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42
lag, there was a fall in the productivity of existing human capital in producing aggregate output. Both bode ill for Kenya’s prospects, quite independently of the outbreak of the AIDS epidemic.

Section 5 analyses two benchmark cases for the period 1990 to 2050, namely, the counterfactual in which the epidemic never arose and the world in which breaks out. Neither involves any explicit form of intervention, though the latter rests heavily on the assumptions adopted by the researchers at the U.S. Bureau of the Census, whose projections form the basis of the benchmarks. AIDS does not bring about a catastrophic – economic collapse, but it does inflict large economic costs and very many deaths.

Independent, though indirect, support for our conclusion that the epidemic will exact a heavy economic toll is provided by micro-economic evidence that premature adult mortality in rural areas of Kenya has strongly adverse effects on school attendance and advancement from one grade to another. Yamano and Jaynes (2005) find the effect to be especially adverse among poorer households and young girls. Evans and Miguel (2005), who use a still larger and in some ways more detailed panel, albeit one restricted to a single district in which the HIV-prevalence rate is high, confirm the former finding but not the latter. They also establish that maternal death produces a bigger setback than a paternal one, and that children whose test scores were low before bereavement are more likely to be withdrawn from school. The cross-country evidence for sub-Saharan Africa yields the same general finding where schooling is concerned; see, for example, Kalemli-Ozcan (2006).

The question of how public policy can alter the course of the epidemic or mitigate its impact upon the accumulation of human capital is taken up in Section 6. Since health and education are clearly intertwined in this setting, the design of public policy should be concerned with both domains. The relationship between spending on combating the epidemic and the profile of age-specific mortality is speculative territory, for it involves the efficacy of various policy interventions that seek to reduce premature adult 4
Vet = Yie-s An Egov2, + Yie-s Azy Egov3n., + BXct + Nc tAg + Vy X Ag + Ect Vet Egov2y., Egov3n., Egov2 Egov3 Egov2_s., Egov3_s., Egov2s., Egov3s., Egov2_,., Egov3_,., Xet Nc At Vr Vr X At Ect Ain Q2n Qin Gon ne

[0; 5] Ain Aen n € [—5; —2]

1.0 a2n
Table B.4: Probability of being a Monopsonist, Probit Estimate

|VARIABLES|Monopsony|
|---|---|
|_ωit_|-0.208***|
||(0.0771)|
|Small Size Firm|1.206***<br>(0.138)|
|Medium Size Firm|0.493***<br>(0.106)|
|Skill Ratio|-1.017***<br>(0.307)|
|Foreign Ownership|-0.361***<br>(0.107)|
|Unionisation of Workers|-0.0274<br>(0.113)|
|Firm Average Years of Education|-0.0170<br>(0.0172)|
|Number of Apprentices|0.138***<br>(0.0392)|
|Location: Kumasi<sup>_⋆_</sup>|-0.136*|
||(0.0798)|
|Location: Takoradi|-0.158|
||(0.147)|
|Location: Cape Coast|-0.357*|
||(0.204)|
|Time|0.101*|
||(0.0574)|
|Time Squared|-0.00570<br>(0.00409)|
|Constant|2.965***<br>(1.016)|
|Observations|1,531|
|Pseudo _R_<sup>2</sup>|0.2143|
|Log Pseudolikelihood|-817.288|
|Sector Dummies|Yes|

**_Source:_** Data for the study comes from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service.

**_Note:_** Table report Probit estimate on the probability of a firm being a monopsonit. _⋆_ The capital city, Accra, is used as the base variable. Robust standard errors in parentheses _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1.

38
scale—but the less likely they will grow rice and vegetables—crops that can be grown on small fields.

In Table 4, we calculate the marginal effects of temperature and precipitation on crop choice. Across China, farmers are more likely to select cotton, wheat, oil crops and maize as temperatures warm and they are less likely to select rice, vegetables, soybeans, potatoes and sugar. With more precipitation, farmers are more likely to pick rice, cotton, vegetables, soybean, oil crops and sugar and less likely to pick maize, wheat and potato. However, the seasonal effects are quite different from the annual effects. For example, warmer summers favor cotton, wheat, and oil crops whereas warmer falls favor rice, vegetables and potato. Wetter springs are very bad for vegetables, oil crops and maize but good for wheat, potato, cotton, and rice. Wetter winters are good for most of the crops (cotton, oil crops, vegetables, soybeans, maize, sugar and rice) but bad for wheat and potato. The actual impact in each location is therefore going to depend on the seasonal distribution of temperature and precipitation, and not just the annual average.

# **V. CLIMATE FORECAST**

The empirical results in the previous section describe how farmers in China have adapted to the climate that they currently face. They have altered their irrigation and crop choice to fit the climate they live in. In this section, we project how these decisions would change in the future if climate changes. We assume that all other features of each farm will remain the same and that only climate will change. This is obviously a simplification as the technology, capital intensity, and other features of the farm are all likely to change over the next century. However, the analysis does provide a sense of the role of climate and the extent of endogenous adaptation to expect.

We examine a set of climate scenarios from climate models for 2050 and 2100. These climate scenarios are complicated as they predict very different changes in each season, they vary from one region to another within China, and they involve a combination of temperature and precipitation effects. The climate scenarios come from the PCM, HADIII, and CCM2 climate models. All the models are assuming a business as usual (no abatement) A2 emission scenario. Table 5 presents the average change in temperature and precipitation for China according to each climate scenario. The PCM 10
> **12** A large body of literature discusses this issue; see for example, Searchinger and others (2008) and Fargione and others (2008).

> **13** The low energy prices between mid‐1980s and early 2000s prompted most analysts to argue that the high prices of the 1970s were an aberration and that the pre‐1973 levels were the norm. For example in its March 6, 1999 edition, the _Economist_ ’s leader article entitled “Drowning in Oil” concluded that (p. 19): “$10 might actually be too optimistic. We may be heading for US$ 5. Thanks to new technology and productivity gains, you might expect the price of oil, like that of most other commodities, to fall slowly over the years. Judging by the oil market in the pre‐OPEC era, a ‘normal’ market price might now be in the US$ 5‐10 range. Factor in the current slow growth of the world economy and the normal price drops to the bottom of that range.” Indeed, most energy analysts were forecasting real prices to average between US$ 15/barrel and US$ 20/barrel in the long run. For example, the World Bank’s nominal crude oil price forecast in 1999 was US$ 18/barrel for 2005 and US$ 19/barrel for 2010. The December 2008 WTI futures contract opened at US$ 18.88 in January 15, 2002, when it was first introduced. During 2008, crude oil prices averaged US$ 97/barrel, almost five times higher than the highest forecasts.

> **14** Here the model is the same as the one used above for co‐movement, except that _Pti_ denotes the prices of food commodities and _Pt_<sup>_j_</sup> denotes the energy price index. The rest of the coefficients and variables have the same interpretation. However, in contrast to the price co‐movement regres‐ sions, regressing food prices on energy prices has a well‐defined endogeneity pattern; energy affects food prices but not vice versa. Thus, we estimated only the regressions with the energy price index (along with deflator and time trend) as the explanatory variables. The estimates can be viewed as energy price transmission elasticities rather than just co‐integration parameters. See Baffes (2009) for the structure of the indices.

> **15** Table 7 indicates that the elasticities for food commodities are higher than those for raw mate‐ rials and metals. This is consistent with the input‐output table of the GTAP database, which shows that the direct energy component in the US agriculture and manufacturing sectors is 12 percent and 3 percent respectively.

**_Page 21_**
# INTERCOMMODITY PRICE TRANSMITTAL% ANALYSIS OF FOOD MARKETS IN GHANA

# 1. INTRODUCTION

Since establishing a stable macroeconomic environment for long run development, the government of Ghana has explored a number of specific policies aimed at stabilizing food prices between seasons and across years. These policies have raiiged from income support and employment generation programs to improving marketing infrastructure. Options under consideration have also included the possiblity of increase government involvement in inter- and intrayear storage; in recent years government held storage capacity has been increased despite the fact that there is not yet a clearly articulated policy on the objectives of such otorage. The implementation of storage and other stabilization policies depends, in part, on the existing efficiency of trader operations.

Similarly, the effectiveness of other possible interventions to guarantee food security that do not involve the government's direct handling of grain also depends on knowing which market channels operate effectively. For example, the potential for cash grants and food for work programs in districts with temporary production shortfalls to stabilize consumption is enhanced when markets in areas with low or variable levels of food consumption are linked to surplus regions via effective market channels.

As part of such policy oriented analysis, a number of studies of markets in Ghana (Asante et al.) as well as other developing countries have analyzed the relationship of the price of a single commodity in various markets. With proper caveats, such studies are used to make inferences on the spatial flow of information and commodities. However, given that households in the regions of
**Table 12a:** Regression of log total per capita consumption expenditure – household head characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.020|0.024|0.052|-0.105|-0.002|-0.115**|0.019|-0.021|-0.229**|
||(0.038)|(0.038)|(0.045)|(0.097)|(0.049)|(0.058)|(0.059)|(0.067)|(0.103)|
|Recall long-list|0.095**|0.092**|0.062|-0.130|0.077*|0.037|0.124*|0.071|-0.137|
||(0.038)|(0.039)|(0.044)|(0.109)|(0.043)|(0.055)|(0.063)|(0.066)|(0.100)|
|Recall short-list*educated|||-0.101||||||-0.138|
||||(0.120)||||||(0.108)|
|Recall long-list*educated|||0.139||||||0.110|
||||(0.104)||||||(0.100)|
|Recall short-list*employed||||0.166|||||0.192*|
|||||(0.111)|||||(0.101)|
|Recall long-list*employed||||0.290**|||||0.215*|
|||||(0.137)|||||(0.111)|
|Recall short-list*non-wage|||||0.036||||-0.041|
||||||(0.111)||||(0.110)|
|Recall long-list*non-wage|||||0.027||||-0.037|
||||||(0.107)||||(0.098)|
|Recall short-list*floor||||||||||
|material||||||0.217***|||0.240***|
|||||||(0.075)|||(0.082)|
|Recall long-list*floor||||||||||
|material||||||0.088|||0.039|
|||||||(0.073)|||(0.081)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.075||-0.029|
||||||||(0.094)||(0.113)|
|Recall long-list*water||||||||||
|<br>(rainy)|||||||-0.000||-0.013|
||||||||(0.097)||(0.135)|
|Recall short-list*water||||||||||
|(dry)||||||||0.116|0.056|
|||||||||(0.088)|(0.110)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.098|0.078|
|||||||||(0.089)|(0.131)|
||11.334**|10.876**|11.233**|11.393**|11.279**|11.066**|11.145**|<br>11.112**|<br>11.029**|
|Constant|*|*|*|*|*|*|*|*|*|
||(0.040)|(0.047)|(0.038)|(0.080)|(0.042)|(0.039)|(0.048)|(0.058)|(0.070)|
|Observations|2,118|2,108|2,118|2,118|2,118|2,118|2,118|2,108|2,108|
|R-squared|0.003|0.281|0.102|0.009|0.036|0.215|0.084|0.096|0.291|

Note: Dependent variable is the log of per capita total expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes head of household and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

43
_Table 3: Average weekly hours of jobs aspired to at age 12._

|||Average hou<br>|rs per week<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Female|‐2.098***|‐2.089***|‐1.205|‐1.491|
||(0.155)|(0.154)|(1.153)|(1.150)|
|PPVT||0.035***|0.043***|0.011|
|||(0.010)|(0.014)|(0.015)|
|Female*PPVT|||‐0.015|‐0.010|
||||(0.019)|(0.019)|
|Other controls||||_Yes_|
|R2|0.134|0.144|0.144|0.180|
|N|1,180|1,180|1,180|1,180|

It is possible that social norms governing career aspirations may become stronger after the age of 12 but before entering the labor market. This would be consistent with the evidence from several studies that find that gender differences in performance and psychological traits begin to emerge around the age of 10‐12 and widen thereafter. To confirm that this is the case, we would repeat this type of inquiry for girls and boys aged 15 or 16, if the data become available for future rounds of the Young Lives survey. The analysis of the STEP data in the next section provides some initial support for this argument. In addition to any barriers in the school‐to‐work transition, there appears to be some streaming before the labor market – women are overrepresented in lower paying fields of study compared to men (Figure 12).

One explanation that does not seem to be backed by the data is that social norms may be changing in Vietnam. Although we cannot rule out the possibility that the female YL respondents will enjoy higher salaries than male YL respondents when they eventually enter the labor market, analysis of the change in the gender wage gaps across different cohorts suggests that this is unlikely. Figure 5 suggests that gender gaps are more or less at the same level across different cohorts and do not show signs of having narrowed in younger cohorts, which would be an indication of changing norms.

It is also worth considering some of the limitations of our analysis. The choice of future occupations in the YL data was limited to a set that is much narrower than the options reported in the LFS data. Consequently, when interpreting results, we should keep in mind that the children were choosing from a limited number of options. Nonetheless, the results suggest that at the age of 12 and at least in aspirations girls are not constrained by restrictive social norms about what jobs are appropriate for men and what jobs are appropriate for women.

# _iii. Labor market transition_

Our third hypothesis about the emergence of streaming is that streaming resulting in gender wage gaps does not occur prior to the labor market, but happens during the school‐to‐work transition. Gender differences in the school‐to‐work transition could occur in the presence of gender‐specific barriers to entry for some fields. One indication of such differences between genders would be a higher fraction of women working outside of their field of study. We use data on field‐of‐study available in the World Bank’s STEP survey to check whether we observe a mismatch between field of study and occupation more often 25
# **5. Conclusions**

In this paper we analyzed the regional growth patterns in Mexico with a special focus on the role of governance. We used the relatively recent creation of a state level corruption and good governance indicator and built a growth regression with cross-state data using traditional control variables from the neo-classical growth regressions literature. We also incorporated more recent state growth period extending into the mid 2000s.

Following the Mexico specific literature, we looked at the convergence process by two different approaches, one by geographical regions and one by income clusters, incorporating more recent years of data on state growth. The conclusion is that there has been convergence among Mexican states between 1970 and 1985, which is in line with the findings in the previous literature. When Mexico started to open its economy in the mid 1980s, a divergence process divided the country between winning and losing states. The Border States are the “winning” states, which could converge to the welfare of the metropolitan area of Mexico City. Together they form the higher income regions of Mexico, where we also include the southern tourism-related state of Quintana Roo. The rest of the country has relatively lost in per capita income terms since the mid 1980s. It should be noted that while divergence occurred over the 1995-2006 period, it appears to have slowed to a much slower pace of divergence relative to the 1985-1995 period (see Table 3).

Reviewing traditional growth regressions for Mexico’s states and including a corruption variable, this paper does not find any significant relationship between corruption and average per capita GDP growth in Mexico during the period 2001-2005. On the other hand, it finds that corruption is negatively associated with the level of GDP per capita. It could well be that these contrasting results imply that more years of data are needed in order to find a statistically significant relationship between the state corruption measure and economic growth. The impact on levels could be due to the lagged effect and longer term impact on growth of longer standing differences in levels of corruption across states. These differences may predate the creation of the _Transparencia Mexicana_ Index. A statistical outlier is Mexico City with high income per capita and high corruption index values, as measured by _Transparencia Mexicana_ . Similar to the existing literature, the results of the growth regression suggest that there are several other variables, which seem to be of importance for state growth in Mexico, like the proximity to the U.S. border and several infrastructure variables, including the share of households with electricity or the road network, some education supply indicators (e.g., student-teacher ratio) and structural variables (e.g., share of rural population). The additional years used in this paper do not lead to substantially different results in the more traditional neoclassical growth regressions.

19
7

**Figure 2:  Trends in Years of Schooling**

<!-- Start of picture text --> United States<br>Japan<br>Thailand<br>Korea, Rep.<br>Singapore<br>Malaysia<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year<br>Source: Barro-Lee (2010)<br>6<br>5<br>4<br>3<br>Average years of schooling, ages 15+<br>2<br><!-- End of picture text --> We also find that the distribution of access is fairly even across socio-economic groups in Malaysia and less so in Thailand. Although in 2002 there was a 17 percent point gap in secondary school participation rates between the poorest (76 percent participation rate) and richest (93 percent participation rate) children in Thailand, this gap had narrowed -- from 24 percentage points in 1994. Such progress could be attributed to more equitable distribution of public education expenditures across household income (Benveniste 2008). However, despite achievement in promoting equal access to education, challenges remain in closing the differences in learning outcomes between urban, particularly Bangkok, and rural areas in Thailand. This disparity is largely due to differences in the quality of the education systems and unequal resources for different school types (World Bank 2012a).
Table 6: SNAP participation and effect of SNAP-benefit changes on prices and real sales by product eligibility

|SNAP eligibility|(1)<br>Inelig|(2)<br>ible|(3)<br>Eligi|(4)<br>ble|
|---|---|---|---|---|
|VARIABLES|Log price index|Log real sales|Log price index|Log real sales|
|_Baseline_|||||
|Log benefts p.p.|0.0898***|-0.126|0.0724***|0.0605|
||(0.0286)|(0.107)|(0.0269)|(0.108)|
|_Interaction_|||||
|Log benefts p.p.|0.0750**|-0.123|0.0657**|0.0376|
||(0.0303)|(0.103)|(0.0277)|(0.104)|
|x Participation rate|0.234***|-0.0489|0.106**|0.362***|
||(0.0418)|(0.179)|(0.0401)|(0.117)|
|Observations|382428|382428|382140|382140|
|Number of units|7968|7968|7962|7962|
|Number of clusters|48|48|48|48|

Notes: Robust standard errors are in parentheses, clustered at the state level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables as well as store and period fixed effects are included. Log benefits p.p. refers to log benefits per population. Participation rate refers to the ratio of SNAP participants to population in each county fixed to the pre-period of 2006. SNAP-eligible goods account for around 75% of revenue in grocery stores.

42