rafmacalaba/datause-encoder-data huggingface dataset:
improving survey design in terms of gains in precision.

Self-reported (subjective) and objective measures of knowledge and adoption of sustainable land management (SLM) practices were surveyed to measure the impact of an agricultural extension intervention (Kondylis, Mueller, and Zhu, 2014). Kondylis, Mueller, and Zhu (2014) exploit this special feature of the survey design to formally document the importance of perceptions versus information accuracy in inuencing behavior. Other strands of the literature have performed similar analysis. House et al. (2004) and Knight (2005) nd that subjective knowledge signicantly determines a consumer's willingness to accept a genetically modied product or biotechnology application.

We next investigate gender-specic determinants of misreporting. Building on previous eorts in health (Butler et al., 1987; Baker, Stabile, and Deri, 2004; Johnston, Propper, and Shields, 2009), agriculture (House et al., 2004; Knight, 2005) and development (Beegle et al., 2012; Deininger et al., 2012; de Nicola and Giné, 2013), we document how dierences in educational attainment, farm size, training exposure, and the allocation of labor within the household will dierentially aect the reporting accuracy of men and women farmers. Response accuracy is shown to be inuenced by the respondent's educational attainment (Butler et al., 1987; House et al., 2004; Beegle et al., 2012; Deininger et al., 2012), and farm size (Beegle et al., 2012; Deininger et al., 2012). This is further motivated by the well-documented gender gap in access to inputs and extension services, labor and liquidity constraints, and tenure insecurity (Ragasa, 2012; Croppenstedt, Goldstein, and Rosas, 2013).

Finally, we provide a cost-eectiveness analysis to illustrate the feasibility of incorporating knowledge exams and eld measurement of adoption into rural household surveys. The costs of these survey tools are compared to the improvement in precision gained. Final recommendations for future monitoring and evaluation practices are based on these assessments.

We nd that, while the incidence of false negative and positive response is similar across men and women, the determinants of misreporting vary with the gender of the respondent. We document that men and women are systematically more likely to underreport their knowledge of agricultural practices and over-report their adoption. Despite clear dierences in human and physical capital, there are no obvious gender dierences in the precision of knowledge and adoption measures. Having been trained on a given technique is associated with lower knowledge misreports and higher adoption misreports for both men and women. Other determinants of reporting error dier by gender.

3
demonstrated. Third, there is no explanation for why the subsidies should assume a noncash form. Why not use standard channels of directed credits, for example?

We extend the GI framework by incorporating the strategy employed by energy monopolies to also gain from the nonpayments system. We demonstrate that, through tax arrears and unpaid dues to extra-budgetary funds, the energy sector substantially passes on the costs of the implicit transfers to the general fiscal accounts. This means that the subsidies eventually show up in the accumulation of public debt. Sometimes, this pass-through is explicit, as with the export tax breaks received by Gazprom until early 1996, or with continuous growth in real terms of Gazprom's arrears to the enlarged budget. Another avenue is ever re-emerging tax offsets, of which Gazprom is one of the major beneficiaries. Further, the energy monopolies, both Gazprom and its counterpart in electricity, RAO UES, have made a number of acquisitions by converting overdue receivables into equity in selected companies - a policy, which has significant monopoly economic costs for the country.<sup>5</sup> The question of why the subsidies assume a noncash form is closely tied to the government's main macroeconomic objective over 1995-98, which was to lower inflation as soon as possible by fixing the exchange rate and controlling credit.

# _Hard Budget Constraints and Growth_

Empirical results obtained by Fischer, Sahay and Vegh (1996) make a crucial link between stabilization and growth: "[...I inflation falls substantially [...] as fiscal deficits are brought under control. [ ...] after two years growth is positive. [...] reducing high inflation is a precondition for the revival of growth." This work, which clearly underpinned the Russian economic agenda between mid-1995 and the meltdown, views stabilization as a strictly macroeconomic phenomenon, with enterprise reform interpreted largely as privatization. However, the accumulated experience with transition since 1990 makes two points about hard budget constraints for enterprises: 6 (i) they are an indispensable microeconomic complement to macroeconomic stabilization, as documented here; (ii) they are necessary for the enterprise restructuring that supports the initial revival of growth, and for the credibility of reforms. 7 Later, the manner in which Russia pursued stabilization (not by cutting fiscal deficits) is discussed, while the rest of this section dwells on hard budget constraints.

> 5 For Gazprom, ferrous metallurgy and petrochemicals have become main targets, while UES concentrates on penetrating non-ferrous metallurgy.

> 6 This term was coined by Professor Janos Kornai. For the seminal article, see Komai (1986). 7The earliest published evidence on the efficacy of hard budgets and competition for transition economies is contained in Pinto, Belka and Krajewski (1993). This result has been confirmed most recently by a 3000-firm survey conducted by EBRD and reported in Carlin, et al. (1999), and discussed in Chapter 7 of the EBRD Transition Report, 1999.
4 The pattem is dIfferent for the middle Income countries group, to which Brazil belongs. Here, the share of total secondary enrollments in vocational schools has remained fairly constant, hovering at around 20 percent, while overall secondary school enrollments as a proportion of the relevant age group has risen dramatically from 18 to 55 percent. This Indicates that vocational schooling in middle Income countries generally has expanded at about the same rate as the secondary school system as a whole. We shall comment further on this more favorable experience in middle income countries, In the concluding section. But first, we consider the case of Brazil.

# 2. Vocatlonal Schooling In Brazil

The average educational attainment of the Brazilian population lags substantially behind that of other middle income countries. In 1980, the mean years of schooling of the male labor force was about 5.6 years with 67.9 percent of the male workers having less than secondary schooling (Psacharopoulos and Arriagada, 1986). Available data for secondary school enrollment sugges **t** hat there has been a general underinvestment in this level of education in Brazil. In 1987, enrollments at the secondary level represented only 37 percent of the school age population compared to 59 percent for other middle income countries. Table 1 presents secondary school enrollment rates for selected countries.

Table 1 Secondary School Enrollment Rates Selected countries (1965 and 1986) <!-- Start of picture text --> Country 1965 1986<br>Brazil 16 37<br>Korea 35 95<br>Chile 34 70<br>Mexico 17 55<br>Average Upper<br>Middle-income<br>Countries * 29 59<br><!-- End of picture text --> Source: World Bank (1989) <!-- Start of picture text -->
*  Defined as per capita GDP between US$ 1,810 and $ 7,410 in<br>1988.<br><!-- End of picture text -->

Available analyses of secondary schooling In Brazil indicate that the system experienced rapid growth between the 1960s and 1970s, averaging 11 percent a year between 1960 and 1980, and
**Figure 1. Aid Intensity Over Time For 88 Developing Countries**

<!-- Start of picture text --> + denotes SSAo Full distribution on axes. Lines indicate quartiles.<br>27 --<br>6<br>Bat<br>z<br>+ Nih<br>o ~~~~~~+ ++<br>n +<br>hi 79 + Tan<br>z<br>3.1-- +tg+ n<br>1.1-- +<br>.8 5.5 14.2 60<br>Average Net ODXAGNP (%), 1990-96<br><!-- End of picture text --> A second response recalls the views of early aid critics and the recent debate over welfare reform in the United States. Authors like Bauer (1971) and Friedman (1958) predicted that foreign assistance would displace processes of institutional maturation that were essential to economic development. Aid would create dependence. The long-run result of high aid levels would be relative economic regress. A striking feature of these views is that many of their essentials were shared by the radical left. Andre Gunder Frank (1966), for example, argued that foreign assistance represented a side payment to elites in recipient countries, designed to buy their compliance in maintaining the economic and political dominance of the industrialized countries.

The diversity of actual experience indicates that neither the gap-filling view nor the dependency view provides an adequate account of the dynamics of aid and development. In this paper, we present a model that captures some of the features of both viewpoints, but permits a multiplicity of outcomes-including the very different aid profiles of, say, Korea and Tanzania. To do so we begin with a standard public-finance problem, to which we add a specific process of institutional maturation. Foreign aid is explicitly government-to-government in this model, and in an accounting sense it fills a "fiscal gap." At the same time, it retards the development of institutional competence in the recipient's public sector. For concreteness we locate institutional development in the recipient's revenue-collecting ministry. The foundational question becomes the one asked by Kaldor (1963): "Will developing countries learn how to tax?" The answer depends on initial conditions, on how much aid is received, and most importantly, on the degree to which donor and recipient internalize learning-by-doing externalities in the recipient's public sector. The model exhibits two steady states, one involving high aid and low institutional development, the other low aid and strong institutions. Whether a country 3
Table 2: Impact on HIV outcomes

|**Panel A: HIV indexes**||||||||
|---|---|---|---|---|---|---|---|
|_Dep. Var. (Yt):_|_HIV kno_|_wledge_|_HIV at_|_titudes_|_HIV te_|_sting_||
||(1)|(2)|(3)|(4)|(5)|(6)||
|Treated|0.898***|0.782***|0.344**|0.339***|0.353**|0.335**||
||(0.244)|(0.215)|(0.135)|(0.103)|(0.148)|(0.128)||
||[0.001]|[0.001]|[0.022]|[0.003]|[0.022]|[0.012]||
|Treated*Y_t−_1||-0.065*||0.008||-0.033||
|||(0.035)||(0.037)||(0.032)||
|||[0.201]||[0.826]||[0.523]||
|Y_t−_1||0.390***||0.360***||0.472***||
|||(0.029)||(0.030)||(0.027)||
|R-squared|0.085|0.198|0.050|0.179|0.092|0.261||
|P-value test joint sig<sup>(</sup><sup>_a_)</sup>||0.000||0.001||0.011||
|Observations|4986|4986|4986|4986|4986|4986||
|Mean Dep. Var. (Control)|0.0400|0.0400|0.0439|0.0439|-0.139|-0.139||
|Std Dev Dep. Var. (Control)|6.106|6.106|3.474|3.474|4.145|4.145||
|**Panel B: HIV individual outcomes**||||||||
||_H_|_IV testing_||||_HIV+ bo_|_y should be_|
|_Dep. Var. (Yt):_|_Objective_<sup>(</sup><sup>_b_)</sup>|_Self-re_|_ported_|_Knows wi_|_ndow period_|_allowed to _|_play football_|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Treated|0.031**|0.025***|0.026***|0.049***|0.045***|0.051***|0.080***|
||(0.013)|(0.009)|(0.009)|(0.012)|(0.012)|(0.016)|(0.028)|
||[0.014]|[0.014]|[0.004]|[0.001]|[0.003]|[0.012]|[0.022]|
|Treated*Y_t−_1|||-0.038||-0.007||-0.046|
||||(0.063)||(0.062)||(0.036)|
||||[0.550]||[0.955]||[0.581]|
|Y_t−_1|||0.253***||0.286***||0.300***|
||||(0.054)||(0.048)||(0.030)|
|R-squared|0.023|0.031|0.059|0.028|0.072|0.044|0.123|
|P-value test joint sig<sup>(</sup><sup>_a_)</sup>|||0.005||0.000||0.006|
|Observations|3828|4982|4971|4986|4986|4986|4986|
|Mean Dep. Var. (Control)|0.033|0.086|0.086|0.129|0.129|0.662|0.662|
|Std Dev Dep. Var. (Control)|0.180|0.280|0.280|0.335|0.335|0.473|0.473|
|Controls<sup>(</sup><sup>_c_)</sup>|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. FWER adjusted p-values in square brackets, based on 10,000 replications. Increasing values of the dependent variable correspond to outcomes more consistent with the messages of Shuga. (a) P-value test of joint significance: p-value for the null that the sum of the coefficient on Treated and Treated* _Yt−_ 1 (evaluated at the mean) is zero. (b) For this outcome we cannot estimate the model including the lag of the dependent variable because testing through health-camps was only offered at follow-up.

(c) Controls in each regression include: female, age, currently attending school, years of education, english spoken at home, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks Yoruba as a native language.

37
- Trujillo, L. and Nombela, G. _Privatization and regulation of the seaport industry_

region, due to three reasons. First, there is a strong need to seek capital to finance investments, since most governments are highly constrained in their budgets, due to debt servicing payments. Second, rapid economic growth is generating new traffic that demands new facilities and more efficient services. And third, fierce competition makes it necessary for ports to upgrade their facilities or risk being displaced by rivals.

Given the relevance of seaport reforms in Latin America, the rest of this section is devoted to describing three interesting cases: the experiences of Chile, Argentina and Brazil. Even though these particular countries are chosen, there are many others in the region that have already transformed their ports, or have advanced plans for that purpose. Other remarkable experiences in this region are those of Mexico, Panama, Nicaragua, Costa Rica, Colombia and Peru. A brief description of the reform processes of all these countries, and also experiences from other world regions can be seen in _appendix 2._

# **3.4.1. - Seaports' reform: the case of Chile**

Chilean international trade is served by 38 seaports, of which 11 are publicly owned, and are organized by the public agency _Empresa Portuaria Chilena (Emporchi),_ while 27 belong to the private sector. Of this last, 11 are of private use (owned by mining and other companies), and 16 are privately owned but publicly used. _Box 10_ shows the distribution of cargoes between the different ports. Overall, in 1997 Emporchi handled 94% of containerized cargo, 69% of general cargo, 18% of dry-bulk cargo and 11% of liquid-bulk cargo. In terms of total volume, the 11 public ports handled 37% of total tons that passed through Chilean ports (Tortello, 1998).

**_Box_ 10. Chilean ports. Distribution of cargoes by port type**

|**Port types**|**Containeri**<br>**cargo**|**zed**|**General ca**|**rgo**|**Dry-bulk c**|**argo**|**Liquid-**<br>**cargo**|**bulk**<br>|**Total car**|**goes**|
|---|---|---|---|---|---|---|---|---|---|---|
||Tons|%|Tons|%|Tons|%|Tons|<br>%|Tons|<br>%|
|**Private**|1,702|0|194,501|2|12,394,187|<br>49|6,870,439|<br>75|**!Syntax**<br>**Error, )**|<br>**38**|
|**Private 2**|427,347|6|2,900,055|29|8,247,183|<br>33|1,334,878|<br>14|**!Syntax**<br>**Error, )**|<br>25|
|**Emporchi**|6,375,130|94|6,804,283|69|4,481,230|**18**|987,764|<br>11|**!Syntax**<br>**Error,)**|<br>37|
|**Total**|**!Syntax**<br>**Error,)**|**100**|**!Syntax**<br>**Error,)**|**100**|**!Syntax**<br>**Error,)**|**!Syn**<br>**tax**<br>**Err**<br>**or,)**|**!Syntax**<br>**Error,)**|**!Syn**<br>**tax**<br>**Erro**<br>**r,)**|**!Syntax**<br>**Error,)**|**!Syn**<br>**tax**<br>**Erro**<br>**r,)**|

Private ports for exclusive use by owners2 Private ports for public use _Source:_ Own calculations using data from Tortello (1998).

33
(right-hand side), where the units are the national currency. We presume that for a particular product, the prices of exports and imports can differ from domestic output and consumption. Such price differences always occur in practice, which is why we incorporate them here, without considering why the price differences arise. We distinguish the prices pij > 0 for domestic output and consumption, used to multiply qij, i = 1,...,M+N, from those for exports and imports, pij<sup>x></sup> 0 and pij<sup>m></sup> 0 respectively.<sup>6</sup> Consistent with the System of National Accounts (SNA), the export prices are measured _net of tariffs and freight, including any subsidy to the buyer but not to the seller_ , i.e. as the f.o.b. (free on board) price in the exporting country.<sup>7</sup> Likewise, the import prices are measured _net of tariffs._<sup>_8_</sup> m x M + N x M + N m With these conventions for p and ij pij _,_ let X j = ∑ i = M0 + 1pij x ij and M j = ∑ i = M0 + 1pij mij denote the value of exports and imports at tariff-free prices, so that nominal GDP measured on the expenditure side is: Using (4), we can re-write (5) as: where the first line is obtained by using qij = (yij – zij) – (xij – mij) for the final goods i=1,...,M, whereas the intermediates have qij = 0, so that [(yij – zij) – (xij – mij)] = 0 for i=M+1,...,M+N. Then the second line follows because xij = mij = 0 for the non-traded goods i=1,...,M0. We can interpret (pij<sup>−</sup> pijm ) as import tariffs (subsidies if negative), and (pij<sup>−</sup> pijx ) as exports subsidies (taxes if negative). So the final summation on the second line is interpreted as import revenue

> the real GDP number is an output measure.

> 6  In principle we should also distinguish producer from consumer prices, which can differ due to taxes and retail margins, but do not incorporate that distinction here.

> 7  See <u>http://unstats.un.org/unsd/sna1993/introduction.asp, paragraphs 6.235, 6.237 and 15.35.</u>

> 8  The SNA recommends that transport costs also be removed from import prices, but that is not possible using the unit-values from UN data, where imports are measured c.i.f. (cost, insurance, freight).

8
program, such as roads, or sanitation. The solution to undertake a cost-benefit analysis, which puts a monetary value on the benefits generated by the programs, and compares these values with the costs.

**Table 15.  Cost Effectiveness of Social Fund for Development Programs (in LE per outcome)**

||Regres-<br>sion|A vs. B<br>Nearest<br>Neigh-<br>bor|Kernel|Regres-<br>sion|D vs. F<br>Nearest<br>Neigh-<br>bor|Kernel|Regres-<br>sion|C vs. E<br>Nearest<br>Neigh-<br>bor|Kernel|
|---|---|---|---|---|---|---|---|---|---|
|**Education (LE 200m)**||||||||||
|Per certificate obtained|909||1,000|1,866||1,555|1,667|||
|Per one less illiterate|1,842|||3,682|||5,385|||
|Per one more job|||||||-9,723|||
|Per one fewerpoor|-89,952|-2,508|-3,683|||||||
|**Health (LE 390m)**||||||||||
|Per one less U-5 death|183,176||||||196,170||184,762|
|Per LE1 saved on health spending|13.1|||33.7|37.8|26.5|21.8||24.8|
|Per one fewerpoor||||15,594|9,814|12,568|-10,866|-8,241|-11,525|
|**Potable water (LE 620m)**||||||||||
|Per LE1 saved on water|146.2|130.4|157.9|250.6||403.4|322.5|170.3|236.6|
|Per one less U-5 death|||||||551,786|||
|Per LE1 saved on health spending|14.6|9.4|16.2|33.2|43.8|41.8|27.0|18.4|25.8|
|Per one fewerpoor|63,161||||||40,458|||
|**Sanitation (LE 220m)**||||||||||
|Per person getting access to sanitation|||||||24,399|5,741|9,946|
|Per LE1 saved on sanitation spending|203.6||||||678.9||508.0|
|Per person fewer with renal disease|-1,387|||-374||-421|4,045||3,541|
|Per person fewer with bilharzia||||||3,371||||
|Per one fewerpoor|37,552||24,684||||145,668||101,268|
|**Roads (LE 500m)**||||||||||
|Per LE1 of transport spending saved|27.8|60.7|31.1||||47.3|67.8|50.5|
|Road utilization||||0.002||||||
|Per one more job|59,903||54,457||||87,674||93,519|
|Per LE1 of additional farm income|12.9|12.1|17.2|17.3||16.6|35.4|||
|Per LE1 of additional non-farm inc|-36||-32|28|||-41|-43|-38|
|Per one fewerpoor||||-95,188||||||

_Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

There are two challenges in undertaking the cost-benefit analysis: first, one has to measure the total impact of the programs, which we have done above; and second, it is necessary to value each output. For the latter, we drew on secondary sources, using the values that are set out in Table 16. We suppose that the benefit of pulling someone out of illiteracy is the avoided cost of schooling per child that completes fifth grade. This treats literacy as a right that needs to be provided, rather than valuing it solely in terms of labor market outcomes. We value an additional job by measuring the economic rent that accrues when someone who was not working now has full-time employment. This may be measured as the wage _Impact Evaluation: Egyptian Social Fund for Development Version of June 29, 2009_ _Page 25 of 36_
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30
# Are Labor Markets in Developing Countries Dualistic?

_William F. Maloney*_ Keywords: Dualism, informal sector, Mexico, segmentation, transitions, wage differentials.

* This work was undertaken while I was on leave from the World Bank, 1994-95. I am grateful to Enrique Davila Capalleja, Aslan Cohen, Rene Cortazar, Bill Dickens, Hadi Esfahani, Ariel Fiszbein, Alec Levenson, Frank Lysy, Doug Marcouillier, Gustavo Marquez, David Nielson, Ana Revenga, Eric Rice, Bill Savadoff, and Guilherme Sedlacek for helpful discussions. I also thank Roberto Flores Lima and Agustin Ibarra Almada of the Mexican Secretariat of Labor and Social Welfare for their advice and help with the project. Finally, I thank the Mexican National Institute of Statistics, Geography, and Information (INEGI) for the use of the data. INEGI is not responsible for any incorrect manipulation of the data or erroneous conclusions drawn from it. This paper was formerly titled "Dualism and the Unprotected or Informal Labor Market in Mexico: A Dynamic Approach."
**Figure 12: Livestock and changing land use in ECA**

<!-- Start of picture text --> Hectares Animal units<br>(millions) (thousand)<br>400 400<br>Meadows and grazing land<br>350 350<br>300 300<br>Land devoted to crops<br>250 250<br>200 200<br>Livestock<br>(right axis)<br>150 150<br>100 100<br>1961 1966 1971 1976 1981 1986 1991 1996 2001 2006<br><!-- End of picture text --> Source FAO (2011) 66