rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6: Estimated impacts of changes in neighboring large farm area/distance on smallholders’ improved seed use</u>**

||**Maize**|**Wheat**|**Sorghum**|**Teff**|
|---|---|---|---|---|
|||**Panel A: Dista**|<br>**nce measures**||
|**Distance any farm**|||||
|Distance|2.55e-05|0.000176|-0.000125**|0.000159|
||(0.000656)|(0.000445)|(4.67e-05)|(0.000136)|
|Distance<sup>2</sup>|2.64e-06|-6.63e-07|6.72e-07***|-3.28e-07|
||(4.12e-06)|(2.69e-06)|(1.71e-07)|(9.07e-07)|
|R<sup>2</sup>|0.323|0.071|0.013|0.050|
|**Dist. same crop**|||||
|Distance|-7.69e-05|-0.000166|-4.71e-05|-8.92e-05|
||(0.000544)|(0.000318)|(3.83e-05)|(0.000146)|
|Distance<sup>2</sup>|<br>2.09e-06|<br>6.22e-08|<br>1.22e-07|<br>1.38e-07|
||(1.85e-06)|(1.28e-06)|(1.35e-07)|(6.10e-07)|
|R<sup>2</sup>|0.323|0.071|0.013|0.050|
|||**Panel B: Ar**|**ea measures**||
|**Area, all farms**|||||
|0 - 25 km|0.000284|-0.000202**|2.20e-05|-2.12e-05|
||(0.000181)|(9.18e-05)|(1.56e-05)|(5.14e-05)|
|25 - 50 km|<br>-0.000424|<br>-3.70e-06|<br>-1.24e-05|<br>3.40e-05|
||(0.000292)|(9.67e-05)|(7.48e-06)|(4.42e-05)|
|50 - 100 km|9.90e-05|-2.83e-05|-3.71e-06|1.15e-05|
||(0.000116)|(4.13e-05)|(2.24e-06)|(2.63e-05)|
|R<sup>2</sup>|0.324|0.071|0.013|0.050|
|**Area, same crop**|||||
|<br>0 - 25 km|0.000926***|-0.000166|2.38e-05|0.000861**|
||(0.000334)|(0.000161)|(2.63e-05)|(0.000342)|
|25 - 50 km|-0.000181|-4.64e-05|-1.48e-05|0.000120|
||(0.000655)|(0.000152)|(1.08e-05)|(0.000227)|
|50 - 100 km|<br>0.000567**|<br>5.75e-05|<br>-3.79e-06|<br>-0.000167|
||(0.000277)|(8.11e-05)|(2.92e-06)|(0.000116)|
|R<sup>2</sup>|<br>0.324|<br>0.071|<br>0.013|<br>0.050|
|No. of obs.(hhs)|170,519|52,885|89,557|92,435|

_Note:_ Woreda fixed effects and year trends included throughout. Standard errors clustered at woreda level.

22
# **3.1 Reforms initiated by Ukraine moving towards a market-oriented economy**

Since gaining independence in 1991, Ukraine has undertaken a sweeping set of land reforms to move from a centrally planned economy where all land was state-owned to a market-oriented system characterized by predominantly private ownership. This change was pivotal in repositioning Ukraine as a significant global supplier of agricultural products, leveraging its vast arable land resources to contribute to global food security. The trajectory of land reform in Ukraine comprises several key phases, each marked by legislative milestones and institutional changes that together facilitated a shift to market-based land relations.

_1990–1992: Initiation of Land Reform and Legal Pluralism in Land Ownership:_ The early 1990s marked the inception of land reform in Ukraine, coinciding with the dissolution of the Soviet Union. The adoption of laws recognizing state, communal, and private land ownership was revolutionary. This period established the foundational legal framework necessary for a market economy, challenging the system of collective farms ( _kolkhozes_ and _sovkhozes_ ) and exclusively state ownership of land.

_1992–2001: Adoption of the Land Code and Large-Scale Privatization:_ In 1992, Ukraine enacted its first Land Code, codifying principles that facilitated the transition to a market economy. This phase was characterized by extensive privatization campaigns, resulting in the redistribution of approximately 27 million hectares of agricultural land to private individuals and entities by 2001. The privatization process aimed to dismantle collective farming structures and empower individual landowners, thereby increasing efficiency and productivity in the agricultural sector.

_2001–2013: Legislative Development and Institutional Strengthening:_ The adoption of a new Land Code in 2001 marked a significant advancement in land legislation. This period saw the introduction of specialized laws concerning land management, protection, valuation, and compulsory acquisition for public needs. Notably, the Law on Land Valuation (2004) sought to standardize valuation practices; however, the reliance on "normative monetary valuation" methods persisted, often leading to discrepancies between assessed values and market realities (OECD, 2018). These normative valuations failed to reflect actual market conditions, resulting in inefficiencies and inequities in property taxation.

_2013–2020: Digitalization, Delineation, and Decentralization:_ Between 2013 and 2020, Ukraine focused on modernizing its land administration system through digitalization and the delineation of state and communal land ownership. The launch of the digital State Land Cadastre in 2013 and the State Register of Real Property Rights enhanced transparency and accessibility of land information. By 2020, over 75% of land parcels were registered electronically, streamlining transactions and reducing opportunities for corruption (Ministry of Agrarian Policy and Food of Ukraine, 2020). Decentralization of land management 9
Countries in the former region are characterized by weaker democracies and more regulation, as well as more pervasive graft. Among countries in our sample, the median polity level is -1 in Africa and 8 in Latin America. Moreover, the “Ease of doing business” percentile rank is .67 in Africa and .49 in Latin America. The estimates in column 6 of Table 6 imply that strengthening democracy and reducing regulation from their respective median levels in Africa to those of Latin America would reduce the probability that firms are victims of graft by 16.2 percentage points from its average level of 19 percent; that is, the incidence of bribery in Africa would fall by 85 percent under this scenario. Thus, our back-ofthe-envelope calculation suggests that fostering democracy and reducing excessive regulation would go a long way in improving Africa’s business climate by reducing corruption.

# **5 Conclusions**

In this paper we argue that existing measures of corruption around the world are an inaccurate gauge of the true incidence of graft on the typical firm. Such measures are often based on surveys of experts, of specific types of firms, or of households, which do not necessarily match one-to-one with the views held by the typical firm. Moreover, existing indicators are often based on perceptions and not necessarily on hard data. Yet, we present evidence showing that average firm perceptions adjust only gradually to changes in the business environment. We show that firms that request licenses or infrastructure services and are not asked for bribes hold a more sanguine view about the pervasiveness of graft, relative not only to firms that did fall prey to corrupt officials but also to firms that did not request such services and hence would have not been affected by bribery. Then, for example, if a country were to effectively launch an anti-corruption campaign, firms’ views on corruption would change, but only gradually, to the improved business climate.

In order to remedy the shortcomings of existing corruption measures, we introduced an experience-based index, the _Graft Index of Firm Transactions_ , which measures the probability that a firm will be asked for a bribe in order to complete a specified set of business transactions. We estimated the index using data on approximately 10,000 firms from the World Bank’s _Enterprise Surveys_ in 33 countries in Africa and Latin America. Our index has several advantages: It is based on firms’ direct encounters with corruption and not on perceptions; it is free of ambiguities as it focuses on a common set of business transactions in all countries; and it reflects the incidence of graft on the typical firm of a country since it is based on nationally representative data. On the downside, our index focuses on petty bribery and does not capture other possible forms of corruption.

11 See Marshall and Jaggers (2005). Data and documentation available at <u>http://www.cidcm.umd.edu/polity/</u>
funds are systematically buying Latin American equities when Russia's returns are high, and vice versa. This is especially true during the Russian Crisis, which squares with informal accounts of the extraordinarily intense contagion at that time. Even during the Russian Crisis, however, fund managers remained cool-headed: there is no evidence they engaged in contagion trading. The contemporaneous relation with **U.S.** equity returns is the only one of the five return benchmarks that is concentrated at the manager level. It is also the only significant effect that is negative. This negative **LOC** statistic for the **U.S.** return implies that fund managers systematically buy Latin American equities when **U.S.** returns are low (controlling for fund inflows/redemptions). Though past work has shown clear links between emerging-market returns and **U.S.** interest rates, this is the first evidence of which we are aware that links actual portfolio shifts to U.S. equity returns.

Table **_5_** focuses on contagion trading during three specific crises: the Mexican, the Asian, and the Russian. The reaction of investors to Russian equity returns during the Russian crisis was particularly strong: investors systematically sold Latin American equities when Russian equity returns were low. Note, though, that this link to Russia is not operative at the manager level. In the case of the Mexican crisis, the effect is smaller, but still significant, and there is some evidence that managers were involved in that case. In the case of the Asian crisis, there is no discernable link to the trading of Latin American equities. The last three columns show the link to **U.S.** market returns during each of these three crises. Given the proximity **to** Mexico, and the importance of economic links between the two countries, it is not surprising that the link between Latin-American portfolios and **U.S.** returns is strongest during the Mexican crisis. Interestingly, the contagion trading statistic is negative, and is significant at both the manager and investor levels. This suggests that, during the Mexican crisis, managers and investors tended to sell Latin American equities when **U.S.** returns were high, and vice versa. One interpretation is that strong **U.S.** returns in the face of Mexico's crisis bodes well for Mexican equities, which induces a portfolio shift away from the rest of Latin America.

In closing this section on contagion trading, it is worthwhile re-emphasizing the qualitative difference between the results above and the existing contagion literature. The difference is that we measure quantities, as well as prices, and address their joint behavior, whereas much of the literature focuses on correlation in prices only.

**18**
of results).<sup>22</sup> As expected, the presence of a visa provision boosts the bilateral settlement of immigrant among countries having different income levels. Namely, the presence of a visa provision in an RTA between Developing-Developed pairs increases bilateral migrant stocks by 13.2% – see column (3).

# – Table 3 about here –

The underlying mechanism through which visa related provisions are expected to affect the bilateral settlement of migrants is the actual increase in the number of visas released among RTAs signatory countries. Easier administrative procedures to get visas will increase the number of visa requests (and granted) among member countries, and this will reflect (in the long-medium run) into larger stocks of bilateral migrants. Testing the effect of visa provisions on the number of bilateral visas for the full set of countries covered in the present study is not possible because of data constraints (i.e. lack of data on the number of bilateral visas granted among all the country-pairs considered here). However, we benefit from data on the number of visas released by the US embassies to each country of origin to test such underlying mechanism.<sup>23</sup> Results, reported in Table A3 support our reasoning: the presence of visa-related provision in RTAs signed by the United States increases the number of visas that US embassies grant to each specific origin.<sup>24</sup>

# – Table A3 about here –

The positive effect of _bilateral_ visa provisions on migration comes with a tiny (negligible) negative _spillover_ effect for immigrants coming from third-origins. In Table 4 we show the effect of visa provision in bilateral _ij_ RTA on the immigration stocks in country _j_ from the Rest of the World (RoW) – excluding _i_ -specific origin.<sup>25</sup> The stock of immigrants from RoW is only marginally affected by the presence of bilateral visa provision. When origin and destination _ij_ share an RTA including visa provision, the stock of immigrants in _j_ from the rest of the world reduces by 0.1%. The result is robust to the inclusion of the Schenghen dummy control.

– Table 4 about here –

> 22We base on the World Bank classification of country by income levels, and consider “North” as high-income countries, and “South” all remaining low and middle-income countries.

> 23Data on the number of visas granted by US embassies is from the Report of the Visa Office ( `https://travel. state.gov/content/travel/en/legal/visa-law0/visa-statistics.html` ). We have data on the number of working-permit related visas granted yearly in the period 2000-2020.

> 24When we include origin fixed effects in estimation, the non-linear PPML estimator discards all those origins whose number of visa does not change over time. This explains the reduced number of observations in column (2) of Table A3.

> 25By excluding _i_ -specific origin from the total stock of immigrants in _j_ , the dependent variable used in Table 4 is indeed _ij_ specific and the number of observations almost the same as in the baseline specification (not exactly the same because some observations are dropped by perfect collinearity with fixed effects).

11
the population with mobile phone access between the non-agricultural population and the most dependent on agriculture is more than 40 percentage points.

_<mark>Figure 2. Share of population with mobile phone access, by share of total household income from agriculture.</mark>_ <!-- Start of picture text --> Income from agriculture > 30% No income from agriculture<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source: Authors’ calculation based on the FAO Rural Livelihoods Information System (RuLIS) database_ A similar pattern in mobile phone access between agricultural and nonagricultural households is observed in the countries where the World Bank HFPS were implemented. Using HFPS and LSMS-ISA data from Ethiopia, Malawi, Nigeria, and Uganda, Ambel et al. (2021) demonstrate that the sample of households who own or have access to mobile phones are considerably different from the sample without access to a mobile phone. Households without access to a mobile phone tend to be poorer and have lower living standards (across multiple dimensions) relative to the sample of households with access to a mobile phone (Ambel et al., 2021).

This issue of coverage is common to all three of the predominant phone survey sampling approaches. In what follows we discuss possible approaches to mitigate this risk during different stages of survey implementation, and in Section 3.3 we discuss post-survey adjustments that attempt to manage this problem.

# _Nonresponse_

While the issue of coverage is critically important for the representativeness of phone surveys, another important factor is the pattern of nonresponse. In phone surveys in particular, nonresponse can be linked not only to refusals, but to failure of the interviewers to establish contact. Just like refusals, this type of nonresponse can be systematic and therefore introduce bias. For example, in a low-income country setting, nonresponse tends to be higher in areas with unreliable network or electricity which makes it more difficult to successfully reach respondents. Areas with poor mobile network or electricity are likely to be rural and poorer, so higher nonresponse in these areas will bias the sample toward urban and richer 7
this regard, target setting, and monitoring are key elements for innovation. Data suggest that Chinese firms are, on average, stronger in monitoring, followed by the development of human resources policies (incentives); the capacity to set long-term targets emerges only as the third performing area (Figure 16). Vietnamese firms are also stronger in monitoring, and in this case with a superior performance in comparison to China; the capacity to set long-term targets is the second highest performing area. When taken together, these results suggest that the performances of both China and Vietnam are below the median countries in all domains (except for incentives, in the case of China), which points to a potential scope for improvement.<sup>14</sup>

**Figure 16. Average score across managerial dimensions: 1(“worst practice”) to 5(“best practice”)**

<!-- Start of picture text --> 5.00<br>4.50<br>4.00<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>operations monitoring targets incentives<br>China Vietnam top performer(country) median (country)<br>Source: Own elaboration based on WMS dataset.<br>Note: The country score is calculated as a simple average across firms, for each country<br><!-- End of picture text --> Moving the focus of the analysis from the average to the entire distribution of management quality score, Figure 17 plots the kernel density functions of China and Vietnam against the United States, the top performing country in the survey. The Kolmogorov-Smirnoff test (for which the null hypothesis is the equivalence of compared distributions) is also reported; in both cases, there is evidence to reject the hypothesis. Overall, data show a wide variation in all three countries, but a distinguishing feature for the United States is that the mean of its distribution is more skewed to the right when compared to China and Vietnam. In addition, the tail of poorly-run firms (here defined as those with management score lower than 2) is much fatter in China and Vietnam when compared to the United States. Almost 13 percent of Vietnamese firms were classified as poorly managed; six and half times more than in the United States. In China, the proportion of firms classified as poorly managed is 5 percent. Even the best Chinese performer (with a score of 4.22) would need to improve its performance to meet the level of the 95th percentile firms in the United

> 14 In a separate analysis – see World Bank (2017) - Malaysian firms are shown to present stronger performance in monitoring, followed by talent management (incentives).

19
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39 <!-- Start of picture text --> 4.5<br>United States<br>4<br>Japan<br>3.53 India ChinaIndonesia BrazilRussia GermanyCanadaItalyUnited KingdomFrance<br>Poland<br>2.5 Nigeria Colombia Israel Czech Rep.Portugal South Africa<br>Pakistan2 New Zealand Ukraine MalaysiaChile Egypt PhilippinesIreland<br>Ethiopia MozambiqueCameroon 1.5 Tanzania 1 BangladeshUruguayCôte d'IvoireGhanaSri LankaKenya KazakhstanBulgaria<br>Burkina Faso Namibia<br>Mongolia 0.5<br>0<br>-0.75 -0.25 0.25 0.75 1.25 1.75 2.25 2.75<br>Log Nominal Public Sector Wage Bills, US$<br>Source: IMF, International Financial Statistics and country reports; World Bank, World Development Indicators and staff reports.<br>Note: Most recent data used. 2010: Brazil, Bulgaria, Chile, Czech Republic, Egypt (estimate), France, Germany, Indonesia, Ireland, Italy, Pakistan, Philippines,<br>Poland, Portugal, United Kingdom, United States; 2009: Canada, Colombia, Ghana, Israel, Japan, Kazakhstan, South Africa, Tanzania, Ukraine; 2008: Bangladesh,<br>Kenya, Mongolia, Nigeria, Russia, Sri Lanka; 2007: Burkina Faso, Côte d'Ivoire, Mozambique, Uruguay; 2006: Cameroon, China, India; 2005: Ethiopia, New Zealand;<br>2004: Namibia; 2003: Malaysia.<br>Log Nominal GDP, US$<br><!-- End of picture text --> Support to small and medium-sized enterprises should be envisaged as a potential tool for overcoming market failures in the financial sector. One possible instrument could be the provision of special lines of credit funded by multilateral banks and new institutions such as the African Investment Bank<sup>23</sup> and from which banks could lend to small businesses. If designed to support the development of industries that are consistent with the country’s comparative advantage, these instruments could help both on the supply and on the demand side of Africa’s labor market.

Training programs to help new workers and those laid off in recent years accumulate of regain skills could contribute to the increase productivity if such programs are targeted to the neediest groups (the youth, the disadvantaged or women). Youth-oriented programs designed with private firms to assess demand for skills and to provide corresponding tailored training programs for young people (following the German model) seem to yield good results in some African countries. It must be acknowledged, however, that such programs are often costly, and may not yield tangible results if the rate of economic growth does not accelerate. To ensure maximum chance of success, they should be designed and implemented in close partnership with the private sector and tailored to suit the business needs of the potentially competitive industries in each country.

> 23 For a discussion of the rationale for an African Investment bank (in addition of the existing African Development Bank), see Monga (2012a).