rafmacalaba/data-use-mentions-extended huggingface dataset:
workers are considered to not pay personal income taxes. For social security, we estimate individuals’ pensions contributions and employer contributions (FGTS and Cota patronal, see the details in World Bank, 2020, pp. 7-9). Informal workers who reported to contribute to pensions are estimated to pay through the voluntary regime.

To estimate IPTU/ITR, we use data from POF 2017/18 to fit a regression that explains the behavior of the sum of IPTU and ITR as a function of disposable income, area of residence (urban or rural), Brazilian state, 19 There are some differences in terms of the CEQ approach between this paper and that from Higgins and Pereira (2014) for Brazil relative to 2008/2009. First, Higgins and Pereira (2014) use survey responses at face value, as in Silveira and Palomo (2023), while our paper models and simulates direct taxes. Second, our study has a broader coverage of direct transfers. Third, Higgins and Pereira (2014) account for electricity subsidies, while we do not. Fourth, the health imputation figures for 2009 were made using the health module of PNAD 2008, while we use PNS 2019. The authors calculated the average benefit received per health visit facility visit and then imputed it to households that reported attending a public health facility. The estimates from Higgins and Pereira (2014) were calculated by state. In 2019, estimates consider the age population composition using PNS, so both are valid but with a different emphasis on group differences (geographical vs. age group). Finally, education estimates in 2019 used per-pupil INEP estimates, but for the year 2018. For 2009, the authors used state-level estimates, one value for all education except tertiary and another for tertiary with data from national accounts. So, the 2009 estimates could better reflect geographic differences, while ours may probably better capture differences in educational per capita benefits. Nonetheless, there is suggestive evidence that differences in the results of this work and Higgins and Pereira (
There is evidence that most of the recent growth in Chinese exports is due to foreign firms. Several studies also argue that foreign firms, which are typically engaged in processing trade activities, drive the skill content upgrading of China’s manufacturing exports (Amiti and Freund 2010; Xu and Lu 2009). Moreover, estimations of growth equations indicate that the income gains from export performance and export upgrading are confined to the improvements made by domestic firms. Jarreau and Poncet (2012) find that the positive association between GDP per capita growth and export sophistication at the province level is limited to ordinary export activities undertaken by domestic firms. These results, together with the results emphasized by Amiti and Freund (2010), suggest that the export activities of foreign firms in China do not matter for the economic growth of Chinese provinces once domestic exports have been controlled for. However, although there are no direct gains from the export upgrading of foreign firms in terms of GDP per capita, there may be room for foreign firms to indirectly affect domestic firms through export spillovers. There are two channels through which export spillovers can act. Foreign firms can provide specific information on export markets that can help domestic firms to reduce their fixed export costs (e.g., information about the tastes of foreign consumers or distribution networks abroad). Foreign export spillovers can also be linked to the mutualization of fixed or variable export costs (participation in international fares, marketing, transport costs). Thus, it is worth investigating whether foreign firms in China act as export catalysts that foster the creation of new export transactions by domestic firms.

Using panel data from Chinese customs that record provincial export flows for the period 1997-2007 by product, destination country, type of firm, and type of trade, this paper shows that the capacity of Chinese domestic firms to begin exporting new products to new markets responds positively to the export activity of neighboring foreign firms. These export spillovers are found to be very specific both in terms of activity and in terms of the
product or without interacting with the buyers [or sellers] in person” (p.37). They refer to the challenges of connecting rural producers to e-commerce in China, but these transactional barriers are likely to be present for small informal firms in other countries and even in urban settings. In fact, in their study in China, they find that the expansion of e-commerce has had no effect on producers and the only positive effects have been for richer and younger consumers who benefited from access to lower priced goods and services. This is another example of the potential polarization from the digital economy.

In sum, it is not obvious from the existing evidence whether the rise of e-commerce increases existing gaps between incumbent larger formal firms and informal ones, or it helps ‘level the playing The new empirical evidence from Chaldal and Daraz sellers’ data, the two case studies in this paper, is mildly positive. As reported by the firms selling on these platforms, the main reason for joining the platform is to access more customers. Most of these sellers report an expansion in their business after joining the platform. They also report an increase in their incentive to register their business and in their visibility to tax authorities. Other, less widespread channels of impact reported by firms include the adoption of new or improved business practices and technologies, better access to finance and greater flexibility in balancing home and work life.

The businesses selling on these platforms range widely in terms of size and other characteristics, and many of them are micro, small and medium-sized. In general, the reported impacts do not vary significantly by firm size and degree of formalization, suggesting that even micro and small informal firms that have joined e-commerce platforms can benefit from the greater market access facilitated by the platforms. However, firms using e-commerce, even the small ones, are from a selected group: they are owned and managed by individuals who are more educated and younger than the owners and managers of more typical firms.

The findings on the two case studies
expressed in log form. Private consumption is comprised of non-durable goods and services. Government spending is government purchases (i.e. the sum of current consumption and public investment). Net tax revenue is the sum of personal and corporate income tax revenues, social security contributions and taxes on production and imports, net of transfers to households and private corporations. Real net tax revenue is obtained by dividing the nominal tax revenue by the GDP deflator (2002 = 100). The definition of these variables is in line with previous studies, in order to facilitate consistent comparisons.

Except for the REER, all the variables in the Canadian block are from Statistics Canada’s CANSIM database. The REER, based on relative unit labor costs in the manufacturing sector, is from the Federal Reserve Bank of St. Louis (FRED) database. A rise in the index as shown in Figure A1 (the net-exports-to-GDP ratio declines with an increase of the REER) in the Appendix represents a deterioration in the country’s competitiveness. The second block, _y_ 2 _t_ , includes the logarithm of U.S. real GDP (2005=100) per capita and real oil prices. U.S. GDP is from the U.S. National Income and Product Accounts database. Oil prices are expressed in real terms by dividing the West Texas Intermediate Oil Price (in U.S. current dollars per barrel) taken from the IMF-WEO database by the U.S. GDP deflator(2005 = 100). I chose the GDP deflator instead of the consumer price

> 11The _jth_ element _rja_ ( _k_ ) of _ra_ ( _k_ ), _j_ = 1 _, .., m_ is the impulse response at horizon _k_ of the _jth_ variable to the impulse vector _a_ .

7
0.2% 1.1% 3.9% 1.2%<br>0 0mil.$ 0.3% 0.0%<br>2006 2007 2008 2009 2006 2007 2008 2009<br>cont'd cont'd new dest new firm new prod cont'd new dest new firm new prod cont'd new dest new firm new prod cont'd cont'd new dest new firm new prod cont'd new dest new firm new prod cont'd new dest new firm new prod<br><!-- End of picture text --> _Source:_ Authors‟ calculation based on Lao PDR Customs data What we see in the two tables is that there is a considerable amount of experimentation: some 25-60 percent of FPDs in any one year are _new_ , and 2007 appears to have been particularly active. On the dollar value side, however, the incumbents dominate by far, accounting for some 75-97 percent of the value in the three years. This is consistent with the findings of Cadot et al. (2010) on African countries, as well as the conclusions of Brenton and Newfarmer (2007) on the importance of the intensive margin in explaining export growth in developing countries.

This simple demonstration of the fact that exploration and export discovery, even if actively encouraged, account for little of the export value presages the survival analysis which will be presented in section 4. It also illustrates the need to support exporters beyond the exploration phase of their activity (the sustainability margin) in order to have any significant value impact from this exploration (the intensive margin).

# **_3.3 Survival Statistics_**

As mentioned in the introduction, after the seminal work of Besedes and Prusa (2006), several other papers have confirmed the finding that the median duration of export spells in developing countries is very short, typically two to three years. We investigate this aspect in the Lao data and present a simple view of the
a victim who triggers the explosive. There are other issues which make landmine explosions problematic as a measure of a violence shock. The date of occurrence is delayed with respect to when the mine was planted, although this is a minor concern if the interest is in the stress effect. More importantly, minefield are heavily concentrated in a few municipalities and locations are strongly correlated with the conflict intensity and the local presence of armed groups.<sup>24</sup> Finally, although landmine victims are not targeted in advance, they have a specific profile: victims are disproportionately children and people who work as manual eradicators of illicit crops, as well as indigenous people (CNMH and Fundación Prolongar, 2017). Moreover, landmines they are almost exclusively placed in rural areas (98% of them, according to the CNMH and Fundación Prolongar (2017)), which means that their visibility and potential impact is low in urban parts of the country. This means that they can be perceived as less indiscriminate, limiting the fear generated in the wider population as those falling outside of these profiles or living in urban areas may not feel at risk. For these reasons, I prefer violent attacks over landmine explosions as a measure of violence events.

The records of violent attacks for this study come from the compilation by the CNMH for its 2013 report _Basta Ya! Memories of War and Dignity_ . Since these records stop in 2012, I update them until March 2016 going back to the original source, the _Centro de Investigación y Educación_

> 21 Combats or battle deaths measures do not fit the profile of modern armed conflicts, where most of the violence is not the product of military confrontations, rather aims to control population and territory and is targeted towards civilians (Kaldor 2006, 2013).

> 22 These would be attacks against a population, colloquially referred to as guerrilla incursion ( _toma guerrillera_ ).

> 23 This definition shares various components with, for instance, the one used in the United States: ‘Premeditated politically
# **Supplementary Material:**

A spreadsheet with the (GEGI) for each country (and background data used in its construction) are available for download [link]and at the author’s website: https://sites.google.com/site/stevenpennings/

# **References**

Bertay, A, L. Dordevic, and C. Sever (2020) “Gender Inequality and Economic Growth: Evidence from Industry-Level Data” IMF working paper WP/20/119

- Cuberes D. and M. Teignier (2014) “Gender Inequality and Economic Growth: A Critical Review,” _Journal of International Development,_ 26, 260–276

- Cuberes D. and M. Teignier (2016) “Aggregate Effects of Gender Gaps in the Labor Market: A Quantitative Estimate”, _Journal of Human Capital_ 10(1).

Galor, O., and D. Weil (1996). “The Gender Gap, Fertility, and Growth.” _American Economic Review_ , 86(3), 374-387.

- Goldin C. (1995) “The U-Shaped Female Labor Force Function in Economic Development and Economic History.” In: Schultz TP (ed), _Investment in Women’s Human Capital and Economic Development_ . U. Chicago Press: 61-90.

- Goldman Sachs (2007) “Gender Inequality, Growth and Global Ageing”, Global Economics Paper No: 154

- Jones C. (2016) “The Facts of Economic Growth”, Chapter 1 in J.Taylor and H. Uhlig _Handbook of Macroeconomics, Volume 2A_ , Elsevier

- Kim Y. and N. Loayza (2019) “Productivity Growth: Patterns and Determinants Across the World” World Bank Policy Research WP 8852 [link]

- Klasen S. (2019) **“** What Explains Uneven Female Labor Force Participation Levels and Trends in Developing Countries?” _World Bank Research Observer_ , 34(2).

> 44 I thank Kathleen Beegle, Dimitria Gavalyugova, Aart
Musacchio (2018) to eliminate another 30 firms that have minority state ownership (any firm with more than 5 percent state ownership) in the roads, airports and airlines, power, and railway sectors.<sup>8</sup> Having some partially privatized firms in our set of private firms would be a problem only if we find that SOEs outperform private firms. In this case, the partially privatized firms could be reducing the mean performance of private firms, biasing our matching results to indicate better performance for SOEs. If, however, we find that SOEs underperform a sample of private firms and partially privatized SOEs, it is likely that our estimates represent lower bounds on the “treatment effect” of being state owned and that the effect would actually be larger if all partially privatized firms were eliminated from the comparison group.<sup>9</sup> As we explain below, our results show that SOEs perform worse, are larger, accumulate more liabilities, and have higher employment costs than private firms. We are therefore confident that our results present either a lower-bound estimate of the effect of state ownership or at best a good reflection of the average effect of being an SOE.

# **_5.2 Matching_**

In order to estimate the effect of state ownership on several indicators of performance, we first perform a matching exercise in which we treat our SOE sample as the treatment group and a dataset of private firms as the control group. We use matching techniques, because our database is observational rather than randomly selected; there can therefore be systematic differences in characteristics between the treated group (SOEs) and the untreated subjects (private firms) that can cause significant imbalances between these groups.

We use the coarsened exact matching (CEM) algorithm described in Iacus, King, and Porro (2012). This technique minimizes imbalances between groups and provides a larger comparison group for each of our SOEs than we would get with exact matching techniques, which tend to produce only a few matches
largely devoid of the central bank information effect, the notion that monetary policy announcements also reveal information regarding the central bank’s future macroeconomic outlook (Nakamura & Steinsson 2018). Finally, the Bu, Rogers & Wu (2021) monetary policy shock series is largely unpredictable from available information, including Blue Chip forecasts, “big data” measures of economic activity, news releases and consumer sentiment.<sup>9</sup> These considerations make it more plausible to assume that the shock series is exogenous and thus appropriately to be placed first in the ordering of a Sims (1980) style VAR. Finally, we use Swanson (2021)’s forward guidance shock (SS-FG), the extended “path factor” of Gurkayanak et al. (2005) that covers the post-2005 period.

We plot all three shock series in Figure 1. In subsection B.3, we also apply the sign restriction identification in Jarociński & Karadi (2020) to disentangle the pure monetary policy shock (JKMP)

> 8It is the first principal component of unexpected changes over 30-minute windows around FOMC announcements in five underlying variables: the Fed funds rate following the FOMC meeting, the expected Fed funds rate following the next meeting, and three-month eurodollar future rates with two, three, and four tenors. The original NS series spans 1995-2014, and was extended to 2019 by Acosta & Saia (2020). We drop unscheduled FOMC meetings to avoid potential noise and set shocks in no-meeting months to zero as the standard practice.

> 9See Ramey (2016), Miranda-Agrippino (2016), and Bauer & Swanson (2020) for critiques of earlier monetary policy shock series that exhibited predictability.

5
14

# **3.1. Exports**

After the Cambodian apparel industry took off in the late 1990s, exports quadrupled within a decade, growing from US$578 million in 1998 to US$2.4 billion in 2004 (see figure 2). According to the Garment Manufacturers’ Association in Cambodia (GMAC), apparel accounted for only 3 percent of Cambodia’s total exports in the early 1990s, but by 2003 it constituted 76.4 percent. Sri Lanka, already a seasoned apparel exporter in the mid-1990s, experienced a healthy but more moderate growth than Cambodia, with exports rising from US$1.7 billion in 1995 to US$3 billion in 2004 (figure 2). In the late 1990s, however, export growth slowed, and exports even declined in 2001, 2002, and 2008 (figure 2).<sup>14</sup> <!-- Start of picture text --> Figure 2: Total Apparel Exports (Millions US$)<br>(HS61 and HS62)<br>1995 2000 2005 2010<br>Period<br>Cambodia Sri Lanka<br>6000<br>4000<br>2000<br>0<br><!-- End of picture text --> _Source:_ United Nations Commodity Trade Statistics Database (UN Comtrade). _Note:_ Exports are represented by imports reported by partner countries. Apparel Classification: HS 1992: Woven: HS62; Knit HS61.
6

|4|The Health Zone provides training for 20 volunteers on maintenance of latrines, water supply systems, and<br>sanitation, conflict management, and petty cash management. The community elects a village WASH<br>committee.|
|---|---|
|5|The community spends ten hours over three days describing a community vision, analyzing the barriers to<br>reducing diarrheal diseases, choosing improvements to drinking water, sanitation, and hygiene, and<br>formulating an action plan. The community is asked to identify practical, low-cost solutions with a minimum<br>of outside assistance. New infrastructure is evaluated in terms of accessibility, technical feasibility, and<br>technical capacity.|
|6|The community builds new infrastructure over 90-180 days, supported by project funds. Key messages<br>about sanitation and hygiene are discussed during sensibilization meetings or during visits to families by<br>the WASH committee, community health workers, or other volunteers. Health Zone staff are expected to<br>visit the community monthly during this time; Health Area staff weekly.|
|7|The community self-evaluates again, to measure progress since Step 2. The Health Zone conducts<br>additional KAP surveys and hosts three hours of meetings to assess the findings and make a plan to<br>maintain progress.|
|8|The CMO spends one day in the community to assess whether or not the community has completed its<br>action plan and achieved the seven norms. If they have, a certification ceremony is held. The CMO and the<br>village WASH committee develop a Community Action Plan for Maintenance so that the changes achieved<br>through the program can be sustained over time.|

The IHfRA data collection team used electronic tablets and transmitted data to a cloud-based server, allowing the research team to conduct quality control measures in real-time, checking for consistency and errors. Additionally, IHfRA randomly selected 15% of
education, cannot assess the impact of education on inequality without bias since they do not control for characteristics correlated with education.

The methodology is applied to five rounds of the nationally representative Household Expenditure Surveys (hereafter FES) of the Bangladesh Bureau of Statistics (hereafter BBS) spanning the years 1983 to 1996. Bangladesh is an important case study because of the extent of poverty there and the lack of research on inequality. For poverty, this paper follows up on work by Rahman and Haque
Policy Research Working Paper 7959

# **Abstract**

Although a large literature highlights the impact of personality traits on key labor market outcomes, evidence of their impact on agricultural production decisions remains limited. Data from 1,200 Ghanaian rice farmers suggest that noncognitive skills (polychronicity, work centrality, and optimism) significantly affect simple adoption decisions, returns from adoption, and technical efficiency in rice production, and that the size of the estimated impacts exceeds that of traditional human capital measures. Greater focus on personality traits relative to cognitive skills may help accelerate innovation diffusion in the short term, and help farmers to respond flexibly to new opportunities and risks in the longer term.

This paper is a product of the Agriculture and Rural Development 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 dali1@worldbank.org, bowenfh@mcc.gov, and kdeininger@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, 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
As is clear from the data presented in Appendix Table 1, the decline in the importance of resource-based products has not been confined merely to a few countries. Manufactures have become the dominant exports of a wide range of developing countries. Even countries in Sub-Saharan Africa such as Malawi, Mozambique, Tanzania, Zambia and Zimbabwe have increased the share of manufactures in their total exports to the point where manufactures make up almost a quarter of the exports of the group (Martin 2001b).

To ensure that the changes in Figure 1 reflect changes in output volumes, rather than simply changes in product prices, the commodity output shares were re-estimated in 1965 prices using deflators from the World Bank’s Development Prospects’ Group. Specifically, agricultural exports were deflated by the World Bank’s index of agricultural product prices for developing country exports; mineral exports were deflated by the price of oil; and manufactures export prices were deflated by the UN Manufactures’ Unit Value index. The resulting commodity shares are presented in Figure 2.

# **Figure 2. Change in Developing Country Export Shares at 1965 Prices**

<!-- Start of picture text --> Quantity Shares in Merchandise Exports of Developing Countries<br>90<br>80<br>70<br>60<br>50 Agric<br>Minerals<br>40 Mnfcs<br>30<br>20<br>10<br>0<br>1965196619671968196919701971197219731974197519761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998<br><!-- End of picture text --> 13