rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 8: Determinants of demand and willingness to pay for title</u>**

||<br>**Would like to obtain title**|<br>**Willing to pay for title**|**Log amount willing to pay**<br>**per ha**|
|---|---|---|---|
|Parcel with chief certificate|0.108***|0.0104|-0.378***|
||(0.0217)|(0.0133)|(0.0734)|
|State land with no title|0.133***|0.0475***|0.458***|
||(0.0271)|(0.0165)|(0.0895)|
|Log parcel area in ha|0.0192**|-0.00866*|-0.822***|
||(0.00765)|(0.00517)|(0.0330)|
|No. of years since acquisition|<br>0.00177***|<br>-0.0000694|<br>0.000721|
||(0.000667)|(0.000423)|(0.00233)|
|Good soil quality|-0.0411|0.0184|0.0499|
||(0.0382)|(0.0240)|(0.134)|
|Fair soil quality|-0.0981**|0.0102|0.140|
||(0.0382)|(0.0242)|(0.135)|
|Flat land|0.0404|0.0368|0.330**|
||(0.0361)|(0.0236)|(0.133)|
|Gently slopped land|0.0604|0.0496**|0.486***|
||(0.0372)|(0.0243)|(0.137)|
|Male headed household|0.0103|0.0180|-0.0236|
||(0.0239)|(0.0153)|(0.0849)|
|Log head's age|0.0751**|-0.00494|0.164|
||(0.0319)|(0.0208)|(0.114)|
|Head is literate?|0.0844***|0.0259**|0.000215|
||(0.0193)|(0.0127)|(0.0701)|
|Member of farmers group|0.0147|0.0171|0.0216|
||(0.0174)|(0.0111)|(0.0612)|
|Leader in local organization|0.00625|0.00785|-0.169**|
||(0.0194)|(0.0122)|(0.0675)|
|Log value of assets in ZMK|<br>0.0161***|<br>0.0110***|<br>0.202***|
||(0.00559)|(0.00360)|(0.0201)|
|Constant|<br>0.152|<br>0.776***|<br>3.141***|
||(0.136)|(0.0895)|(0.492)|
|Number of observations|<br>2,475|<br>1,896|<br>1,754|
|Mean of dep. var.|0.77|0.94|4.30|
|R-squared|0.0557|0.0223|0.304|

_Notes:_ Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010 28
The experience of the late 2000s is instructive. In 2006-2007 and again in 2008-2011, a series of shocks created a gap between global demand and supply of food, leading to sharp increases in food prices (Figure 1). Many governments intervened, implementing export restrictions that aimed at isolating domestic food markets from global market developments. These measures contributed to reduce global supply, leading to even higher food prices. Giordani, Rocha and Ruta (2016) found that uncooperative trade policies alone were responsible for an increase in global food prices by 13 percent, on average. This effect was even higher for specific food markets. Martin and Anderson (2012) found that trade policy changes accounted for 45 percent of the increase in the world price of rice and 30 percent of the world price of wheat.

The situation today is in part different due to more favorable initial conditions. Back in the mid- to late 2000s, global food stocks were low, oil prices were high and weather-related shocks affected production in major exporting countries (FAO, 2009). Today, production levels of all major staples (wheat, rice, maize) are above the average of the past five years, oil prices are low and stock levels relative to consumption for major grains are 70-100 percent higher than in the late 2000s (Voegele, 2020). Indeed, as shown in Figure 1, global food prices have been relatively stable in recent years and they have remained low in the first months of 2020.

**Figure 1. Food Price Index (January 2000 – February 2020)**

<!-- Start of picture text --> 130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>Food Price Index, 2016 = 100<br>2000M1 2000M12 2001M1 2001M12 2002M1 2002M12 2003M1 2003M12 2004M1 2004M12 2005M1 2005M12 2006M1 2006M12 2007M1 2007M12 2008M1 2008M12 2009M1 2009M12 2010M1 2010M12 2012M1 2012M12 2013M1 2013M12 2014M1 2014M12 2015M1 2015M12 2016M1 2016M12 2017M1 2017M12 2018M1 2018M12 2019M1 2019M12 2020M1 2020M2<br><!-- End of picture text --> _Source:_ International Monetary Fund Primary Commodity Prices (January 2000-February 2020). _Note:_ Food Price Index, 2016 = 100, includes Cereal, Vegetable Oils, Meat, Seafood, Sugar, and Other Food (Apple (non-citrus fruit), Bananas, Chana (legumes), Fishmeal, Groundnuts, Milk (dairy), Tomato (vegetables)).

Despite favorable initial conditions, the virulent and rapid global diffusion of Covid-19 could lead to the emergence of problems in global food markets in the coming months. Declines in food supplies could result from a number of factors, including labor shortages as people become sick or are prevented to go to work due to containment policies and to disruptions in supply chains due to logistics bottlenecks within 2
39 Table 1: Returns to Work Experience by Development Status

|Dependent Variable:||E|stimated|Returns|to Poten|tial Work E|xperienc|e||
|---|---|---|---|---|---|---|---|---|---|
|Return Specifcation:|(1) N|o Cohor|t FE|(2) De|cadal Co|hort FE|(3) Eve|nts Coh|ort FE|
|2 Development Groups:|G|D - G|D|G|D - G|D|G|D - G|D|
|1. Baseline|1.7***|1.5***|3.2***|1.1***|1.7***|2.8***|0.8***|1.8***|2.6***|
|2. Log Hourly Wage|1.8***|1.3***|3.1***|0.8***|1.8***|2.6***|0.4**|2.0***|2.4***|
|3. Log Hours Worked|0.0|0.3***|0.3***|0.3***|0.0|0.3***|0.4***|-0.1|0.3***|
|4. 0-25 Exp. Bins Only|2.0***|1.8***|3.8***|1.3***|2.1***|3.4***|1.1***|2.1***|3.2***|
|5. 0-15 Exp. Bins Only|2.4***|2.4***|4.8***|1.7***|2.7***|4.4***|1.4***|2.8***|4.1***|
|6. 5-35 Exp. Bins Only|1.5***|1.1***|2.6***|0.9***|1.3***|2.2***|0.6***|1.4***|2.0***|
|7. Lagakos et al 2018 (m)|1.9***|1.6***|3.5***|0.9***|2.1***|3.1***|0.6***|2.2***|2.7***|
|8. Lagakos et al 2018 (h)|1.9***|1.5***|3.4***|0.9***|2.1***|3.0***|0.6***|2.1***|2.6***|
|9. Cntry Pop. 2017 as Wgts|1.8***|1.8***|3.6***|1.5***|1.5***|3.0***|1.1***|1.7***|2.8***|
|10. Yrs of Educ._≤_13|1.7***|1.7***|3.4***|1.4***|1.6***|3.1***|1.4***|1.6***|3.0***|
|11. Yrs of Educ._>_13|2.1***|1.2***|3.2***|1.7***|1.2***|3.0***|1.5***|1.2***|2.7***|
|12. Yrs of Educ._≤_13 & Wgts|1.7***|2.0***|3.7***|1.8***|1.5***|3.3***|1.7***|1.7***|3.4***|
|13. Yrs of Educ._>_13 & Wgts|2.4***|1.0***|3.5***|2.2***|1.1***|3.3***|1.9***|1.2***|3.1***|
|14. 0-25, Educ._≤_13 & Wgts|2.0***|2.4***|4.4***|2.2***|1.8***|3.9***|2.0***|2.0***|4.1***|
|15. 0-25, Educ._>_13 & Wgts|2.8***|1.4***|4.2***|2.5***|1.4***|3.9***|2.2***|1.5***|3.7***|
|16. Row 14, Lagakos et al ’18|2.2***|2.2***|4.4***|2.2***|1.7***|3.9***|2.3***|1.6***|3.9***|
|17. Row 15, Lagakos et al ’18|2.9***|1.6***|4.5***|2.7***|1.3***|4.0***|2.4***|1.3***|3.7***|

_Notes:_ This table shows the constant, i.e. the mean return for developing countries (“ _G_ ”), and the coefficient of the developed country dummy, i.e. the difference between the mean returns of developed countries and developing countries (“ _D-G_ ”). The mean return for developed countries (“ _D_ ”) is obtained by adding the constant and the coefficient of the developed country dummy. See text for details for each row and each column (N for row 1 = 145; 12; 122). Robust SEs: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
process in which participants find effective ways to make use of existing limited institutions and, importantly, to build substitutes for public institutions based on private associations and private networks. To the degree that this is so, market reforms that rely on imperfect private substitutes for public institutions may nevertheless create expanded opportunities for trade.

Against this background, our paper takes up the question of whether policy reforms that create new incentives and opportunities can give rise to better performing markets, even when public institutions remain weak. The paper relies on three well timed surveys of agricultural prices in Bulgaria. The first survey took place in 1995 just prior to the 1996 economic crises that would eventually result in a new government and new policies. The second took place in 1997 just prior to a series of agricultural policy reforms that led to the full liberalization of agricultural prices in 1998. The third took place in 2001, following a four-year period when agricultural policies were stable and market-based, but when related institutions remained weak. We also introduce a simple way to measure changes in the performance of spatial markets when information on the underlying determinants of transfer costs is unavailable.

The remainder of the paper is organized as follows. The next section describes events and policies related to food market reforms in Bulgaria during the study period. In section 3, a conceptual model is developed to describe markets that range in their level of completeness and efficiency and an applied version of the model is specified. The data is discussed in section 4. Section 5 contains estimation and simulation results and section 6 concludes.

# **2 Background**

Following the fall of the communist government in 1989, Bulgaria began a process of integrating its economy and institutions with the European Union. Eventually in this process, Bulgarian policy would focus on the transformation of institutions and the building-up of administrative capacity, driven largely by the need to take up and implement the European Union’s _acquis communautaire_ as a condition of EU membership (Preston 1997; Dimitrova 2002). However, for most of the period between 1989 and the start of negotiations for EU membership in 2000, policy reform in Bulgaria, though uneven, centered on freeing up markets and building private ownership rather than developing institutions. This is particularly evident in the related set of policies that influenced food markets during the seven year period covered in this study.<sup>4</sup> Political uncertainty marked much of the first decade of reform in Bulgaria and this was reflected in uncertain and inconsistent economic policies. Early on, as the restructuring of the

> 4 While the broad criteria for Bulgaria’s eventual membership in the EU was laid out by the Copenhagen European Council in 1993, Dimitrova notes that the European Union’s emphasis on institutions and administrative capacity began around 1997.

2
12 _“_ Quality _”_ is, at the outset, a comparatively arbitrary term. In previous postal studies it has been measured using indicators such as number of letters delivered on time. It may also encompass the linked concept of reliability—are letters delivered at all? Again, we do not have such data at the cross-country level. Instead, we use a measure of trust in the postal system (survey answers to a question regarding willingness to send an object worth $100 through the posts) as a proxy for quality. This trust measure might plausibly be related to measures of efficiency—bloated public monopolies frequently provide a lower quality of service than efficient ones (World Bank, 1995). Trust, and a broad definition of _“quality”_ might also be related to the extent of service provision.

In order to empirically examine our posited relationships, a range of relevant variables were selected from the UPU database as well as information from Kirkman et. al. (2002), and the World Bank’s _World Development Indicators_ database. These are described in Table 4, with descriptive statistics in Table 5.<sup>19</sup> There is considerable variation across countries in most of the indicators—for example, the number of letters delivered per year per employee varies from 196 to 174,930, receipts as a percentage of post office expenditures varies from 11 percent to over 400 percent, privately operated post offices as a percentage of all offices varies from none to 95 percent and the average geographic coverage of post offices varies from less than one square kilometer to nearly 30,000 km<sup>2</sup> .

> 19 It should be pointed out that much of the data is subject to significant error.  For example, the UPU collects its data from the incumbent postal operator and it has limited capacity to check the reliability of this information. Further, while such operators usually have an official monopoly over at least parts of the postal delivery system, especially in developing countries with weak operators, much mail is delivered by unofficial operators—which is not captured in the statistics.  The trust index could reflect trust in express or highly guaranteed mail services rather than the country’s core mail service.
in the PICS-BEEPS data. The second question is addressed by testing whether the estimated effects for Serbian firms are different from those for firms in other countries.

The 2003 Serbia PICS survey covered 408 firms; the 2002 Serbia BEEPS survey covered an additional 230 firms. About half of these firms were in services, about 10 percent were in construction, and the rest were manufacturing firms of varying sorts. Two thirds of the sample were small and medium-sized firms (employing 100 or more persons). Over half (57 percent) of the sample was composed of new private firms, 29 percent were sociallyor state-owned, and the remainder had been privatized. Almost one-quarter of the sample were exporting, and about one in ten had significant foreign ownership. Because of the relatively small Serbian sample, the estimations of pool manufacturing, construction and services firms and employ sector dummy variables; the results are similar if the regressions are done separately by sector. Table 3 shows the results of the TFP estimations.

**Table 3: TFP regressions using firm-level data – Serbia vs. other PICS-BEEPS countries**

||**49 country**<br>**surveys**<br>**Serbia**|**Serbia different**<br>**from 49 others?**|
|---|---|---|
|Fixed capital|0.374**<br>0.394**|No|
|Labor|0.693**<br>0.590**|No|
|Privatized|0.359**<br>0.599**|No|
|New private|0.359**<br>0.846**|Yes*|
|Foreign-owned|0.205**<br>0.282|No|
|Exporter|0.216**<br>0.089|No|
|Capital city|0.089**<br>0.398*|No|
|Country-survey dummy variables included|Yes||
|Sector dummy variables included|Yes||
|Number of observations|15,050 firms from 51 country<br>surveys||

* Significant at the 5 percent level; ** significant at the 1percent level; heteroskedastic-robust standard errors

The estimations for Serbian firms show that new private firms in Serbia have productivity levels that are 85 percent higher<sup>15</sup> than the benchmark socially-owned firms, and the productivity of privatized firms is 60 percent higher. The estimations based only on the Serbian firms in the sample do not show that foreign ownership and export activity are associated with high TFP levels, but this result is probably due to the relatively small sample size of the Serbian surveys. The last column in Table 3 compares the effect of the firm characteristics in Serbia to those in other countries: there is no evidence that Serbia is any different from other countries, with one exception — new private firms are even more strongly associated in Serbia with higher TFP than they are in other countries. Thus Serbia provides positive evidence in support of the general pattern found internationally, and no evidence against it.

These findings extend to the other three measures of performance used above.<sup>16</sup> New private firms in Serbia are more likely to have higher employment growth, to have reinvested in profits, and to have invested in new plant, than state/socially-owned firms; Serbian exporters are also more likely to have invested in new plant. Moreover, there is no evidence that Serbian firms are any different from their counterparts in other countries 8
in Figure 11. Control of the economy was gradually regained and consolidated after the first quarter of 2003.

## **Figure 11**

**Short term policy interest rate (Selic) and exchange rate in Brazil 2000 – 2003**

<!-- Start of picture text --> 4.4 28<br>4.0 26<br>Exchange rate<br>3.6 24<br>3.2 22<br>2.8 20<br>2.4 18<br>Selic<br>(right scale)<br>2.0 16<br>1.6 14<br>2000 2001 2002 2003<br><!-- End of picture text --> Source: Bacen 30. The run on mutual funds investments highlights two critical issues for monetary and financial policies: first, the importance of having balance sheets that adequately reflect the value of intermediaries’ assets and, hence. mark-to-market regulation; second, the relevance of monitoring the risk positions of intermediaries that invest in long term government paper funded with liquid deposits. Had government securities been registered at their market value rather than their face value, part of the herd behavior could have been avoided. Hence the need to move in that direction was desirable. However, the transition from one accounting system to another should be done during tranquil times, not during crisis episodes. Liquidity risk originated in the transformation of liquid deposits into long-term government securities was not monitored accurately and ended up in the central bank market intervention described below. Additionally, the improper disclosure of information does not allow the end-investor to properly assess risk and hence is not conducive to the creation of a diversified public debt holder base, an important element in risk management of the public debt.

# **II.  PUBLIC DEBT MANAGEMENT: INSTRUMENTS THAT FACILITATE DEBT STABILIZATION**

31. Public debt management may be geared to compensate structural budget rigidities, but may also amplify the negative effects of external shocks if the cost-risk trade-offs are not adequately balanced. Ideally, public debt composition would be such that, in response to a negative shock to the budget, debt service would decrease. However, the Brazilian structure of public debt, short- run and indexed to the exchange rate or to interest rates, amplified the negative effects of the supply and international capital markets shocks. Another feature of Brazilian debt is the concentration of debt 15
Table 7 summarizes the effect of progressively adding the various controls using the Transparency International measure for corruption and the ACD measure of conflicts;<sup>32</sup> column (3) shows that both, the conflict variable as well as the corruption variable, decrease in size when they are jointly included, suggesting that part of the higher costs in conflictaffected countries is because these countries also have higher levels of corruption, and vice versa.

The main significant correlation between costs and the additional variables on the business environment and government investment capacity is with the time it takes to enforce a contract. A 10% increase in the number of days it takes to enforce a contract is associated with 2.9% higher unit costs. The inclusion of the Doing Business Indicators reduces the correlation of unit costs and the conflict variable, leaving the coefficient on the corruption variable largely unchanged. The coefficient on the Public Investment Management Index is not significantly different from zero and its inclusion does not affect the size and significance of the coefficients. Overall, column (6) illustrates that part of the higher costs in conflict countries is due to a worse business environment, but this does not explain the whole effect.

# **A Closer Look at the Role of Geography and Population Density**

As noted above, most of the explanatory variables are measured at the country level, and several are constant over time within countries. The aggregation involved in the construction of such variables clearly implies some loss of variation in the data, which could lead to misleading results. This is more of a concern for variables that vary a lot within countries, for example, population density and certain geographical characteristics, than for variables for which there is relatively little within-country variation, for example, corruption. To investigate whether we can obtain sharper findings regarding the relationship between unit costs and geographical characteristics and population density, we conducted a separate analysis of more disaggregated data obtained for a selected subset of countries. Specifically, for Bangladesh, Ethiopia, Ghana, India, the Philippines, Thailand and Uganda, we

> 32Tables S.10 and S.11 in the appendix show the results separately for conflict and corruption.

21
102 lower skilled jobs and employers find no need to pay them more than comparabie Senegalese.

TAB 7.1 SaiepL~ Itates of JnA a Earaap PwIm BEed one P&aml and Enacia Cu t C (Vab.. in puarbes= we ntandard arm)

||Dependent Varable-<br>Monthly Base Salary|Dependent Variable-<br>Monthly Total Earnings|
|---|---|---|
||1|2|
|azercept|9.639*<br>(0.137)|9.942'<br>(0.194)|
|male|0.178e<br>(0.071)|0.192"<br>(0.100)|
|**Other**Aian|02S5<br>(0.188)|.237<br>(0266)|
|Non-Acan|1.420<br>(0.193)|1.23?<br>(02)|
|Union Member|-0.016<br>(0.060)|0.133<br>(0.085)|
|EducaOion|0.074'<br>(0.006)|0.111'<br>(0.007)|
|LaborForce<br>Ezperenc|0.007'<br>(0.003)|').016<br>(0.004)|
|Firm.pecdIc<br>Exp.riene|0.018*<br>(0-003)|0.020'<br>(0.004)|
|Trainbg|_0.108'_<br>(0.038)|0.098<br>(0.054)|
|% Senegene|Q0a24|0.669"|
|<br>Stae Owned|(0.M)|(038S)|
was much lower than that in Honduras due to people’s perception to the survey company. Therefore, we expect that this letter would create impression that this experiment was part of the government’s official data collection. As shown below, both non-response and attrition rates in this experiment were as good as those of Serbia LFS and HBS.

### **_Survey logistics in F2F and CATI surveys_**

In the F2F survey and the baseline in the CATI survey, enumerators recorded all responses in paper questionnaires and sent them to the headquarters where all data were entered into the computer. On the other hand, at the telephone interview in the CATI survey, operators recorded all responses into a data enter program directly while they were calling. The same data entry program software (BLAISE) was used for entering data collected from both F2F and CATI surveys. The data collection was done from June to July in 2013.

# **5 Results**

## **5.1 Comparison of Variable Means**

This subsection examines whether data collected by F2F and CATI modes are similar. To do this, means of variables necessary for consumption projections are calculated in both data collected by F2F and CATI, and whether differences in the means are statistically significant by Pearson Chi-square tests. Note that since the household size and education variables were balanced in the sample for these census variables, the difference found here can be considered to be due to the difference in the survey modes. The results are shown in table 6.

Among the two variables that were balanced ex ante, we did not find a difference in household sizes, but we found a difference in the education variable between the two modes especially in the rural areas although the size of the difference is not very large. The mean household size is slightly larger in CATI than in F2F but the difference is statistically insignificant. The educational attainments of household heads are also similar between CATI and F2F data sets, and all but vocational school attainment in rural areas record statistically insignificant differences.

In labor and income variables, we did not find any statistically significant difference between two surveys except for months worked. In demographic and purchase variables, we do not find any statistically significant difference. For dwelling and durable good ownership variables, we found statistically significant differences in five and two out of 18 items in urban and rural areas, respectively.<sup>7</sup> In urban areas, more CATI households have district or local heating and freezers and washing machines than F2F households, but we found the opposite for satellite dish. So, it is not easy to find a pattern for the response in this category. Lastly, for subjective variables, more CAPI households are likely to feel worse off than F2F households in rural areas, but the difference is small.

> 7 The questions on housing quality (water installations, sewerage, electricity, district or local heating, fixed telephones and number of rooms) are not included in the model but included in questionnaires.

15