rafmacalaba/data-use-mentions-extended huggingface dataset:
one industry to another. Among those who switched, income risk was higher among those who moved to non-manufacturing sectors than those who switched within manufacturing sectors. In light of their ...ndings, Krishna and Senses (2009) conclude that the impact on labor income risk needs to be taken into account when calculating the costs of openness to international trade.

Robustness tests by Krishna and Senses (2009) reveal that controlling for o¤shoring causes the coe¢ cient on import penetration to increase. In addition, the o¤shoring variable is negative and signi...cant, suggesting that an increase in o¤shoring in a particular industry is associated with a decrease in income risk in that industry.

Artuç, Chaudhuri, and McLaren (2010) and Artuç and McLaren (2010) estimate and simulate the dynamic model of labor adjustment developed in Cameron, Chaudhuri, and McLaren (2007) and Chaudhuri and McLaren (2007) to assess the distributional e¤ects of trade shocks. The former study uses the US CPS data and the latter uses 2004-2006 data from the Household Employment Survey of the Turkish Statistical Institute. The studies estimate both the average cost of switching industries and the variance of idiosyncratic switching costs, and use the estimates to simulate a trade shock to the manufacturing sector. In both cases, the authors ...nd that, due to the high the costs of switching from one industry to another, the economy takes a decade to reach the new steady state after liberalization. During this time, workers move from the manufacturing sector to other sectors, wages in the manufacturing sector ...rst drop then rise as labor supply to that sector falls, and wages in other sectors at ...rst rise and then fall as labor supply to those sectors rises. However, throughout these ‡uctuations, the real wage of the manufacturing sector remains below that of the tari¤ steady state while the non-manufacturing sector real wage remains above it.

Importantly, the distributional e¤ects of
only implicit measure available on household wealth. Instead, we construct a wealth index based on recorded household assets. Components of the index include durables, such as radio and bicycle, source of drinking water, characteristics of floor materials, and type of toilet facility. While the 2000 and 2005 surveys record a larger number of assets than the 1992

> 5 This description of the sample design refers to the 2005 RDHS, with slightly different designs used in the two previous RDHS waves. The 1992 RDHS builds on the 1991 Census as a sampling frame. At the time of the 1992 survey collection, a civil war was ongoing, with most actions of warfare taking place along the Ugandan-Rwandan border. Due to security concerns, 44 rural sectors in the provinces of Byumba and Ruhengeri in northern Rwanda were excluded from the sample frame at the outset. The 2000 RDHS builds on the listing of enumeration areas outlined for another household survey, the _Enquête Intégrale sur les Conditions de Vie des Ménages_ (EICV) collected in 2000, as no other population records were available at the time. The sampling frame of the EICV itself is based on the pre-genocide Census of 1991. Three strata were used – Kigali, other urban areas, rural areas – and rural areas were further stratified into provinces, resulting in 13 strata (Ministère des Finances et la Planification Economique 2003). The sample design of the 2000 RDHS is only representative of rural areas of each province and Kigali City.
The previous finding of more on-farm tree planting in forest rich environments is confirmed in the multivariate setting. The generally positive association between nearby tree cover and presence of trees on farms merits further analysis, especially using panel data, to better understand how farmers respond to changes in the availability of forest resources—by increasing the density of trees on their farms as forest resources decrease (Arnold and Dewees, 1995) or by continuing agroforestry systems which may already have an integral relationship with nearby forest (e.g. Brottem, 2011; Degrande et al., 2006). Onfarm trees also tend to be more prevalent in environments where the temperature is higher. Overall, geoclimatic variables were found to be very important. They accounted for around for 18.5% and 31% of the total variation in the adoption of fruit trees and tree cash crops respectively (Tables 6a and b). This result buttresses previous findings about biophysical conditions as key drivers of farmer decisions to adopt and maintain trees on-farm (Pattanayak et al., 2003; Place and Garrity, 2015). Surprisingly, no systematic correlation was found across countries with population density (or soil fertility).

Finally, country fixed effects also explained an important share of the variation (more than 40% for adoption). This underscores the importance of national level institutions and policies in shaping whether farmers decide to adopt trees on their farms or not. In many countries, forest regulations create disincentives for on-farm tree management (Place and Garrity, 2015; Ribot, 1999). But changes to such regulations can spur changes in practice as shown in the recent regreening in Niger, which has been catalyzed by political openings and reforms to colonial-era forest and rural policies that allowed local innovation in land management (Sendzimir et al., 2011).

# 4 Concluding Remarks

In this paper we used nationally representative household-level data to explore and compare the prevalence and economic contribution of trees on farms across five African countries. Three main findings emerge. First, trees on farms
Policy Research Working Paper 9883

# **Abstract**

Across a wide variety of regions and contexts, surveys have found high rates of disagreement within couples on matters of household decision making. Using a unique data set from a spousal survey of 421 agricultural households in the Philippines, this paper finds that 50.2 percent of couples disagree about who makes any given decision in the household. The paper systematically explores the empirical relevance of theoretical explanations from the existing literature for this spousal disagreement. Spouses are no more likely to agree on specific decisions compared with general decision making, are more likely to agree on the decision-making process, and are less likely to agree on decision making for activities in which both take part. Moreover, women are more likely to report that their husbands were involved in decision making when speaking with a female enumerator. The findings suggest that intrahousehold disagreement is not driven by differing interpretations of which decisions count as “major,” or by asymmetric information. Although the paper finds evidence of enumerator effects, their magnitude is small and cannot explain the observed rates of spousal disagreement over decision making. Rather, spousal disagreement appears to stem primarily from systematic gender differences in interpreting what it means to be a decision maker. The paper discusses the implications of the findings for the measurement of intrahousehold decision making in household surveys.

This paper is a product of the Gender Innovation Labs of East Asia and the Pacific Region and Africa Region. 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 hjohnson1@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
model, where the impact of the intrinsic climate and technology uncertainty is brought into the model optimization stage. Specifically, we generate 1,000 sample paths of optimal global land use under di↵erent realizations of the stochastic crop productivity index. The results are presented as the di↵erence between the stochastic path and deterministic reference solution. **5.1 Optimal Path of Global Land Use under Crop Yield Uncertainty** Figure 3 depicts the optimal allocation of global land use over the next century. The left-hand side of Figure 3 shows the deterministic paths of di↵erent types of land considered

> 25The model converges to its stationary state around 2150. The di↵erences in land use allocations between 2100 and 2150 are small and therefore not reported.

15
availability of evidence about the epidemic and the use of that information when formulating the local response.<sup>31</sup> The first was conducted by the MPS with the 36 DTSs, as part of an effort to evaluate the PEN 2008-2011.<sup>32</sup> None of the 16 DTSs that responded to the survey reported having conducted elaborated specific studies about the prevalence and epidemiological profile of HIV/AIDS on their local population during the years the PEN was in use. The second survey was conducted in August 2011 by the MPS with those same DTSs in the context of the Study.<sup>33</sup> It revealed that all DTSs but one had generated studies about the epidemic in their territories. However, a number of DTSs reported that they had not identified the most vulnerable groups, and other DTSs included a long list of various social groups—or even the entire population—as a vulnerable group.

Similarly, the degree of knowledge that EPSs have of the epidemic among their ensured populations is variable—as reflected by the survey conducted by the MPS among the EPSs.<sup>34</sup> Some EPSs have produced their own indicators to monitor persons registered in the care programs, to analyze the quality of data reported by IPS and SIVIGILA, to assess the information flow with the Observatorio Nacional, and other variables.

In any case, these initiatives have sought to improve the information about the insured people for managerial and accounting purposes rather than for elaborating the HIV/AIDS epidemiological map to inform future strategic and clinical decisions. Furthermore, only four of the 16 responding EPSs reported having set up initiatives for data collection and technical reports elaboration (PEN activity A-66, linked to goal 12).

# **_(b) Programmatic Framework: National Level_**

At the national level, Colombia has been developing the necessary normative to sustain the programmatic framework of the HIV/AIDS response. The country has passed laws, decrees and agreements that
fragility of the mechanisms governing public sector works and the difficulty in controlling for multiple conflict of interests.

Concessionaires led by the Autostrade group try to block any government intervention aimed at the introduction of a more harmonious tariff system. In 2006 Autostrade provocatively threatened to sue the government and ask for compensation in case a new tariff law would be enacted.<sup>23</sup> Nevertheless, statistical data on this subject are less univocal. As Figure 4 shows, the evolution of the tariff before and after 1999 (the dotted line) does not seem to show a significantly different trend, although from 2003 onward tariffs seem to be growing more than inflation.

However one possible explanation may be given for the “tariff rage” of both concessionaires and users. Before 1999, tariffs were linked to the highest one imposed by Autostrade, so the whole network would have a nationally-fixed tariff system, either directly or through shadow-tolls. After 1999, a new system was introduced, which linked the tariff applied on each highway both to general aggregates (inflation) and to concession-specific variables (quality and productivity). For this reason, the geography of the highway tariff has evolved unevenly since 1999. This may also explain consumer aversion and the haste against the new concession system.

Figure 4: Tariff and Inflation Rate Evolution<sup>24</sup> <!-- Start of picture text --> 130<br>125<br>120<br>115<br>110<br>105<br>100<br>95<br>90<br>85<br>80<br>1991 1993 1995 1997 1999 2001 2003 2005<br>Inflation Rate Tariff<br><!-- End of picture text --> _Source: Italian Statistical Institute, ISTAT. Inflation refers to variation in the consumer price index (CPI); the base year for the two series is 1995=100._ Other transport economists, such as Marco Ponti (Ragazzi
Howes et al. 2022; World Bank 2023).

Combined, the official contribution of the resource sector to overall GDP is high and has been growing over time. The figure was just above 10 percent in 1980, and steadily increased over time to nearly 30 percent of GDP by 2018 (e.g., Howes et al. 2022; World Bank 2023; etc.). The share of GDP contributed by agriculture is also high at approximately one-quarter of GDP, but the sector has grown at the same rate as overall GDP and agriculture’s contribution to GDP has remained relatively stable between 1980 and 2018 (e.g., Howes et al. 2022). As described in the Introduction, the vast majority of employment in the country is in subsistence agriculture, with the most recent estimates illustrating that roughly one-half of households have at least one member primarily employed in agriculture (e.g., World Bank 2024). Thus, any gains that might accrue to the population from growth in the resource sector are likely either through spillovers from the sector or through government transfers redistributing some of the economic gains from the activity (e.g., World Bank 2023).

Economic growth and government revenues are strongly tied to global commodity prices given the country’s resource intensity (e.g., World Bank 2023). The country has had several resource booms associated with spikes in commodity prices since independence that have led to high levels of growth and high levels of revenue collection. The most recent resource boom began in the early 2000s and continued to roughly 2014 (e.g., Fox and Shroder 2018; Howes et al. 2022; etc.).

As discussed in the Introduction, we estimate the change in well-being between two nationally representative surveys conducted during the last resource boom. Specifically, we estimate the change in monetary poverty and access to several essential services using the 2009 Household Income and Expenditure Survey (HIES) and the 2016-2018 DHS. Figure 1 reports both
15 Figure 3b: NTMs coverage ratios by selected industry, theory-based classification <!-- Start of picture text --> Coverage ratio by industry and type of NTM<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Total imports Agriculture Primary Intermediate Manufacturing<br>Consumer Customs Process Product<br><!-- End of picture text --> Source: Authors' calculation based on data from COMTRADE and UNCTAD NTMs database.

While TRAINS has actual information on a more limited set of policy measures, business surveys may provide a more complete picture (although subject to biases discussed earlier). Figure 4 shows the trade barriers that have been reported to the EU Commission between 1996 and 2015 by type of measure. Approximately, 50 percent of the trade obstacles faced by European exporters have to do with domestic regulation in foreign markets, such as TBT/SPS measures. Once we further disaggregate the analysis, we find that the most affected sector is agriculture, where concerns are mainly related to SPS measures. Among manufacturing, the most affected industry is pharmaceuticals.
interact with the sectoral output share of such firms. Fully-owned FDI, in contrast, is more likely to be an export-platform with fewer local linkages and technology leakages.

Finally, income per capita ( _income_ ) positively mediates the FDI spillover effect on domestic labor productivity from partially-owned FDI, while there is no effect for fully-owned FDI (column 11). This reflects the importance of national competitiveness and development, given the possibility of more local linkages for partially-owned foreign firms.

Overall, the results clearly indicate that spillovers from partially-owned FDI are positively mediated by various national and institutional factors. In contrast, national and institutional characteristics exert no or even a negative effect on spillovers from fully-owned FDI.

# **5 Conclusions**

In this paper, we use a cross-section of more than 25,000 manufacturing firms in 78 low- and middle-income countries from the World Bank’s Enterprise Surveys Indicator Database to identify the mediating factors that determine intra-industry productivity spillovers. Besides OLS, this paper also applies an instrumental variables approach to addresses the potential endogeneity between FDI spillovers and domestic firm productivity. We differentiate between major types of mediating factors, namely the FDI spillover potential, domestic firms’ absorptive capacity, and differences in host country factors and institutions. This is the first study to our knowledge that interacts FDI with all three types of mediating factors against productivity outcomes.

We find evidence for negative FDI spillovers overall, which we relate to increased competition in the short term for market share (both domestic and export) and skilled labor. Regarding the FDI spillover potential, we find a positive and significant impact in the overall sample for market orientation in the OLS regressions – the higher the share of FDI output sold domestically, the greater productivity spillovers to domestic firms. Splitting the sample into three productivity groups, we find that larger output share sold to the domestic market and larger share of local inputs contributes to higher spillovers for low- and medium-productivity firms.

Regarding a domestic firm’s absorptive capacity, almost all variables show the expected coefficient signs
**Table 8.  Estimates of model (8), using 50% quantile regression**

||**Dep. var: C**<br>**A**|**PIA clusters**<br>**& B**|**Dep. var.:**<br>**Freedo**|**Economic**<br>**m Index**|
|---|---|---|---|---|
|**Independent variables**|**Coeff.**|**Robust**<br>**s.e.**|**Coeff.**|**Robust**<br>**s.e.**|
|∑���<br>�<br>A�<br>�.�|0.0500|0.0137***|0.443|0.183**|
|Cumulated length of engagement|‐0.00180|0.00082**|‐0.0128|0.0094|
|GDPper capita at PPP|0.0000179|5e‐06***|0.000062|0.000073|
|Aid/GDP|‐0.00357|0.149|‐13.3|4.74***|
|Political Rights|‐0.0512|0.0330|‐0.786|0.268***|
|Country&year fixed effects|Yes||Yes||
|N|2280||2518||
|Number of countries|145||154||

Note: * = significance level of 10% or less; ** = 5% or less; *** = 1% or less. Standard errors are adjusted for 145 clusters.

The coefficients retrieved by quantile
group and the control group. None of the differences are statistically significant, and all are within about a percentage point of the control group response rate. The control group response rate is a tiny bit lower in the long run surveys and a tiny bit higher in the short run ones. But none of these differences control for covariates or even strata fixed effects, as in the next table.

**Correlates of attrition and compliance** We analyze the correlates of attrition in Columns 1 and 2 of Table A.5, which reports an OLS regression of an indicator for attrition on selected baseline covariates.<sup>3</sup> There are not significant differences in attrition by treatment group, substantively or statistically. Those who attrit are slightly wealthier and have slightly poorer mental health. In all, the treatment indicators and covariance are jointly significant at _p_ = 0.047 so attrition is not ignorable. This is one reason we control for covariates in all treatment effects regressions.

# **A.4 Treatment compliance**

Figure A.1 displays the distribution of class attendance for those assigned to therapy. NEPI did not collect attendance data during the first week (three sessions), so for simplicity we assume that all participants who attended at least one session after week one also attended the first three sessions.

We use two definitions of compliance. Our first measure is defined as “attending at least 8 days of therapy”, or about three of the eight weeks. Our second measure is defined as attending at least 80% of sessions (16 classes plus the 3 in the first week).

We analyze the correlates of compliance in Columns 3 through 8 of Table A.5. Being assigned to cash in addition to therapy did not affect the likeliness of attending therapy, which is to be expected since the cash grants were not known to participants until after therapy. The main correlates of

> 3We do so to reduce collinearity and thus ease interpretation. Results with full covariates draw similar
**|**Students Average s core**|**Total**|Percentage|
|Native||||||
|Experimenl|564|14.6|356<br>29.1|13.9|95.3|
|Control|205|23.2|125<br>26.8|4.1|17.7|
|Total - t/test|769|16.9|481<br>28.5|11.4|67.3|
|**Rural**||||||
|Experimernal|645|20.7|421<br>32.9|11.6|56.0|
|Control|208|20.1|128<br>29.7|8.2|40.6|
|Total -ttest|853|20.5|549<br>32.1|10.8|52.5|
|**Urban**||||||
|Experimental|337|26.9|238<br>39.7|12.0|44.5|
|Controi|361|26.9|221<br>44.3|15.9|59.3|
|Total - tttest|698|26.9|459<br>41.9|13.9|51.6|

Source: Own calculations based on PARE's database. Note: Difference respect to control group.

7
combined share of 59 percent. The largest exposures of CGSs in these deciles are wholesale and retail trade, construction of buildings, specialized construction activities, and accommodation.

**By combining exposures with GHG emission data we find that 72 percent of the transition risk for CGSs is driven by five sectors.** Figure 3 shows the 15 sectors at a 2-digit sectoral level that have the highest value for the indicator. The top five sectors are crop and animal production, land transport, wholesale trade, retail trade, and electricity, gas, steam and air-conditioning supply. For crop and animal production the transition risk is highly concentrated, with two CGSs having more than 40 percent of their total guarantee portfolios allocated to this sector. For wholesale and retail trade the GHG emission intensity is more limited than for the other three top-5 industries, however their exposure share is much higher (about three times).

Table 2 – Emissions, exposure, and value-added shares per decile (NACE 2 sample, 2020)

|**GHG emissions**<br>**decile**|**GHG emissions**<br>**share**|**CO2 emissions**<br>**share**|**CGS exposure**<br>**share**|**Value added**<br>**share**|
|---|---|---|---|---|
|1|53%|67%|2%|4%|
|2|27%|14%|6%|5%|
|3|6%|4%|4%|7%|
|4|3%|4%|16%|15%|
|5|3%|4%|21%|9%|
|6|3%|3%|22%|13%|
|7|2%|2