rafmacalaba/datause-encoder-data huggingface dataset:
Table 4 presents two sets of results. A common mean for the calorie distribution, which comes from module 7 – the personal diary, is used in Panel A, while module-specific means are used in Panel B. The last column shows the estimates of the proportion of the population that are hungry. Holding the mean calorie availability fixed at the value estimated when using the personal diary method, the rate of malnourishment ranges from 17% to 28%. This is a difference in the estimate of hungry people in Tanzania of nearly 5 million people. And it is due solely to differences in the calculated CV across survey methods.

At present, the FAO hunger estimates rely on CVs which are not always country specific, do not vary over time within countries, and have several adjustments made to suppress variability. As shown in Panel A, abstracting from the challenge of measuring mean food availability from FBS, the variation in CV estimates driven only by survey design can result in large differences in estimates of hunger even when adjustments are made to suppress variability. When we also allow for variation in the measure of average calorie availability, the variation in apparent hunger is even larger. Panel B shows that if setting the mean to the specific value of each module, the proportion of undernourished people varies greatly: ranging from 19% when the personal diary is used to 72% when the estimates come from an HCES that relies on a 14 day recall with a long list of food items. That would constitute a difference of 24m people in a country like Tanzania, which has a total population of roughly 45m. In general the patterns of variation in panel B of Table 4 are broadly similar to those in Table 2, which reflects the importance of the first moment of the food distribution in the resulting hunger numbers.

# **6. CONCLUDING DISCUSSION**

There is a push by the international research community to calculate hunger numbers directly from household surveys (HCES-direct method), as opposed to calculating them from a combination of food balance sheets and household surveys (FBS-CV method) as currently done by the FAO. The FBS-CV method has the advantage of more frequent availability, but there are clear concerns with this method as well. Whereas we increasingly know and understand survey errors, we have very little handle on errors in FBS data. Furthermore, the FBS-CV method cannot allow for the analysis of food insecurity patterns within countries.

Still, despite these critiques, the evidence in this paper cautions against a naive switch to the HCESdirect method. In our survey experiment, we calculate hunger to range between 19 and 68 percent –this is a difference of more than 23 million people in Tanzania (a country with a population of 45 million 17
suppliers’ products, they rely on a measure of textual similarity between firms’ product descriptions in 10K filings for each pair of Compustat firms (from Hoberg and Phillips 2010, 2016), and confirm that the amount of trade credit provided by a supplier to its retailers is significantly negatively associated with product substitutability among suppliers selling to the same retailers.

A related line of research examines the importance of supplier product differentiation and its implications for the strength and exclusivity of their trade credit relations with buyers. Starting from the theoretical conjecture of Burkart and Ellingsen (2004) that differentiated goods are more difficult to divert for private benefits, which makes the supplier more willing to sell on credit, Cunat (2007) posits that suppliers are better able to enforce debt repayment than banks because they hold the threat of stopping the supply of intermediate goods to their customers. As long as there is a surplus that is split between a supplier and its customer if they continue to do business, it is costly for the customer to find alternative suppliers and for the supplier to lose the customer. In that model, this link takes the form of intermediate goods being specific to the buyer, and Cunat (2007) offers suggestive evidence from a large sample of UK firms that the levels of trade credit build up as the relationship between a supplier and customer evolves, and that suppliers provide more trade credit when customers experience temporary liquidity shocks (presumably to safeguard the relationship).

Dass et al. (2015) take this intuition further, focusing on investments that are made by a supplier that are specific to a relationship with a customer. Such relationship-specific investments (RSI), defined generally as investments that increase asset-specificity, can take the form of “building a factory close to a downstream customer, purchasing special machinery, developing specific human capital, or acquiring specific technology” (Dass et al., 2015, pp. 1872-3). A key insight is that trade credit can act as a guarantee if the level of RSI made by a supplier can be ascertained by the customer only over time, and thus the upstream firm has an incentive to underinvest. Through delay in payment and the risk of nonpayment by the customer (if the RSI has not been made), trade credit can make it incentive-compatible for the upstream firm to invest sufficiently in the relationship. In this way, and similar to Cunat (2007), RSI generates a surplus that is specific to the relationship.

In addition, Dass et al. (2015) posit that the relative bargaining power of the upstream and downstream firms affects their shares of the surplus and that, the greater the bargaining 13
allevietion of poverti led to tho formulation of a more comprehensive sector policy in education. The new focus g've impetus to lending for primary and non..ormal education to help build the foundation of literacy, numeracy and problea-solving -ills necessary for national development.

The "1974 Sector Working Paper" updated the 1971 paper and reiterated the importance of meeting the educational needs of the lowest 40% of the income group. The paper emphasized the urgency of improving access of the rural and urban poor to education, making curricula relevant to rural needs, promoting functional adult literacy, and making increased use of the mother tongue. The paper stressed that rural education and training should be practical, designed as an integral part of the education system, linked with rural development activities, and replicable in terms of cost and management. The paper also called for research into the conditions related to basic education, curriculum development, teacher training, institutional models, project design, use of the mass media, manageLant training, administrative reorganization, monitoring and evaluation, and planning and policy formulation by couatries. Furthermore, the Paper urged the Bank to support the local design, production and distribution of textbooks and instructional materials.

As a result, Bank lending to primary education 'ncreased from 5t between FY70-74 to 14% between FY75-79 (See Table 1). Support for nonformal education rose from nil before FY69 to 5.2% of the total education project cost between FY74-79.8 Much of the financing for basic education was for the expansion of the enrollment capacity, such as school building and pre-service teacher training.

Table 1: World Bank Landing for Primary Education and Honformal Basic E4ucation FY70-90 CUSSmi tlian

|**FY**|Total<br>**CS)**|Primary<br>Education<br>**CSO**|Nonformal<br>Basic<br>Education<br>**_CS)_**|Primary Education<br>as 2 of Total Bank<br>Lending for Education<br>(X)|Non-formal Basic<br>Education as**X**of<br>Total Bank**Lending**<br>for Education<br>(X)|
|---|---|---|---|---|---|
|70-74 *|814.9|36.4|n.s.|**5**|n.a.|
|75-79*|1,681.9|236.7|n.a.|14|n.a.|
|80-84|2,964.2|423.7|37.8|14|1.3|
|85-90|5,530.7|1,2V3.2|18.7|23|0.3|
|Total<br>70-90|10,991.|7 1,975.0|56.5|18|0.5|

* **Data** for primary education ***lso** include **nonformal** basic edUcation.

a omain and Armstrong, "Review of World Bank Operations in Nonformal Education and Training." 3
# **4 Empirical application to Malawi**

## **4.1 Model selection and estimation of model parameters**

### **4.1.1 ELL approach: Household level data with household expenditure as dependent variable**

We rely on variables that are available in both the survey and the census. The first step of our model selection procedure groups candidate variables by type: (i) demographics, (ii) education, (iii) employment, (iv) dwelling characteristics, (v) asset ownership, and (vi) village level variables. This latter group includes variables that are created by computing mean values of selected variables from the population census at the enumeration-area (EA) levels (think of local employment rates, share of the population with given levels of education and the penetration of phone use) and variables derived from the publicly available remote data (think of night-time-lights, distance to nearest city and river, local climate, and agricultural land quality).

Next, we regress the log of per capita expenditure (combined with region and urban dummies) on each group separately using Ordinary Least Squares (OLS). Independent variables that are not significant at the 5% level are sequentially dropped from the regressions, starting with those that are least significant. Once all groups have been considered we select the group that provides the best fit, combine this with the group that provides the second-best fit, and continue to trim the variables that cease to be significant. This process is continued until every single group has been given a chance to contribute one or more variables to the model. We allow for interactions with the urban-rural dummy to capture selected area heterogeneities (see Tarozzi and Deaton, 2009; Tarozzi, 2011). Finally, LASSO model selection is implemented to verify that all key variables are accounted for. The same procedure is applied to obtain the heteroskedasticity model describing the variance of the errors. The resulting regression models are included in Appendix 3.

Among the different categories of explanatory variables, dwelling characteristics and household asset ownership rank as the strongest predictors of household consumption expenditure per capita, followed by household head education and employment variables. An interesting observation is that local area night-time-lights, agricultural land quality, and climate data (derived from public remote sensing data that serve as predictors in the SEM approach) are found to be significant when included on their own – but once we control for variables available in the population census, these variables lose their significance and are subsequently dropped from the model.

Once the models have been selected, the model parameters are estimated by going 21
imposed. Controlling for ride conditions (crowding and presence of men) and pooling across opportunity costs does not change the results (Columns 1 and 2, Panel A, Table 2). The results from the event study specification are similar (Figure 2). The effect remains relatively stable all through the sequence, with no evidence of time trends either in the pre- or postperiods.<sup>26</sup> The results across opportunity cost levels in Figure 2 and Appendix Figure A6 indicate that willingness to pay does not vary significantly across opportunity costs.<sup>27</sup>

# **5.3 Why Value the Reserved Space? Avoiding Men.**

How much does presence of men in the reserved space affect riders’ willingness to pay for the reserved space? We exploit variation in the presence of men in the women-reserved space to shed light on potential heterogeneity in riders’ demand for the reserved space. We augment (1) by regressing the respondent’s choice of the reserved space on a set of interacting dummies 1) indicating whether a positive opportunity cost was assigned at ride _t_ , and 2) indicating whether the presence of men in the reserved space at ( _time × location_ ) of ride _t_ measured by platform observers was above or below median.<sup>28</sup> This yields: Chose Reserved Space _i,t_ = _βM_ 1 **1** [ _di,t_ = 1] _×_ Few Men in Reserved Space _t_ Equation (3) does not include a constant, and the categories are mutually exclusive and

> 26We also present results over the full sample of rides in Figure A7, Panel (a), as well as the density of observations for each ride in the sequence in Panel (b). The sample restriction does not affect our conclusions.

> 27Berry et al. (2019) also observe inelastic demand for a proven clean water technology at low ranges of the price distribution.

> 28As discussed in Section 3.1, this measure is the average of observations by third party riders traveling in the same location, day and time slot. Thus it represents the conditions that a participant can expect on her ride when she makes her decision to board the reserved or public space but does not intend to capture her own perceptions from within the train, which could be affected by her choice of car.

20
in a more specific way, which refers to an individual’s subjective appreciation of his or her own life. Accordingly, the concept of ‘happiness’ has been defined as ‘ _the degree to which an individual judges the overall quality of his/her own life-as-a-whole favorably’_ (Veenhoven 1984, chapter 2). This is also commonly referred to by terms such as ‘subjective well-being’ and ‘life satisfaction.’ Thus defined, happiness is something on one’s mind that can be measured using surveys. Common survey questions<sup>10</sup> read: ‘ _Taking all together, how happy would you say you are: very happy, quite happy, not very happy, not at all happy?_ ’ (a standard item in the World Value Studies) or ‘ _Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?_ ’ (a standard item in the Gallup World Poll). Responses to this question from the Gallup World Poll are used in the empirical part of this paper. This question captures predominantly the cognitive component of happiness, also known as contentment.

How happy people are depends on _objective conditions_ and _subjective factors_ , including perceptions and expectations. According to Layard (2006), objective factors such as gender, age, marital and education status, financial situation, and health determine to a large extent life satisfaction, but subjective factors associated with perceptions and expectations about family relationships, work, community and friends, personal freedom, institutional quality, and personal values are also imperative to individual happiness. These domains of life reflect the most important human needs as identified by Maslow (1943). The relative importance of the objective and subjective determinants of life satisfaction vary over time and across individuals.

To analyze the roots of dissatisfaction with life in developing MENA in the wake of the Arab Spring, we used cross-sectional data from the Gallup World Poll for the years 2009-10 and a simple reduced-form life satisfaction model (see Di Tella, MacCulloch, and Oswald 2003; Arampatzi, Burger, and Veenhoven 2015):

**LS jit = Θ Individual_Perceptions jit + Σ Personal_Characteristics jit + ε j+ λ t + μ jit. (1)**

In this model, LS, the overall life satisfaction of individual _j_ in country _i_ in year _t_ , depends on a vector of **Individual_Perceptions** about social conditions and domain satisfactions of individual _j_ in country _i_ in year _t_ , a vector of objective **Personal_Characteristics** of individual _j_ in country _i_ in year _t_ , a vector ε **i** of country dummies to control for time-invariant country-specific characteristics, a vector λ **t** of month-year dummies capturing time-related shocks that are common for all countries in the developing MENA region, and μ **jit** is a residual error. We estimate model 1

> 10 See Veenhoven (2012) for a discussion of the limitations of direct questioning.

10
explains why the ratio of intangible assets is more associated with firm growth in countries with a better protection of intellectual property rights.

A better protection of property rights is also associated with a higher share in all firms of the limited liability corporation, which is an organizational innovation to reduce the risks faced by investors and to increase the ease with which to obtain external finance. What business environments lead to a higher tendency of incorporation? Demirguc-Kunt et al. (2006), using a sample of firms in 52 countries from the World Business Environment Survey, find that businesses are more likely to choose the corporate form in countries with more developed financial sectors, better legal systems, stronger investor rights, lower regulatory burdens and corporate taxes, and efficient bankruptcy processes. In addition, the marginal return of incorporation is higher in countries with good financial and legal institutions—corporate firms grow faster in such environments.

# **Are All Property Rights Created Equal?**

Acemouglu and Johnson (2005) unbundle institutions into “property rights institutions” and “contracting institutions.” Property rights institutions capture how much private property is secure from the “grabbing hand” of the state, for example, through outright expropriation or bribe extraction. Contracting institutions capture the effectiveness of institutions that are used to resolve disputes between private contracting parties, such as the courts and the judicial system. Based on crosscountry evidence, they find that property rights institutions tend to be far more important than contracting institutions in facilitating economic development—that is, only property rights institutions are consistently statistically significant and quantitatively important, whereas contracting institutions are not. Their interpretation is that it is easier for private parties to use alternative mechanisms to get around the contracting issues, but it is harder to avoid government expropriations. Note that the tendency they identified represents average effects across the world.

The unbundling conjecture and the relative importance of property rights over contracting institutions are confirmed by Beck et al. (2005) that relies on the World Bank Environment Survey data between 1996 and 1999 from 54 countries. In their main specification, they relate firm growth rates to standard firm- and country-level controls, along with ordinal measures of firm-perceived degree of obstacles in financing, corruption, the legal system. To avoid collinearity, they enter each firm-perceived obstacle one at a time. Their regression results show that while proxies of property rights institutions (e.g., general bribes, bribes to bank officials, and managerial burdens in dealing with regulators) are negatively associated with firm growth, the speed of the court (i.e., a proxy of contracting institutions) in resolving disputes is not significantly so. To consider the possibility that 13
as obtained from the household surveys.<sup>31</sup> One can easily see the differences in the export and the local/household prices, the comparison of prices supports findings that exportation can aid in alleviating poverty because of the higher prices found on the international market.<sup>32</sup> If road budget allocation does not consider agricultural potential than financially lucrative opportunities may be missed out on. The prices from Table 13, were multiplied by the agricultural potential of that crop, resulting in the potential value of the crop.<sup>33</sup>

**Table 13:  Crop Price Chart: Export and Local Price**

|Crop|Export price<br>(per kg)|Household price<br>(per kg)|
|---|---|---|
|Coffee|$1.62|$0.51|
|Cotton|$1.21|$0.41|
|Maize|$0.24|$0.26|
|Soy bean|$0.23|$0.10|

Note: Export price and household price are based on 2007 exchange rate.

If a district has the financial means to improve its road network then that district has a greater opportunity to sell its agriculture output. For example, if the district of Masindi decided to reach its coffee potential of 107,658,952 kg, then with an improved road system they could sell that coffee at the local market for USD54 million or export for a total of USD174 million. Or if the Pader district in North Uganda chose to grow its

> 31 The prices from the Uganda Export Promotion Board are calculated by dividing the total value of the export for 2007 by the total weight exported, resulting in a price per kilogram in US dollars. Reference: <u>http://www.ugandaexportsonline.com/statistics.htm. For the household surveys, the median price of the</u> crop when it was sold at the market (not to a trader) was used. However, the prices from the household data are in Uganda Shillings. To convert from Shillings to dollars, we collected data on the exchange rate over the month of December 2008 (the month when the household data was collected) and used the average as the exchange rate, 1,960 Shillings per dollar. The road maintenance data has also been converted from Shillings to dollars using an average of the 2006 exchange rate, for a rate of 1,830 Shillings per dollar. 32 In the case of maize, the local price is higher than the international price. This may be due to maize’s position as a staple in Ugandan diet, as well as the large swings in prices. The household data gave a range from 150 Shillings. (USD 0.08) to 5,000 Shillings. (USD 2.55) per kilogram.

33 There is always the possibility that households are consuming some of the output and that not all of the potential output is going to the market.

36