rafmacalaba/data-use-mentions-extended huggingface dataset:
the weak-instrument-robust Anderson-Rubin Wald test in the second-stage regressions. We expect the IV estimates of the coe¢ cients on tari¤s and FDI in‡ows to be larger in magnitude than the LS-BE estimates of those coe¢ cients if measurement errors and political economy forces dominate the confounding reverse causality forces.<sup>32</sup>

# 4 Main Results

## 4.1 Least Squares Between Regressions

Starting with the determinants of backward GVC participation shares, Column (1) of Table 1 shows the baseline LS-BE estimates of Equation (1). Most coe¢ cients have the expected signs and are signi...cant. Factor endowments jointly matter for backward GVC participation, with a strong F-statistic of 6.84, which is signi...cant at the 99% level. Larger land and/or natural resources endowments are linked to lower backward GVC participation shares, while abundance in capital is associated with higher backward GVC participation shares. But geography and domestic industrial capacity also matter. A shorter distance to the GVC hubs is positively correlated with backward GVC participation. Countries with larger domestic industrial capacity (i.e., a potentially larger pool of domestic suppliers) exhibit lower backward GVC participation shares, as domestic inputs may be used to replace imports.

Countries’openness to trade and FDI is also a predictor for the intensity of backward GVC participation. Lower tari¤s and larger FDI in‡ows are associated with higher backward GVC participation shares across countries. Better institutional quality measured by a higher score in the political stability index is linked to higher backward GVC participation. Exchange rate appreciation is unrelated to backward GVC participation, which is not surprising, given that appreciation stimulates imports by reducing its prices, but can also reduce export competitiveness due to higher export prices.<sup>33</sup> The R-squared in Column (1)

> 32We also tried using lagged tari¤s and FDI, as well as neighboring countries tari¤s and FDI as instruments. In these
A small literature has studied the effects of the timing of non-labor inputs application. Jagani et al. (2021) use a household panel in Kenya to show that farmers respond to variation in temperature within the growing season, adjusting input application and labor use to short-run temperature shocks. Islam and Beg (2021) randomize farmers into a treatment that improved the timing of urea application among rice farmers in Bangladesh, reducing urea use by 8% without reducing yields. They find in their baseline survey that farmers are applying urea earlier than recommended in the growing season, and are applying more than recommended quantities. Farmers who experience outreach about this issue reduce applications of urea early in the season, when returns to application are low.

Though accurate measurement of application quantity is undoubtedly important, there has been minimal research to date on this topic. Beegle et al. (2012) suggest that because fertilizer purchase and application are generally salient and singular events, this particular domain of agricultural data is less prone to recall bias than other domains, such as agricultural labor. Indeed, they generally support this hypothesis in their analysis of data from national surveys in Malawi and Kenya, taking advantage of the 12-month fieldwork design to assess the impact of recall duration on quantity reported, where they find no evidence of recall bias with respect to fertilizer use (binary), and minimal evidence of recall bias with respect to quantity of application, though findings do differ slightly by crop type and household headship. More recently, Wollburg et al. (2021) find that longer recall periods lead to recall bias both in fertilizer use (binary) and in the quantity of application. With increasing recall periods, farmers are less likely to report the use of fertilizers but report higher quantities applied.<sup>25</sup> While differences in methods of elicitation exist across research and data collection efforts, head-to-head within-survey experiments to compare methods for measuring quantity of input application does not seem to have been undertaken as yet. Within-survey experiments about mineral fertilizer and
city and its surrounding areas. The aggregate labor market clearing condition determines _<u>v.</u>_ Note that the symmetry in the initial equilibrium breaks down once there is a trade cost shock to one of the international trade routes.

## **Built-up Density**

The empirical analysis utilizes data on built-up area to explore the impacts of CIV crisis on sunk investments that can lead to persistent effects. We define built-up density in terms of density of residential housing and manufacturing structures.<sup>13</sup> The proportion of built-up area is defined as equal to the density of housing and manufacturing in the [ _K_<sup>0</sup> _, K_<sup>_z_</sup> ] zone of the integrated subregion. In rest of the areas, it equals the density of housing defined in turn by population density ( _ni_ ). This definition implies that built-up area should be more concentrated around the city center due to higher population density and concentration of manufacturing.

# **(4.3) Effects of the Transport Shock to One Port (Abidjan)**

We model the CIV-political crisis as an increase in _θk_ and no change in _θj_ along alternative routes. Though international prices at the port of entry were not influenced by the CIV crisis, transport cost from port (Abidjan) to city _K_ ( _θk_ ) increases as a result of the conflict. An increase in _θk_ increases price of import ( _m_ ) to the consumers ( _∂P∂θmKk_ 0 _>_ 0) but reduces prices of exportables ( _∂P∂θqKK_ 0 _<_ 0) to the producers. The CIV crisis did not affect transport cost from port of entry (non-Abidjan) to city _J_ , but it is indirectly affected due to labor mobility between cities. Propositions 1-3 summarize the impacts of a higher _θk_ .

## **Effects of Trade Cost Shock to the Abidjan Route: Testable Predictions**

_A trade shock due
Second, we control for all ...rm-level characteristics by including a parent ...rm ...xed e¤ect and limiting the comparison to establishments owned by the same parent ...rm. Multinationals with only one foreign subsidiary are hence dropped from the sample. In doing so, we establish the role of production linkages by comparing the performance of subsidiaries that belong to the same multinational but with di¤erent degrees of input-output linkages. The e¤ect of ...nancial linkages cannot be estimated in this speci...cation because our measure of ...nancial linkages does not vary within each parent ...rm.

## [Table 11 about here]

Table 11 reports the results. We continue to ...nd vertical production linkages to exert a signi...cant e¤ect on MNC subsidiaries’performance during the crisis. A one-percentage-point increase in the input-output relationship is associated with 1.28 percentage points higher growth rates, exceeding the estimated e¤ect in Table 3. Again, the production linkages do not appear to systematically a¤ect establishment performance in non-crisis years.

# 7 Conclusion

We investigate in this paper the role of foreign ownership in establishment performance during the recent Global Financial Crisis. Using a worldwide establishment panel dataset, we examine whether foreign ownership led to di¤erent resilience to the crisis between establishments with similar economic attributes and how the role varies from non-crisis periods. Moreover, we examine how, conditional on foreign ownership, production and ...nancial linkages explain the di¤erential responses of MNC subsidiaries.

We ...nd that MNC subsidiaries responded on average better to the recent crisis than local controls with similar economic characteristics. The advantage of foreign ownership was clearly pronounced during the crisis, while relatively muted in non-crisis years. Production and ...nancial linkages played critical roles in explaining the di¤erential responses of MNC subsidiaries. Establishments sharing stronger vertical production linkages with foreign parent ...rms
_across Generations Around the World_ . Number 28428 in World Bank Publications. The World Bank.

- Noble, K. G., Houston, S. M., Bartsch, H., Kan, E., Kuperman, J. M., Akshoomoff, N., Amaral, D. G., Bloss, C. S., Libiger, O., Schork, N. J., Murray, S. S., Casey, B. J., Chang, L., Ernst, T. M., Frazier, J. A., Gruen, J. R., Kennedy, D. N., Van Zijl, P., Mostofsky, S., Kaufmann, W. E., Keating, B. G., Kenet, T., Dale, A. M., Jernigan, T. L., and Sowell, E. R. (2015). Family Income, Parental Education and Brain Development in Children and Adolescents. _Nature Nuroscience_ , 18(5):773–778.

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Policy Research Working Paper 10912

# **Abstract**

While cash transfers have emerged as an attractive option to minimize negative long-term impacts of conflict, the scope for targeting and assessing their impact in such settings is often challenging. This paper shows how a digital farmer registry in Ukraine (the State Agrarian Register) helped to target and evaluate such a program, using the country’s $50 million Producer Support Grant in a way that largely avoided mis-targeting. The analysis applies a difference-in-differences design with panel data from 2019–23 on crop cover at the parcel/farm level for the universe of eligible farmers registered in the State Agrarian Register. The findings suggest that the program significantly increased area cultivated, although the effect size remained modest. Impacts were most pronounced near the frontline and for the smallest farmers. The paper discusses the implications in terms of a more diversified menu of support options and the scope of using the State Agrarian Register to help to implement these options, as well as lessons beyond Ukraine.

This paper is a product of the Development Research Group, Development Economics. 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 kdeininger@worldbank.org and dali1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of
.3270|0.2704|22,181|1.3%|
|2) PIT allowance at PLN 5000|05265|03759|0.3397|0.3517|0.2799|8,873|0.5%|
|3) PIT allowance at PLN 8000<br>Sim|.|.|0.3356|0.3477|0.2768|22,582|1.3%|
|4) Family 500+ & PIT allowance at PLN 5000|||0.3107|0.3245|0.2682|29,702|1.7%|
|5) Family 500+ & PIT allowance at PLN 8000|||0.3076|0.3214|0.2652|41,172|2.4%|
|**Extreme Poverty**(percent of population)||||||||
|202014 Baseline|||5.5%|8.9%||||
|1) Family 500+|||3.4%|5.9%||||
|2) PIT allowance at PLN 5000|272%|60%|5.1%|8.5%||||
|3) PIT allowance at PLN 8000<br>Sim|.|.|4.8%|7.9%||||
|4) Family 500+ & PIT allowance at PLN 5000|||3.2%|5.6%||||
|5) Family 500+ & PIT allowance at PLN 8000<br>Source: Own estimates based on HBS 2014.|||3.1%|5.3%||||

29
br>eports the p-value of a t-test that_T_1=0. We use administrative data on recommendations<br>4.|
|---|---|
|(4)<br>idual<br> in 2015<br>p-value<br>T1 vs. C|0.86<br>0.24<br>0.77<br>0.33<br>ers in our study<br>ment dummies a<br>rs in _T_1. In colu<br>d 6 are analogou<br>l group in 2014 a<br>8 compares the<br>armers in_T_1. It r<br>in February 201|
|(1)<br>(2)<br>(3)<br>Fertilizer<br>Indiv<br>application<br>recomm.<br>Control mean<br>p-value<br>Mean<br>in 2014<br>T1-T4 vs. C<br>T1|Urea(kg/ha)<br>267.81<br>0.72<br>151.23<br>(135.25)<br>(32.64)<br>DAP(kg/ha)<br>49.41<br>0.87<br>30.65<br>(65.27)<br>(26.32)<br>KCL(kg/ha)<br>9.45<br>0.78<br>33.27<br>(26.15)<br>(28.16)<br>Cost(pesos/ha)<br>1,895.49<br>0.55<br>1,882.97<br>(1,181.91)<br>(398.83)<br>Observations<br>130<
under an inflation targeting regime. For example, even with an inflation targeting framework, expectations were not well anchored in New Zealand when forecasters did not understand the central bank’s objective function (Kumar et al. 2015). Inflation expectations in 10 advanced economies were not as well anchored during periods of persistently below-target inflation as during periods when inflation was close to target (Ehrmann 2015).

Several studies have examined whether inflation expectations became unanchored during and after the global financial crisis, which was followed by a wave of unconventional monetary policy actions. During the period immediately following the crisis, market-based inflation expectations in the United States and the United Kingdom became more sensitive to macroeconomic news, but neither survey-based nor market-based long-term inflation expectations in the Euro Area became unanchored (Galati, Heemeijer, and Moessner 2011; Galati, Poelhekke, and Zhou 2011).

During a longer post-crisis period in the Euro Area, when inflation fell and was persistently below target, there is evidence that the anchoring of inflation expectations weakened (Grishchenko, Mouabbi, and Renne 2017; Garcia and Werner 2018). The findings, which are based on different methodologies and different measures of inflation expectations, are less consistent for the United States, where anchoring is alternately found to have improved and deteriorated significantly in the post-crisis period (Ciccarelli, Garcia, and Montes-Galdón 2017; Grishchenko, Mouabbi, and Renne 2017). Overall, given the size of the shocks during the crisis, expectations in advanced economies remained fairly well anchored (Miles et al. 2017).<sup>17</sup> Anchoring of inflation expectations in EMDEs. Evidence on the anchoring of inflation expectations in EMDEs is more limited, but some studies suggest that inflation targeting plays a role (Appendix 2). Using monthly survey data from Consensus Economics for a sample of 22 EMDEs and 14 advanced economies in a structural vector autoregressive model, Davis (2014) finds evidence that the introduction of inflation targeting is associated with a statistically significant reduction in the response of 12-month-ahead inflation expectations
with their effects on employment. The estimates presented in Column 3 of Table 4 in the Appendix indicate that in areas close enough to an electric grid, bringing a main road 10 km closer marginally increases the number of hours worked by nearly 1.2 hours a week, but this effect is not statistically significant. Unlike proximity to roads, proximity to the electric grid has a much larger effect. In areas with high access to a main road, bringing the electric grid 10 km closer increases the number of hours worked by 12.5 hours a week. Importantly, it is more beneficial to provide both types of infrastructure simultaneously, as indicated by the positive coefficient on their interaction term. Indeed, bringing a main road 10 km closer increases the positive effect of bringing the electric grid 10 km closer, and by more than five hours of work every week. Using the DHS sample, Figure 7 shows that the impact of increasing proximity to the electric grid on the number of hours worked becomes higher for households that are closer to the road, and vice versa (the impact of increasing proximity to the roads also boosts the number of hours work for households that have greater proximity to the electric grid). Bringing an electric grid 10 km closer increases the number of hours worked by 12.5 hours for households located next to a main road, but only by just 7 hours when households are located 10 km away from the main road network. Similarly, bringing the main road network 10 km closer increases the number of hours worked by 1.2 hours when households are located next to the road, but such proximity actually reduces the number of hours when the household is located 10 km away from the electric grid. That is, increasing the proximity to the road only has a positive impact for workers located next to the electric grid.

Figure 7: Marginal impact of proximity to roads (left) and electricity (right) on employment <!-- Start of picture text --> Marginal impact
# **2 Related Literature**

Previous literature has documented the effects of many weather shocks, including temperature, precipitation, NDVI, windstorms, and soil quality, on economic activity (Dell et al., 2014). These papers study short-run effects, such as on labor (Graff Zivin and Neidell, 2014), on agriculture (Schlenker and Lobell, 2010; Deschênes and Greenstone, 2007), and long-run effects, such as on education and health (Groppo and Kraehnert, 2017; Graff Zivin et al., 2018; Maccini and Yang, 2009), on economic growth (Dell et al., 2012a).

We contribute to the growing literature that studies the effect of weather shocks on consumption, poverty, and vulnerability to poverty. Dell et al. (2012b) use a world sample from 2000 to show that countries are, on average, 8.5 percent poorer per 1 degree warmer weather. Other within-country evidence shows that rainfall deficits affect rice farming households negatively in Ethiopia, who lower non-food expenditures to meet their food expenditures (Essama-Nssah et al., 2011). Baquie and Fuje (2020) find that only nonassistance consumption reduces, and households decrease food consumption in response to the growing season rainfall deficit. Vulnerability to poverty increased by 40 percent in Kenya (Christiaensen and Subbarao, 2004), and 42 and 21 percent of households in rural and urban areas, respectively, were vulnerable to poverty in Ethiopia in response to rainfall shocks (Hill and Porter, 2017). Vulnerability to poverty was also estimated to be higher in drought-prone areas vis-à-vis other ecological zones in Ethiopia (Skoufias et al., 2021). Pape and Wollburg (2019) show an increase in rural poverty and hunger due to vegetation (NDVI) deficits in Somalia (also a conflict-affected nation).

Interestingly, Letta et al. (2018) shows a reduction in rural household income and a slowdown in income convergence due to temperature shocks in Tanzania. In a forthcoming paper, Calcutt et al
.012)|
|Other castes mean education level|−0.012|−0.014|−0.015|−0.0079|
||(0.010)|(0.010)|(0.010)|(0.0098)|
|Observations|1318|1318|1318|1318|
|_R_<sup>2</sup>|0.91|0.91|0.92|0.92|
|Household level controls|Yes|Yes|Yes|Yes|
|Agricultural controls|Yes|Yes|Yes|Yes|
|Group controls|No|Yes|Yes|Yes|
|Control for other group|No|No|Yes|Yes|
|Additional group controls|No|No|No|Yes|

_Source_ : Author’s analysis based on the 1982 and 2006 rounds of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ :<sup>*</sup> _p <_ 0.1,<sup>**</sup> _p <_ 0.05,<sup>***</sup> _p <_ 0.01. Robust standard errors clustered at the village level are reported in parentheses. The coefficients on the other controls are reported in table S1.1 in the supplementary appendix S1. The reference group is defined as individuals from the same caste. The other group refers to individuals from other castes. Group controls and controls for other groups include the number of individuals in each group (in log) and the mean value for the demographic characteristics in each group. Additional group controls include the average amount of land in each group and the amount of transfers within groups.
128|0.053|0.317|0.143|0.078|
|Female Headed|0.277|0.090|0.032|0.209|0.070|0.035|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|
|Source: NCS, 1985|/86 and 199|2/93.|||||

Table 4.19 shows the incidence of poverty by the educational level of head of household. Taking the rural areas first, in 1985 more than _50_ percent of households whose heads had no education were poor. All the poverty measures show an exponential decline with increasing education, in both periods. However, some interesting facts seem to emerge out of this table. There is a decline of 2.4 percentage points in headcount, 3.2 percentage points increase in depth and 3.4 percentage points increase in severity respectively among the uneducated in rural areas, but for primary school leavers while the head count has declined, the depth and severity has increased. For secondary and post secondary school leavers, the results are mixed. For the secondary school leavers, there was a very small (3.1 percentage points) decline in headcount, a 2.1 percentage point increase in depth, and a 2.4 percentage point increase in severity of poverty. For the post secondary school leavers, there was an increase in all measures during the period of our study. In the urban areas, there was a decline in headcount, depth and severity of poverty for households whose heads had below primary education. Again for the post secondary school leavers, the trend was similar to that of the rural areas, resulting in higher depth and severity of poverty.
# **5. Conclusions and Policy Implications**

Limited access to and benefit from technology may hinder women’s access to economic opportunities, especially in conservative societies and in a context where e-learning and working from home became the new normal during the COVID-19 pandemic. Given how the pandemic accelerated reliance on and importance of technology and digital devices in terms of market access and income, these factors will only be more important in the future.

Within this context, the paper tests the hypothesis that digital inclusion and closing the gender digital divide would reduce the gender gap in labor force participation in the MENA region. Using a pooled cross section dataset of 13 MENA countries over four years (2018 to 2021), the female-to-male labor force participation ratio was regressed on an internet inclusion index. Another version of the model is estimated using the gender gap in access to the internet. Control variables include the structure of economic growth, population growth, openness of the economy, unemployment rate, educational attainment, formal and informal institutions, and years dummies.

The results show that digital inclusion would contribute to reducing the gender gap in labor force participation. It is worth noting that accessing the internet might not be sufficient to ensure women’s access to economic opportunities; affordability, relevance, and trust all play a role. Moreover, ensuring online privacy, encouraging girls to study STEM subjects, and providing women with the required digital skills are needed to increase women’s benefit from technology and thus female labor force participation. Other key drivers to close the gender gap include the economic growth structure and informal institutions. Equal gender roles in the society can reduce the economic gender gap.

The analysis has important implications for policies targeting women’s empowerment (SDG5) and aiming to reduce inequalities (SDG10). Ensuring women’s access to the internet is a necessary but not sufficient condition to transform the digital divide into a digital dividend and ensure that women benefit from being online. Bridging the digital divide requires ensuring the relevance of internet