rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 9a: Number of new firms around GEL Figure 9b: Number of new firms around GEL<br>100,000 cutoff – 2008 data (pre-reform) 100,000 cutoff – 2009 data (pre-reform)<br>Local Avg. + Polynomial in GEL 500 Intervals from Cutoff Local Avg. + Polynomial in GEL 500 Intervals from Cutoff<br>50000 100000 150000 50000 100000 150000<br>2008 Declared Revenue 2009 Declared Revenue<br>30 25<br>20<br>20<br>15<br>10<br>10<br>Number of new firms in 2007 Number of new firms in 2008 5<br>0 0<br><!-- End of picture text --> The discontinuity at GEL 100,000 in the pre-reform period displayed in Figure 9 suggests that we cannot use a basic RDD methodology to estimate the impact of the reform on firm registration around GEL 100,000. With an additional assumption, we can, however, use a difference-in-difference analysis. That is, if we assume that the size of the jump in the number of new firms at GEL 100,000 would be constant over time in absence of the reform, we can compare the size of this jump over time and we can attribute post-reform changes in the jump to the reform. The plots for 2008 and 2009 in Figure 9 suggest that this may be a reasonable assumption, i.e. the jump is of similar size using 2008 and 2009 data. In fact, estimating regression (2) for years 2008 and 2009 gives an estimated jump size of 5.3 in 2008 and 4.4 in 2009 (coefficient a1 in the regression represents the size of the jump) – both estimated jumps are statistically significant at the 0 percent level.<sup>25</sup> For the difference-in-difference methodology, we run regressions along the lines of Equation 1, adding a time dimension, i.e. year fixed effects and interaction terms of the variables with these fixed effects. This approach estimates the impact of the reform in each year relative to the first year of data (2008). That is, the estimated impacts are differences with respect to 2008 data.

There is no comparable discontinuity in the revenue distribution around GEL 30,000, suggesting that the basic RDD approach is valid around GEL 30,000. Figure 10 plots the number of new firms in intervals of GEL 100 around GEL 30,000. Here, we can use smaller intervals since we have many more firms in each interval, reducing noise in the data. The number of new firms shows a more uniform pattern around GEL 30,000 than around GEL 100,000, decreasing below and above the cutoff. The 2008 graph displays a small discontinuity at GEL 30,000, but this jump is not statistically significant. We thus use a standard RDD methodology with cutoff GEL 30,000 to estimate the impact of the micro tax regime on firm registration.

> 25 We cannot use data on declared revenue for 2007 to see whether the size of the jump in new firms was similar in 2006, since as discussed above, the 2007 revenue data is much less complete than for later years.

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who completed the baseline survey were exposed to the information, thus guaranteeing _perfect compliance_ .

In order to elicit the information on perceived returns to schooling, the computer software used three subjective expectation questions, similar to the ones included in the _Jóvenes con Oportunidades_ survey:

1. If you were to quit studying right now and hence lower secondary is your highest degree, what do you think is the amount you can earn per month at ages 30 to 40?

2. If you finish upper secondary and do not continue studying, what do you think is the amount you can earn per month at ages 30 to 40?

3. If you get a university degree and do not continue studying, what do you think is the amount you can earn per month at ages 30 to 40?

Similarly, the computer software elicited information about the students’ perception about the returns to schooling for an average person, as opposed to expectations about his or her own returns.<sup>10</sup> Students in the treatment group received three main information contents. First, they were given gender-specific information on the monetary returns to schooling, as computed using data from ENOE second quarter of 2009. The information on the returns to education was given in the form of additional monthly pesos earned by upper secondary working full time as well as the net present value of the additional income flows assuming entry and exit to the labor market at ages 25 and 65, respectively: In Mexico a man (woman) between 30 and 40 years old with a maximum education level of lower secondary earns, on average, $4,832 ($3,179) MX per month.<sup>11</sup> A man (woman), ages 30 to 40, with an upper secondary diploma earns, on average, $6,466 ($4,827) MX per month, or $1,634 ($1,648) MX more per month. Therefore a man (woman) with an upper secondary diploma earns, on average, $784,320 ($791,040) MX more than those with a lower secondary degree throughout his (her) productive life.

Students in the treatment group also received information about the returns to university in a format similar to the one presented above. The second type of information content

> 10The three questions read as follows: (1) what do you think is the amount earned per month by a man (woman) between 30 and 40 years old with a lower secondary degree?; (2) what do you think is the amount earned per month by a man (woman) between 30 and 40 years old with an upper secondary degree?; (3) what do you think is the amount earned per month by a man (woman) between 30 and 40 years old with a university degree?

> 11In November 2009 $1 MX was approximately $0.08 US.

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21 and rstk-analysis software they use be made accessible to bank examiners in differ-nt countries and responsive to the dynamics of financial-institution balanlce sheets.

In the meantime, supervisors in developing countries can usefully be encouraged to develop information systems that are compatible with the PDV methods of bank capital appraisal described in section II. For each of the major portfolio categories a bank's balance sheet and income statements delineate, top managers can be asked under penalty of fraud to project, defend, and discount the range of cash flows they deem likely to accrue.

Concentrating **on Tdentifving and TraCking the Changing Value of Problemn Loans** Within the U.S., state and federal regulators share information about the loars and investments that their respective bank examiners classify adversely. International sharing of supervisory information is equally important and already occurs to some extent through the Bank for International Settlements (BIS). The World Bank and BIS should explore opportunities to extend the scope of this information sharing to a broader group of countries.

More generally, the World Bank or BIS might speed up the spread of the needed information systems and coordinate supervisory standards across developing countries in three ways. First, they could become producers of internationally transferable creditanalysis and regulatory information protocols and valuation software. Second, on an international or regional basis, they could serve as forums for negotiating itemization and valuation rules that would use expert software to recognize incipient loss accruals and to assign accruing losses to specific bank stakeholders. Third, they could test and disseminate risk infornation to help the financial-services industry to set up a global secondary market for developing-country loans. Enhancing the liquidiLy of these loans would tend to reduce prescriptive levels of capital requirements for banks in developing countries and the cost of capital to customers of these banks.

In the years to come, these institutions could usefully disseminate a range of valuation and credit-analysis models that follow behavioral scoring models in being conceived as methods for analyzing pertinent information reported in credit-application data, credit-bureau or credit-rating files, and payment records. These models should strive to follow U.S. software systems in planning to employ scoring models to predict a customer's disposition to pay back a loan when and as it becomes delinquent. The
15 sectors and consumers to take advantage of this revolution. In practice for developing countries this means increasing contestability in the processing and marketing of their farm products and inputs, and focusing rural public expenditures more on reducing underinvestment in public-good institutions and infrastructures to lubricate that value chain rather than on inequitable and inefficient farm input subsidies.<sup>4</sup>

# **Where can the economics profession contribute more to the policy reform process?**

Better empirical research on the extent, causes and effects of policy is needed to encourage developing countries to further reform their sectoral and trade policies.<sup>5</sup> Consider those three areas in turn.

# _Estimating the changing extent of distortions_

Empirical analysis on the degree of interventions in agricultural markets has been provided for almost 20 years by the Secretariat of the OECD (2006) for developed countries plus Korea, Mexico, some of Europe’s transition economies and a handful of large developing countries, in what are now called Producer Support Estimates (PSEs). No such comparable series has been generated for long periods for other developing countries since the Krueger, Schiff and Valdes (1988) study which covered the 19601984 period for 18 developing countries, apart from a nine-year update for the Latin American countries in that sample by the same country authors (Valdes 1996), and a comparable study of 7 central and eastern European countries (Valdes 2000). An
8 Figure 1: Building Area, Height and Volume for New York City’s Region, 2012/2019 _Notes:_ The figure shows New York City’s region in WSF3D (90m*90m resolution). Building area is in m<sup>2</sup> (A). Height is in m in WSF3Dv1 (B) and WSF3Dv2 (C). WSF3Dv2 volume is in m<sup>3</sup> (D). Data is for the year 2012/2019.
- Handa, Sudhanshu. 2002. “Raising Primary School Enrolment in Developing Countries: The Relative Importance of Supply and Demand.” _Journal of Development Economics_ 69:103-28.

- Jalan, Jyotsna, and Elena Glinskaya. 2003. “Improving Primary School Education in India: An Impact Assessment of DPEP-Phase I.” Indian Statistical Institute (New Delhi) and The World Bank. Processed.

- Lanjouw, Peter, and Martin Ravallion. 1999. “Benefit Incidence, Public Spending Reforms, and the Timing of Program Capture.” _World Bank Economic Review_ 13(2):257-73.

- Lavy, Victor. 1996. “School supply constraints and children’s educational outcomes in rural Ghana.” _Journal of Development Economics_ 51:291-314.

- Lloyd, Cynthia, Sahar El Tawila, Wesley H. Clark, and Barbara Mensch. 2003. “The Impact of Educational Quality on School Exit in Egypt.” _Comparative Education Review_ 47(4): 444-67.

- Lloyd, Cynthia, Barbara Mensch, and Wesley H. Clark. 2000. “The Effects of Primary School Quality on School Dropout among Kenyan Girls and Boys.” _Comparative Education Review_ 44(2):113-47.

- Pitt, Mark M., Mark R. Rosenzweig, and Donna Gibbons. 1993. “Determinants and Consequences of the Placement of Government Programs in Indonesia.” _World Bank Economic Review_ 7(3):319-48.

- Paxson, Christina, and Norbert Schady. 2002. “The Allocation and Impact of Social Funds: Spending on School Infrastructure in Peru.” _World Bank Economic Review_ 16(2):297319.

- Pritchett, Lant, and Deon Filmer. 1999. “What Education Production Functions Really Show: A Positive Theory of Education Expenditures.” _Economics of Education Review_ 18(2):22339.

- Younger, Stephen. 2000. “The Incidence of Public Services and Subsidies in Peru: Evidence from Household Surveys.” Cornell Food and Nutrition Policy Program Working Paper No. 103. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=436602.

- Younger, Stephen. 2003. “Benefits on the Margin: Observations on the Marginal Benefit Incidence.” _World Bank Economic Review_ 17(1):89-106.

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**29**

could be driving the differential impact on farm yields. The results from Table 7c suggest that selection into the PFR did not result in any overall gender differences in crop choice. Indeed, parcels from male- and female-headed households are equally likely to have perennial cash crops as a result of PFR demarcation activities. The disaggregated farm labor results from Table 7d also fail to reveal a discernible pattern across male- and female-controlled parcels.

# **4.7 Gender and parcel location**

Our gender-differentiated analysis provides evidence that the effects of land demarcation vary by sex of the household head. However, a puzzle from Table 7b remains: why do agricultural yields drop on treated parcels held by female-headed households (relative to treated maleheaded households)? To shed light on this question and to test the predictions from our model, we exploit the partial coverage of the PFR demarcation process. The program only demarcated those parcels situated within the boundaries of treated villages. This feature of the program allows us to test for differences in treatment effects on parcels located within and outside of the village.<sup>37</sup> Moreover, as the model presented in Section 3 suggests, these effects also likely vary with initial levels of tenure security. Given the relatively weaker tenure security initially experienced by female-headed households, we explore gender as a source of heterogeneity and report our results by gender of the household head.

In practice, we revise Equation 3 as follows:

**y** _ijk_ = _α_ + _β ·_ **t** _jk_ + _η ·_ **outside** _ijk_ + _ν ·_ **t** _jk ·_ **outside** _ijk_ + _φ ·_ **x** _ijk_ + _γk ·_ **lottery** _k_ + _εijk_ (5)

where **outside** _ijk_ takes a value of 1 if parcel _i_ is located outside the village boundaries.<sup>38</sup> We report the coefficients _β_ , _η_ , and _ν_ on our outcomes of interest (tenure security and investment; agricultural activities; crop choice; and, labor supply) in Table 8a through Table 8d. To document the differential effects by gender, we present our results for both split (Panels A and B) and pooled (Panel C) samples.

We begin by examining the location-specific effects of land demarcation on tenure security and investment in Table 8a. We find that, in line with program implementation, assignment to the

> 37A given parcel’s likelihood of being inside the landholder’s village does not vary by gender (see Table A-4).

> 38This indicator variable is based on the household’s self reported location of the parcel. The location variable is also consistent with other parcel distance measures. For example, parcels located more than one hour’s walking distance from the dwelling are significantly more likely to be located outside of the village (see Table A-4).
The MCB is used as a criterion of access to certain privileges, in particular privileges on real estate construction or purchase for young families.

In 2017, one‐third of the population had disposable incomes below the MCB, compared to 18.7 percent of the population in 2014. The scale of this poverty increase corresponds with the results of the poverty analysis based on the cost of basic needs approach, presented in Mazol (2017). An increase in the risk of moderate poverty was observed for every social group, irrespective of the initial level of poverty.<sup>4</sup> Nevertheless, the magnitude of the increase of the poverty risk was lower than average for residents of Minsk, for the working age population with high educational attainment, and for households without children (see Annexes, Table A3). On the contrary, residents of small cities (defined as cities with a population not exceeding 100,000) faced the highest moderate poverty risk increase. As a result, the level of moderate poverty in small cities caught up with the one of rural areas in 2017.

_Figure 10: Official and moderate poverty rates, 2014‐2017_ <!-- Start of picture text --> 40% 8%<br>35% 7%<br>30% 6%<br>25% 5%<br>20% 4%<br>15% 3%<br>10% 2%<br>5% 1%<br>0% 0%<br>Moderate poverty Official poverty<br>2014q1 2014q2 2014q3 2014q4 2015q1 2015q2 2015q3 2015q4 2016q1 2016q2 2016q3 2016q4 2017q1 2017q2 2017q3 2017q4<br><!-- End of picture text --> Note. See methodological notes on definition of poverty concepts. Source: Authors’ estimates based on quarterly HBS data.

# 3.3. Vulnerability

While official poverty did not change substantially during the crisis, this need not indicate that the degree of vulnerability to poverty risk or financial distress of the population throughout the recession remained unchanged. For instance, as discussed in the section above, moderate poverty increased significantly during the 2015‐2017 crisis. Hence, the share of people whose income is above the official poverty line, but very close to it, is increasing. Second, many households go in and out of official poverty during the year. In 2017, 13.4 percent of the population lived below the official poverty line during at least one quarter, while the official poverty rate for the year was 6.8 percent. In other words, official poverty numbers do not fully reflect poverty risks and income insecurity.

To assess the population poverty risks better, we construct the measure of vulnerability following Lopez‐ Calva and Ortiz‐Juarez (2013). We model the probability of being poor (by the official definition) as a function of certain socio‐economic characteristics such as age, residence, socio‐economic status, household type, and

> 4 The profile of the population below the moderate poverty risk threshold is broadly similar to that associated with the official poverty threshold and corresponds to the poverty profile revealed in Mazol (2017).

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Figure 3 graphs the actual CIP and this implicit range over the period of the 3rd and 4th ICoAs, and also the intervention range defined in the September 1993 retention scheme - see section 3.2.'5 The 3rd ICoA had come into operation during the 1975-79 price boom. The consuming country members were initially reluctant to see the reintroduction of quotas, but eventually agreed in September 1980 on the understanding that a group of Latin American producers desisted from attempts to raise the price outside the scope of the ICoA. Helped by frost in Brazil in 1981, the ICO quotas generally managed to achieve stable prices within the implicit ICoA range. Drought conditions in Brazil during 1985-86 coffee year resulted in the suspension of controls in February 1986, to be reintroduced the following year as supply recovered and prices fell back again. "Independence Day" for coffee was 4 July 1989 when quotas were again suspended despite the fact that the CIP had not reached any of the trigger levels for quota reduction.

The technical reason for the July 1989 suspension of the ICoA quotas was that, with the 4th agreement due to end on 30 September, there appeared to be no basis for a new agreement with economic clauses. A number of different factors combined to generate dissatisfaction with the way that the ICoAs had operated, but there was no consensus as to how this should be changed. On the consumers' side, dissatisfaction related to the market distortions perceived to be generated by the ICoA. Consumer tastes have shifted over time towards the high quality arabica beans produced by the Colombian "milds" (Colombia, Kenya, Tanzania) and "other milds" (mainly central American) groups of producers, but the ICO quota allocation tended to generate a higher premium for this type of coffee over the prices of robustas (produced in Brazil, Indonesia and Africa) and unwashed arabicas (produced in Brazil). Dramatic evidence for this was provided by the February 1986 suspension of quotas: over the following year, the premium of mild arabicas over robustas fell from 42% to just 6% (March 1987 compared with March 1986). But consuming countries were also irritated by the fact that the ICoAs permitted unlimited exports to non-member consuming countries at free market prices which, particularly " Source: International Coffee Organization, _Monthly Report on Prices,_ (ICO, London), various issues.

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to crime.<sup>8</sup> The country level controls, _Z_ , include logged GDP per capita, the ICRG index of government efficiency, the firing costs in years of salary, and the interest spread (as a measure of banking competitiveness). All the country-level variables come from the World Development Indicators data base of the World Bank. Since the regressions are at the firm level, while we have country-level explanatory variables, to avoid exaggerating estimation precision, we cluster the standard errors at the country level (Moulton 1986). Table 3 reports the results.

The most important finding is that foreign ownership has a strong and significant relationship with both sales growth and TFP. Increasing foreign ownership by 10 percentage points would increase sales growth by 1.2 percentage points, and TFP by 1.8 percentage points—effects that are non-trivial. To put this in perspective, Table A1 in the appendix provides the comparison of SADC with the East Asia and Pacific region (consisting of China, Indonesia, Malaysia, and Thailand, EAP hereafter) and all nonSADC countries with similar income level as SADCs.<sup>9</sup> Foreign ownership is significantly higher in SADC (17.5 percent) than in EAP (13.5 percent) and the similarincome sample (10.8 percent). In light of the positive effect of foreign ownership and SADC's advantage in this measure, foreign ownership has reduced the disadvantage of SADC toward other regions.

The other findings on performance determinants are also illuminating. The intensity of crime (as measured by the share of sales lost due to crimes) is positively associated with sales growth but negatively with TFP. Firms in countries with higher income levels tend to have higher TFP--a country that is richer by 100 percent tends to have a productivity premium of 7 percent, which could reflect the forces of better institutions and a large market. Unsurprisingly, a higher firing cost and, thus, a higher exit barrier, is associated with lower productivity level. An interpretation is that higher firing costs cause shirking of workers and, therefore, lower efficiency for a fixed amount of capital and labor. This is consistent with what is commonly found in many developing

> 8 We have also tried specifications including the share of losses of sales due to power outage and the average level of schooling of firm employees.  The results including these additional controls are similar. However, several countries do not have these measures. To preserve as many SADC countries as possible in our sample, we opted to report the results without including them.

> 9 That is, with income less than 4600 U.S. dollars of 2000, the level of South Africa.

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