly $ 3 billion. New varieties coming with reform have spread to a portion of this area. National yield data do not show :n impact. Although private companies currently have a small share of the wheat seed market, they have been actively looking for new foreign varieties to introduce, and they have been selling more wheat seed. The reform impact on wheat may be expected to grow as private companies push into the market.

# **4.2.4 Vegetables for Export**

Vegetable exports is an area where one would expect an impact from reforms allowing in new varieties as well as new specialty chemicals such as plant growth regulators. The limited data which are available show a large increase in value of vegetable exports after reform period, but are not sufficient to estimate the increase in value due to regulatory reforms (see Table 4.4). Furthermore, only a fraction of that value would be net economic benefit, since vegetable producers and processors would be taking resources away from other productive activities. This section is based on information from a 1994 FAO and MARA study, Turkey: Horticulture Subsector Review: Approaches to Market-Led Development.

_-25-_
<!-- Start of picture text --> Trends in citizens' trust<br>Trust in army Trust in police Trust in president<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>45%<br>2 0 0 5 / 2 0 0 6 2 0 0 8 / 2 0 0 9 2 011/ 2013 2014/ 2015 2016/ 2018 2019/ 2021<br><!-- End of picture text --> Note: Sample of 16 countries: Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar (except missing data in 2019/21), Malawi, Mali, Namibia, Nigeria, Senegal, Tanzania, Uganda, Zambia, and Zimbabwe Source: Afrobarometer database available at https://www.afrobarometer.org/online-data-analysis/

# Political stability

## **<mark>Long-term trend 4. Political instability is on the rise in Africa and constitutes a major threat to the continent’s ability to claim the 21</mark>**<sup>**st**</sup> **<mark>century.</mark>**

<mark>Political violence is at a historical high in Africa – and the continent fares poorly in regional comparative perspective. As illustrated in Figure 15, after a short-lived decrease in the early 2000s, violence has reached new highs in the last decade. This violence is likely the product of the long-term trends identified above, and reflects the fractured nature of the social contract, and the challenge of ensuring state control over the territory, in particular in fragile states with weak institutions. It remains a core binding constraint to growth on the continent: instability creates policy volatility, shortens policy horizons leading to suboptimal macroeconomic policy decisions, and decreases investors’ confidence. Estimates of the cost of conflict in Nigeria alone in 2021 was $15
# **3 Evidence**

We use two sources of data, firm-level data and customs records. The firm-level data come from the Encuesta Nacional Industrial Anual (ENIA), an annual industrial census run by Chile’s Instituto Nacional de Estad ́ıstica that interviews all manufacturing plants with 10 workers or more. It is a panel. The customs data provide administrative records on firms exports by destination. We manually matched both databases for the period 2001-2005. As a result, we built a 5-year panel database of Chilean manufacturing firms.

The data have several modules. The main module contains information on industry affiliation, ownership type, sales, exports, input use, imports of materials, workers and wages. Industry affiliation is defined at the 4-digit ISIC Revision 3 level, which totals 113 industries.

We are mostly interested in the employment information. The data on workers are presented at detailed categories, which allows us to explore the demand for different tasks. From the detailed employment records, we define the following tasks: management (directors), administrative services (accountants, lawyers), engineers (specialized skilled production workers), blue-collar activities (non-specialized unskilled production workers), and general maintenance services (unskilled non-production workers).

Table 1 presents summary statistics for the key variables in our model. We present the unconditional averages as well as averages for exporting firms and non-exporting firms. In terms of employment, exporters are larger than non-exporters, as expected. On average, 39 percent of workers in Chilean firms are skilled workers and 61 percent are unskilled workers. Exporters utilize a higher share of skilled workers (41 percent) than non-exporters (39 percent). Within the skilled and unskilled categories, we are especially interested in engineering and blue-collar employment. The share of engineering employment is roughly 18.1 percent at both at exporters and non-exporters, while the share of blue-collar employment is 52.0 and 54.6 at exporters and non-exporters, respectively. The ratio of engineers to blue-collar workers is 4.7 at