rafmacalaba/data-use-mentions-extended huggingface dataset:
64.2|75.8|
|Observations|24,526|7,222|8,844|8,460|

_Source:_ Authors’ calculation based on survey data from the LSMS-ISA database (World Bank, 2018). _Notes:_ Agricultural advice measures in-person advice and excludes advice received through media (TV, radio, flyers, etc.).<sup>_a_</sup> Exact wording differs between countries (see Appendix B).

of advice that exclude advice obtained through media. On average, 29% of farmers report having received such advice. In Malawi (which generates 32% of its GDP in the agricultural sector and is known for its relatively strong policy focus on agriculture, see OECD/FAO, 2016), the number is 46%. Conditional on receiving advice, farmers have an average of 4 to 5 contacts a year. While 51% of farmers receiving advice report more than two contacts a year, only a few farmers seem to be involved in regular extension programs (such as monthly village or field school meetings). The majority of farmers (85%) that receive advice rate the received advice as useful, and less than 10% of farmers report having received advice that was useless. On average, less than 8% of households that received advice paid anything for it.<sup>24</sup> In addition, Table 1 indicates that in all countries the majority of extension contacts are solicited by farmers. The share of solicited contacts ranges between 64% in Nigeria to over 80% in Malawi (possibly reflecting the fact that Malawi features a relatively decentralized and demand-driven extension landscape; see Davis, 2008). More details are provided in Table A2 in the appendix.

Table 2 provides additional insights into the topics on which farmers receive advice. Almost all farmers (91%) who have been in contact with extension services received advice on agricultural production and processing. To a large extent, this involves advice about modern
power supply quality, and higher corruption are some of the channels responsible for the differential impact of ethnic fractionalization on small and large firms. We pay close attention to endogeneity concerns. Apart from accounting for several confounding factors, we provide instrumental variables estimation results. Based on the work of Ahlerup and Olsson (2012), we instrument for ethnic fractionalization using the duration of human settlements since prehistoric times. The instrument has been used in other studies (see Awaworyi Churchill and Smith, 2017; Casey and Owen, 2014).

We contribute to the literature in a variety of ways. First, we use firm-level survey data from many developing countries that is cross-country comparable. The same questionnaire and sampling methodology are used to collect these data. We can account for differences across data points within a country, such as the quality of the business environment and firm characteristics, by using firm-level data. In the literature on ethnic fractionalization, the use of firm-level data on developing countries is unusual. Awaworyi Churchill and Valenzuela (2019) do use firm-level data for 62 countries. However, over 70 percent of their sampled firms are from developed countries, such as the U.S., U.K., Germany, and so on.

Our second contribution is that we analyze how the impact of ethnic fractionalization on firms’ labor productivity level and growth rate depends on the size of the firms. To the best of our knowledge, this is the first such attempt. The heterogeneous impact of ethnic fractionalization has been discussed in the literature, but not regarding firm size. Studies show that the negative impact of higher ethnic and cultural diversity on growth and productivity is mitigated at higher income levels (Alesina and Ferrara, 2005), in countries with good institutions (Easterly, 2001), and in countries with more developed democratic institutions (Collier, 2000; Bluhm and Thomsson, 2020). As we find below, a higher ethnic fractionalization hurts the labor productivity of the 4
10 fatalities, 100 affected people, a declaration of a state of emergency, or a call for international assistance. In reviewing event-specific data, we note a decreasing prevalence of missing values (as a fraction of events reported) over time. Missing observations for total affected/deaths hover around 25 percent, while missing observations for damages range from 50 to 75 percent. Consequently, we opt to concentrate solely on population affected and deaths in our intensity measure. Acknowledging that data availability has expanded since 2000, thanks to technological advancements allowing wider 8
2. Using the Framework This section first discusses issues related to applying the framework, and then illustrates its use at the project level and at the country level.

# 2.1 Applications

The ME framework provides an analytic structure that can be used to (1) monitor change and evaluate the impact of a specific project that has empowerment as one of its goals, (2) conduct in-depth research, (3) monitor national-level concerns, and (4) track relative changes in empowerment among different countries. The objective and context of each activity will determine which aspects of the framework to use and how to collect and analyze data.

To illustrate, the framework can be applied to assess whether empowerment objectives are being reached and what specific factors associate with related outcomes. The five case studies of the ME study, for example, use the framework to evaluate the empowerment impacts and identify causal relations in Bank-financed interventions. Each of the five case studies has adapted the framework to fit the nature, context, and objectives of the intervention. For example, the community-based education project in Honduras collects empowerment data for the state and society domains at the local and intermediary levels. The market domain and the state sub-domains of justice and politics are not priorities for investigation, and the intra-household domain is of limited interest.

An in-depth research exercise could seek to understand whether, how, and to what extent marginalized people, such as Indian women, can be empowered. To develop policy and practice recommendations, such research would need to focus on all sub-domains and levels identified by the analytic framework. The framework has yet to be tested in this kind of analysis.

A national-level monitoring exercise can use the ME framework to identify key indicators in each domain and undertake an analysis of empowerment at different levels. Section 2.3 of this paper discusses work currently in progress on developing a survey module that can be added to a Living Standards Measurement Survey (LSMS) or other national household questionnaires.

Finally, using the ME framework concepts of domains
_The American economic review_ , 1997, _87_ (2),

- 184–188.

- Schiavina, M., A. Moreno-Monroy, L. Maffenini, and P. Veneri, “GHSL-OECD Functional Urban Areas 2019,” _EUR 30001 EN, Publications Office of the European Union_ , 2019.

- U.N., “World Urbanization Prospects: The 2011 Revision, Department of Economic and Social Affairs, Population Division,” 2011.

- United Nations, “Statistical Yearbooks,” 1960-1980.

- , “World Urbanization Prospects 2018, Department of Economic and Social Affairs,” 2018.

- , “National Accounts Official Country Data database,” 2020.

- , _Proportion of the Urban Population Living in Slums_ , United Nations, SDG Global Database., 2020.

- , “System of National Accounts (SNA) - Analysis of Main Aggregates (AMA) Database,” 2020.

- USGS, “Mineral Industry Surveys 1960-2020,” 2020.

- Venables, Anthony J., “Breaking into tradables: Urban form and urban function in a developing city,” _Journal of Urban Economics_ , 2017, _98_ (C), 88–97.
28

# **Appendix 1: Vulnerability calculations**

The results by country using the first set of data sources are shown in tables A1.1–A1.3. For these tables, import and export statistics from the IEA are always used if available. Tables A1.4 (1999–2008) and A1.8 (1999–2009) give the results of decomposition analysis by country. Tables A1.5–A1.7 (1999–2008) and tables A1.9–A1.11 (1999–2009) give the distribution of the oil share effect, energy intensity effect, and offsetting coefficients by income and region. Figure A1.1 reports the results in figure 8 for the second period. For decomposition analysis, where net oil imports calculated from the import and export statistics from the IEA differ markedly from those calculated taking the difference between production and consumption statistics from the IEA and EIA databases, the country is dropped. For the remaining countries for which both the IEA and EIA data are available, the data sets of production and consumption statistics that more closely approximate net imports derived from the IEA’s import and export statistics are used.

Table A1.1: Vulnerability and changes in vulnerability (percentage of GDP)

|**Country**|**V**<br>**1999**|**V**<br>**2008**|**V**<br>**2009**|∆**V**<br>**99–08**|∆**V**<br>**99–09**|**Data**<br>**source**|
|---|---|---|---|---|---|---|
|Albania|2.7|4.9|1.3|2.
Table 4: The strong industrial productivity growth

||Value-added/worker<br>growth 1993-97 (%)|
|---|---|
|Total|<br>39.8|
|Agriculture|20.8|
|Industry|54.0|
|Industry less construction|<br>63.0|
|<br>Services|<br>19.2|

> _Source:_ Calculations based on GSO (1998) for output in current prices and on VLSS I and 2 for employment data

# _b) The Industrial Sector_

Table 5 shows that in industry both the State and the domestic non-state sectors have grown rapidly (by 10.2% and 9.2% per annum respectively) over the period 1995-98. In terms of size, however, the State sector still dwarfs the private sector. In 1998, its contribution to industrial value-added was more than twice as large as that of the domestic private sector. Accordingly, a large share of the recent industrial expansion is due to State enterprises. Indeed, table 5 shows that between 1995 and 1998 State companies have accounted for 37.2% of Vietnam's total industrial growth, compared to 16.2% for domestic private companies (foreign investment is discussed in the next section).

The State sector's contribution to employment, however, is much smaller. In 1997/98, it provided jobs for less than 25% of industrial workers. This contrasts with the domestic nonstate sector which, despite its smaller size, employed more than 64% of industrial workers. As can be seen in Table 5, a large proportion of these workers are in household enterprises. But registered "private and mixed companies" are also much more labor-intensive than the State sector. Although accounting only for 7.9% of industrial GDP in 1998, these companies employed more workers than State-owned enterprises.

This picture shows that Vietnam's slow employment growth reflects the large State sector's strong growth
quartiles. Consistent with other patterns of asymmetry already reported, farmers' revisions to their self-reported plot size are more pronounced among those farmers who underestimated their plots.

Incomplete updating is apparent in the scatter plot and nonparametric kernel regression displayed in Figure 3, which plots the percentage measurement error in the post-treatment (postharvest) survey round on the vertical axis, against the pre-treatment measurement error. If the information treatment fully eliminated NCME, the scatter plot would be a horizontal line at the zero mark on the vertical axis. Only 13 percent of treated households report plot size accurately after receiving the GPS measure of the plot. Notably, those observations are heavily concentrated among respondents whose initial measurement error was modest, within roughly the [-50,50] interval. Meanwhile, only another 28 percent of households exhibit some correction of mistaken beliefs, as shown by the clustering of points around the zero value on the vertical axis and within the range bounded by zero and the post-planting measurement error. A plurality of farmers (37 percent) did not change their incorrect beliefs, which remained identically wrong over time, as depicted by the observations along the 45-degree line. It is striking that nearly three times as many farmers did not change their mistaken beliefs at all than fully updated to the correct plot size based on measurement they witnessed on an observable variable. The nonparametric regression indicates a strong, positive correlation between pre- and post-treatment measurement errors, but of shallower slope than the 45-degree line, signaling incomplete adjustment to corrective information, indicating partial learning failures.

Figure 3 reveals two other linear relationships in the pre- and post-treatment data. These reflect the propensity of respondents to report round values for plot area. To see this, define the self-reported plot size from survey round t (PP or PH), SRt. true value, T (which does not vary over time, thus no subscript), and rounded values, Ri (with i indexing different positive integer multiples of 0.5, e.g., 0.
stunting of children of ladino mothers is less than _50%_ while it is almost 80% among children of indigenous mothers who do not speak Spanish. Differentials are also very important in terms of both mothers' and husbands' education. Note the very low prevalence of stunting (8%) among children whose fathers have postsecondary education. The mean z-score in height-for-age increases monotonically across the quartiles of the sample distribution of expenditure peir capita.

# **4.4 Geographical Variation of Stunting in Rural Guatemala**

Table 2 shows the mean z-score for height-for-age and the prevalence of stunting by age of the child at the time of interview for both sexes combined, for the complete sample and each of the surveyed

> for-height and height-for-age; and (2) studying weight-for-age does not add any additional information to that provided by studying the other two indicators, weight-for-height and height-for-age.

> 1995 DHS data also indicate that the prevalence of stunting in the rural conmnunities of the four departments surveyed by the EGSF is 63.1%.

5
business, as well as support the implementation of the project. Deploying a unit costs approximately 40,000 Reais (US$19,000) for the first year, which includes the cost of training, computer equipment, furniture, and running costs.

The opening of the Minas Fácil Expresso offices was covered by local newspapers in at least some of the municipalities (Jornal S’Passo, 2011; Gxp, 2011), although it is unclear how widespread advertisement of the new system to firms was.

# **3. Administrative Data**

We use three forms of administrative data, all provided by the _Junta Comercial de Minas Gerais (JUCEMG)_ , the Minas Gerais Chamber of Commerce. The first is a list of the municipalities in which the express office was planned to be implemented, and of the municipalities in which it was actually 5
Policy Research Working Paper 11067

# **Abstract**

Viet Nam is widely regarded as a success story for its impressive economic growth and poverty reduction in the last few decades. Yet, recent evidence indicates that the country’s economic growth has not been uniform. Compiling and analyzing new, extensive province-level data from the Vietnam Household Living Standards Surveys spanning 2002 to 2020 and other data sources, this paper finds within-province inequality to be much larger than between-province inequality. Furthermore, this inequality gap has been rising over time. Despite the country’s fast poverty reduction, the poor were increasingly segregated in certain provinces, particularly those with a larger ethnic minority population. The analysis finds a beneficial impact of economic growth on poverty reduction, but this can depend on inequality levels. It also finds that greater inequality has had negative effects on economic growth but varying negative effects on different poverty indicators. The paper provides supportive evidence of the beneficial impact of economic transitions from agriculture to non-agriculture. The results suggest that policy makers in Viet Nam should focus on reducing spatial disparities and income inequality to attain sustainable economic development.

This paper is a product of the Development Data Group, Development Economics and the Poverty and Equity Global Department. 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 authors may be contacted at hdang@worldbank.org and vietcuong@vnu.edu.vn.

_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
and sanitation, crowding,<sup>24</sup> subsistence capacity,<sup>25</sup> and precarious materials.<sup>26</sup> Therefore our estimates are not subject to bias from any unobservable determinant of poverty or income that is time varying but common across all households in Central America.

Our study period in this section involves only 13 years of analysis (2000‐2013). Given this shorter period –compared to the nearly 30 years for the per capita GDP analysis– we consider only three events: Hurricane Felix (2008) for Honduras, Hurricane Matthew (2011) for Guatemala and Honduras, and Hurricane Harvey (2012) for Honduras. The three hurricanes are part of SS scale 1, therefore we cannot evaluate the impact of major hurricanes. The other countries (Costa Rica, Panama, and El Salvador) did not have events within our study period and for the years where there is an available and comparable survey.<sup>27</sup> As discussed in the previous section, wind speed data was aggregated by year. When more than one event occurred in a calendar year in the same country, only the strongest event was taken into account. In this case, we also take into account the time when the household survey was conducted. This is key since household surveys usually record the income of the preceding month. We show two set of results: (i) linearly weighted by the difference between the event’s date and the survey’s date (divided by 12), and (ii) unweighted results (i.e., regardless of the number of months that have passed since the strongest event). The rationale for this is that the effect of a hurricane on household income (or poverty) might be underestimated if the interval is bigger. Noy (2009) discusses the importance of a weighting function to evaluate impacts on GDP since events observed earlier in the year may have higher impact on GDP
Obstacles to doing business|||0.095**|0.039|
|(index)|||(2.09)|(0.58)|
|Level of Competition||||0.020|
|<br> (index)||||(0.19)|
|**Pseudo R-Squared**|0.07||0.07|0.11|
|***,**, * Significant at 1,5, and 10 percent level. T-statistics in parenthese|s.||||
|Source: Authors‘ calculations based upon World Bank Enterprise Surveys<br>Note: See Table 3 for additional notes|||||

42
>0.00|<br>0.55|

The dynamic panel regression is performed for each of the crops. First, the standard lagged value instruments are found to be largely valid, except for the model for cereal production ( **Table 10** and **Table 11** ). Second, cereal production is least sensitive to transport connectivity. The statistical significance of the estimated coefficients is generally low. It seems that the way of producing cereal in Kazakhstan would not change dramatically because of transport connectivity.

It is found that local market connectivity has almost always have a positive impact on crop production, meaning that these crops are produced more when farms are away from local populated areas. This can be interpreted as the fact that agricultural production normally takes place in nonurban areas.

The regional connectivity measured by transport time to the nearest border point is important to produce more potatoes, oilseeds and vegetables. These are among the important emerging export crops from the country. Particularly, the estimated coefficients are statistically significant for oilseeds and vegetables. The elasticity is about -0.3, meaning that a 10 percent reduction in transport time to the border point (including border crossing time) could increase oilseed and vegetable production by 3 percent. This impact is significant.

Last, the impact of domestic market accessibility measured by _MAI_ on crop production is heterogeneous across crops, possibly depending on the domestic consumption behavior. For potatoes and vegetables, the domestic market access is important. The coefficients are estimated to be positive and significant. It implies that these crops are important not only as export commodities but also for domestic consumption. Particularly, vegetables are often perishable. The
# **_2.2 General equilibrium biofuel analyses_**

General equilibrium analyses provide a more encompassing assessment of the biofuel emergence on the whole economy and food prices because these multiple linkages are explicit in the model. The disadvantages of many of these analyses are the aggregation of crops in a few sectors, the lack of realistic biofuel policy parameterization, and trade elasticities, which tend to dampen trade responses to changing relative agricultural prices. Taheripour et al. (2007) describe extensions of the GTAP 6 database to account for biofuel production and their linkages, upstream and downstream, calibrated to 2001 data. The impact from biofuel expansion on land is through reallocation of land into corn, sugar cane, and oilseeds sectors. Biodiesel is treated as crushing oilseeds and producing meal as a byproduct (as DDGS are in the case of ethanol), <!-- Start of picture text --> Li Lj Ln<br>σ1<br>L _<br><!-- End of picture text --> Figure 1. Land allocation in the standard GTAP model a drastic assumption. Land supply is not explicitly described but presumably it follows the standard one level CET assumption incorporated in the GTAP model structure as shown in Figure 1. Land adjusts sluggishly according to relative rents obtained in respective activities. All countries have a common cost structure. The trade matrix is rather limited. It analyzes several policy shocks such as the increase in crude oil prices, the phasing out of MTBE, and biofuel subsidies. Keeney and Hertel (2009) propose a model incorporating land, yield, and trade responses to the biofuel expansion. Land demand is derived from a constant-return-to-scale <!-- Start of picture text --> L1 Lj Lk Ln<br>σ2 = -0.5<br>L L L<br>forest & other pasture crops<br>σ1 = -0.11<br>L _<br><!-- End of picture text -->