rafmacalaba/data-use-mentions-extended huggingface dataset:
out of poverty aimed at: (i) small farm sector intensification, (ii) improved employment opportunities in dynamic commercial agriculture, (iii) growth of the rural non-farm sector, (iv) migration of the young, and (v) provision of safety nets for those “trapped” in poverty. The recommended measures include improving human capital endowments, reforming the land, labor and financial markets, enhancing research and extension, improving the supply of public goods and services, and transfer programs.

The rural poverty profile and determinants of poverty analysis provide guidance on a social agenda and poverty alleviation strategy for rural Mexico. The strategic principles for reducing poverty involve seeking to strengthen the key assets of the poor, taking into account geographic differences in the poverty situation and priorities.

The government of Mexico could apply a three-pronged poverty-reduction approach: _First,_ targeted programs should focus on the extreme poor and prioritize among groups in rural areas, especially in the dispersed rural areas. Given the distribution of poverty, first 34
usefulness of WHO's FFC index and the alternative Aronson et al. approach, using data on out-of-pocket payments in Vietnam as an illustration. As indicated in the Introduction, this is not an uninteresting case study, since around 80% of health spending in Vietnam is paid out-of-pocket [2]. Furthermore, three key changes occurred during the 1990s [2]. First, user fees in the public sector rose. The increase was especially pronounced for hospital care, where fees appear to have risen by over 1000% in real terms between 1993 and 1998, but were also noticeable in commune health centers even though these were still supposed to be free in 1998. Second, there was a large rise in fees for private clinics and doctors. These apparently rose by nearly 600% over the period 1993-98. Third, expenditures on drugs actuallyfell over the period 1993-98, due to a 30% fall in the real price of medicines during the period in question. The latter seems to have been due in part to deregulation of the pharmaceutical sector and in part to increased donor assistance in drug supplies.

## _DATA, VARIABLE DEFINITIONSAND COMPUTATIONS_

The data are taken from the 1992-93 and 1997-98 Living Standards Measurement Surveys (LSMS) undertaken jointly by the government of Vietnam and the World Bank. For the purpose of this exercise, the household is taken as the unit of analysis. After deletion of cases with missing information, the sample contained 4800 households in 1993 and 5999 in 1998. Household prepayment income is measured by total household consumption, gross of out-ofpocket payments for health services. Household postpayment income is simply prepayment
**Table A.23:** Citizen Attitudes and Trust Towards the Village Government

||**Citizens survey**||
|---|---|---|
||Interactions withgovt<br>Perceivedgovtquality|Trust ingovt|
||(1)<br>(2)|(3)|
||**Panel A: Full Sample**||
|New village head|0.177<br>-0.018<br>(0.159)<br>(0.142)|0.027<br>(0.126)|
|Observations|8815<br>8790|8789|
|Control mean|-0.12<br>-0.034|-0.029|
|Robust p-value|0.223<br>0.949|0.724|
|<br>MSE-opt. bandwidth|17.1<br>17.7|17.5|
|Effective obs.|3752<br>3892|3812|
|<br>|**Panel B: First-Degree Connection**<br><br>||
|New village head|0.294<sup>_∗_</sup><br>-0.034|0.169|
||(0.179)<br>(0.161)|(0.160)|
|Observations|2638<br>2631|2630|
|Control mean|0.21<br>0.23|0.23|
||**Panel C: Second-Degree Connection**||
|New village head|0.398<sup>_∗_</sup><br>-0.105|-0.128|
||(0.218)<br>(0.179)|(0.169)|
|Observations|2201<
Policy Research Working Paper 7802

# **Abstract**

Trees on farms are often overlooked in agricultural and natural resource research and policy in Sub-Saharan Africa. This paper addresses this gap using data from the Living Standards Measurement Study–Integrated Surveys on Agriculture in five countries: Ethiopia, Malawi, Nigeria, Tanzania, and Uganda. Trees on farms are widespread. On average, one third of rural smallholders grow trees. They account for an average of 17 percent of total annual gross income for tree-growing households and 6 percent for all rural households. Gender, land and labor endowments, and especially forest proximity and national context are key determinants of on-farm tree adoption and management. These new, national-scale insights on the prevalence, economic contribution and determinants of trees on farms in Africa lay the basis for exploring the interaction of agriculture, on-farm tree cultivation, and forestry. This will improve our understanding of rural livelihood dynamics.

This paper is a product of the Program on Forests (PROFOR) and the “Agriculture in Africa—Telling Facts from Myths” project managed by the Jobs Group and the Office of the Chief Economist, Africa Region of the World Bank. 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 lchristiaensen@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
unexpected result may be that the firm production does not exhibit agglomeration economies. The estimated elasticity is positive but statistically insignificant. There are several possible reasons for this. First, the variation in firm location is very limited in the BEEP data set, leading to very little variation in the number of firms in the same province as a firm is located. Second, our agglomeration variable may be too broad. Each province is very large in Mozambique. The potential intangible impacts of neighboring firms may not be so clear at the provincial level. Unfortunately, however, more granular data are not available.

31. To examine a more dynamic aspect of improved transport infrastructure, the firms’ locational choice is estimated by the conditional logit model. The estimated results show that firms prefer to locate themselves where (i) other firms exist (i.e., agglomeration economies), (ii) local connectivity, measured by road density, is higher, (iii) transport costs to port are low, and (iv) the local market size is large ( **Table 7** ).

32. The policy implications are straightforward: First, the existing clusters and cities will continue to grow because of agglomeration economies. This calls for efforts toward improving efficiency and sustainability in urban transportation and mitigating congestion. Second, port connectivity is essential to keep competitiveness of firms. The share of Mozambican firms involved in international trade to purchase inputs or sell outputs is still
years of study; and (iv) entry into municipalities within the same urban area is likely to be highly correlated. Given these problems, we decided instead to use the concept of “urban agglomerations,” defined in a comprehensive urban study developed by IPEA, IBGE and Unicamp (2002), and used by Da Mata, Deichmann, Henderson, Lall and Wang (2005a and 2005b).

Even though we had RAIS micro data available from 1986 to 2003, we found the data from 1993 to 2001 to be more reliable and therefore use only these years for the analysis. For each year, we identified new establishments with at least 10 employees, and recorded the urban agglomeration in which they entered. If the entering establishment belonged to a multi-establishment firm, we also identified the agglomeration of the parent.

As can be noted from Table 1, most parent establishments are located in the Southeast (58.15% in 1993). Note, however, that the percentage of entering parent firms over the period 1993-2001 (i.e., the CF years) falls to 42.7% in the Southeast, but rises to 21.01% in the Northeast (from 12.49% of the parents in 1993). Thus, considering only the spatial distribution of parents (which are footloose by definition), there is some evidence that the CF’s were effective. There is also evidence in this table, however, that if branches are closely tied to parents, the CF’s have a lot to overcome in vertically integrated industries, given the initial distribution of parent firms in 1993.

How empirically relevant is the “pull” of the parent firm? The answer depends upon the sector being considered. Table 2 shows percentage of entrants over years 19932001 that have headquarters. We would expect proximity to the parent to be particularly important for Metallurgy, Communcations and Electronics, Transportation, Chemicals, and two situations from the data, the econometric results presented in the following sections show evidence of
absorptive capacity to learn from their neighbors as we have shown in previous heterogeneity analyses. Second, new importers will be less experienced with import procedures, often lacking the correct knowledge, manpower or internal business systems to handle the import process. They will have more barriers and costs to overcome before they start to import. Hence, in both cases, the knowledge and signals about import markets from their neighbors will not be as useful to them.

All the heterogeneity variables are included in the regression and presented in column 6 of Table 6. There is no change to the statistical significance and magnitude of the effects for each firm characteristic. This shows that each of the previously discussed effects operate independently of each other, with no two effects cancelling each other out when included simultaneously. The regression shows that learning from neighbors is most beneficial for the – base group of firms small, domestic, high productivity and experienced importers that are importing from EU markets. A one standard deviation increase in the density of neighbors is associated with a 1.3 percentage points increase in the probability of import entry. A corresponding increase in the signal from neighbors is associated with a 2.4 percentage points increase in the probability of import entry. The signal is boosted by the density of neighbors and the probability of import entry can increase by 2.3 percentage points if the interaction term increases by one standard deviation. This group of firms are precisely the type of firms that lack the resources to learn about import markets themselves (i.e., small and domestic), with the absorptive capacity to use the information (i.e., high productivity and experienced importers) and are learning about more complex markets in the EU.

# **5 Conclusion**

In this paper, we use data on Serbian firms to estimate the impact of information spillovers on the decision to enter new foreign sourcing markets. Our framework combines the model of importing in Antras, Fort, and Tintelnot (2017) (which suggests sharp distinctions between
try based on the sum of the contributions of that broad category at each grid-cell within the country.

**Urbanization data–** We use data measuring urban and rural catchment areas from ref.<sup>37</sup> This data provides a pixel level estimate of the travel time to the nearest urban area (URCA). Pixels within an urban areas are assigned values that indicate they are within an urban area of various sizes. These values range from 1, indicating the pixel is in a city of at least 5 million people, to 30, indicating the pixel is in a rural hinterland. Values in between 1 and 30 indicate the pixel is in an urban area of increasingly smaller sizes (e.g. 20,000-50,000 people) or is less than 1 hours travel, 1-2 hours travel, or more than 2 hours travel time from a city of various sizes (e.g. 1 hour from a city of 20,000-50,000 people). We define pixels that fall within urban areas of any size as urban, those that are less than 1 hours travel from a city of any size as peri-urban, and those that are more than an hour’s travel from a city as rural. These classifications are only for explanatory purposes, in all analysis we use the assigned score from 1-30. To classify RWI pixels we assign it the URCA value of the nearest URCA pixel. The plurality of grid-cells in our sample are in the peri-urban category (Figure SI-6).

**Economic concentration–** We use data from the Global Human Settlement Layer (GHSL) data product BUILT-V that measures the built-up volume of a pixel as the number of cubic meters of built volume in that pixel. We use a pixel size of 100m. We collected data on the NRES version of the BUILT-V product that only include non-residential built up area in the calculation of the built volume. For each city in our data we calculate the standard deviation in the BUILT-V-NRES value
or after 2006q2. Altogether the period spans from 2005q3 to 2007q1, and the number of times that each individual worker changed firm type (from formal to informal or vice versa) or size (from any one of the four firm sizes considered to any of the other three) is recorded.

**Table 7: Mobility of individual workers across firm size and type, 2005q3 – 2007q1**

_<u>(shares)</u>_

||**_Firm type _**|**_Firm size_**|
|---|---|---|
|**_Education_**|**_Change*_**|**_Change*_**|
|_Incomplete_<br>_Primary_|15.26|31.86|
|_Complete_<br>_Primary_|16.41|33.93|
|_Incomplete_<br>_Junior High_|20.09|36.84|
|_Complete_<br>_Junior High_|19.33|38.98|
|_Incomplete_<br>_Senior High_|21.02|44.83|
|_Complete_<br>_Senior High_|20.3|45.99|
|_University_|21.15|47.67|

_Source:_ Authors’ computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note:_ *Change refers to at least one movement over one year. _Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High education but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and
# _Enrollment_

To measure enrollment, we use school enrollment rosters collected each year. From these data, we also construct school-grade-level total enrollment, by gender and pooled across girls and boys.

# _School Management_

To understand the impact of the program on school management outcomes, we collected the number of School Management Committee (SMC) meetings held, and the number of School Improvement Plans (SIPs) prepared and implemented at each school. These were collected monthly from July 2012 to January 2013, for a total of seven observations per school.

# **3.3 Baseline Sample Students and Pre-Program Balance**

Students who were enrolled in grades three and four at the beginning of 2011 (prior to the intervention) comprise our _Baseline Sample_ (N=7,327). Table 1 presents average statistics and balance tests of pre-program student and school-level characteristics. On average, 55 percent of the students are girls, with the majority of students belonging to a scheduled caste (31 percent), scheduled tribe (15 percent) or other backward caste (40 percent). Less than half – 38 percent – of the students are in schools with electricity, few – 10 percent – have access to a computer, and just over 80 percent have access to clean drinking water.

Column 2 presents the regression coefficient testing the difference in means of pre-program variables between the treatment and control groups. We see no significant differences between treatment and control students across gender, caste, or school characteristics such as type of school (upper or lower secondary), having electricity, a computer, or drinking water, although students in treatment schools are younger on average. There are slightly fewer students enrolled in treatment 12
**26** **<u>│</u>**

Lastly, on the question of how these wage differences spill over to domestic firms, the evidence is mixed. Foreign investment may induce wage increases in domestic firms by shifting up the labor demand curve, thereby increasing equilibrium wages (a so-called ‘reference wage effect’) or ‘wage spillovers’, which can operate through labor mobility and bargaining (Tomohara and Takii, 2011). As an example, studies have found that a 10% increase in the share of former foreign MNE workers in domestic firms increases an incumbent worker’s wage in domestic firms by approximately 0.6% (Poole, 2013). Negative effects have also been identified in the literature; for example, to wages paid by domestically owned enterprises. The possible reason for negative wage spillovers is that foreign firms ‘steal’ the best workers, i.e., high wage workers from the local firms (so-called “labor-market crowding out”); moreover, the competition between local and foreign firms in the product market decreases the profitability of local firms which, in turn, may lead to wage reductions in these firms (so-called “productmarket crowding out”) (Pittiglio et al., 2014).

# Geographical Diversification

Marked regional differences in the distribution of foreign investment are a fact in most developing countries and have motivated a substantial stream of literature on the choice of FDI location within countries. Access to customers as well as suppliers of intermediate inputs (Amiti and Javorcki, 2005); institutions and risk appetite due to cultural differences among investors (Du, Tao and Lu, 2012); agglomeration effects, follow-the-leader strategies (Deichmann, Karidis and Sayek, 2003; Hilber and Voicu, 2010) as well as the availability of qualified labor and good infrastructure have all been identified as strong determinants of MNE spatial concentration or diversification in various contexts.

The Enterprise Surveys confirm that foreign multinational activity tends to be less widespread than domestic activity, either
, we discuss the data to be used in the main analysis. In Section 4, we introduce the empirical strategy and show that the introduction

> 5 Based on our preferred estimate of -0.565, and the corresponding mean cohort birth rate: (−0.565 × 100)/41.0. 6 The World Bank reports teenage birth rates (in births per 1000) of 82.2 for Brazil, 12.7, 28.1 and 46.2 for Germany the United Kingdom and the United States. The highest birth rate was for Niger, at 217.2 births per 1000 teens (World Bank 2020).

> 7 This limits in many instances the data that is available on the rollout of school expansions in these settings. Brazil provides a great setting for our study by being able to investigate the largescale school expansion making use of highquality administrative data for the entire country.

6
Sub‐Saharan African countries, Mensah (2016) identifies reductions in firms’ productivity by 0.6% to 1.1% for a percentage increase in outage intensity. A similar study based on a panel of 23 African countries estimates that a one percent increase in electricity outages would account for a loss in firms’ total factor productivity of 3.5% (Mensah, 2018). Ramachandran et al. (2018) use Enterprise Survey data as well but allow for heterogeneity among firms’ experiences of outages. By clustering firms according to their affectedness by outages and their growth behavior, they find a large group of firms seemingly unaffected by outages, a group that seems to be able to cope with outages through the usage of generators, and a group severely affected by power outages that cannot cope even when using generators.

Increasing the reliability of infrastructure services has been found to significantly improve outcomes both at the firm‐ and macro‐economic level. For instance, Ilmi (2011) estimates the marginal impact of electricity reliability on firm costs, using firm‐level data collected by the Business Environment and Enterprise Productivity Survey (BEEPS) in 26 countries in Europe and Central Asia. Eliminating all electricity outages would allow firms to reduce costs by about 1.4%, on average. In the Western Balkans, firms loose on average 5.8 percent of their annual sales due to electricity issues, according to the same survey (Kresic, Milatovic and Sanfey, 2017). The losses caused by outages are particularly pronounced in Africa. One study 9
development indicators and firm growth in what follows, relationships that vary with per capita income.

Our approach relies on a measure of firm growth from the World Bank Enterprise Surveys (WBES) database – the percentage increase in the number of workers over the two years prior to the survey. While labor growth is a topic of intense interest, there are also practical reasons for focusing on that measure in this analysis.<sup>4</sup> Other measures of firm growth (such as sales growth) are also available, but our measure of labor growth is available for a much larger set of firms and countries. Testing the hypothesis that financial structure affects firm growth differentially, depending on a country‘s level of economic development, requires as wide a sample of countries as possible, since the financial structure indicators are measured at the country level. Our most expansive regression models employ information from over 49,000 firms in 89 countries.<sup>5</sup> Relying on labor (rather than sales) growth has another side advantage: labor is likely to be measured with less error than sales for both accounting and tax reasons, and such measurement errors may differ systematically by the level of development.

To foreshadow our main result, we find that firms grow faster in countries with low levels of income per capita when the banking system is relatively well-developed. We find no such results for other measures of financial structure, including measures of stock market development. Moreover, we find no strong relationships between financial structure and firm labor growth for countries with high levels of per capita income, in line with previous findings in the literature (Levine, 2002; Demirguc-Kunt and Levine, 2001). Our results are robust to using the instrumental variables method to deal with the potential endogeneity of our financial structure variables.

We also apply the regression methodology pioneered by Rajan and Zingales (1998) (henceforth RZ) to test whether firms in industries that rely heavily on external financing have higher labor growth rates in countries