rafmacalaba/data-use-mentions-extended huggingface dataset:
100**|
|---|---|
|Proportion of ministerial/senior government positions held by women|10|
|Proportion of seats held by women in national parliament|20.3|
|Extent to which the country has laws mandating women’s workplace equality|20.0|
|Proportion of women who hold a bank account at a financial institution|26.0|
|Proportion of women who have made or received digital payments in the past year|7.4|
|Proportion of women with access to internet services|27.6|
|Level of personal autonomy, individual rights and freedom from discrimination|31.3|
|Openness of gender statistics|45.0|

**The participation of women in Myanmar’s labor force is less than that of men, as is their representation in senior or decision-making roles across the public and private sectors.** Despite women accounting for 51.8 percent of Myanmar’s population, a United Nations Capital Development Fund Assessment of women and girls’ financial inclusion estimated that women owned only around 25 percent of all MSMEs in Myanmar (28 percent of small enterprises and 20 percent of medium enterprises), predominantly in services, but also in the manufacture of textiles

> 3 Women’s agency in Mon and Kayin States, World Bank, June 2020.

4
Policy Research Working Paper 9201

# **Abstract**

This study examines whether foreign direct investment inflows facilitate upgrading of export quality in host countries. The analysis focuses on the Russian Federation and uses customs data merged with firm-level information from Orbis. The results show a positive relationship between the quality of products exported by domestic firms and the presence of foreign affiliates in the upstream (input-supplying) industries. This relationship is present irrespective of export destination or foreign direct investment origin. The results are robust to using different proxies to measure product quality.

This paper is a product of the Finance, Competitiveness and Innovation 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 author may be contacted at s.poupakis@gmail.com.

_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
. _Annals of Economics and Statistics/Annales d’ ́economie et de statistique_ , 307–328.

- Dustmann, C., Fasani, F., & Speciale, B. (2017). Illegal migration and consumption behavior of immigrant households. _Journal of the European Economic Association_ , _15_ (3), 654–691.

- El-Mallakh, N., & Wahba, J. (2021). Upward or downward: Occupational mobility and return migration. _World Development_ , _137_ .

- Gleditsch, N. P., Wallensteen, P., Eriksson, M., Sollenberg, M., & Strand, H. (2002). Armed conflict 1946-2001: A new dataset. _Journal of Peace Research_ , _39_ (5), 615–637.

- Gleeson, S., & Gonzales, R. G. (2012). When do papers matter? An institutional analysis of undocumented life in the United States. _International Migration_ , _50_ (4), 1–19.

- Gonzales, R. G. (2011). Learning to be illegal: Undocumented youth and shifting legal contexts in the transition to adulthood. _American Sociological Review_ , _76_ (4), 602–619.

- Gonzalez-Barrera, A. (2015). _More Mexicans leaving than coming to the US._ Washington.

- Hall, M., & Greenman, E. (2015). The occupational cost of being illegal in the United States: Legal status, job hazards, and compensating differentials. _International Migration Review_ , _49_ (2), 406–442.

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# **Appendix A ‐ Stylized summary of risk management practices in four countries**

|**Process/**<br>**country**|**Colombia**|**Sweden**|**Turkey**|**Indonesia**<sup>**51**</sup>|
|---|---|---|---|---|
|**Context and**<br>**defining**<br>**characteristics**| USD 700 m. exposure<br>managed by DGCPTN<br> SOEs and sub-nationals<br>potential beneficiaries<br> 3 staff in credit risk team| USD 4 bn. exposure<br>managed by Guarantee<br>and Lending unit at SNDO<br> Beneficiaries mostly<br>SOEs, no sub-nationals<br> 11 staff| USD 11.2 bn. exposure<br>managed by credit risk<br>team at Treasury<br> Beneficiaries SOEs, banks,<br>municipalities<br> 5 staff| USD 16.7 bn. exposure<br>managed by CL team at<br>DGFRM<br> Beneficiaries SOEs and<br>infrastructure fund<br> 8 staff|
|**Credit risk**<br>**analysis**| No internal risk<br>assessment; public credit<br>rating used| Credit rating models<br>favored, simulation<br>models, and structural<br>models;<br> Industry-specific score<br>cards using rating<br>agencies’ methodologies| Statistical regression<br>model for each type of<br>beneficiary<br> Model calibrated using<br>historic data of<br>beneficiaries performance<br>vis-à-vis Treasury| Industry-specific
predetermined with respect to present-day aid, but they may have been located in areas that were more suitable for missionary work, or areas where missionaries could survive and be self-sustained. If these areas are more or less likely to be selected for aid projects for some reason other than mission station presence, _β_ will be biased. Detailed information is available on the determinants of the location of mission stations from historical sources, notably Johnson (1967) and Robinson (1915). Moreover, increasing amounts of detailed and spatially disaggregated historical data are available, so the regression analysis can plausibly control for most determinants: precolonial ethnic institutions, distance from coast and rivers, water accessibility, malaria prevalence, altitude, terrain characteristics, historical population and cities, and distance from historical routes. In addition to controlling for these factors, Oster (2019) bounds are also calculated to formally assess the extent of the bias due to unobserved controls.

A second source of bias is lack of common support in the distribution of the control variables. Even if the set of controls **X** _ik_ fully accounts for the selection problem, the simple OLS estimator will still be biased if the cells hosting a historical mission (the treated observations) are very different in their covariates from cells without a historical mission (the control observations) and if the control function is incorrectly specified (Imbens & Rubin, 2015). Several measures are taken to deal with this issue. First, the sample always excludes cells where more than 90 percent of the surface area was covered by desert or forest in the 18th century. Second, in some specifications the sample is further restricted to coastal cells and cells that intersect one of the main African rivers.<sup>11</sup> Third, in other specifications the analysis is conducted on subsamples obtained using propensity score matching.

Finally, a third source of bias derives from the spatial nature of the data. Since mission stations are spatially clustered, the regression analysis may be overestimating or underestimating the impact
<!-- Start of picture text --> 5.3.<br>Section<br>in<br>1 1 2 discussed<br>1 and<br>Specifications 0 Wholesale 0 Wholesale Wholesale 1<br>0<br>Liability<br>-1 -1 Table<br>Years to Reform Retail Data Years to Reform Retail Data -1 Years to Reform Retail in<br>VAT Data<br>-2 -2<br>2016 2016 -2<br>Alternative Net 2016<br>to C. -3 OLS DiD (Log): -0.022 (0.074). N Retailers:2822, N Wholesalers=3014 -3 OLS DiD (Log): 0.020 (0.049). N Retailers:17356, N Wholesalers=13077 -3 OLS DiD (Log): 0.026 (0.049). N Retailers:17416, N Wholesalers=13176 presented<br>Appendix<br>1 Excludes 1 Includes 2 tests<br>Excludes<br>99,<br>p95, p 1<br>0 0<br>p99,<br>Estimations Wholesale at Wholesale at Wholesale robustness<br>VAT at 0<br>-1 -1 the<br>Years to Reform Retail Years to Reform Retail -1 Years to Reform Retail of<br>Output -2 -2<br>-2<br>B. winsorinzing winsorizing<br>Winsorizing -3 OLS DiD (Log): 0.047 (0.041). N Retailers:2822, N Wholesalers=3014 -3 OLS DiD (Log): -0.027 (0.036). N Retailers:17356, N Wholesalers=13077 -3 OLS DiD (Log): 0.004 (0.037). N Retailers:17416, N Wholesalers=13176<br>representation<br>1
The aggregate gains from trade in Vietnam are 1.99 percent. These gains are mostly explained by gains from lower tariffs on Other Processed Food (0.92 percent), Bananas (0.88 percent) and Cigarettes (0.61 percent). These are mostly consumption gains (with minor or even absent income gains). Fish contributes 0.37 percentage points to the aggregate gains, with a consumption gain of 0.50 percent and a lower income loss of 0.13 percent. The case of Rice is interesting because lower tariffs (and lower prices) create large income losses of –1.57 percent. Other important products are Corn (with income losses of –0.13 percent), Alcohol (with consumption gains of 0.17 percent), Pork (0.28 percent) and Tea (0.13 percent).

The pro-poor index in Vietnam is –1.70. This anti-poor bias of agricultural liberalization is due to the fact that the gains for the rich (2.74 percent) are higher than the gains for the poor (1.04 percent). Tariff liberalization in products such as Bananas and Cigarettes yields the largest pro-poor biases, but the tariff cuts in Corn, Other Processed Food and Rice are distinctly anti-poor. Note in particular the role of the rice income effect: because of lower prices and because the poor are major producers and sellers of rice, the net rice income loss for the poor is –2.35 percent, much higher than the net rice income loss for the rich (of only –0.58 percent).

To end, we report in Table 6 the results from scenarios where tariffs are eliminated only in cereals (namely, corn, wheat, rice and other cereals—sorghum, barley—in the templates). This scenario is interesting because it combines a simulation for a set of related goods that accounts for more than 50% of global calorie intake,<sup>11</sup
|Metropolitan Region|0.506|2.639|0.0420**|
|---|---|---|---|
||(0.258)|(3.587)|(0.0547)|
|Los Ríos Region|0.717|24.65*|0.0119**|
||(0.452)|(42.52)|(0.0260)|
|Arica and Parinacota Region|0.817|0.224|0.110|
||(0.446)|(0.540)|(0.192)|
|Ñuble Region|0.880|3.390||
||(0.534)|(5.513)||
|Constant|0.138***|0.00119***|0.0524*|
||(0.0783)|(0.00254)|(0.0820)|
|Observations|763|129|129|
|Robust standard errors in parentheses||||

*** p<0.01, ** p<0.05, * p<0.1

# **6. Discussion and policy implications**

Eradicating violence against women requires a political agenda that addresses both the causes and the consequences of violence. The elimination of all forms of violence against women is a target of Goal 5 on Gender Equality, one of the Sustainable Development Goals adopted by United Nations member states to promote all persons' economic and social well-being. The large percentage of women who report having been victims of some type of intimate partner violence, together with the number of femicides that occurred in 2020, underscore the need to develop a policy agenda that includes prevention, protection, and reparation programs for violence against women to guarantee that women can exercise their right to live a life free from violence.

To design
Policy Research Working Paper 5658

# **Abstract**

Institutional features of the African setting—large extended families and imperfect credit and land markets—matter to the equity and efficiency roles played by intergenerational linkages. Using original survey data on Senegal that include an individualized measure of consumption, this paper studies the role played by land inheritance, other bequests and parental background as influences on an adult’s economic welfare and economic activities. Although intergenerational linkages are evident, the analysis finds a seemingly high degree of mobility across generations, associated with the shift from farm to non-farm sectors and the greater economic activity of women. Male-dominated bequests of land and housing bring little gain to mean consumption and play little role in explaining inequality, although they have effects on the sector of activity. Inheritance of non-land assets and the education and occupation of parents (especially the mother) and their choices about children’s schooling are more important to adult welfare than property inheritance. Significant gender inequality in consumption is evident, although it is almost entirely explicable in terms of factors such as education and (non-land) inheritance. There are a number of other pronounced gender differences, with intergenerational linkages coming through the mother rather than the father.

This paper is a product of the Development Research Group and the Gender Group of the Poverty Reduction and Economic Management Network. 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 mravallion@worldbank.org and dvandewalle@ worldbank.org.

_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
invest in knowledge inputs that can be transformed into innovation outputs according to an innovation function. At a later stage, these innovation results impact on firm-level productivity, contingent on the capacity of firms to transform innovation outputs into improvements in product quality and efficiency.

As a result, the CDM model requires the estimation of three main components: (i) the knowledge function, which involves estimating the R&D and ICT functions, (ii) the innovation equation, and (iii) the productivity equation. When estimating these components, there are two critical choices to be made. The first is to define the scope of the knowledge inputs and innovation outputs to be included in the analysis. This is often related to the availability of data in the innovation surveys. In most of the CDM applications, data are restricted to the use of R&D for knowledge activities; for innovation outputs, either product and process innovation dummies, or the number of patents and the revenue associated to new products are used. Regarding innovation outputs, we focus on technological innovations -either product or process innovations – and look at revenue originated from product innovations, since patenting activity in the South Asian countries is minimal.

The second choice when examining these three innovation components relates to how the model is solved to produce economic estimates that can be interpreted in a causal way. A parameter of a model is causal, or structural, sometimes referred to as a deep parameter _,_ when it untangles cause from effect, and is derived from a theoretical model where the directions governing the laws of motion are straight forward (Heckman, 2000). In contrast, in a reduced form setting, cause and effect are confounded in the presence of unobserved factors. Decisions regarding knowledge inputs and innovation outcomes can be made simultaneously and there can be feedback effects, especially between innovation outcomes and productivity in the sense that more productive firms may have an advantage to develop new products and/or processes (such feedbacks would be accounted for by including lagged terms
17 As summarized in Table 1c, these indicators are constructed based on responses to the following questions asked in the Enterprise Surveys: ―Has your company undertaken any of the following initiative in the last three years: Agreed to a new joint venture with foreign partner?‖ and ―Has your company undertaken any of the following initiative in the last three years: Obtained a new licensing agreement?‖ 18 We classify firms into four size categories based on the number of full-time permanent employees: micro (1 to 10), small (11 to 50), medium (51 to 200) and large (more than 200). Establishment age is determined by responses to ―In what year did your firm begin operations in this country?‖ and is used to separate firms into three age classes: young (less than five years old), mature (five to 15 years old) and old (more than 15 years since started operations). 19 We use R&D intensity in the innovation equations as it may more accurately capture the differential effect of additional spending on R&D on the likelihood of innovation. Using an R&D indicator variable (as is estimated in equation 1) in place of R&D intensity in the innovation equations does not substantively alter our results or conclusions.

> 20 We calculate TFP by estimating a Cobb-Douglas production function from our enterprise data separately for each two-digit ISIC industry. Output is the real value of enterprise sales and inputs are the real value of fixed assets, total labor costs (actual or ILO wages) and materials expense. All variables are in logs. Each firm‘s residual from its

15
risk” as in the earlier literature on the subject, or broken down into different components as above) is mainly borne by the owner of the dwelling, and relates to the certainty of present and future cash flows from the investment. On the other hand, renters with high moving costs may care about physical eviction risk.

In this paper we clarify these issues. We show that appropriate data on rents and prices of dwelling units in the formal and informal sectors can be used to isolate two economic variables: the difference in rates of return on housing investment, which reflects the risks borne only by investors, and the premium on rents for the formal sector, which reflects conditions prevailing in rental markets. These two quantities have different economic meanings, and should therefore be analyzed separately. We consider a simple model of investment in the housing market, where investors can choose between two sectors: the formal sector, where physical investment faces no risk of destruction, and the informal sector, where investment, in each period, is subject to an exogenous risk of destruction. Construction costs differ between the two sectors. On the demand side, all households are renters.<sup>9</sup> Renters shop for dwelling attributes and do not care about the sector (formal or informal) _per se_ . There is no moving cost, and renters can choose freely between the formal and informal sector (no segregation in the housing market).

The implications of this simple model are the following. Free mobility of renters implies that rents in the two sectors must be equal. On the supply side, the higher risk faced by investors in the informal sector must be reflected in a higher return on equity invested. Since rents are equal in the two sectors, the values of houses will be lower in the informal sector. Returns on investment, measured by the rent-to-value ratio, will thus be higher in the informal sector. This result relies only on investment side considerations and is independent of assumptions on the households’ side
_m_ ( _xh_ ; _β_ ) if the data call for it. In our application the value of the household’s house (or rental value) will serve as the predictor _xh_ .

To obtain some intuition for the implications of Assumption 1 it may be helpful to verify what it implies for the relationship between the expenditure share on housing and household income. Note however that we are concerned with predicting household income per capita rather than household income. (In

> 7 Alternatively one could for example also look to data on mortgages or credit card statements. However, this approach may not be feasible in countries with underdeveloped or non-existing mortgage markets.

7
product classi...cation fraud whereby exporters misdeclare the country or misclassify products in their customs declaration forms. Finally, the model shows that the relationship between trade discrepancies and tari¤s depends on the e¤ect of tari¤s on the detection rate of the importing countries.

The empirical section of the paper heeds the message of Goldberg and Pavcnik (2016): “Measure (trade barriers) before you estimate (trade impacts)! ”We ...rst led the NTM data collection e¤orts of the UNCTAD and the World Bank which allow us to generate an NTM database of 50 importing countries at the detailed 6-digit Harmonized System (HS) level with bilateral variation across 96 trading partners.<sup>11</sup> We then rely on product level quantitybased gravity regressions to estimate the bilateral AVE of NTMs at the HS 6-digit level. Our estimated AVEs are reasonable and in line with previous studies, with a sample mean of 4.7 percent when NTMs are present. Using the estimated AVEs, this paper then assesses whether the presence of Border NTMs contributes to large trade discrepancies as shown in the theoretical model. The results show that exporting countries that face higher AVEs have larger trade discrepancies, consistent with the hypothesis of country origin fraud in order to gain more favorable market access conditions. Similarly, the regression results also demonstrate that the products that have higher AVEs are those that have larger trade discrepancies, consistent with the hypothesis of product classi...cation fraud with the purpose of reducing the costs of compliance with Border NTMs. These results are consistent with the theoretical model and are robust to simultaneity and measurement errors in AVEs, which we address with the use of instrumental variables. Overall, this paper ...nds that a 10 percentage point increase in the AVEs could lead to a 15 percent increase in trade discrepancies, while a comparable increase in tari¤s leads to only an 8 percent increase in trade discrepancies. These ...ndings suggest that a
0.7). When using the 2016 survey, the predictions from the household-level model are more accurate than the sub-area model both in and out of sample. The area-level model is generally less accurate than the sub-area model, with in-sample correlations of 0.8 when using the 2014 data and 0.75 when using the 2016 data. The exception is out-of-sample predictions using the 2016 data, where area-level estimates are more accurate than sub-area estimates but remain less accurate than the household model. Both the household-level and area-level models underestimate uncertainty, relative to the sub-area model, in the 2016 data. In artificial simulations, when compared with predictions from

> 23 It is possible that more sophisticated models or methods that allow for non-linearities and interactions may better predict out of sample, especially if the correlations of predictors vary by things like municipality size. This is a topic for further research.

32