rafmacalaba/datause-encoder-data huggingface dataset:
25 <!-- Start of picture text --> Cold Turkey versus Gradual Price Control<br>Yc V cr ~ SEct HSct<br>Ya-Yn- 5U<br>0-<br>o p 1<br><!-- End of picture text --> Figure 6 The second clear result relates to credibility (the equilibrium value of p). Since P* > Pg. the VDS schedule rotates further, clockwise and still crossing the same zero point (compare VDS,t with VDS8 in Figure 6). The cold turkey equilibrium is at the intersection of VDSct and HSct, at Ect. Since there is no hoarding under a cold turkey approach, there will be a high _observed_ supply elasticity and thus a low probability of program collapse p,t (lower, for example, than p(O)).

For comparison of the cold turkey decontrol strategy with a gradualist approach, consider two possible configurations for the latter. If the initial distortion is so small that there would be no hoarding at all in the gradual case either (i.e. A - 0 and net supply equals Y.), Cyyy < 0 would imply a smaller supply response per unit of price increase than observed under the CT approach. This in turn would imply a larger assessed probability of collapse. So even if there is no hoarding under gradualism (mild initial distortions and A - 0), gradual programs will be less credible as cold turkey programs if Cyyy
received by program participants. On the other hand, after only two seasons in their new locations, beneficiaries are likely still learning how to make the most of the resources they now control. This may be particularly true for households that relocated great distances and must adapt to unfamiliar agro-ecological environments, cultural settings and new markets. Indeed, the degree to which participants benefited from the program, and the ways in which they did, differed depending on the distance they moved. Impact also varied across gender of the household head – female-headed beneficiary households increased assets significantly more than male-headed households – while impact rarely varied across levels of farming experience.

The remainder of the paper is organized as follows: Part II describes the background of the project, Part III discusses both the qualitative and quantitative analytical approaches, Part IV describes the empirical strategy, Part V discusses the quantitative and qualitative findings, and Part VI concludes.

# **II. Background**

## **CONTEXT**

The 2007 Malawi Poverty and Vulnerability Assessment reports that 52 percent of the population in this landlocked country of 14 million lives below the poverty line, and there has been little progress reducing poverty since 1998 (World Bank, 2007). Poor growth performance over this period was primarily attributed to erratic weather patterns that adversely affected agriculture, the most important economic sector in the country. Severe droughts in 2001 and 2005 devastated production of rain-fed maize, the primary crop of Malawi’s 1.8 million smallholders who each own an average of one hectare of land. Eighty-five percent of the country’s employment is in agriculture, and land distribution, especially when large estates are included, can be very unequal. Land inequality is particularly a concern in areas with very high popula6
PHN 1991 Sector Revew 36

# **Table 6:** Population **Health &** Nutrition **Sector** Reports Completed in FY91

|Region/<br>Country|<br>Project<br>|Report<br>Date|Report<br>Color|Final|
|---|---|---|---|---|
|**MFRICA**|||||
|Cameroon|*Women in Development.|11/90<br>|Green|Yes|
|Kenya|*Human Resources: Improving<br>|6/91|Grey|Yes|
||the Quality and Access.||||
|Kenya<br>|*Food and Nutrition.<br>|3/91<br>|Grey|Yes|
|Malawi<br>Nigeria|Population Sector Study.<br>Implementing the National<br>Policy on Population.|6/91<br>12/90|Green<br>Yellow|No|
|Nigeria|<br>Strategy for Food & Nutrition<br>Security.|6/91|Grey|Yes|
|Sao Tome & Pr.|Human Resource Dev. Strategy.|6/91|White|No|
|Senegal|Women in Development.|6/91|White|No|
|Sudan|*Toward an Action Plan for<br>|9/90|Grey|Yes|
||Food Security.||||
|Tanzania|Women and Development.|10/90|Yellow|No|
|Zimbabwe<br>Regional|Strategy for Women in Dev.<br>Gestion du Secteur de la Sante<br>outils de Gestion des Programmes<br>Sectoriels.|3/91<br>6/91|Green<br>Green||
|ASIA|||||
|Indonesia|*The World Bank and Indonesia's<br> <br>|2/91|Grey|Yes|
||Pop.<br>Program.||||
|Indonesia<br>Lao PDR|<br><br>*Health Planning and Budgeting.<br>Population, Health & Nutrition.|1/91<br>11/90<br>|Red<br>White|Yes|
|Papua N.Guinea|Health & Pop. Roview.|2/91<br>|Grey|Yes|
|Papua N.Guinea|Management, Manpower, Money.|2/91|Grey|Yes|
|Papua N.Guinea*|Structural<br>Adj., Growth & Human<br>Resource Development|5/91|Grey||
|Philippines|New Direction in Family Planning.|6/91|Green||
|B4ENA|||||
|Egypt|Poverty Alleviation and Adj.|6/90|Green||
|Maghreb|Demographic Challenge to<br>|8/90|Green||
||Sustainable Econ. Development.||||
|Poland|Health System Reform.|3/91|Green||
|Romania|<br>Accelerating the Transition:<br>Human Resource Strategies for<br>|7/91|Green|No|
||the 1990s.||||
|Saudi Arabia|<br>*Review of Roles, Functions and<br>Operations of the Ministry<br>of Health.|4/91|Yellow||
|Turkey|Issues and Options in Health<br>Financing.|9/90|Grey|Yes|
between government and particular interests, by providing information more economically than it would pay government to obtain the same information by itself. Of course, lobbies are not able to provide exact information about the true demand, only signals. But these signals enable the elimination of inefficient equilibria. Essentially, lobbies are able to create surplus due to such informational efficiencies, which politicians and lobbies share and are left better off. Perhaps, this model is appropriate for explaining the boom in the number of lobbies that are issue-based such as green lobbies. The theory is difficult to test since it offers few clues about what the “reduced-form” function for (some measure of) lobbying might look like.

A case is strongly made for the relevance of informational lobbying in Hansen’s (1991) study of the politics of U.S. agricultural support policies. His informal, yet intuitive, theory is based on his observations about the interactions between Congress and the farm lobby over the period 1919-1981. The theory is designed to explain three stylized facts about how the farm lobby gained and lost access to Congress over this period.

- In the 1950s and 1960s, farm policy makers dropped the American Farm Bureau Federation (AFBF) from its dominant position in agricultural politics. AFBF had been agriculture’s leviathan for a generation.  In the 1960s and the 1970s commodity organizations replaced the position previously occupied by the AFBF.

- In the 1960s and the 1970s farm policy makers paid less and less attention to the advice of farm lobbies.

- In the 1970s and 1980s, this was reversed, and they scarcely paid attention to the advice of consumer lobby.

In sum, during those 40 years, Congress reallocated access within the farm lobby, Congress restricted access for the farm lobby but Congress denied access to the consumer lobby. Hansen develops a theory to explain these changes in access. His theory is developed around (i) competitive advantage in a lobby’s ability to deliver better information to politicians than their rival groups, and (ii) recurrence of issues around which a lobby makes its case to the politicians. Hence, politicians grant access to lobbies on the basis of their _informational advantage_ over other lobbies, and also the _permanence_ of the issues and positions conveyed by the lobbies. Hansen’s theory of access is based not so much on the direct monetary contributions by lobbies as on their informational contributions. In his view, 8
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# **3.3.2 Are Public Sector Wages too High?**

We saw at the end of Section 3.2.2 that public sector wages seemed to have pulled ahead of private formal sector wages in recent years. But even if the figures of mean earnings quoted above seem to suggest this, we cannot conclude that the public sector has unambiguously higher levels of earnings without further probing. There are two reasons for this:

- a. The quality of labor found in the two types of employment is different. To measure the "true" earnings difference between the sectors we must at least control for differences in measurable human capital characteristics of the work force employed by them.

- b. We shall soon see that there are large differences in wage levels within the private wage earners. In particular the wage level increases significantly with the size of firms in the private sector. We need to make some judgment as to where in this hierarchy of earnings the supply price of labor lies. It is possible that the level of wages in the large private firms reflects the strength of union pressures or other institutional influences.

The first set of issues is easier to tackle than the second. The distribution of workers by educational levels in the sample covered by the GLSS does show that the public sector employees are better educated, as shown in the Table below (Table 3.3), although the gap might have been reduced in terms of years of education. Thus we would expect a differential in favor of the public sector on this account. On the other hand the somewhat larger proportion of females in the public sector would tend to bring the differential down if in fact there is a significant gender gap in earnings.

**Table 3.3 Percentage Distribution of Employees by Education and Gender**

|Year|Sector|Primary|Middle|Secondary|Post-<br>Secondary|Males|Females|
|---|---|---|---|---|---|---|---|
|**1987**|Public<br>Formal<br>Private|4.8<br>12.2|63.5<br>71.4|17.9<br>15.5|13.8<br>0.8|75.3<br>82.4|24.2<br>17.6|
|**1992**|Public|_5.1_|59.3|32.1|2.9|71.3|28.7|
||Formal<br>Private|5.7|70.9|23.1|0.4|80.3|19.7|

_Source: GLSS._ 13
ther do I find evidence consistent with a model in which lower status women tend to join the labor force in response to a negative economic shock, which might both frustrate her husband and incite domestic violence. Crucial to disentangling these stories is the fact that the data I collected contains both information on assets and income (including from agriculture and household enterprise) as well as measures of domestic violence, which are rare to have in the same data. Finally, while I find that the results for age at marriage are also inconsistent with a reverse causality story in which lower-status women enter the labor force in response to domestic violence, I cannot rule out this possibility for less educated women.

Previous literature has not focused on the heterogeneous relationship between labor force entry and domestic violence based on a woman’s characteristics upon entering the labor force. This heterogeneity is important in the Bangladeshi context studied. For instance, the relationship between age at first marriage and domestic violence in the entire sample of women is driven by the much stronger relationship between age at first marriage and domestic violence among women who work; there is a small and only borderline statistically significant relationship between age at marriage and domestic violence among women who do not work. Knowledge of this heterogeneity is important to policymakers who would like to target which working women are particularly likely to suffer domestic violence. Additionally, these results can help explain how studies in various settings have found both positive, negative, and zero correlation between domestic violence and women’s labor force participation, suggesting that it depends on the baseline level of of women in a the bargaining power particular population. Finally, bargainmodel which is consistent with the results in the that while ing paper suggests raising women’s bargaining power high enough can allow her to escape domestic violence, increased bargaining power among women who have low baseline bargaining power may increase domestic violence. Policymakers should consider this possibility when designing programs meant to improve women’s access to the labor force or empowerment more generally.

4
four-digit HS level. One might think that unit values would be quite similar across countries at that level of disaggregation, but that is not the case. Clark et al. (2004) found the same results for maritime transport costs. Additionally, Feenstra (1996) shows that there is a large variation in unit values even at the 10-digit HS level. He cites the example of cotton shirts for men, which the U.S. imports from almost half of its 162 trading partners. The unit values range from $56 (Japan) to $1 (Senegal). These differences in unit values lead to large differences in insurance costs per kilogram, even for “homogeneous” products. Thus, it is not surprising that we find that the more expensive the product per unit of weight, the higher the insurance and hence the overall transport cost.

Directional imbalance in trade between the US and the source country has the expected negative sign and is significant at one percent. If we move from a favorable unbalance (from the point of view of the exporters to the US<sup>31</sup> ) of 50 percent to a negative one of the same amount, air transport costs increase around 16 percent.

Finally, the coefficient associated to airport infrastructure is negative and significant (at 1%): the greater the investment in infrastructure, the lower transport costs are. In our sample, an improvement in airport infrastructure from the level of Colombia (p25) to the level of Great Britain (p75) reduces air transport costs in 15 percent. This result may be due to the following reverse causality. Airport infrastructure reduces air transport costs, but at the same time, low air transports costs increases trade and may induce investments in airport infrastructure. To control for this reverse causality in column (2) we instrumentalize the level of foreign airport infrastructure of column (1) with indexes of telephones per capita and paved roads. In addition, as a robustness test, in column (3) we use a five-year lag of our infrastructure variable. In both cases we obtain very significant effects, similar to the results obtained in column (1).

In columns (4), (5) and (6), besides the level of airport infrastructure, we include institutional variables to observe their effect on air transport costs. We should expect a better institutional framework -measured by regulatory quality and/or government

> Coast  (see Appendix B, Table B4.).

> 31  For foreign exporters, the larger is the unbalance of US bilateral trade (exports – imports divided by bilateral trade) the lower should be the transport costs they face because of the low capacity utilization of the airplanes coming back to the US.

15
# **2.5. Household access to finance in international comparison**

As discussed above, the share of households with a formal bank account is lower in Africa than in developing countries outside Africa. Behind this low median, however, there is substantial variation within the region. While in Kenya, 42 percent of households use a formal account, this share is below 5 percent in DRC, Guinea, the Central African Republic and Niger. The share is even greater in upper-middle income countries, such as South Africa (54 percent) and Mauritius (80 percent).

The Global Findex survey not only allows an aggregate picture of the share of households using formal financial services, but also a more detailed look into who uses what kind of financial services. One striking finding is that the gender gap in the use of formal financial services is larger in Africa than outside Africa. While men are more likely to use a formal financial account than women across the developing world, the gap is significantly larger inside Africa than outside. It is important to note, however, that these are unconditional comparisons. Using more detailed financial sector surveys for a number of Eastern and Southern African countries, Aterido, Beck and Iacovone (2013) show that when key observable characteristics of individuals are taken into account the gender gap disappears. The lower use of formal financial services by women can be explained by gender gaps in other dimensions related to the use of financial services, such as their lower level of income and education, and by their household and employment status.

Household data on the use of financial services reconfirm the importance of leapfrogging with the help of technology. While the share of households with a formal account is larger outside than inside Africa, the share of households that have used mobile phones for payment services is larger inside than outside Africa. Specifically, 5 percent of population in the median African country reported using mobile phones for payment services, compared to 3.8 percent in non-African developing countries.

21
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yields on GIIPS countries’ debt, to obtain market-based indicators of banks’ exposures to sovereign risk.

In particular, we estimate the following equation for each bank: We regress a bank’s stock return (ri;t) on the returns on a broad market index (rt<sup>M)aswellasonthe</sup> return to S (=10) different sectoral indices (rt<sup>s).ThesectoralindicesarebasedontheIndustryClassifica-</sup> tion Benchmark (ICB). More specifically, we use the level 2 decomposition, which divides the total market into 10 industries: oil & gas, basic materials, industrials, consumer goods, healthcare, consumer services, telecommunications, utilities, technology, and financials. As we are interested in exposures to sector-specific news (and not the movement in sectoral indices due to economy-wide news), we first orthogonalize each of the rt<sup>sserieswithrespecttomarket-widereturns(r</sup> t<sup>M)andthefinancialsectorreturns.4Doingso,we</sup> clean the sectoral returns from market-wide news as well as their dependence on financial sector (shocks). Subsequently, we standardize the orthogonalized exposures, which facilitates comparing the exposures to different industries. The estimated �<sup>s</sup> coefficients then reflect both the exposure to as well as the riskiness (volatility) of the sectoral shocks. The residual, "<sup>i</sup> t<sup>, captures the idiosyncratic or bank-specific news compo-</sup> nent. We estimate Equation (1) for each bank and for each year using daily returns, such that we end up with a panel database on sectoral exposures that varies at the bank-year frequency. The panel dataset of estimated exposures consists of 12; 689 bank-year observations, covering 2; 005 banks from 77 countries over a ten year period starting in 2002. We do not impose constraints on the coefficients and hence allow that a bank has a negative exposure to, and hence is short in, a specific industry. Information on the estimated exposures is reported in Table 1.

> 4The returns on the financial sector index are orthogonal with respect to the market.

7
yield requirements of around 10% and 14% - higher than Basel’s 8% and higher than current levels in Mexico (8%) and in Brazil (11%).

However, this discussion uses the IRB foundation approach with the actual default experience to measure the default probabilities. Using the S&P corporate calibrated mappings from ratings to default probabilities and the rating of the relevant sovereign we obtain much lower figures. Such a rating is interpreted by rating agencies as the floor to non-government ratings and therefore the associated PD, EL and UL can be considered as a floor for corporate sector’s PD, EL, and UL. Table 3 shows that, notwithstanding the lower level of capital requirements derived from S&P ratings and probabilities of default, increased capital charges are likely to emerge for all Latin America and Caribbean countries starting from countries enjoying an investment grade rating such as Colombia, where the sum of EL and UL already exceeds the value of the all encompassing 8% capital requirement of the current Accord.

To conclude this section, our results suggest that, while the IRB foundation implies a rather generalized increase in capital requirements, it may not afford the 99.9% protection advocated by the BCBS given the default probabilities encountered in emerging countries. This result also calls into question the calibration of the published curve for use in these environments. Assuming provisions cover expected losses, our results indicate that to achieve 99% protection would require capital levels significantly higher than the Basel I’s 8% and around a 15% overall requirement. To achieve 99.9% protection would require higher levels of capital. While these levels were close to Argentina’s overall capital requirement in 2001, it would represent a steep increase in capital requirements for many countries. We now turn to consider how potentially 13
whether the ways different PCs affected productivity is consonant with our previous conjectures.

_Effects of PC Provisions_ To calculate the effect of different PC provisions, we re-ran the FE and first difference GMM models for both the pooled and participants only samples, for each of the variables that we presumed would affect information, incentives and commitment.'<sup>5</sup> In exploratory runs, we allowed the PC variables to affect both the productivity level and growth rate. In many cases, only the level or rate effect was found to be statistically significant; in these cases only the significant term was kept in the final specification, although we kept at least one type of effect (level or rate, depending on which was more statistically robust) for each PC feature. In addition, to address the potential selfselection problem with PC provisions, we allowed the sample of firms with PC-feature-j to have a sample-specific growth rate throughout the sample period. Most of the sample control variables for the PC features were found to be insignificant; consequently, we dropped these controls. In the end, we only kept two sample control variables: (year x PCparticipation dummy), to capture the specific growth rate of the sample of participants in the pooled sample, and, (year x bidding dummy) to capture the sample-specific growth rate for those PC participants that selected the CEO through bidding in both the pooled and participants samples. The positive and, for the GMM estimates, significant sign of

- 15 Since FE decisively outperformed OLS (and random effects) specifications in all tests we do not report the OLS (and random effects) estimates of the effects of PC provisions.

20