rafmacalaba/datause-encoder-data huggingface dataset:
# **Impact of the Russian Invasion on Ukrainian Farmers’ Productivity, Rural Welfare, and Food Security**

# **1. Background and motivation**

With a total agricultural area of close to 45 million ha of very fertile land,<sup>1</sup> Ukraine has long been a major breadbasket and exporter of agricultural commodities. Beyond causing immediate hardship and triggering a large exodus of displaced people, the Russian Federation’s military invasion of Ukraine and the economic blockade of its Black Sea export routes have also led to sharp increases in grain prices <mark>(von CramonTaubadel 2022)</mark> and raised concern about global food security. While fears of such consequences have eased somewhat with partial opening of maritime export routes in the context of the UN-brokered grain deal, there is little doubt that the war and associated political changes will profoundly affect Ukraine’s farm sector and rural areas.

To better understand impacts on the welfare of Ukraine’s farm population as well as the profitability, food supply, and prospects for future development of the country’s agriculture sector, this paper reports results from a survey of small and medium sized farms for the 2021 and 2022 agricultural seasons. We aim to answer three questions: First, has the invasion created an imminent threat of decapitalization that might permanently undermine the rural economy’s capacity to invest including in human capital? Second, how have area cultivated, profitability, crop choice, and level of market participation been affected by the war overall, what are the underlying mechanisms, and how do war effects vary across farm size group and regions? Third, what is the extent of informality in Ukraine’s agriculture sector and how do productivity and war impact differ between farms in the formal and informal sectors?

On the first issue, studies show that, if it forces households to liquidate assets to survive, exposure to conflict can have persistent effects, beyond short term impacts on food security (George _et al._ 2020; Muriuki _et al._ 2023) or agricultural output (George _et al._ 2021). In Colombia, violence led households to shift to activities with lower but less risky returns and, in the extreme, revert to subsistence (Arias _et al._ 2019). While direct effects may be of temporary nature only (Miguel & Roland 2011), violence can have long term effects if it is associated with refugee flows via forced displacement (Ibanez _et al._ 2022), reduces households’ investment in human capital (Büttner _et al._ 2022), leaves residues such as land mines (Arcand _et al._ 2015), or is used as cover for opportunistic land holding expansion by elites (Tellez 2022). Adverse long-term impacts of violence via human capital accumulation are documented for Nigeria’s 1967–1970 Civil War

> 1 Ukraine’s agricultural land endowment is larger than the agricultural area of France (18 million ha), Germany (12 million ha) and Poland (11 million ha) combined.

2
Table A7: Rwanda - Educational attainment of children and their parents by five-year birth cohort

||Average years<br>of schooling|No schooling|Primary|Secondary|Tertiary|
|---|---|---|---|---|---|
|Son||||||
|1931-1935|2.4|44.7|50.4|4.3|0.7|
|1936-1940|2.8|38.2|54.3|7.5|0.0|
|1941-1945|2.6|41.5|51.3|6.8|0.4|
|1946-1950|3.4|33.7|56.6|7.7|2.0|
|1951-1955|3.6|29.2|60.4|9.5|1.0|
|1956-1960|3.7|30.8|55.8|11.3|2.0|
|1961-1965|4.4|25.9|57.7|12.8|3.6|
|1966-1970|5.2|21.3|54.1|20.3|4.3|
|1971-1975|5.2|20.2|54.8|22.2|2.8|
|1976-1980|5.2|13.5|64.8|20.4|1.3|
|Daughter||||||
|1931-1935|0.6|82.8|16.2|1.0|0.0|
|1936-1940|0.6|81.4|17.5|0.7|0.4|
|1941-1945|1.1|71.4|25.7|2.9|0.0|
|1946-1950|1.4|66.8|28.9|3.8|0.4|
|1951-1955|1.9|54.7|39.8|5.3|0.2|
|1956-1960|2.4|48.4|44.5|6.7|0.5|
|1961-1965|3.3|43.2|43.4|12.4|1.0|
|1966-1970|4.3|29.7|51.1|18.2|1.0|
|1971-1975|4.8|20.4|60.9|17.0|1.6|
|1976-1980|5.2|15.8|63.1|19.8|1.3|
|Mother||||||
|1931-1935|0.0|99.4|0.6|0.0|0.0|
|1936-1940|0.1|98.6|1.4|0.0|0.0|
|1941-1945|0.1|97.3|2.7|0.0|0.0|
|1946-1950|0.2|96.6|3.3|0.1|0.0|
|1951-1955|0.3|94.0|5.9|0.1|0.0|
|1956-1960|0.3|93.8|5.8|0.4|0.0|
|1961-1965|0.5|87.6|11.8|0.6|0.0|
|1966-1970|0.9|81.2|17.1|1.5|0.2|
|1971-1975|1.3|73.4|22.9|3.5|0.3|
|1976-1980|1.9|60.1|35.2|4.4|0.2|
|Father||||||
|1931-1935|0.3|96.3|2.8|0.6|0.3|
|1936-1940|0.2|95.8|3.5|0.7|0.0|
|1941-1945|0.4|93.9|5.1|1.0|0.0|
|1946-1950|0.5|91.2|7.5|1.3|0.0|
|1951-1955|0.8|86.3|12.6|1.1|0.1|
|1956-1960|1.0|81.4|16.7|1.8|0.1|
|1961-1965|1.5|72.5|24.1|3.1|0.3|
|1966-1970|2.1|63.9|30.5|5.1|0.5|
|1971-1975|2.7|54.1|38.8|6.4|0.8|
|1976-1980|3.2|43.8|46.9|8.3|0.9|

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**Table 5.** Relationship of trees on farms and daily consumption per person

Dependent Variable = Log. Real Daily Consumption per person (in 2011 PPP)

|||(I)|(II)|(III)|(IV)|
|---|---|---|---|---|---|
||Trees On Farm (yes = 1)<br>|0.597***<br>[0.037]||||
|_Ethiopia 2011-12_|Fruit Trees On Farm (yes = 1)<br>Tree Cash Crops on Farm (yes = 1)||0.382***<br>[0.053]|0.612***<br>[0.039]||
||Trees for Timber or Fuelwood on Farm (yes = 1)||||0.132|
||||||[0.134]|
|_Malawi 2010-11_|Trees On Farm (yes = 1)<br>Fruit Trees On Farm (yes = 1)|0.000<br>[0.031]|-0.006<br>[0.010]|||
||Trees for Timber or Fuelwood on Farm (yes = 1)||||-0.323***<br>[0.103]|
||Trees On Farm (yes = 1)|0.212***<br>[0.035]||||
|_Nigeria 2010-11_|Fruit Trees On Farm (yes = 1)||0.252***<br>[0.046]|||
||Tree Cash Crops on Farm (yes = 1)|||0.177***||
|||||[0.030]||
||Trees On Farm (yes = 1)|-0.002<br>[0.030]||||
|_Tanzania 2010-11_|Fruit Trees On Farm (yes = 1)<br>Tree Cash Crops on Farm (yes = 1)||0.011<br>[0.010]|0.032***<br>[0.011]||
||Trees for Timber or Fuelwood on Farm (yes = 1)||||0.010|
||||||[0.010]|
||Trees On Farm (yes = 1)|0.010<br>[0.025]||||
||Fruit Trees On Farm (yes = 1)||0.102***|||
|_Uganda 2010-11_|Tree Cash Crops on Farm (yes = 1)||[0.032]|0.002<br>[0.010]||
||Trees for Timber or Fuelwood on Farm (yes = 1)||||0.002<br>[0.021]|

_Note:_ Sampling weights and fixed effect were used for all regressions. * p<0.10 ** p<0.05 *** p<0.01.

28
If a country’s distortion is larger than that of the frontier country, then _τ >_ 0, since by design it produces less. Figure 7 summarizes the estimated distortions by industry in Peru, Colombia, Chile, Costa Rica, Mexico and Brazil. Except for Mexico, all the countries have significant distortions in ICT relative to Korea - in the case of Costa Rica up to 40%.

Two aspects should be noted from these estimates: (1) Output distortions have been rising over time - our hypothesis is that they limit entry, make markets more concentrated and have slowed productivity growth. (2) Input distortions in the ICT sector are high relative to frontier economies, but remained constant over the sample period.

These higher distortions are correlated with low spending by ICT firms on research and development (R&D) relative to middle-and high-income countries (Figure 8) and with low patent registrations (Figure 9).

# **3 ICT Misallocation and Productivity**

This section presents a framework to examine the role of ICT production and competition in aggregate productivity. It builds on Edmond et al. (2015) who analyzed the impact of competitive gains from international trade with varying markups and that where there is extensive misallocation, greater competition reduces markup distortions by creating competition and hence reduces productivity losses. In an extension, Edmond et al. (2018) decompose markup costs into three distinct channels: (1) Aggregate markups act as a tax on output. Competition within a market is low when markup dispersion is higher among firms in that market. (2) The result is a misallocation of factors of production. This is often associated with reallocation of production from firms with high labor shares to ones with low shares and (3) it leads to inefficient entry.

An interesting result of the Edmond et al. (2018) framework calibrated using U.S. census data is that while competition reduces the markup of incumbents, it also reallocates market share toward the largest incumbent firms, leaving the net effect of markups unchanged.

We recast Edmond et al. (2015) in the domestic market as two sectors, ICT and nonICT and three types of firms: final good producers, traditional intermediate goods firms and ICT intermediate goods firms.

## **3.1 Final Goods Producers**

A single consumption good is produced in a perfectly competitive industry using inputs _y_ ( _s_ ) from a continuum of sectors: 6
increase in the Lerner index. Table 5 reports the average for the Lerner index across the same two periods we examined in the case of the H-statistic, namely 1994-2001 and 2002-2008. Also, this table reports the p-values for a t-test of whether the average of the Lerner index changes across periods. With the exception of Egypt, where the Lerner seems to have declined over time, and Morocco and Tunisia, where we cannot reject the null of no change, for all other countries, our tests indicate an increase in market power among banks over time.

Relative to other regions, the Lerner index for both Non-GCC but, especially, for GCC countries is higher. Table 6 reports the average Lerner index across regions for the period 19942008 and for the recent period, 2002-2008. Over the most recent period, we see that the average Lerner for GCC countries is higher than those for all other regions. Furthermore, these differences are statistically significant in all cases. In the case of Non-GCC countries, the Lerner index is higher for these countries relative to that for Eastern Europe, Latin America, and SubSaharan Africa, but the differences do not appear to be statistically significant. Overall, the evidence based on the non-structural measures of competition suggests that banking sectors in MENA, and, in particular, those in GCC countries are less competitive than those in most other regions.

# **4. Methodology and data to evaluate what drives differences in competition across regions**

In order to understand why banking sectors in MENA are less competitive than those in other regions in the developing world, we conduct an empirical analysis of the determinants of competition across countries. We use the H-statistic as our summary measure of competition across countries.<sup>8</sup> In particular, using bank-level Bankscope data for the period 2002-2008, we calculate the H-statistic for all countries for which at least 50 observations are available. With these data we run cross-country regressions in order to investigate the potential factors that explain banking competition in MENA.

Our estimations include the following two steps. First, as shown in equation (4) below, we regress the H-statistics against a dummy for countries in MENA (i.e., a variable that takes

> 8 However, results do not change significantly if we use the Lerner index instead.

8
15 -

-

obtained from individual states have then been aggregated to obtain the All India situation.9/ Table 3 presents the empirical results on inequality and poverty measures aggregated over the rural areas of 16 major states. Average consumption per capita rose by 2.3 percent per annum froni 1977-78 and then the growth rate stabilized at around 2 percent per annum.

The total inequality in per capita consumption (measured by Theil's index) rose dramatically from 1973-74 to 1977-78, but fell from 1977-78 to 1983. However, from 1983 to 1986-87, inequality did not change lo/ significantly.Between state inequality contributed 6.8 percent to the total inequality in 1973-74, which means that the within-state inequality is the major determinant of the inequality at the All India level. This suggests that policies aimed at redistribution of income and assets within the poorer states such as Bihar deserve greater emphasis. The share of between-state inequality grew, however, from 6.8 percent to 11.7 percent between 1973-74 and 1983, but fell dramatically to 7.1 percent between 1983 and 1986-87. Thus, the between state disparities in average living standards have shown a tendency to decrease over the most recent period. But this phenomenon is

- 9/ It is worth repeating that this procedure, unlike the procedures adopted by many previous researchers, appropriately takes account of the regional price variations while deriving poverty at the All India level.

lo/ It needs to be stressed that these are rural growth and inequality trends. These changes trigger off changes in urban growth and inequality - an aspect not examined in this paper.
respondents’ access to water and sanitation services, and their use of water and sanitation infrastructure. It also included questions on household expenditure, health and hygiene.

From the NWSS household survey , we were able to construct seven access to WASH indicators, informed by the Sustainable Development Goals (SDGs) (WHO/UNICEF 2015). These indicators are: (1) access to improved water, (2) access to basic water, (3) access to improved water on premises, (4) access to piped water on premises, (5) lack of access to fixed-point sanitation (also known as open defecation), (6) access to improved sanitation, and (7) access to sewerage connection, with definitions as follows:

- (1) **_Improved water_** sources are those which, by the nature of their construction and when properly used, are adequately protected from outside contamination, particularly fecal matter. Such sources include piped water to yards/plots, public taps or standpipes, tube wells or boreholes, protected springs, and rainwater.

- (2) **_Basic water_** satisfies the requirements of “improved water” while also satisfying the additional requirement that it take less than 30 minutes, round trip, to collect the water in question.

- (3) **_Improved water on premises_** fulfills the same requirements as basic water, but further implies that the water is available directly on household premises.<sup>6</sup>

- (4) **_Piped water on premises_** fulfills the same requirements as improved water on premises, but is provided through pipes.

- (5) **_Fixed-point sanitation_** involves a pit or other containment structure, regardless of the quality of the structure or whether it is hygienically maintained. While it includes both improved and unimproved facilities, it stands in contrast to open defecation, which is defined as not having access to any type of toilet.

> 6 The global SDG indicator for water is defined as the “percentage of population using safely managed drinking water services,” and covers those improved drinking water sources that are (1) located on premises, (2) available when needed, and (3) compliant with fecal and priority chemical standards. Unfortunately, at the time the FMWR commissioned data collection for the National Water and Sanitation Survey (NWSS), this SDG indicator had not yet been defined, so we did not include access to safely managed water in the MBG model.

4
# World Bank-Supported Adjustment Programs: Country Performance and Effectiveness

by Vittorio Corbo and Patricio Rojas

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Initial Conditions, External Shocks, Policy<sup>Stance, and a First</sup><br>Look at Performance|3|
||Country Performance<br>Policy Stance|5<br>6|
|3.|Statistical Analysis of Country Performance|8|
||The B'fore-After Approach<br>Control-Group Approach<br>A Modified Control-Group Estimator|8<br>10<br>11|
|4.|Overview of the Data|16|
|5.|The Empirical Results|17|
|6.|Conclusions|21|
|Table|s|23|
|Appe|ndix A|33|
|Refe|rences|37|
marketing of such solar products were limited as SHSs are expensive for ultra-poor households residing in _Chars_ .<sup>1</sup> Hence, coverage of business activities by these NGOs was minimal. As there exist hardly any alternative sources of electricity, most _Char_ residents use kerosene-based lighting equipment, such as open-wick lamps and covered-wick lamps (known as “Hurricane” lanterns), as their main source of nighttime lighting, particularly for activities like studying and cooking. Some households used battery-powered flashlights for emergencies. However, these flashlights (also known as “Torch” lights) have very limited power to perform any additional tasks, and the required batteries are expensive and often unavailable to _Char_ dwellers.

# **Solar devices**

To study the impact of portable solar lanterns on education, we used d.light solar lanterns, which are certified and recognized under the World Bank Lighting Global Project (https://www.lightingglobal.org/). We collaborated with Gana Unnayan Kendra (GUK), a northern-based NGO in Bangladesh, in implementing our research project. In total, we had 500 units of the d.light S250, along with an additional 300 units of the other two types of product called the S10 and the S2, with the financial support provided by Kopernik, Daiwa Securities and BRAC. The details of these products are as follows (see also Figure S.1 in the supplemental appendix):

- S250: The d.light S250 is their flagship product, which provides bright light for a maximum of four hours and illuminates a room to the same degree as a 3 to 5-watt compact fluorescent lamp (CFL). This unit also has the functionality of charging cellular phones. The S250 has a separate lightweight solar panel that needs to be used to recharge the unit.

- S10: The d.light S10 is a portable solar light-emitting diode (LED) lamp that provides bright light for a maximum of four hours. This unit does not possess the functionality of charging mobile phones. The solar panel of the S10 is combined with the main unit.

- S2: The d.light S2 is the simplest and most highly efficient LED; it provides a focused light for a maximum of four hours. The illumination capacity of this unit is lower than that of the other two units. Like the S10, this unit also lacks the functionality of charging mobile phones. The solar panel of the S2 is also combined with the core unit.

In terms of lighting capacity, the S250 could provide, on average, 110 lumens of light (the lumen is the measure of light emission) based on the top brightness setting after a full day’s solar charge; the S10 and S2, on the other hand,

> 1The minimum amount to be paid for a 10-watt panel with a 2.5 LED tube-light or a 5-watt CFL with a two-year installment package is 8100 taka (approximately 105 USD), which provides lighting for only four hours. See http://www.gshakti.org/index.php?option=com _\_ ~~c~~ ontent _\_ &view=article _\_ &id=115 _\_ &Itemid=124.

6
considering separately the periods before and after the global financial crisis, namely 2003-2007 and 2008-2011.

# **4.3.  Ex-post differences in firm performance**

To examine the firm dynamics around the use of markets and whether the ex-ante differences persist in the aftermath of the capital raising activity, we estimate regressions that trace the evolution of the firm attributes around the year in which firms issue bonds or equities, considering the run-up year and the year after this activity.<sup>27</sup> We compute this type of event studies for each of the firm-level attributes analyzed above using dummy variables that estimate a three-year window around the use of capital markets. The regressions use all the available data for all firms in our data set. The coefficients on the dummy variables measure whether the firm attributes around the use of markets are statistically different from those of our control group in these regressions, namely, the non-users and the users during the years outside the three-year window around the capital raising activity. We also test whether the coefficients on these dummy variables are equal to each other, which allow us to gauge whether the use of markets is associated with a different performance around the issuance. All regressions include industry dummies (leaving out as a base the dummy for the manufacturing sector) and time dummies.<sup>28</sup> Consistent with the evidence presented in the previous section, the results show that before it uses capital markets, the average firm is larger and grows faster, has a longer maturity structure

> 27 In unreported results, we use for the pre-issuance period all years before the first capital raising takes place and for the post-issuance period all years after that issuance. For each firm, we only consider the first bond and the first equity capital raising activity during our sample period. If a second issuance takes place, we exclude the observations from that event onward. The results are qualitatively similar to the ones reported here. As an additional robustness exercise, we also estimate fixed-effect panel regressions to explore the within variation among users of capital markets. The results are also broadly consistent with the ones reported here.

> 28 The use of time dummies allows us to control for common factors driving firm performance in a given year, such as the possible market timing associated with capital raising activity. For example, firms might issue capital during stock market boom years and subsequently underperform as market conditions deteriorate. Heterogeneity among investors could be one of the explanations why some investors might be willing to invest in these firms even when they underperform (Campbell et al., 2012).

25
methodology of directly converting quantities of each food into calories using the calories per unit weight for each item reported in (Gopalan et al. 2004) is used, with additional supplemetal data from the MedIndia web-site.<sup>10</sup> This covers 90.8% to 95.4% of average household food expenditure between 1983 and 2012.

For goods that have missing units or that are classified as “other” within a major category heading (e.g. fruits, vegetables, meat, dairy), calories proportional to the average calorie per rupee calculated within that category and NSS region are assigned. This increases the share of food expenditures covered by an additional 0.5%. The rest of food expenditures fall under the processed food and beverages categories, where the lack of units and/or vague classification make the use of a caloric conversion difficult if not impossible, e.g. the count of cooked meals or “cold beverages bottled/canned” or unitless expenditures on “prepared sweets” or “salted refreshments.” Calories from reported tea, coffee, and alcohol consumption are straightforward to incorporate because their units are reported. For the other goods, the reported estimates assume that calories per rupee are equal to 50% of the calories per rupee that can be directly converted across all goods. Ingredient costs appear to make up 40% of the sale price at large Indian restaurants ((Federation of Hotel and Restaurant Associations of India 2004)) while the value for richer countries is typically in the 20-35% range. A value of 50% puts the calories/rupee of processed foods, beverages and cooked meals roughly equal to the dairy category, while a value of 66% puts it equal to pulses or pure sugar.

The other adjustment made is similar to the official NSS estimates, which incorporate survey information on the number of meals consumed by household members at home and away on payment (which are drawn from the calories measured in the survey), the number of free meals consumed away from home through employers, schools and other households (which are not included in the survey quantities), and meals given to non-household members (which are drawn from the survey quantities but do not contribute to household caloric intake). The simple adjustment factor is based on the formula _adjustment factor = (meals at home + meals away from home free)/(meals at home + meals to others)_ . This assumes that households that consume more calories per meal at home both give and receive free meals that are proportionately higher in calories, and that free meals given and received en-

> 10See http://www.medindia.net/calories-in-indian-food/index.asp.

23