rafmacalaba/data-use-mentions-extended huggingface dataset:
substantially higher in cantons with larger reliance on direct democracy, including fiscal referendums.<sup>150</sup> Somewhat more broadly, research has argued that tax collection and compliance often increase following the election of new governments, because of renewed popular trust following successful elections.<sup>151</sup> The importance of accountability in shaping trust and tax compliance among business taxpayers has been comparatively under-researched, though some key messages have begun to emerge. In general, relative to individuals, businesses appear more concerned about the creation of specific, and comparatively narrow, forums for participation in shaping government action, as these forums may most directly address their core interests and concerns. In Ethiopia, concerns about the arbitrary application of presumptive taxes on small businesses led state governments to invite business associations to joint assessment committees.<sup>152</sup> In Ghana, initial efforts to tax the informal sector were pursued through the introduction of “associational taxation” by which tax collection

> 147 Sjoberg, F., Mellon, J., Peixoto, Hemker, J., & Tsai, L., (2019). Voice and Punishment: A Global Survey Experiment on Tax Morale. . _World Bank Policy Research Working Paper 88558855._ Washington: World Bank.

> 148 Timmons, J., & Garfias, F. (2015). Revealed corruption, taxation and fiscal accountability: Evidence from Brazil. _World Development_ , 70:13-27.

> 149 Touchton, M., Wampler, B., & Peixoto, T. (2019). Of governance and revenue: Participatory institutions and tax compliance in Brazil. _World Bank Group, Policy Research Working Paper 8797._ Washington: World Bank.

> 150 Torgler, B. (2005). Tax morale and direct democracy. _European Journal of Political Economy_ , 21:525-531.

> 151 Prichard, W. (2015). _Taxation, responsiveness and accountability in developing countries: The dynamics of tax bargaining_ . Cambridge: Cambridge University
temperature (above 32 ̊C) during the second half of the vegetative phase and the reproductive phase reduce yields. The last phase (maturation phase) lasts between 20 to 35 days.

Planting later in the season ensures that the seed germinates quicker, however waiting too long does not allow the crop to complete the maturation stage before the growing season ends. Furthermore, specific to Mexico, in July and August there is a period of mid-summer drought called _canícula_ (Figure 2) affecting farmer‘s planting decisions. In general, farmers want the corn to flower (for the ear formation stage to be complete) before the onset of the _canícula_ in order to better the odds of the crop survival in case it is a drier than normal _canícula_ (Eakin, 2000). This implies that the months leading up to the _canícula_ are of special importance in Mexico.

**Figure 2: Agricultural Cycle in Mexico**

|Feb|Mar|Apr|May|Jun<br>Jul|AugSep|Oct|Nov|Dec|Jan|Feb|Mar Apr|May|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dr|y|||Wet Season||||DryS|eason||Wet s|eason|
|||Pr|e-_caníc_|_ula_<br>_Caníc_|_ula_||||||||
|||||||1999:|ENN||||||
|||2001: M|xFLS I||||||||||
||||||2005:|MxFL|S II(un|til 200|7)→||||

For the household data we use the first two waves of surveys from the Mexico Family
The economy could benefit from other parts of the reforms, such as reflecting the power supply costs in the retail pricing of electricity. This could be a natural extension of the study in future.

# **References**

Chen, Wenying. 2005. “The costs of mitigating carbon emissions in China: findings from China MARKAL-MACRO modeling” Energy Policy, 33:885-896.

China Electricity Council (CEC). Electricity industry statistics 2010-2015.

Dupuy, M., and F. Weston and A. Hove (2015). Stronger Markets, Cleaner Air and Power Sector: Deepening Reform to Reduce Emissions, Improve Air Quality and Promote Economic Growth. The Paulson Institute. Chicago.

- Ho, M.S, Z. Wang, and Z. Yu (2017). China’s Power Generation Dispatch. Discussion Paper, Resource for the Future (RFF), Washington, DC.

Jamasb, T. R. Nepal and G.R. Timilsina (2017). A Quarter Century Effort Yet to Come of Age: A Survey of Electricity Sector Reform in Developing Countries. The Energy Journal, Vol. 38 (3), pp. 195-234.

Joskow, P.L. (1998). “Electricity Sectors in Transition.” The Energy Journal, Vo. 19, No. 2, pp. 25–52.

Lei, N., L. Chen, C. Sun and Y. Tao (2018). Electricity Market Creation in China: Policy Options from Political Economics Perspective. Sustainability, Vol. 10, pp. 1481.

Max Dupuy, M., F. Weston and A. Hove (2015). Power Sector: Deepening Reform to Reduce Emissions, Improve Air Quality and Promote Economic Growth. Paulson Dialogue Paper. The Paulson Institute, Chicago.

Newbery, D.M. (1999). Privatization, Restructuring and Regulation of Network Utilities, MIT Press.

Teng, F., F. Jotzo, X. Wang, 2017. “Interactions
||||
|Students dropped out of school|0.10|-0.02<br>(0.01)|0.09|-0.00<br>(0.02)|0.11|-0.03<sup>_∗_</sup><br>(0.02)|
|Education assessment score|0.44|0.06|0.41|-0.02|0.47|0.07|
|||(0.06)||(0.08)||(0.08)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

24
pervisors are different from the non-exposed lines on observable characteristics measured before the start of the promotion program.<sup>43</sup> I use daily line-level production data we collected and harmonized from the factories for four important production outcomes: efficiency, alteration rates, absenteeism and product complexity. Before the start of the promotion program, the lines exposed to the new female supervisors had very similar production statistics as the non-exposed lines. These groups did not differ in their efficiency, their complexity of garments produced and their alteration rates. The exposed lines have only slightly lower absenteeism rates, about 0.34 percentage points lower than the non-exposed mean of 4.23%.

The main specification for the role model analysis is similar to the one I employ for the promotion analysis. For outcome _Y_ of each exposed and non-exposed subordinate operator _s_ in factory _f_ , _exposed_ is the covariate of interest and defined as a dummy variable indicating whether the individual was working on a line to which a selected participant of the promotion program was assigned, where the individual’s line code was collected at the factory follow-up for the promotion program. _exposed_ is equal to zero if the individual was working on a line unrelated to the promotion program.<sup>44</sup> Note that this comparison is likely to be a conservative estimate, since the female supervisor can be expected to have been visible to operators working on neighboring non-exposed lines, too. _X_ is a vector of potential control variables, which consists of all 15 variables for which I tested balance on in Table 1 and their squared terms, in addition to factory, enumerator and month dummy variables. The set of control variables to be included are selected using the PDS Lasso algorithm as explained in Section 3.5.1. Standard errors are clustered at the production-line level, following Abadie et al. (2017). I again limit the sample to those women who report having another adult decision-maker in the household.

# **4 The effects of a promotion
# **<u>4. Estimation Strategy</u>**

The experiment is designed to disentangle the impact of the behavioral change parenting component from the cash transfer program. This section describes the estimation strategy. It also outlines how data from the follow-up sample (Table 1) are used in the comparisons on which the estimation strategy relies.

# _<u>4.1 BCC Value-Added and Local Spillovers</u>_

Table 1 provides the composition of the follow-up sample by actual beneficiary status for the cash transfer program. In the control group, all households are non-beneficiaries. In each treatment group, there are three types of households in the sample: non-beneficiaries, beneficiaries from the baseline sample, and beneficiaries from the booster sample.

The experiment is designed to provide precise estimates of the value-added of the behavioral change parenting intervention. Based on randomized assignment of villages to the two treatment arms, we can disentangle the impact of the behavioral change component on cash transfer beneficiaries and local spillovers on households that do not benefit from cash transfers within treated villages.

First, we estimate the value-added of the BCC component for cash transfer beneficiaries by comparing outcomes between cash transfer beneficiaries in villages assigned or not to BCC. This estimate is obtained through the following regression model excluding observations from the control group: _i_ refers to an individual or household (depending on whether the outcome of interest is a household-level or individual-level outcome), _Y_ is the outcome of interest, _BCC_ takes the value of 1 for cash transfer villages randomly assigned to the behavioral change component, _Z_ is a set of randomization strata fixed effects, and _ε_ is the error term. C takes the value of 1 for observations in the control group, 0 otherwise. B captures the actual beneficiary status from the cash transfer program. Robust (White-Huber) standard-errors are clustered. To maximize power, this estimation is undertaken by including observations from the booster

> between the two teams, the list of children was extracted by the coordination team from the household
return compared to both documented return migrants and non-migrants. This suggests that undocumented migrants do not gain from their overseas migration experience in terms of skill acquisition the way documented migrants do. Our results show that the longer undocumented migrants stay abroad, the more their human capital is depleted, while the opposite is true for documented migrants. Indeed, we find evidence that illegal migrants experience a waste of human capital overseas, as they have lower earnings and end up in lower-ranked occupations while abroad, which might explain their lower earnings upon return.

Our results are the first to show the negative impact associated with illegality on the migrants, even after they return to their country of origin. These findings have important policy implications, as they suggest that there is a need to raise awareness among potential migrants about the negative consequences of illegal migration experiences. Furthermore, countries of origin should maximize the benefits of migration by supporting legal migration and curbing illegal migration.

# **References**

- Abrego, L. J. (2011). Legal consciousness of undocumented Latinos: Fear and stigma as barriers to claims-making for first-and 1.5-generation immigrants. _Law & Society Review_ , _45_ (2), 337–370.

- Ambrosini, J. W., Mayr, K., Peri, G., & Radu, D. (2015). The selection of migrants and returnees in Romania: Evidence and long-run implications. _Economics of Transition_ , _23_ (4), 753–793.

- Assaad, R., & Krafft, C. (2013). The Egypt labor market panel survey: introducing the 2012 round. _IZA Journal of Labor & Development_ , _2_ (1), 8.

- Awad, I., & Aziz, N. A. (2017). Egyptian irregular migration in the GCC countries. In _Skillful survivals: Irregular migration to the Gulf._ Cambridge: Gulf Research Centre Cambridge.

- Azose, J. J., & Raftery, A. E. (2019
1990), Uganda 2014 (refugees).

74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons.

75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank’s Central Microdata Catalog.
firms may face when enrolling in a SEBRAE service.

The third version, motivation, includes the same information as the basic version, but at the top of the page provides an additional sentence asking: “Did you know that being one level below your competitors (adopting between 1 and 5 fewer business practices) makes your firm on average 26 percent more likely to die within one year?<sup>1</sup> ” This statement was meant to provide tangible information on the benefits of adopting more business practices. It may also make the threat of competition more salient, thereby increasing firms’ incentive for adopting new business practices (Bloom et al. 2015).

The fourth version, motivation plus pre-enrollment, includes the information in the basic version plus the motivating sentence and pre-enrollment in the training session.

> 1 This correlation is based on SEBRAE data.

6
**Self-control GoNoGo** Task-based Pakistan Pilot - - - - - 0.78 402 -

_Notes_ . This table reports estimates of the reliability of socioemotional skills measures. “Cambodia” refers to data collected in that country in 2017. A random 13% of respondents were resurveyed. “Pakistan Pilot” and “Pakistan” refer to data collected in that country in 2018. Half of the pilot respondents were re-surveyed. Appendix D describes each instrument in detail. Columns (1), (2), (4), and (5) provide Cronbach’s alpha statistics for the test (columns 1 and 4) and the re-test (columns 2 and 5). Columns (3) and (6) show test-retest correlations. Following Laajaj and Macours (2021), we applied an acquiescence bias correction when applicable (left panel); we also provide uncorrected measures (right panel). Sample sizes differ due to item non-response and different survey modes (in-person vs. phone surveys).

42
Changes in the priority that policy makers gave to education in government budgets also generally contributed little to the increases in public education spending. For example, upper-middle-income countries had the largest increases in public spending on education overall but devoted a smaller share of their government spending to education in 2015 than they did in 2000. The exception was low-income countries, where making education a higher priority in government budgets did lead to greater public education spending (Figure 6), although this represented a relatively small proportion of the overall increase. Over this period, the share of the budget devoted to the education sector increased from an average of 15 to 16 percent in those countries (see Table 1).

**Figure 6: Economic Growth Has Been the Main Driver of Increased Public Spending on Education** _Decomposition of public education spending changes (%), 1998_ **–** _2017_

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>High-income Lower-middle-income Low-income Upper-middle-income<br>Contribution of GDP growth to growth in education spending<br>Contribution of share of education in total government budget to growth in educaiton spending<br>Contribution of total government spending growth to growth in education spending<br>Total change in education spending - 2000–2015<br>%ge change between 2000 and 2015<br><!-- End of picture text --> _Source:_ World Bank calculations based on UIS and IMF data.

_Note:_ Average percentage growth in all variables calculated as the percentage change from the four-year **– –** average in 1998 2001 to the four-year average in 2014 17, for all countries with data available in at least one year across both four-year periods.

Regions with the fastest growth in public education spending were those that experienced rapid economic growth and managed relatively large increases in
**Graph 2: Prior government effectiveness and operational performance of public sector governance operations**

<!-- Start of picture text --> Prior government effectiveness and project outcome ratings<br>HS2<br>S 1<br>MS<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>MUS-1<br>US-2<br>n=134<br>HUS-3<br>Worldwide Governance Indicators for Government Effectiveness (2000)<br>Project outcome ratings<br><!-- End of picture text --> Note: project ratings range from: HUS = highly unsatisfactory, to HS = highly satisfactory. WGIs rate countries on a scale from 0 = no government effectiveness to 100 = very high government effectiveness. Colored arrows indicate governance percentiles (0-10, 10-25, 25-50, 50-75, 75-90 and 90-100) as defined by the index. Coverage: 134 public sector mapped development policy and investment lending operations implemented in 67 countries. Several countries had more than one operation, sometimes rated differently. Each operation is counted separately.

**Graph 3: Prior government effectiveness and operational performance of public sector governance operations -- investment loans only**

**Prior Government Effectiveness and Project Outcome Ratings for** **_Investment Operations_**

<!-- Start of picture text --> HS2<br>S 1<br>MS<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>MUS-1<br>US-2<br>HUS-3<br>Worldwide Governance Indicators -- Government Effectiveness (in 2000)<br>Project outcome ratings<br><!-- End of picture text --> Coverage: 58 public sector mapped investment lending operations implemented in 43 countries. Several countries have had more than one operation, sometimes rated differently; each operation is counted separately.

28
de...ned as wages above what workers would earn elsewhere in the labor market. Because of the nature of their data, Kaplan and Verhoogen are able to decompose plant level wages into a component that re‡ects skill composition and a component that re‡ects wage premia. Their identi...cation strategy is based on the peso devaluation of 1994 that they argue di¤erentially a¤ected incentives to export within industries. Comparing across plants within industries, they ...nd that roughly two thirds of the higher level of wages in larger, more productive plants are explained by wage premia and that nearly the entire di¤erential within industry wage changes induced by the shock to exports is explained by wage premia and not by skills. The authors conclude that sorting on individual ability is not responsible for the well-documented correlation between exporting and wages.

As the ...rst contribution in the literature to account for both ...rm heterogeneity and intermediate trade in their analyses, Amiti and Davis (2008) o¤er a theoretical and empirical examination of the impact of tari¤ cuts on workers’wages that accounts for the extent of a ...rm’s engagement in international trade. Using Indoneisan manufacturing census data for 1991-2000, a period that encompasses Indonesian trade liberalization, the authors develop a general equilibrium model to estimate this relationship. They ...nd that the impact of a given tari¤ change on a ...rm’s workers’wages is dependent upon that ...rm’s role in the global economy. That is, a 10 percentage point decrease in output tari¤s will lower wages of importcompeting ...rms by 3 percent but will raise wages at exporting ...rms by 3 percent. Likewise, a decrease in input tari¤s by 10 percentage points will increase wages by 12 percent at ...rms that rely on imports but will have an insigni...cant impact on the wages of
(PMT) and community-based targeting (CBT), two targeting methods that are commonly employed, and compare their performance as implemented in practice, within and across countries. Since in low-income settings, PMT and CBT are almost always applied after geographical targeting, our analysis allows us to understand the actual contributions of PMT and CBT once _geographical_ targeting has been applied. In addition, we explore the role of measurement choices across multiple dimensions, including three key ones. First, we consider both per-capita consumption and food security as well-being metrics, the latter being a major policy objective in humanitarian interventions. Second, we explore the role of program coverage (or the share of beneficiaries selected) in the performance of targeting. Third, we rely on multiple measures used in the literature to evaluate targeting, with a preference for distribution-sensitive indices (i.e. measures that consider distances to poverty lines). To provide additional insights, we conduct simple regressions to identify whether distinct types of households are selected or excluded by different targeting methods. Finally, we collect cost data, an area where evidence is particularly limited, although the cost of targeting instruments is often cited as an argument against targeting.

Several important results emerge from this study. We show that measurement choices play a decisive role in the performance of targeting. At first sight, PMT seems to perform well in reaching the poorest households.<sup>2</sup> The median PMT-targeted program provides 20 percent more resources to the poorest than would random allocations. This is only 6 percent for CBT, which performs systematically worse than PMT based on consumption per capita across all programs studied. Nonetheless, when considering distributionsensitive measures of performance, none of the methods seems to perform significantly differently from each other and from random or budget-neutral universal delivery of benefits. This result is driven by the high poverty rates and the significant homogeneity present in the program areas studied. A different picture emerges when relying on food insecurity as a well-being metric: only four targeting schemes perform better than
Policy Research Working Paper 5864

# **Abstract**

The 2007–2008 upsurge in agricultural commodity prices gave rise to widespread concern about investors causing a “global land rush”. Large land deals can provide opportunities for better access to capital, transfer of technology, and advances in productivity and employment generation. But they carry risks of dispossession and loss of livelihoods, corruption, deterioration in local food security, environmental damage, and long-term social polarization that led some countries to recently pass legislation restricting foreign land acquisition. To stimulate evidence-based debate, this paper explores determinants of foreign land acquisition for large-scale agriculture. It quantifies demand for land deals, showing it focused on Africa where land expansion is about 20 times the level it was in the past. The analysis uses data on bilateral investment relationships, together with newly constructed indicators of agro-ecological suitability in non-protected and forested areas with low population density as well as land rights security. It estimates gravity models that can help identify determinants of foreign land acquisition dedicated to large-scale agriculture. The results confirm the central role of agro-ecological potential as a pull factor. In contrast to the literature on foreign investment in general, the quality of the business climate is insignificant, whereas weak land governance and tenure security for current users make countries more attractive for investors. Implications for policy are discussed.

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 rarezki@imf.org, kdeininger@worldbank.org and hselod@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