rafmacalaba/datause-encoder-data huggingface dataset:
prices started to fall in 1996/97, the number of market participants almost instantaneously decreased.

What evidence of pass-through do we get from our household survey data during the 1990s? As shown in Figure 1, the three surveys used in our analysis cover one period preceding the coffee boom (1992/93), one period right after the boom (1995/96), when world market prices for Robusta coffee had already considerably dropped, and a later period (1999/00) that follows a further significant decline in world market prices. Thus, two low-price periods and a relatively high-price period can be compared.

Figure 1: ICO robusta international price in US cents per kg and survey coverage, 1990-2001 <!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>01/90 01/91 01/92 01/93 01/94 01/95 01/96 01/97 01/98 01/99 01/00<br><!-- End of picture text --> Source: International coffee organization (www.ico.org), September 2005.

Figure 2 shows, for each survey period, how world market price fluctuations relate to the prices received by coffee producers. It turns out that mean prices paid to growers (converted to current US cents and averaged over each survey period) increased more than fourfold between the first and second survey and were almost halved between 1995/96 and 1999/2000. In 1992 Ugandan Shillings, these prices fluctuated around 200 USh per kg in 1992/93, then rose to around 500 USh in 1995/96, and fell again to around 330 USh in 1999/00. This 8
**Table 6:  Estimated effects of community radio access on household attitudes towards giftgiving politicians**

||No Radio X|Children i|nteraction|Radio X|Children int|eraction|
|---|---|---|---|---|---|---|
||Bivariate|Radio|All|Bivariate|Radio|All|
|||Controls|controls||controls|controls|
|Number of radio stations|||||||
|Public stations||0.025|0.026||0.025|0.026|
|||(0.33)|(0.39)||(0.32)|(0.38)|
|Private commercial||0.029|0.019||0.028|0.019|
|stations||(0.18)|(0.37)||(0.18)|(0.37)|
|Religious stations||-0.037|-0.031||-0.036|-0.030|
|||(0.32)|(0.45)||(0.34)|(0.46)|
|**Private non-commercial**|**-0.002**|**-0.003**|**-0.0004**|**-0.018**|**-0.019**|**-0.014**|
|**(community)**|**(0.83)**|**(0.78)**|**(0.97)**|**(0.15)**|**(0.14)**|**(0.28)**|
|**Private non-commercial X**||||**0.006**|**0.006**|**0.005**|
|**number of children**||||**(0.03)**|**(0.03)**|**(0.05)**|
|Number of children listed|||-0.010|-0.020|-0.020|-0.022|
||||(0.07)|(0.01)|(0.01)|(0.01)|
|Number of children listed|||-0.013|||-0.013|
|aged 0 - 5|||(0.21)|||(0.21)|
|Secondary Education -|||-0.083|||-0.083|
|<br>Respondent|||(0.00)|||(0.00)|
|Is Yoruba - Respondent|||0.015|||0.0172|
||||(0.69)|||(0.65)|
|Number of HH members|||-0.0002|||0.001|
|aged over 60|||(1.00)|||(0.97)|
|Catholic Religion -|||-0.007|||-0.008|
|<br>Respondent|||(0.82)|||(0.79)|
|Household income below|||-0.076|||-0.077|
|30,000 CFA|||(0.00)|||(0.00)|
|Dwelling characteristics|||||||
|Wall - Brick/Semi-rigid|||-0.121|||-0.120|
||||(0.00)|||(0.00)|
|Floor - Cement|||-0.044|||-0.045|
||||(0.05)|||(0.04)|
|Rooms - Five or more|||0.032|||0.032|
||||(0.28)|||(0.27)|
|Distance to closest urban|||-0.0003|||-0.0003|

29
# COVID-19 School Closures, Learning Losses and Intergenerational Mobility<sup>∗</sup>

João Pedro Azevedo, Alexandru Cojocaru, Veronica Montalva Talledo and Ambar Narayan<sup>1</sup> _Keywords: COVID-19, learning losses, inequality, intergenerational mobility_ _JEL: I20, I24, I25, O15_

>  We are grateful to Maria Ana Lugo, Shwetlena Sabarwal and Benu Bidani for useful comments on an earlier draft of the paper and to Daniel Gerszon Mahler for discussion and suggestions on the use of GDIM data. All remaining errors are those of the authors. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> 1 Affiliations: Azevedo: Education Global Practice, The World Bank and UNICEF. Cojocaru, Montalva Talledo and Narayan: Poverty and Equity Global Practice, The World Bank. Alexandru Cojocaru is the corresponding author: <u>acojocaru@worldbank.org</u>
4~~~~~~~ financing.' There was a significant acceleration of reforms in 1991, when a liberalization and stabilization program was launched amidst a harsh environment of output decline. Privatization howevei moved slowly.5 On the other hand, private sector development was significant. The private sector contributed less than 5% of GDP at the end of 1989, and 36.8% in mid-1993; and whereas there were registered 24,500 private enterprises at the end of 1989, there were 315,000 enterprises registered in mid-1993.' The expansion of the private sector took place against the background of the characteristic declines in GDP associated with the transition. The state enterprise sector was making significant losses: in 1992 these losses were in the neighborhood of 7% of GDP (which equalled the government's budget deficit for that year), and for the first three quarters of 1993 preliminary estimates indicated combined losses of the state-enterprise sector of approximately 15% of GDP.<sup>7</sup> The private sector was in

> 41 Outstanding debt has not been necessary for difficulties in access to external finance. Romania, which had no substantial debt before it embarked on the transition, also faced a severe external financing constraint, due to perceived low creditworthiness, even in the absence of large pre-existing debt.

> S/ Only two large state enterprises had been privatized by the end of' 1993, and a small number of joint ventures had been author'.zed by the Ministry of Industry.

O/ Source: Small and medium enterprises division, Ministry of Industry, Government of Bulgaria.

> 7/ Source: World Bank staff astimates.
The adjusted supply-side gap is thus the difference between full coverage and the coverage that would be achieved taking into account first the current level of availability of the network in areas (the A variable), and second the take-up rate expected when there is no affordability issue. As before, the sum of the adjusted pure demand-side gap, the adjusted supply-side gap, and the coverage rate is equal to one: _APDSG_ + _ASSG_ + _C_ = 1 (8) The third step is to decompose the adjusted supply-side gap into two components. First, the adjusted pure supply-side gap is defined as follows: _APSSG_ = _ASSG_ × _U_ * = 1( − _AU_ *) × _U_ * (9) Finally, the adjusted mixed demand and supply-side gap is defined as follows: _AMDSSG_ = _ASSG_ × 1( − _U_ *) = 1( − _AU_ *) × 1( − _U_ *) (10) The proportions of the deficit in coverage due to demand-side, supply-side, and combined problems can then be computed using the above adjusted definitions, with the sum of the three proportions still being equal to one. The results are provided in tables 3 and 4. The findings are fundamentally reversed versus what was obtained with the simple statistical decomposition. The proportion of the deficit in coverage attributable to demand-side factors is now small for piped water, at 19 percent (population weighted data) to 23 percent (unweighted data). For electricity, the corresponding figures are 39 percent (unweighted data) to 52 percent (population weighted data). By contrast, the proportion of the deficit in coverage that is attributable only to supply-side factors is now much larger, at 41 percent to 42 percent for piped water depending on whether country population weights are used or not, and at 37 percent to 39 percent for electricity. The combined demand and supply-side problems account for 35 to 39 percent of the coverage deficit for piped water, and 11 to 21 percent for electricity on average for all the countries in the sample. Given that the combined supply and demand factors reflect first a supply issue (these are urban areas where the network is not available today), it is clear that supply appears to be a larger constraining factor than demand in terms of explaining coverage deficit in urban areas in Africa.
The California Climate Action Registry,<sup>11</sup> for example, considers all thinning of forests to send carbon to the atmosphere even though thinning reduces the risk of an even greater fire burning down an entire stand. “If, by contrast, that same forest is not thinned and instead experiences a catastrophic fire, the C stock baseline is simply _reduced_ , as if no CO2 emissions had occurred during the fire” (Hurteau et al., 2008). Scientists criticize the carbon accounting methodology as a “one size fits all” policy that does not account for the diversity of ecosystems involved and may penalize actions that help restore forest ecosystems rather than destroy them ( _ibid_ .). As Boyd (2009: 3) reflects, “solving problems through centralized controls and global blue prints tends to create its own vulnerabilities in the long term.” Since the Bali round of negotiations held in December 2007, efforts to reduce emissions from deforestation and degradation (REDD) have been added to the portfolio of activities authorized under the Kyoto Protocol. It is undoubtedly very important to recognize that forest ecosystems store an immense quantity of carbon and that harvesting timber and converting forests to other uses is as important a source of greenhouse gas emissions as power production. Thus the scientific foundation for adopting REDD is quite strong. Designing REDD projects so that new projects do not just lead to further leakage is a substantial problem. Further, ensuring that the rights of indigenous people are at least protected, and ideally enhanced, as a result of support of their management of forest ecologies is a goal that is widely shared by social activists at multiple scales. Accomplishing this goal while expanding the amount of forested land in developing countries would be economically efficient but a difficult challenge.<sup>12</sup> Currently there

> 11. California Climate Action Registry, _Forest Sector Protocol_ (2007), http://www.climateregistry.org/PROTOCOLS (accessed January 15, 2009).

> 12. John Vidal, in an article in _The Guardian_ (October 17, 2008), stressed that recognizing forest community rights would be a more cost-effective mechanism for reducing emissions than paying organizations to plant trees. “A study by Jeffrey Hatcher, an analyst with Rights and Resources in Washington, found that it costs about $3.50 (€2) per

26
<mark>WPS3576</mark>

# **Poverty in Rural and Semi-Urban Mexico during 1992-2002**

By

**Dorte Verner**<sup>**1**</sup> **Dverner@worldbank.org**

## World Bank Policy Research Working Paper 3576, April 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 I am very grateful to Jose María Caballero for inviting me to write this paper and Mario Torres Falcon for excellent and invaluable research assistance. This paper complements a larger World Bank effort to understand poverty in Mexico, the so-called Mexico Programmatic Poverty Work.
- 40 -

||**T**|**able 5. Estimate**|**d bid fun**|**ction**|||
|---|---|---|---|---|---|---|
||OLS||2SLS w|ith ztnb|IV||
||Coef.|Std. Err.<br>|Coef.|Std. Err.|Coef.|Std. Err.|
|ln_num_|-0.134|(0.050) ***|-0.285|(0.123) **|-0.338|(0.179) *|
|ln_securitybefore_|0.234|(0.046) ***|0.237|(0.045) ***|0.234|(0.042) ***|
|ln_dist_|0.045|(0.040)|0.054|(0.039)|0.037|(0.040)|
|D(_ethnicity_)|0.222|(0.156)|0.258|(0.161)|0.219|(0.165)|
|ln_cost_|0.959|(0.038) ***|1.015|(0.059) ***|0.999|(0.049) ***|
|ln_length_|0.061|(0.048)|0.067|(0.048)|0.057|(0.050)|
|ln_lane_|0.092|(0.225)|0.145|(0.236)|0.102|(0.217)|
|ln_thickness_|0.030|(0.028)|0.048|(0.030)|0.037|(0.028)|
|ln_gravel_|0.017|(0.012)|0.013|(0.013)|0.019|(0.012)|
|ln_bitumen_|-0.028|(0.010) ***|-0.037|(0.012) ***|-0.029|(0.010) ***|
|ln_earth_|-0.008|(0.013)|0.014|(0.016)|0.015|(0.022)|
|ln_brick_|0.014|(0.017)|0.016|(0.017)|0.012|(0.016)|
|ln_gabion_|0.006|(0.011)|0.013|(0.012)|0.012|(0.012)|
|ln_exavate_|0.003|(0.014)|0.007|(0.015)|0.006|(0.015)|
|ln_cement_|0.002|(0.017)|-0.018|(0.023)|-0.014|(0.022)|
|D(_postqualify_)|0.162|(0.153)|0.022|(0.176)|0.006|(0.220)|
|Constant|-0.910|(0.482)*|-1.658|(0.692)**|-1.301|(0.591)**|
|Obs.|599||591||591||
|R-squared|0.893||0.891||0.889||
|_F_-statistics|875.0||808.1||||
|Wald chi2|||||38941||
|No. of dummy variables:|||||||
|Project districts|18||18||18||
|Bidders' home districts|25||25||25||

Note: The dependent variable is the number of bidders, num. Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively. 2SLS with ztnb = two stage least squares with the zero truncated negative binomial regression as a first stage. For this model, _num_ is replaced with the predicted number of bidders from the first stage.

# **_Bidder participation_**

To explore the bidders’ entry strategy, a generalized count regression is performed. In addition, a probit model is also estimated with the observations of not only bidding firms but also firms that showed interest but did not enter the market. The results indicate that higher costs of bidding documents deter potential entrants from entering the market (Table 6). In the negative binomial model, the coefficient is negative and significant. The implied elasticity associated with the document price is estimated at -0.51 (Table 7). Therefore, a 10 percent
We observe a reduction in the probability of reporting an expected income (oneself) lower than $4,000 MX among boys and girls in the treatment group, compared to those in the control group. Among girls in the treatment group, we observe an increase in the probability of reporting an expected income between $4,000 and $7,000 MX, and no changes in the probability of reporting an expected income above $7,000. Among boys in the treatment group, we instead observe no change in the probability of reporting an expected income between $4,000 and $7,000 MX, but an increase in the probability of reporting an expected income in all the bins above $7,000 MX. The graphical evidence is supported by the regression results presented in Table 8. In summary, both boys and girls seem to update their beliefs in response to the information received as part of the intervention. However, while girls adjust their perceptions in line with the statistics provided, a significant fraction of boys report values higher than information provided by ENOE.

It is puzzling that while both boys and girls update upwards their perceptions regarding the monetary benefits of finishing EMS, only girls report higher effort. We next assess whether more objectives measures of effort support this conclusion. Until 2012, students attending technological EMS schools could choose among three different subtracks: 1) physics and mathematics, 2) economics and accounting, and 3) chemistry and biology.<sup>24</sup> Each subtrack has a large set of optional courses, and among those, students have to choose two (for a total of ten weekly hours) during the last semester of high school. The subtrack of physics and mathematics is the one with the widest choice of math related courses, followed by the economics and accounting, and chemistry and biology.

We compare the school subtrack distribution for boys and girls in treatment and control schools. Results are reported in Table 9. Among boys we do not find any significant difference in the subtrack distribution between treatment and control groups; a vast majority of students prefer the physics and mathematics subtrack (48%) followed by economics (20%) and chemistry (13%). For girls, the percentage of students who prefer physics and mathematics is 27% and is not statistically different in the treatment and the control group. We do find instead a much larger fraction of girls undertaking economics in the treatment groups (35%) vis-a-vis the control one (19%), with a consequential reduction in the uptake of chemistry and biology. A Kolmogorov Smirnov test allows us to reject the null hypothesis that the subtrack distribution is the same in the treatment and the control group for girls, but we can not reject the null hypothesis for boys. This evidence shows that one of the mechanisms linking the information treatment with improved learning outcomes among

> 24Starting from 2012, as part of a major curriculum reform, students can have a fourth optional subtrack, that covers humanities and social sciences subjects (for details see http://cosdac.sems.gob.mx/riems.php).

19
Dauphin, A. 2013. “The Role of Polygyny in the Intra-Household Efficiency of Agricultural Production in West Africa.” CIRPEE working paper 13-23, Gatineau (Québec).

Dauphin, A., and B. Fortin. 2001. “A Test of Collective Rationality for Multi-Person Households.” _Economics Letters_ 71 (2): 211–16.

De Laiglesia, J. R., and C. Morrison. 2008. “Household Structures and Savings: Evidence from Household Surveys.” Working paper. OECD Development Centre Working Papers 267, Paris.

DeVreyer, P., S. Lambert, A. Safir, and M. Sylla. 2008. “Pauvreté et structure familiale: pourquoi une nouvelle enquête?” _Statéco_ 102 : 5-20.

Fenske, J. 2015. “African Polygamy: Past and Present.” _Journal of Development Economics_ 117: 58–73.

González, L., and B. Özcan. 2013. “The Risk of Divorce and Household Saving Behavior.” _Journal of Human Resources_ 48 (2): 404–34.

Grossbard, A. 1976. “An Economic Analysis of Polygyny: The Case of Maiduguri.” _Current Anthropology_ 17 (4): 701–07.

———. 1980. “The Economics of Polygamy.” _Research in Population Economics_ 2: 321–50.

Grossbard, S. 2014. _The Marriage Motive: A Price Theory of Marriage: How Marriage Markets Affect Employment, Consumption, and Savings_ . Springer-Verlag, New York.

Grossbard-Shechtman, A. 1986. “Economic Behavior, Marriage and Fertility: Two Lessons from Polygyny.” _Journal of Economic Behavior & Organization_ 7 (4): 415– 24.

Jacoby, H. G. 1995. “The Economics of Polygyny in Sub-Saharan Africa: Female Productivity and the Demand for Wives in Cote d’Ivoire.” _Journal of Political Economy_ 103 (5): 938–71.

Jacoby, H. G, G. Li, and S. Rozelle. 2002. “Hazards of Expropriation: Tenure Insecurity and Investment in Rural China.” _American Economic Review_ 92(5): 1420– 47.

Kureishi, W., and M. Wakabayashi. 2013. “What Motivates Single Women to Save? The Case of Japan.” _Review of Economics of the Household_ 11 (4): 681–704.

Lambert, S., M. Ravallion, and D. van de Walle. 2014. “Intergenerational Mobility and Interpersonal Inequality in an African Economy.” _Journal of Development Economics_ 110: 327–44.

23
level controls: firm size, age, access to finance, whether or not the firm invests in fixed assets, reported losses due to breakage or spoilage in transport (as proxy for transportation infrastructure), security costs, female ownership, exporting and ownership status (foreign versus domestic), use of foreign technology, formal registration, generator ownership, and electricity outages. These are comprised in the vector φ�,� in equation (1). All countries are pooled in the baseline regression, and we include region (within country) and sector fixed effects (region� and sector� ). In the regressions that include the rainfall shocks, we replace region fixed effects (region�� with country fixed effects (country�). The reason for this is that the geographical units of the weather observations are of similar size to regions within a country, and thus the inclusion of region fixed effects absorbs much of the variation in the weather variables. Finally, ε�,� represents a random error term. Survey weights are applied in all estimations.

3.2 Modelling the impact of poor water supply on firm performance Through equation (1), we ask how well water infrastructure can cope with rainfall shocks; as a next step, we take this analysis further and ask whether inadequate water infrastructure may be detrimental for the total factor productivity (TFP) of a firm, which in turn may lower output. Theoretically, estimating this impact is a two-stage process – first estimating the production function and obtaining the estimates of TFP. Second, estimating the effect of the infrastructure variables on the resulting TFP. However, this approach is problematic. Omitting the infrastructure variables in the estimation of the production function will result in omitted variable bias and thus incorrect estimates for TFP (Wang and Schmidt 2002; Moyo 2011). A common approach is to therefore include the determinants of TFP and output into one equation. We adopt this approach and estimate the following equation assuming a translog production function<sup>4</sup> :

> 4 The water infrastructure and firm productivity literature has adopted different functional forms. A few studies have estimated Cobb-Douglas production functions (Bogetic and Olusi, 2013; Arnold et al., 2008; Moyo, 2011) while others have estimated translog production functions or translog cost functions (Escribano et al., 2010; Iimi, 2011). As it is a more flexible functional form relative to the Cobb-Douglas, we opt-for a translog specification for the production function. The estimation of a translog cost function has also been used for power infrastructure and firm productivity estimations (Fisher-Vanden et al., 2015). Due to revisions in the cost section of the Enterprise Surveys over time, estimating a cost function would be challenging. Thus, we estimate a production function.

8