rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6: Estimation results for number of mortgages by gender of owner, 2000-2019</u>**

||<br>**Total**|<br>**Male**|<br>**Female**|**Joint**|
|---|---|---|---|---|
|**Panel A: Entire sample**|||||
|Spor. reg. plots pre-2010 (_θ1_)|0.0160***|0.0088***|0.0206***|0.0297***|
||(0.0010)|(0.0010)|(0.0012)|(0.0038)|
|Spor. reg. plots post-2010 (_θ2_)|0.0273***<br>(0.0008)|0.0301***<br>(0.0015)|0.0141***<br>(0.0009)|0.0283***<br>(0.0007)|
|Syst. reg. plots post 2010 (_θ3_)|0.0016***|0.0009|0.0007**|0.0019***|
||(0.0003)|(0.0009)|(0.0003)|(0.0002)|
|Constant|-0.0380***|-0.0120**|-0.0118***|-0.0002|
||(0.0091)|(0.0056)|(0.0037)|(0.0041)|
|No. of obs (blocks)|38,600|38,600|38,600|38,600|
|Linear combinations|||||
|_θ2_-_θ1 _|0.0113***|0.0213***|-0.0066***|-0.0015|
||(0.0014)|(0.0020)|(0.0017)|(0.0041)|
|_θ3_-_θ2 _|-0.0257***|-0.0292***|-0.0133***|-0.0264***|
||(0.0008)|(0.0017)|(0.0009)|(0.0007)|
|_θ3_-_θ1 _|-0.0144***|-0.0079***|-0.0199***|-0.0279***|
||(0.0011)|(0.0013)|(0.0012)|(0.0038)|
|**Panel B: Restricted sample**|||||
|Spor. reg. plots pre-2010 (_θ1_)|0.0267***<br>(0.0033)|0.0161***<br>(0.0032)|0.0067*<br>(0.0039)|0.0325***<br>(0.0102)|
|Spor. reg. plots post-2010 (_θ2_)|0.0092***|0.0119**|0.0106***|0.0150***|
||(0.0022)|(0.0050)|(0.0015)|(0.0025)|
|Syst. reg. plots post 2010 (_θ3_)|0.0014***|-0.0010|0.0010|0.0029***|
||(0.0006)|(0.0022)|(0.0006)|(0.0006)|
|Constant|-0.1100***|-0.0452***|-0.0051|-0.0001|
||(0.0219)|(0.0153)|(0.0084)|(0.0083)|
|No. of obs (blocks)|9,100|9,100|9,100|9,100|
|Linear combinations|||||
|_θ2_-_θ1 _|-0.0175***|-0.0041|0.0038|-0.0174|
||(0.0045)|(0.0063)|(0.0045)|(0.0108)|
|_θ3_-_θ2 _|<br>-0.0077***|<br>-0.0129**|<br>-0.0096***|<br>-0.0122***|
||(0.0023)|(0.0056)|(0.0017)|(0.0025)|
|_θ3_-_θ1 _|-0.0253***|-0.0171***|-0.0057|-0.0296***|
||(0.0033)|(0.0038)|(0.0039)|(0.0102)|

_Note_ **:** Standard errors in parentheses: * p<0.10, ** p<0.05, *** p<0.010 26
||rm|%|%|%|%|%|0%|
|---|---|---|---|---|---|---|---|
|in|Fi|7|43|1|47|2|10|
|Spa|SOE|3%|45%|1%|45%|6%|100%|
|n Fed.|Firm|18%|45%|0%|25%|12%|100%|
|Russia|SOE|7%|49%|0%|24%|19%|100%|
|ania|Firm|50%|33%|0%|17%|1%|100%|
|Rom|SOE|6%|56%|0%|37%|1%|100%|
|gary|Firm|25%|48%|1%|21%|5%|100%|
|Hun|SOE|3%|73%|0%|14%|10%|100%|
|Rep.|Firm|37%|47%|0%|14%|2%|100%|
|Slovak|SOE|4%|68%|0%|20%|8%|100%|
|bia|Firm|15%|59%|1%|24%|1%|100%|
|Ser|SOE|1%|71%|1%|20%|7%|100%|
|ria|Firm|30%|39%|1%|26%|5%|100%|
|Bulga|SOE|3%|56%|1%|25%|15%|100%|
|||rm|dividual|rty|litician|E||
|||Fi|In|Pa|Po|SO||

32
27 These results differ from other incidence analyses of production taxes in developing countries which generally reflect an assumption of full forward shifting. Because these taxes are usually simply allocated among income ranges using data on consumption expenditures, such studies usually find that these taxes are regressive while also arguing that this regressivity could be reduced through the use of differential tax rates by commodity, and excises on "luxury goods".

Here, the incidence profile is a mix of negative and positive effects by household group. Larger redistribution occurs than with the income tax because of the large relative price effects stemming from the removal of selective sales taxes, including the VAT and border tax adjustments.

# _Export Taxes_

Incidence results in Table 3 for export taxes show a pro-poor effect from the removal of export taxes in CMte d'Ivoire, suggesting that these taxes are regressive. Indeed, current tax reforms aim at gradually eliminating these taxes. In part, these incidence effects from the model reflect the impact of the replacement taxes in also applying to urban households consuming imported products. These results are in contrast to those from other incidence studies, which assume that he incidence of export taxes falls exclusively on producerexporter groups, and which yield a progressive incidence pattern, since these groups are in the higher income ranges. One study consistent with our findings is that of Sri-Lanka by Jayasundera (1986) who also considers implicit subsidies to domestic consumers associated
# **I. Introduction**

A family preference for sons over daughters may manifest itself in a variety of ways. An especially stark dimension is the excess mortality among girls which has been documented in several Asian countries (see for example Zeng et al 1993 for China; Muhiri and Preston 1991 for Bangladesh; and Das Gupta 1987 for India). A similar phenomenon has also been documented in the Middle East (Yount 2001). Son preference can also manifest itself through lower investments in the human capital of girls. Pande (2003) documents lower nutrition and lower immunization rates among girls in India. School enrollment among girls lags behind that of boys in many South Asian, Middle Eastern and North African countries (Filmer 2005).<sup>1</sup> This study focuses on one particular definition of "preference" for sons, namely different propensities for continued childbearing given the gender composition of existing children in the family. Such behavior could be the result of taste-based gender discrimination, or economic factors such as differences in the costs of investing in girls versus boys, or differences in the pecuniary return to investments in girls versus boys. The paper does not, therefore, use the term "preference" only in the sense of inherent tastes but in the looser sense of gender-related differences in behavior—regardless of the cause of those differences.

There are numerous possible reasons for observing son preference in the developing world. Typically, these derive from conditions found in many traditional rural societies such as inheritance systems which pass assets to sons, inter-generational insurance systems in which sons care for parents in old-age, or production systems with low pecuniary returns to women's work (and to investments in women's human capital). General development processes and modernization, including urbanization, the dissolution of traditional rural communities, and

> 1 A more general discussion of differences between boys and girls in inputs and outcomes can be found in World Bank (2001).

1
**Table 3: Summary Statistics The Relationship between Formality and Firm Size and Age**

||**Registe**<br>**federal t**|**red with**<br>**reasury**|**Any worke**<br>**with**|**rs registered**<br>**IMSS**|**Enume**<br>**1989 C**|**rated in**<br>**ensus**|
|---|---|---|---|---|---|---|
|Revenue**decile**|Mean|**Obs**|**Mean**|**Obs**|**Mean**|**Obs**|
|1st decile|.080|889|0|6|.087|472|
|2nddecile|.125|910|0|11|.109|512|
|3rd decile|.184|917|0|19|.136|523|
|4th decile|.257|1197|.024|84|.196|730|
|5th decile|.401|558|.075|67|.299|341|
|6th decile|.444|1024|.102|167|.317|590|
|7th decile|.581|816|.219|206|.444|441|
|8th decile|.600|788|.279|219|.442|443|
|9th decile|.710|909|.331|381|.547|537|
|10th decile|.855|865|.606|538|.751|535|
||Registe<br>**federal**|red**with**<br>**treasury**|**Any worke**<br>**with**|**rs registered**<br>**IMSS**|**Enume**<br>**1989**C|**rated in**<br>ensus|
|Years in business|Mean|**Obs**|Mean|Obs|Mean|**Obs**|
|less than one year|.323|876|.237|131|0|0|
|1 year|.374|838|.221|131|0|0|
|2 years|.416|1094|.343|181|0|0|
|3 years|.455|857|.345|171|0|0|
|4 years|.463|559|.391|110|.309|537|
|_5_years|.432|562|.336|125|.309|538|
|6to7years|.411|672|.397|156|.316|648|
|8to 9 years|.401|504|.290|100|.287|492|
|10 to 12 years|.441|975|.415|193|.312|952|
|13 to 15 years|.416|551|.418|122|.353|541|
|16 to19years|.467|304|.333|69|.391|297|
|20 to 29 years|.438|765|.321|159|.359|744|
|30 years or more|.435|476|.414|99|.376|468|

28
Inverting the coefficient yields an estimate of _a._ The quantitative test of the model is whether these estimates of _a_ are reasonable.

Empirical testing of the Grossman-Helpman model using nontariff barrier (NTB) data from the 1980s in U.S. manufacturing has been undertaken in two studies, by Gawande and Bandyopadhyay, and Goldberg and Maggi.<sup>17</sup> Since U.S tariff reductions were the subject of multilateral reductions in the Kennedy and Tokyo rounds, while the Grossman-Helpman model is one of unilateral protection, NTBs are more appropriate for testing the model. NTBs are measured as coverage ratios, that is, the percentage of imports covered by such barriers. Both studies qualitatively affirm the Grossman-Helpman model prediction. They find that protection increases with the inverse import-to-output ratio in politically organized industries (as measured by existence of PACs).<sup>18</sup> However, they find the implied value of the government preference parameter _a_ to be surprisingly high – over 100 in the Goldberg-Maggi study and over 1000 in the Gawande-Bandyopadhyay study. If those estimates were taken literally, then the U.S. government would approximate the textbook ideal of welfare-maximizing entities!

17 Gawande and Bandyopadhyay (2000) and Goldberg and Maggi (1999).

18 Gardner’s (1987) study is prescient about the Grossman-Helpman model’s key features. In Gardner’s model government maximizes the weighted sum of buyer’s surplus (B) and producer’s rents (R), B + θ R. In turn, B and R are functions of farm output quantities. Efficient redistribution using production controls in this framework requires choosing quantities of farm products to maximize W. Unlike Grossman and Helpman, who adopt a formal model of the lobbying process and, as a result, are able to provide microfoundations for their objective function, Gardner does not provide micro-foundations for W. He employs Peltzman’s (1976) “majority generating function” as the argument for using this type of objective function. He attributes the same forces that determine lobbying effectiveness as determining the value of the parameter θ . Gardner measures these forces for seventeen farm commodity by the number of producers, their geographical dispersion, the stakes from redistribution (output per farm), and the stability of the industry (variability of production patterns). He also estimates (long-run) demand and supply elasticities for those commodities. Pooling data across the seventeen commodities over the period 191280 yields a sample of 1124 observations for Gardner’s empirical analysis. The dependent variables are variants of the nominal protection coefficient. Gardner finds that the lower (greater) the (inverse) demand elasticity, the greater the level of intervention, that is, the higher the price relative to it’s non-distorted price. This is a confirmation of the Grossman-Helpman intuition that it is most efficient to tax commodities with the lowest price elasticities of demand, and is the basis for Gardner’s conclusion that interventions in U.S. agriculture have been efficient.

9
Policy Research Working Paper 7003

# **Abstract**

Market integration is key to ensuring sufficient and stable food supplies. This paper assesses the impediments to market integration in Central and Eastern Africa for three food staples: maize, rice, and sorghum. The paper uses a large database on monthly consumer prices for 150 towns in 13 African countries and detailed data on the length and quality of roads linking the towns. The analysis finds a substantial effect of distance and share of paved road on the level of market integration, as measured by relative prices. Furthermore, the paper evaluates the additional domestic and cross-border impediments to market integration in the region and represents them on a regional map. The analysis finds heterogeneous levels of domestic market integration across countries and significant “border effects” for the majority of contiguous countries in the sample, which reveal that markets are more integrated within than between countries. Countries that are members of the same regional trade agreement have substantially “thinner” borders with other members. Finally, the analysis shows that countries with less integrated domestic markets and “thicker” borders with their neighbors also have a higher prevalence of food insufficiency. These findings support policy efforts in tackling domestic and border impediments to transactions such as reforming customs, simplifying nontariff measures, addressing corruption, improving the quality of roads, and deepening regional trade agreements.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pbrenton@worldbank.org, and aportugalperez@worldbank.org.

_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 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._ Produced by the Research Support Team
household and area models, when one defines bias with respect to both sample design (in our case simple random sampling within AGEB) and the model parameters, the design-model bias vanishes. This occurs because the sample mean of the predictor variables is an unbiased estimator of their population means, under a simple random sample design, and follows from the concept of design-model bias discussed in Valliant et al. (2000, p. 120).

In many applications, it is difficult or even impossible to apply a nested error regression model with household-level auxiliary variables, either because household-level auxiliary variables are not available for the entire finite population or due to the presence of measurement error in the household-level auxiliary variables. In these situations, the unit-context model would be a reasonable option. Like a nested error regression model with household-level auxiliary variables, a unit-context model is very flexible in producing estimates of a wide range of linear and nonlinear parameters (e.g., different FGT indices) using a single model. This is in sharp contrast with an area or sub-area level model, which requires separate models for each estimated parameter. The unit-context model also permits incorporation of uncertainty incurred due to estimation of different model parameters, such as the variance of the sampling error.

# 3. Data

We evaluate the ability of data derived completely from satellite imagery to predict municipalitylevel poverty in Mexico. In this section, we discuss the different data sources used to construct the requisite variables as well as the data used to validate the performance of the models.

## 3.1. Household data

We conduct the main analysis using monetary poverty estimates at the household level from the 2014 MCS-ENIGH Household Survey data. The survey is conducted every two years by the Mexican National Institute of Statistics and Geography (INEGI) to understand the income, spending, socio-demographic, and employment situation of the country. It is a nationally representative, cross-sectional household survey. The 2014 MCS-ENIGH survey covers 58,125 households, of which approximately 75% are urban and 25% are rural.<sup>10</sup> The survey samples 896

> 10 Rural areas are defined as localities with fewer than 2,500 inhabitants.

11
simultaneous shortage of capital and the contraction in the supply of new loans that affected banks in New England during the early 1990s recession in the United States A capital crunch could result in the reduction of total bank assets or alternatively in a shift toward less risky assets such as government bonds. An extensive survey of the empirical evidence available for industrialized economies, has concluded that "there is some evidence that bank capital pressures during cyclical downturns in the US and in Japan may have limited lending in those periods and contributed to economic weakness in some macroeconomic sector" (Basel Committee on Bank Supervision, 1999). Recent empirical evidence shows that the introduction of more severe capital regulation may have reduced bank credit supply also across emerging economies (Chiuri et al., 2002).

These concerns have recently been addressed by policy makers as well. The Financial Stability Forum, for instance, has raised the question whether several features of the new capital regulation currently discussed by the Basel Committee on Banking Supervision could increase the cyclical fluctuations of the economy. In response, the Basel Committee has confirmed that risk-based capital requirements are inevitably procyclical (more capital is required during recessions exactly because credit risks in banks' portfolios increase in cyclical downturns) and suggested that the cyclicality question should be addressed by means of different instruments. For examnple, national supervisors (under Pillar II of the new accord) could request banks to comply with higher than minimum capital requirements and leave bank capital free to fluctuate above that level.

See Bernanke and Lown (1991) and Peek and Rosengren (1995) for evidence in favor of the presence of a capital crunch during the 1990-91 recession in the US. A contrary view is taken by Berger and Udell (1994).

4
at least 15 years), including experience with firms in developing countries as well as advanced economies.

The source of external senior consultants in the last layer of quality control varied across sector. For agriculture and livestock, the validation exercise was conducted with agricultural engineers and researchers from Embrapa, an agricultural research institution from Brazil. For food processing, wearing apparel, pharmaceutical, transport, and retail, as well as for the general business functions, the team hired external consultants through a large management consultant organization. For automotive sector, the team has hired a senior consultant directly. For health, the team invited directly five physicians with different field of specializations and practical experience in hospitals in clinics in the United States and low income countries in Saharan Africa.

The validation exercise with sector specialists were organized as follows. First, the team would explain the purpose of the project, present the initial findings, and share a draft with identified business functions and technologies associated with them. The sector specialists would have between one and two weeks to reflect on the material to validate them or propose a new combination of business functions and technologies associated with them. After receiving the revised material, a second meeting with sector specialists would be organized with the FAT survey team to discuss the proposal and converge towards an updated combination of business’s functions and technologies.

In what follows we describe the grids for both types of business functions.

# **A.1.2 General Business Functions**

Figure 1 in Section 2 shows the 7 general business functions in FAT and the possible technologies used to conduct them. The business functions identified are: business administration (HR processes, finance,accounting), production planning, procurement and supply chain management, marketing and product development, sales, payment methods, and quality control. These are business functions that in addition to being central in the functioning of the firm, are also retained in some capacity (or some tasks) within the firm. The technologies used for these business functions tend to be more available and off-the-shelf technologies, often ICT technologies. For example, for administrative processes, these range from handwritten processes (the least sophisticated) to the use of enterprise resource planning which are software that allow for real time, integrated management of the main business processes. With the help of management consultants, we identify the technologies feasible for each business function and develop similar rankings of sophistication based on the consultants understanding of the number of tasks and complexity that the technologies can handle.

One important characteristic of the grid is that the sophistication rankings are not fully 62
before short term credit. (b) **_Non-Miracles at the Micro level:_** This is the case of the following 5 episodes - Mexico, Indonesia, Russia and the two Turkey episodes – where there is no credit-less recovery. We can further split the non-miracles into _Phoenix Miracles only at the Macro level_ and _Non-Miracles at the Macro and Micro levels._ The macro evidence based on GDP and Private Credit in section 4.1 showed Phoenix Miracles at the macro level in Mexico, Indonesia, and Russia. However, as seen in Figure 5C, in each of these countries both Sales and Short Term Borrowing recover at the same time (at t+1 in the case of Indonesia and Russia and t+3 in the case of Mexico). In both the Turkey episodes (see Figure 5D), we find that both corporate sales and credit collapse during the crisis and show no signs of recovery even two periods after GDP recovery.

To summarize the results from sections 3.1 and 3.2, based on just the macro data, we could categorize only 6 of the 9 3S episodes as potential Phoenix Miracles. When we look at firm-level data aggregated up to the country level, we find that only 4 of the 9 3S episodes appear to be Phoenix miracles where corporate sales recover before a recovery in short-term borrowing.

# **4. Microeconomic Foundations of Phoenix Miracles**

# **_4.1 Identification of Firm-level Miracles_**

In this section, we disaggregate our analysis to the firm level to first identify how many firms recover in each of the economies and then to identify firms that may be classified as potential miracles. We next focus on their balance sheet and cash flow statements over the two periods following the trough of the crisis. Since we are aggregating across all firms in the economy, we use a balanced sample of firms with key non-missing balance sheet and cash flow statement data<sup>17</sup> in each of the five years from two years before the crisis (t-2) to two years after the crisis (t+2). We also drop firms in the Utilities and Finance sector (Banks, Insurance, and Financial Services) and any erroneous observations such as negative sales or total assets. This leaves us

> 17 Our sample is restricted to non-missing values of the following variables from t-2 to t+2 : Sales (Income Statement), Short-term borrowing (Balance Sheet) and the following Cash Flow Statement variables -  dividends, increase/decrease in short-term borrowing, increases in long-term borrowing, decreases in long-term borrowing, increases in capital stock, decreases in capital stock, increase in investments, decrease in investment, disposal of fixed assets, capital expenditures, cash from financing activities, cash from investing activities, and cash from operations, and cash from other financing activities.

14