rafmacalaba/data-use-mentions-extended huggingface dataset:
now have different currencies, and prices are no longer equalized across regions and republics.

Still, as a robustness check, we use the fitted values of past trade from the simple gravity equation in the regression (Column five of Table 3). The results remain robust implying that a significant amount of current trade is determined by past linkages.

# 3.4.1 Interpretation and Implications **for the Future**

The results in this paper show that trade has been reoriented within Russia, primarily as a result of trade barriers, and that trade flows within the FSU are still significantly impacted by past linkages. We have also shown that the elasticity of trade with respect to tariffs increases over time, likely as a result of a reorientation of production, infrastructure, and networks. This implies that the Russian bias we estimate will continue to increase over time. Decreasing trade between Russian regions and republics will affect production, infrastructure maintenance and development, and business networks. As these linkages for trade deteriorate, the cost of international trade will increase, which will cause trade between FSU countries to decline further.

At present this process is likely show up in only marginal improvements or neglect of existing infrastructure. To examine the extent to which this process has begun, we first examine passenger train speed within the FSU as a proxy for investment in infrastructure that links regions to republics, and then we discuss how nascent projects in the FSU countries are likely to alter international linkages.<sup>8</sup> Using train schedules between FSU countries and Russian regions, we find that trains got faster between most Russian regions from 1989 to 1996, and trains to the former republics slowed. Table 4 reports the average percent change in time of travel between and among regions and republics from 8Train schedules for 1989 and 1996 are from the National Railways archives in Moscow.

16
33 2012). The regulator announced that this new regulation was intended to “rid the power sector of corruption and protect electricity consumers from fraud and extortion often committed against them by electricity companies.” As one potential indication of progress in this area, the percentage of households reporting informal payments declined from those in the 2010 survey by 3.8 percentage points nationally, 2.0 percentage points in urban areas, and 5.8 percentage points in rural areas. The decline was primarily among lower‐decile households, while the top decile statistics remained largely the same.

Figure 8: Percentage of households who paid a bribe to get grid connection <!-- Start of picture text --> 60<br>40<br>20<br>0<br>Malawi urban Malawi rural Malawi total Nigeria urban Nigeria rural Nigeria total<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

In Senegal, 86 percent of households said that corruption had increased over time. However, only 1 percent of those connected to the grid said that they had been asked by utility officials to pay bribes. The question asked in Senegal is not comparable to that in Malawi and Nigeria. It is possible that this more positive outcome in Senegal was due in part to the fact that the question concerned only the past 12 months, and if bribes are associated primarily with the initial connection, those households that had obtained grid connection more than 12 months earlier would not have reported bribe payments.

# Community versus Household Access

In Malawi, Niger, and Nigeria, the survey asked whether their communities had electricity.<sup>11</sup> The data can be used to examine electrified communities and compute the percentage of households that have chosen to connect to the grid. In urban Nigeria, 92 percent of all households living in electrified communities had connected to the grid, whereas less than half of such urban households in Niger and Malawi had
. Those households with stronger ties in the community are more likely to be able to cope with economic/financial losses and this reflects on children nutritional status. Kanaiaupuni et al. (2005) look at mothers’ social networks in Northern Mexico and find that social interactions help sustain healthier children especially among economically marginalized families.

Some previous work has been done using YL to study the association between maternal social capital and child health. Moestue et al. (2007) and Galab and Lives (2006), using the Andhra Pradesh sample find a positive association between a child height-for-age z-score and the size, and the literacy rate of the maternal network. Harpham et al. (2006), explore the association between maternal social capital and child physical and mental health in Vietnam. Consistent with the findings of the present analysis, they find stronger relations between maternal social capital and the health of 1 year old children than 8 year old children.

None of these papers using YL exploits the longitudinal setting of the data to study the causal effect of maternal participation. Furthermore, there are no papers on individual social capital and child health using the Peruvian sample. To the best of my knowledge, the only paper related to individual social capital in Peru (Cueto et al., 2005) analyzes the association between cognitive and structural capital measured at the community level and children’s educational progress and achievement. The only positive association they find is between cognitive social capital and children’s educational progress. They find that cohesive communities which experience more trust

> 6 However, the opposite might also occur: those who have a wider network might also be more likely to be involved in any community organization.

5
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Rail networks in Angola**

|Company|Port|Region|Lines (km)<br>Total|Operating|%<br>operating|Gauge<br>(mm)|
|---|---|---|---|---|---|---|
|Caminhos de Ferro de Benguela (CFB)|Lobito|Central|1,333|246|18|1,067|
|Caminhos de Ferro de Luanda (CFL)|Luanda|Northern|479|181|38|1,067|
|Caminhos de Ferro de Moçamedes<br>(CFM)|Namibe|Southern|907|425|47|1,067|
|Amboim|Amboim|Central|122|0|0|760|
|Total|||2,841|852|30|-|

_Source:_ Bullock 2009.

# **Challenges**

Angola will need to rehabilitate or rebuild the 60 percent of its current rail network that is currently out of operation. This is a tremendous task, since in many cases it requires expensive works to remove mines and complete replacement of the obsolete or deteriorated rails. In addition, the performance of existing lines is very poor. Freight density and labor productivity are low in Angola in comparison to other African countries, and will need to be tackled through institutional reform (table 10).

**Table 10. Railway indicators for Angola and selected countries, 2000–05**

||CFM (Angola)|BR (Botswana)|CEAR (Malawi)|Nacala Railroad<br>(Mozambique)|Beira Railroad<br>(Mozambique)|Ressano Garcia Line<br>(Mozambique)|Transnamib (Namibia)|Spoornet (South Africa)|RSZ (Zambia)|NRZ (Zimbabwe)|
|---|---|-
145,765|65,243|53|46|1|51.3|50.7|
|Total above|108,619|202,636|94,017|51|47|2|69.1|70.5|
|Memo:|||||||||
|All ECA Countries|157,082|287,317|130,235|44|58|-2|100.0|100.0|

Note: In addition to EU Eastern Enlargement countries, ECA includes South Eastern Europe (Albania, Bosnia and Herzegovina, Macedonia, Serbia and Montenegro) and CIS (Commonwealth of Independent States) economies (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, Russia Tajikistan, Turkmenistan, Ukraine, and Uzbekistan). Source: Computations based on UN COMTRADE Statistics with the EU as a reporter.

Turkey’s export performance in EU markets also stands out against other European and Central Asian countries. While the share of Bulgaria and Croatia in total EU-oriented exports from the European and Central Asian region remained stable, those of Romania, Turkey and EU8 significantly increased demonstrating superior performance vis-à-vis CIS economies. Turkey’s share grew from 10 percent to 12 percent in 2000-04. Overall, the share of EU Eastern Enlargement economies increased from 69.3 percent in 2000 to 70.5 percent in 2004.

# F _. Concluding observations_

The analysis provides empirical support to the following observations. First, thanks to steady expansion of trade in goods and services since the mid-1980s, Turkey has become highly
2016). For aluminum, copper, lead, and zinc, wavelet-autoregressive integrated moving average (ARIMA)-based models have outperformed traditional ARIMA models in terms of forecast accuracy (Kriechbaumer et al. 2014). For lead and zinc, He et al. (2015) found that a curveletbased multiscale forecasting approach was superior to traditional benchmark models such as ARMA and random walk. For lead, ARIMA models have generated slightly better forecasts than models based on lagged forward prices (Dooley and Lenihan 2005).

# **2.2.3. Multivariate time series models**

Multivariate time series models such as VARs have generally outperformed the no-change benchmark and, in many cases, univariate models. In a comprehensive analysis of price forecasts for copper, lead, nickel, tin and zinc, Issler, Rodrigues and Burjack (2014) using annual and monthly data from 1900 to 2010 found that model performance differs by data frequency and commodity. For annual data, univariate autoregressive models were the best for aluminum and copper prices _,_ while VARs produced the best results for lead and zinc, and VECMs had the best results for nickel and tin. But for monthly data, VECMs of all metal prices and U.S. industrial production showed superior forecasting performance. Castro, Araujo and Montini (2013) also 9
Since there are so many unobserved variables and the theoretical determination of interest rates is so complex, it may be useful to approach the question of modeling the entire set of world interest rates as being determined by a small number of unobserved common factors plus idiosyncratic local factors. This approach has been employed, for example, by Koedijk and Kool (1994), in order to analyze European interest rates, and the method is potentially even more effective when data. on a larger number of countries is available.<sup>4 0</sup> It can allow both identification of a core world interest rate for comparative purposes (measure mean deviation in real rates), and also allow the experience of different developing countries to be grouped. The interesting issues include: (a) How many significant common factors are there in real interest rates across the world? (b) What are the characteristics of countries have low correlations with the main common factors? In particular, what is the role of financial repression; and of national inflation vol]atility in determining low correlations.

In practice, we find that on the larger samnple of 24 developing country wholesale rates<sup>4</sup> ' the picture that emerges is of a diverse and volatile interest rate experience, with conisiderable persistence in real interest rate rnovements. The degree to which the movements are common across countries was assessed by computing the eigenvalues and principal components of the real interest rate data. With the first principal component accounting for only some 28 per cent of the total variance it is clear that the degree of commnonality is rather modest. Indeed, a three-factor model explains only 61 per cent of the total variance, and a six factor model is required to reach 80 per cent). With such modest explanatory power, it is not surprising that no evident economic interpretation could be placed on these principal components, nor are they highly correlated with inclustrial country interest rates.

A itechnical difficulty that emerges in using factor models is that high volatility countries
**<mark>Monetary transfers</mark>**

Monetary transfers are captured directly from the survey. They refer to pensions (without separating between contributory and non-contributory), unemployment and maternity benefits, Child Money program, student benefits and several other benefits. The food stamps program, a small but only means-tested program in Mongolia, is not included in the survey. These monetary transfers are classified into three groups: pensions, Child money program and social welfare benefits. No assumptions or computations are made about these. Selection of beneficiaries and amount of benefit received is taken directly from the survey.

# **<mark>In‐kind transfers</mark>**

Education expenditures are allocated to a given household based on the presence of school-aged children attending public school and official education expenditure per aimag. Education expenditures at the aimag level are available for Pre-School and Basic education (which includes both Primary and Secondary). Annual pre-school expenditure per child varies from 1,587,000 MNT in UB to 2,381,000 MNT in Dundgovi. Expenditures per student at the primary and secondary level are less than expenditures for pre-school, ranging from MNT 687,000 in UB to 1,529,000 in Govi-Altai. Tertiary education expenditures are not available at the aimag level, the national average expenditure on tertiary education per student is 3,537,140 MNT.<sup>17</sup> Attendance in pre-school is not captured in the HSES, since these children are too young, thus an attendance rate at the aimag level is assumed, about one-third. By aimag, a random sample of preschool aged children are selected to receive in-kind education. For primary, secondary, and tertiary students, attendance is based on responses in the HSES.

Finally, if a household indicated they used public health services in the HSES, an in-kind health transfer is applied. The amount of the annual transfer per household is 1,655,041.8 MNT, based on total expenditures in public
, firm surveys that explore</mark> the <mark>business costs of natural hazards and infrastructure disruptions are rare in most developing countries, and virtually nonexistent for Tanzania. As a result, there is only limited understanding of how firms are affected by and cope with natural shocks and the associated infrastructure disruptions. This makes it difficult for governments to fully assess economic losses after a disaster, and to identify and prioritize resilient infrastructure investments to ensure business continuity and enhance firms’ resilience to natural shocks.</mark> <mark>To address this gap, we conducted a specialized firm survey in Tanzania to explore the impacts of natural shocks—particularly floods—on firms and the infrastructure services they rely on. Analyzing the results of the survey, this study addresses several interrelated questions:</mark>

1. <mark>What are the impacts of natural shocks on firms? For example, destroyed assets, disrupted water, electricity, transportation, and telecommunications services; impacts on workers.</mark>

2. <mark>What is the role of supply chains in propagating and multiplying disruptions? In other words, from suppliers to clients and end-users.</mark> 3. <mark>What adaptation strategies are used by firms? For example, additional inventories, own generators, and own water sources or tanks.</mark> The results from this study confirm that natural shocks have substantial impacts on firms. A single flood in 2018 directly damaged and destroyed an estimated $7.8 million worth of Tanzanian firms’ buildings, machineries, and inventories. The study also finds that, in the aftermath of a disaster, indirect effects— particularly those resulting from disrupted transport infrastructure—prevent employees in up to 40 percent of firms from coming to work and hinder firms' ability to receive supplies on time or to maintain sales. It also finds that 30–50 percent of all supply and delivery delays of firms in the study region can be attributed to the propagation of shocks
in a live birth or a miscarriage: This identification strategy poses several concerns. We assume that pregnancy results in three possible outcomes: live birth, abortion, and miscarriage.<sup>7</sup> Following Hotz et al. (2005), we can also

> 7 ENADID survey distinguishes between stillbirth and miscarriage: if a pregnancy loss occurs in the first 5 months of pregnancy, it is recorded as miscarriage ( _aborto_ ); if the loss of pregnancy occurs after 5 months of

6
- Jansen, E., Daniels, L., & Nicholson, J. (2012). The dynamics of parenting and early feeding constructs and controversies: A viewpoint. _Early Child Development and Care_ , _182_ (8), 967–981.

- Jenkins, J. M., & Handa, S. (2019). Parenting skills and early childhood development: Production function estimates from longitudinal data. _Review of Economics of the Household_ , _17_ (1), 121–147.

- Kalil, A., Ryan, R., & Corey, M. (2012). Diverging destinies: Maternal education and the developmental gradient in time with children. _Demography_ , _49_ (4), 1361–1383.

- Kohn, M. (1969). _Class and conformity: A study in values_ . University of Chicago Press.

- Maccoby, E. E., & Martin, J. A. (1983). Socialization in the context of the family: Parent-child interaction. In P. Mussen & E. Herrington (Eds.), _Handbook of child psychology_ (pp. 1–102). Wiley.

- Maurin, E., & McNally, S. (2008). Vive la revolution! Long-term educational returns of 1968 to the angry students. _Journal of Labor Economics_ , _26_ (1), 1–33.

- Mazumder, B., Rosales-Rueda, M., & Triyana, M. (2019). Intergenerational human capital spillovers: Indonesia’s school construction and its effects on the next generation. In _AEA Papers and Proceedings_ (Vol. 109, pp. 243–49).

- NICHD. (2002). Early child care and children’s development prior to school entry: Results from the NICHD Study of Early Child Care. _American Educational Research Journal_ , _39_ (1), 133–164.

- Oreopoulos, P., Page, M. E., & Stevens, A. H. (
# 6. Conclusions and Recommendations

Fertilizer subsidies account for a large share of the support that the GoI provides to the agriculture sector, and in 2008 they were almost double the entire budget for the Ministry of Agriculture. Although there are circumstances under which subsidies can lead to efficient outcomes, by addressing market constraints, we argue that this is not the case in Indonesia. In addition, in light of limited fiscal resources, there is a trade-off between providing subsidies for private inputs and the provision of public goods and services. As such, the Government of Indonesia is spending large amounts on fertilizer subsidies at the expense of providing support in other areas that may matter more to farmers and the agriculture sector in general (extension services, R&D, irrigation).

The findings from the two rural household surveys in 2003 and 2007 show there was no targeting of benefits for the fertilizer subsidy program. Thus, most rice producers benefit from subsidized fertilizer regardless of whether they had small/large paddies or their level of wealth. The effect of this policy is regressive and the 40 percent largest farmers capture up to 60 percent of the total subsidy. Fertilizer shortages also mean that very few farmers (less than 10 percent in 2007) pay the maximum price as stipulated by the Ministry of Agriculture.

The subsidies contribute to an increased use of urea, which in some cases has resulted in an overuse that has a negative impact on yields. Thus, overall the relationship between fertilizer use and rice yields is best described as an inverted U-relationship, supporting the existence of an optimum level of fertilizer use, beyond which additional consumption has an adverse effect on output. The maximum level of urea use, at which the relationship with yields reverses from positive to negative, is in line to that recommended by the MoA. However, most farmers report that they use higher than recommended levels of fertilizer. Finally, the findings of this paper suggest that the costs associated with the fertilizer subsidy program, both fiscal and economic, outweigh the benefits from
the survey in Croatia covered both manufacturing and services, in this paper we present comparisons with manufacturing only. It is also worth reminding that although much of the lagging regions in Russia is not surveyed, the survey also skips bigger cities and agglomerations such as Moscow, Moscow Oblast, Saint Petersburg and Leningrad Oblast. Due to restricted coverage on location and size of the firms in Russia, management score could go in either direction. For example, we do not cover smaller firms (less than 25 employees) in Russia (which are covered in both Mexico and the US), and likewise, we do not cover much of the lagging regions in Russia as well. A broader coverage could therefore also pull down the current management score, depending on the share of firms that is added on either end of the location or size distribution. Furthermore, as we will learn later in the paper, a low response rate in Russia is likely leading to an overestimation of the observed management score.

19 This interesting finding is explored more in Annex table A.2, where we compare the average score of firms surveyed in Russia in each of the 16 questions on management practices to the scores of the US firms as detailed in Bloom et al. (2019a).

10
worker upskilling and alternative models for revenue sharing in the economy (Guellec and Paunov, 2019; Autor et al., 2020).

The main contribution of our study relative to the growing literature on the pandemic’s effects on trade is the emphasis on the role of product resilience. We provide empirical evidence to inform two debates: whether reliance on inputs from abroad, and China in particular, caused more vulnerabilities and how social distancing measures affected global production and exports. Thereby, our paper adds to the studies of the impacts of COVID-19 on trade and GVCs (e.g., Bonadio et al. 2021; Cerdeiro and Komaromi, 2020; Crozet et al. 2021; Demir and Javorcik 2020; Espitia et al. 2022; Fujiy, et al., 2021; Lachitew and Socrates, 2020). Using cross-country-productmonth level data, Berthou and Stumpner (2021) show that lockdown measures implemented by exporter and importer countries impacted trade. Bricongne et al. (2021), Pimenta et al. (2021), and Lucio et al. (2021) also show that lockdown stringency in destination markets reduced, respectively, French, Portuguese, and Spanish firms’ exports at the onset of the COVID-19 crisis.

Closer to our work, Liu et al. (2021) show better performance in the early phases of the pandemic of Chinese exports of medical products, products with a high share of work from home, high contract intensity, and capital goods, while Lafrogne-Joussier et al. (2021) look into one production vulnerability, the reliance on inputs from China, showing that French firms with higher such reliance suffered more input shortages that translated into a decline in their exports through input-output linkages. These two papers differ from ours in that they focus on single countries’ exports (China and France) and single or few specific dimensions of how production was affected by COVID-19, while our analysis covers exports by all countries to the 29 largest markets and an encompassing set of product vulnerability dimensions. Differently from
<!-- Start of picture text --> No Yes No Yes<br>Outages but Power is not the biggest obstacle Outages and Power as the biggest obstacle<br>AFR EAP ECA LAC MNA SAR AFR EAP ECA LAC MNA SAR<br>2.7<br>2.7<br>2.6 2.6<br>2.5<br>2.5<br>2.4<br>Total factor productivity 2.4 Total factor productivity<br>2.3<br>2.3 2.2<br><!-- End of picture text --> Figure 3: The combined effects of outages and other power constraints ized differences in means between these two groups with the corresponding level of significance in each case.

Table 4: Descriptive Statistics (T=Power Outages)

||[1]|[2]|[1]_−_[2]|
|---|---|---|---|
||Outages|No Outages|Diff.|
|Firm size|1.802|1.831|-0.038***|
|Firm’s longevity|29.690|29.737|-0.003**|
|Own website|0.386|0.471|-0.173***|
|Foreign private participation|8.082|7.222|0.034**|
|GDP growth (annual %)|3.321|4.995|-0.511***|
|Infation (annual %)|7.726|7.480|0.043***|
|log(Bank concentration in %)|4.047|4.084|-0.108***|
|log(FD index)|-1.496|-1.243|