rafmacalaba/datause-encoder-data huggingface dataset:
offered subsidized registration (Benhassine _et al._ 2018) would fall back into informality, a notion supported by a number of randomized trials from Latin America or South Asia (Bruhn & McKenzie 2014). If, on the other hand, entry barriers or other factors reducing the benefits of formality apply only to a subset of firms, eliminating these could set off large gains in terms of competition, output, and wages for skilled labor (Ulyssea 2018).

We contribute to this debate in two ways. First, to measure the size of informality in the agriculture sector, we make use of the fact that informal farms operate unregistered land to combine our sample frame with estimates of agricultural crop cover based on satellite imagery. We find that 14.5 mn. ha, or about 32% of the 45.7 mn. ha of Ukraine’s cultivated area, is operated informally. Although informal farms are smaller, their total factor productivity (TFP) is indistinguishable from that of formal enterprises of comparable size, suggesting that, rather than productivity differences, either other barriers to farm entry (Deininger _et al._ 2018) or factors affecting the benefits from formalization for specific farms may have precluded formalization.

One candidate for such barriers is restrictions on transferability and inheritance to rights of ‘permanent use’ of land that was given to emergent farms in the early 2000s. As use rights are concentrated with the smallest farms, such restrictions could partly explain variation in TFP across farm size groups. Moreover, although they can be transferred to a legal entity established by the original assignee, such personal rights would not survive the death of the original assignee, reducing the benefits from formalization and incentivizing farms with larger shares of land in use rights to remain informal. Exploring this in greater detail is an important area for follow-up research with potentially far-reaching implications for Ukraine’s future development.

The paper is structured as follows. Section 2 provides conceptual and methodological background by discussing evidence of the aggregate impact of the invasion and details of constructing the sample frame and ways to infer the size of the informal sector. Section 3 provides evidence from our data at household and farm level for 2021 and 2022 to show how asset positions and factor market participation changed with the invasion. Section 4 provides evidence on how direct and indirect war effects changed land allocation, profitability, and productivity by farm size. Section 5 concludes and draws out policy implications.

# **2. Motivation and context**

This section discusses studies that assessed the impact of the Russian invasion of Ukraine on agricultural exports and global food security, the channels through which such impacts could come about, and the micro data underpinning such estimates. It highlights how representative farm level data can complement existing data sources and, by providing information on economic factors at a more granular level, informs estimates of negative conflict impacts and ways to try and minimize them.

4
# _Learning from regulatory reform_

The impact evaluations profiled above used randomized experiments to randomly offer the program to selected individuals, firms, banking branches, farming localities, and slum areas. However, this approach to evaluation may not be possible with some forms of FPD projects, such as reforms in the regulatory environment. Nevertheless, in many cases rigorous impact evaluation is still possible. We illustrate this through consideration of two recent studies which have conducted impact evaluations of regulatory reforms.

The view that burdensome regulations are an important barrier to private sector development was famously expressed by de Soto (1989), who calculated that it would take 289 days, 11 permits, and over $1,000 to legally register a small business in Peru. This emphasis on regulatory reform has been further spurred by the World Bank’s _Doing Business_ project, which ranks countries each year on both the overall ease of doing business, and on the extent of reforms undertaken in the previous year. The 2009 report notes that almost 1000 reforms have been recorded in the areas measured by Doing Business have occurred in the past six years, with the most common reform being one which makes it easier to start a business by reducing the costs and number of procedures needed. Yet despite the huge number of reforms, there is almost no rigorous impact evaluation of these reforms.

An exception is found in Bruhn (2008) and Kaplan et al. (2007), who study the impact of business registration reform in Mexico. The reform was organized by a federal agency, but implemented at the municipal level since many business registration procedures were set locally. Due to staffing constraints, the federal agency could not implement the reform in all priority municipalities at once, but instead staggered the reforms, introducing them first in some municipalities and then later in others. Among the municipalities identified as priorities for implementation, there was no specification of which should go first. This allows the author to use municipalities in which the reform was introduced later as a control group for the municipalities in which it was introduced earlier, using a difference-in-differences estimation methodology. This estimation essentially looks at the period where the first few municipalities had reformed and others had yet to. It then compares the change in the number of registered businesses (or in other outcomes of interest) for those municipalities where the reform was introduced early to

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more stable in Poland and Slovenia. Similar patterns apply for financial corporations. (In both cases, the data refer to aggregate liabilities, which in part will take the form of equity.)

**Table 3.6b.  Private Sector Credit, Households, Percent of GDP**

||Country|Privat|e Credit,<br>GDP|% of|Priv<br>Hou|ate Cred<br>seholds,<br>GDP|it to<br>% of|Pr<br>House<br>|ivate Cre<br>hold Mo<br>% of GD|dit<br>rtgage,<br>P|
|---|---|---|---|---|---|---|---|---|---|---|
|||2004|2007|2009|2004|2007|2009|2004|2007|2009|
|New Member States|Bulgaria<br>Czech Republic|35.2|62.8|75.3|10|23|28.2|2.7|10.4|13.5|
||Estonia|39.6|86.1||19.7|43.3|52.5|14.6|37.7|46.5|
||Hungary|44.3|59.5|66.5|12.8|21.6|26.8|9.5|16.4|21.2|
||Latvia|50.3|88.3|103.3|17.6|42.7|47.5|12.4|33.7|36.9|
||Lithuania|28.8|60.4|69.8|7.1|24.4|29.2|5.5|17.2|22|
||Poland|31|44.6|55.2|10.6|20|28.7|3.8|9.9|15.8|
||Romania|15.6|35.6|40.7|4.8|17.7|19.1|0.5|1.4|4.5|
||Slovakia|30.4|42.4|51.1|8.6|16.3|22.2|2.9|4.5|6|
||Slovenia|48.1|78.8|92.7|12.2|19.2|22.6|2.8|6.2|9|
|Candidates and|Albania|9.6|30|37.2|2.8|10.6|12.5|1.4||8.5|
|Potential Candidates|Bosnia and Herzegovina|32.3|44.4|50.2|13.6|26.2|26.3||||
||Croatia|51.8|67.1|69.6|30.4|41.1|36.9|10.1|16.4|15.9|
||Macedonia, FYR|22.1|36.8|42.9|5.6|13.5|||2.6||
||Serbia|24.8|35.3|45|4.9|12.6||0.7|3.8||
||Turkey|17.3|29.5|33.6|4.9|11.6|14.4|0.5|3.9|4.8|
|Eastern Partnership|Armenia|7|13.6|23.6|2.9|7.1|9.9||1.7|2.7|
|Countries|Georgia|9.7|27.1||2.8|8.8|10|1|2.6|5.1|
||Moldova|0|0|0|0.9|5.5|3.3|1.1|4|3.9|
||Ukraine|25.2|58.2|73.3|6.6|22.5|26.4||6.5|14.5|

Source: European Bank for Reconstruction and Development.

Finally, Table 3.10 shows the data for the category of “other monetary financial institutions”. This group saw a substantial decline in net financial assets (often negative) between 2002 and 2007 but there was a marked deleveraging between 2007 and 2009.

**Table 3.7. Household Balance Sheets**

|||Ho<br>Financ|usehold,<br>ial Asset<br>GDP|Net<br>s, % of|Household Gr<br>Liabilities,|oss Fina<br>% of G|ncial<br>DP|Ho<br>Lia|usehold<br>bilities,|Gross L<br>% of G|oan<br>DP|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Country|2002|2007|2009|1999<br>2002|2007|2009|1999|2002|2007|2009|
|New Member States|Bulgaria|50|84|57|7|26|50||4|23|29|
||<br>Czech Republic|53|54|55|18<br>25|28|35|7|10|25|31|
||Estonia|52|62|67|10<br>18|58|69|7|14|48|58|
||Hungary|62|64|64|5<br>11|50|54|4|11|30|38|
||Latvia|34|5|10|2<br>4|33|42|3|9|48|51|
||Lithuania|45|29|39|8<br>14|33|41|2|3|27|33|
||Poland|41|51|43|8<br>16|25|33|6|13|24|32|
||Romania|33|56|47|4<br>4|26|31|1|2|20|24|
||Slovak Republic|39|13|12|21|29|33|5|9|24|34|
||Slovenia|67|79|79|9<br>16|34|48||16|25|29|

Source: Eurostat.

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**Figure  2.   Demand for Representative CRTS good g in Regional Market r**

<!-- Start of picture text --> Armington Aggregate Demand<br>for CRTS good g in RM r<br>CES<br>σ = sigmadm a /<br>Russia goods<br>Rest of World Imports<br>CES<br>σ = 1.5 * sigmadm<br>Own Regional Market Other Russian Imports<br>CES<br>σ = 2 * sigmadm<br>Imports from Imports from<br>Imports from<br>Regional Market 1 Regional Market q<br>Regional Market m<br><!-- End of picture text -->

> **<u>a/</u> sigmadm = 3 in CRTS sectors, except in OTH (other goods producing sectors). For OTH we rely on estimates from Ivanova (2005).**

53
15 4 convey the same message, i.e., Africa is now among the regions which are most highly dependent on a relatively few export products and, unlike all other regions, this trade dependence has _increased sharply_ over the last three decades.

# IV. OECD Protectionism and the External Environment for African Exports

Did external protectionism play a role in the marginalization of Africa in world trade that occurred over the last three decades? Such could be the case if foreign tariffs and nontariff barriers discriminate against Africa specifically, or against the types of products Africa exports. Statistics on OECD trade barriers compiled by UNCTAD can provide direct evidence which bears on this question."

# A. OECD Tariff Barriers Facing Africa

A problem one faces in attempting to provide answers is the widespread departure from the mostfavored-nation (MFN) principle in OECD trade regimes. Recently these departures have taken the form of regional trade preferences such as the North American Free Trade Arrangement (NAFTA), EU-EFTA preferences for trade within and between member states, or EU and EFTA preferences for Eastern Europe and Mediterranean countries."<sup>2</sup> Sub-Saharan Africa receives trade preferences under the OECD's Generalized System of Preference (GSP) schemes, and through the European Union's Lome "To assist developing countries in the Uruguay Round the World Bank and UNCTAD developed a database on tariffs and nontariff barriers with related software for analyzing this information. Since the trade barrier data was matched with tariff line level trade statistics it allowed developing countries to analyze the impact of trade barriers on their exports and to help formulate national strategies for negotiating in the Round. The system, called SMART (Software for Market Analysis and Restrictions on Trade) has been installed in over 40 developing countries.

'<sup>2</sup> Braga and Yeats (1994, Table 1) estimate that almost 50 percent of world trade in manufactures occurs under preferences. European intra-trade accounts for almost two thirds of this total with the EU and EFTA arrangements being of particular importance. Aside from the intra-trade of countries within these two groups, which is all duty free, a protocol allows for duty free trade in manufactures between EU and EFTA. According to Braga-Yeats tabulations the European arrangements cover a trade value more than _seven times greater_ than that of NAFTA intratrade. See Harmsen and Leidy (1994) for a listing of free trade arrangements that have been notified to the GATT.

I
In Section 4 we analyze the empirical implications of the model, studying the effects of natural resources on GDP per capita and on its rate of growth. We find that, when interactions with human capital are ignored, an increased abundance of natural resources reduces the rate of growth but increases income. When we add to the regression analysis a term that interacts human capital and natural resources, we find that, for high levels of human capital, the rate of growth also increases with the abundance of natural resources. Section 5 concludes.

Scandinavia is perhaps the most striking case of development based on natural resources. For this reason, we motivated our theoretical model by presenting in the next section the experiences of Scandinavia and Latin America. As our review indicates, since the second half of the nineteenth century, a high level of human capital along with other factors made successful development possible for the first group of natural resources-rich countries.<sup>3</sup>

# **2 Human Capital and Natural Resources: Scandinavia vs. Latin America**

A closer look at the history of Scandinavia and Latin America shows that, during the late nineteenth and early twentieth centuries, both groups of countries enjoyed similar levels of GDP per capita, and, more important from this paper’s perspective, both were mostly exporters of natural resources. In 1870 Finland, Norway, and Sweden had incomes per capita of $1107, $1303 and $1664, respectively, whereas Argentina and Chile had incomes per capita of $1311 and $1153 , respectively. However, the long-run evolution of the two groups of countries was quite different: the Scandinavian countries developed, but the Latin American countries did not. By 1990 the divergence in income levels was striking. Whereas Finland, Norway, and Sweden by that year had incomes per capita of $16604, $16897 and $17695, respectively, Argentina and Chile had fallen far behind, with incomes per capita of $6581 and $6380 dollars, respectively (table 1).<sup>4</sup>

> 3See Leamer (2003) for more details on the relationship among distance, natural resources, and trade.

> 4All figures come from Maddison (1995).

4
leads firms to invest in job training. The estimates in columns (1) to (3) show that firms that adopt complex software do not change their behavior regarding training provided to workers but they increase the likelihood of providing ICT-specific training to the manager by approximately 23 percentage points. We also test for the possibility that firms adopting the complex software engage in a reorganization process, for example by outsourcing activities. As an example, one could think of the adoption of complex software that includes the services of workers needed for its implementation. To test this mechanism, we consider as a new dependent variable in Equation (4) an indicator variable for whether the firm engages in outsourcing. Column (4) shows a positive though insignificant effect of complex software on firm outsourcing.

# **9. Conclusion and Policy Recommendations**

A large body of evidence, mainly for developed countries, documents that labor markets are becoming more polarized, with employment and earnings shifting from middle-skilled jobs to both high-skilled and low-skilled jobs. This pattern has given way to several concerns on the extent to which technology adoption could be a core contributing factor, automating routine tasks and potentially displacing middle-skilled occupations. Recently, however, additional concerns arise, as more advanced technologies used by typically more educated workers are increasingly replacing also cognitive and analytical tasks. At the same time, many economists argue that technology adoption will, at least in the _medium run_ , significantly increase firm productivity and, under certain policy conditions, ultimately lead to an expansion of jobs. The overall impacts of technology adoption on overall employment and on the skills composition of occupation is therefore an empirical question.

To our knowledge, ours is the first paper exploiting a firm-level data set to estimate, in the medium term, the impact of the adoption of more sophisticated technologies on firm-level employment and skills composition of jobs within firms. We exploit a simple reduced-form equation relating adoption of complex software with labor outcomes at the firm level. We mitigate concerns with the potential endogeneity of our measure of firm technology adoption by exploiting changes in technology adoption at the firm level between 2007 and 2013, and instrumenting firm technology adoption with a measure of the local propensity for technological progress whose impact we allow to differ across sectors. Our prior is that firms are more likely to adopt complex 23
Table 12: Share of immobile inputs

||_FEj_ = ln <sup>1</sup><sup>_−βj_</sup><br>_β_<sup>_j_</sup>|_sd_(_FEj_)|[_Ho_:_FE_ICT =_FEj_]|_β_<sup>_j_</sup>|
|---|---|---|---|---|
|ICT|1.8|0.07||0.14|
|Manufacturing|1.5|0.17|0.09|0.19|
|Retail|1.4|0.03|0.00|0.20|
|Other Industries|1.5|0.17|0.13|0.18|

_Notes: FEj_ = ln<sup>1</sup><sup>_−_</sup> _β_<sup>_jβj_</sup> are fixed effects from a regression of the (log of the) share of land to labor on industry-specific fixed effects, _FEj_ , and location-specific fixed effects, _FEi_ . Given first-order conditions for the firm, industry-specific fixed effects map onto industry-specific input shares. OLS standard errors reported in the second column. P-values reported in the third column. _Sources:_ Authors’ survey.

of equations:

- if _j_ = _{_ ICT _,_ manufacturing _,_ retail and wholesale trade _,_ other industries _}_ . Total expenditure is set such that the trade balances in equation 13.

# **6.3 Mobile and Immobile Factors of Production**

Mobility restrictions might affect differently the industries in the economy if they use mobile and immobile factors of production in different proportions. Land is, by definition, immobile: changes in the geography of the economy will translate into changes in the rental rate of land, and therefore higher costs of production for industries that more heavily use this input.

To pinpoint this channel, we use the fact that firms located in the same town face the same prices for land and workers. Therefore, differences in shares of input used between firms in the same town but in different industries can be attributed to differences in the production function rather than to differences in factor prices.

where _FEj_ = ln<sup>1</sup><sup>_−_</sup> _β_<sup>_jβj_.29</sup> In our survey we ask the following questions, which inform this part of the analysis:

- How many square meters did the company own for its business operations?

- How many square meters did the company rent for its business operations?

> 29In practice, the fixed effects _FEj_ are defined up to a scaling factor, which is sensitive to the units of measure used for the factors of production. So, we rescale the industry fixed effects such that the average is 0.2, which is the consensus in the literature on the economy-wide value for this parameter.

28
# **Tables**

**Table 1: Timing of the Surveys**

||ES|N|COV-ES, R1|COV-ES, R2|
|---|---|---|---|---|
|El Salvador|03/2016-08/2016|719|06/2020 - 08/2020|11/2020 - 01/2021|
|Georgia|03/2019-01/2020|581|6/2020|10/2020- 11/2020|

Note: This table shows dates of implementation of ES and two rounds of COV-ES. ES stands for Enterprise Surveys, and COV-ES, R1, and COV-ES, R2 stand for COVID follow-up Enterprise Surveys Round 1 and COVID follow-up Enterprise Surveys Round 2, respectively. See Section 4 for more details.

34
while only 11% of businesses cited it as a moderate obstacle (World Bank, 2006: Annex 1). World Bank (2004B) examines constraints to trade in Central Asia and concludes that the road networks in the region are relatively extensive and largely sufficient to meet the needs of users, although roads are in poor condition because of inadequate maintenance. It argues that these countries should focus on the maintenance of existing roads, focusing on the core road networks that can be maintained on a sustainable basis within the resources available in each country.

Investment in hydroelectric power generation, mainly for export, could make an important contribution to economic growth. Tajikistan has enormous hydropower potential and neighbors countries which are energy scarce and are potential export markets. However, while hydropower exports could boost GDP growth, foreign exchange earnings and government revenue, they will create very few jobs in the long run, once the construction of these projects is completed, and hence make little direct contribution to poverty reduction.

Improving labor productivity is also necessary to sustain long-term growth. Tajikistan has the lowest labor productivity among CIS countries, although this may reflect other factors besides the quality of the workforce, such as the high cost of doing business in the country (World Bank, 2005D: 3). Since independence, the quality of the labor force has been undermined for two reasons. First, educational attainments have deteriorated, as measured by falling school enrolment and completion rates. It is also likely that the quality of education which pupils receive in schools has deteriorated sharply, although it is difficult to measure this. The main reason for this is the paucity of resources allocated to public education, as a result of which teachers are very badly paid (and often absent from work), there are shortages of teaching materials and school buildings cannot be properly maintained. Secondly, many of the most productive workers have left the country. Many ethnic Russians, who comprised a large share of the professional cadres in Tajikistan, left the country in the 1990s. More recently many Tajik citizens have migrated to work in Russia, where wages are much higher than in Tajikistan; about a third of all males of working age now work outside the country on a permanent or seasonal basis (World Bank, 2005D: 58).

8