rafmacalaba/datause-encoder-data huggingface dataset:
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modeling the poverty, wage, production, investment, and terms of trade equations, estimate returns of different public investments to growth and poverty reduction. They find that agricultural research and development (R&D) and education would considerably increase agricultural GDP, and that public road and telecommunication investment would more benefit nonagricultural growth. The results for Thailand are more or less the same (Fan _et al_ ., 2004). A computational general equilibrium (CGE) model is also considered as a version of this general approach (e.g., Nordås, 2004; Mlachila and Yang, 2004; USITC, 2004).

The following analysis adopts the partial equilibrium approach with agricultural trade data. This approach has the advantage of relatively low data requirements and high tractability; based on micro-foundations, it can easily focus on specific issues in question. Though, it may overlook some possible side effects that would occur in the economy beyond the model. In contrast, the general equilibrium approach is good at providing a broad picture of the economy but will risk complicating the structural specification and requiring much extensive data; it preferably requires disaggregate data on the sub-national level. Concern also grows over misspecification and critical omitted variables, as the model includes more equations. Moreover, the “black-box” criticism, to which the CGE model is typically vulnerable, would be applicable.

The paper attempts to relate product market outcomes to agricultural-related infrastructures for two commodities: coffee (together with cocoa in our following empirical specification), and dairy. These are economically and socially important in Africa. In addition to the traditional infrastructure variables, various agricultural (or rural) infrastructure proxies are examined: rural telecommunications adoption index, rural water access, electricity consumption for agricultural purposes, and rural access index.

The following sections are organized as follows. Section II describes an overview of agriculture in Sub-Saharan Africa. Section III develops an empirical model to estimate the supply and demand functions of each farm product. Section IV quantifies the infrastructure
compared to the PIPE sample, a significantly larger fraction of the SEO sample reports negative operating income for the fiscal year before the equity issue.

We also report mean and median values for the information asymmetry proxies we employ in our later tests in Table 3. The univariate analysis of these measures provides no evidence in support of the hypothesis that PIPE issuers suffer from higher levels of asymmetric information than SEO issuers and is at odds with the profile of PIPE issuers in the US (Dai, 2007, Brophy et al. 2009, Chen et al. 2010 and Gomes and Philips, 2012). Asset tangibility as measured by NPPE to Total Assets is significantly higher for PIPE issuers compared to SEO issuers. However, trading volume for an average PIPE issuer is lower than that of an average SEO issuer. PIPE and SEO issuers are statistically similar with regard to analyst coverage and market to book ratio. And finally, the age of PIPE issuers is marginally higher than that of SEO issuers.

Overall, the results of the univariate tests described in Tables 2 and 3 suggest that while PIPE and SEO issuers appear to be of similar size, from similar industries, and have similar levels of asymmetric information, there are differences in terms of their operating performance. PE financed PIPEs in Asia thus appear to bear little resemblance to similar financing in the US where PIPE issuers are commonly described as young, small and risky firms both in absolute terms and relative to SEO issuers. However, the statistics presented so far only explore the characteristics of PIPE and SEO issuing firms in a univariate way. In the next section we employ multivariate regression models to test more rigorously for differences between PIPE and SEO issuers. We also use event studies to study the stock market performance of firms pre- and post issue to examine whether there are significant differences by issue type.

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# **Appendix 2: Simulation techniques**

We treat household expenditure as a random variable that comes from some distribution the parameters of which we estimate. This random variable is a sum of observed and unobserved components. The observed component is a product of household characteristics and the returns on these characteristics in a particular migration state. The unobserved component is determined by the choice of the migration state according to rules (A.1) and (A.2). We cannot recover the exact value of the unobserved component but can only estimate the parameters of the distribution of that component in each counterfactual state. We need to simulate the distribution of counterfactual expenditures in order to calculate the poverty and inequality measures in various counterfactual scenarios.

To simulate the expenditure distribution for each household in different states of migration we draw error terms ε1, ε2, μ1, μ2, μ3 from unconditional 5-variate normal distribution with the estimated variance-covariance matrix (A.3). In every draw _m_ household _i_ is assigned to a particular migration state _s_ , according to rule (A.1 and A.2): _Uis_ − _Uij_ =φ ˆ _sjZi_ + ε _im_ ( _sj_ ) > 0; _Uis_ − _Uik_ =φ ˆ _skZi_ + ε _im_ ( _sk_ ) > 0 (A.12) where φ ˆ _sj_ , _sk_ are the estimated parameters as in (A.1) and ε _im_ ( _sj_ ),( _sk_ ) are the values of the error terms in draw _m_ . The expenditure of household _i_ in draw _m_ is: ˆ _m m Cis_ = β _s X i_ + μ _is_ (A.13) So, in every draw only migration choice is realized and the counterfactual expenditure derived for that choice. By repeating this process _M_ times for all households in our sample we generate the simulated expenditure distribution in all migrations states. Any distributional statistics could be calculated using this distribution. For example, the simulated poverty rate for households with migrants working outside Nepal in case of no abroad migration is: 44
The data provides for a plot of T( γ ) estimated with vs. transit time for exports by rail in Central Asia (see Figure 8). The multiplier effect is over 4.

# **Figure 8. Rail Transit Time vs.  Hedging Inventory Level Estimated from Road Premium**

<!-- Start of picture text --> 160 y = 4.155 8x<br>140 R 2 = 0.49 58<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>0 5 10 15 20 25 30 35<br>Rail Transit Time Days<br>Hedging Inventory (days)<br><!-- End of picture text --> _Data Source: Ojala 2005._

# **7.2 An Empirical Application to the Northern Corridor**

The Northern Corridor is the main transport artery linking the landlocked countries in East and Central Africa (Rwanda, Uganda, Burundi, Eastern DR Congo and South Sudan) to the Mombasa port (Kenya). Up to Kampala (Uganda) transit cargo is _via_ trucks or on the Kenya-Uganda railroad (Figure 9).

The corridor’s performance is hampered by two factors related to infrastructure management and quality: (1) Kenya’s infrastructure quality and (2) rail poor performance. The Governments along with donors (the World Bank and the EU) and complementary efforts by trans-national entities (NCTTCA and COMESA) are also addressing facilitation related issues through harmonizing transport and documentation policies (e.g. third party insurance and mutual recognition of insurance). Despite 51
Besides these static aspects that limit the effectiveness of CRS, the lack of longterm commitment from both the government and the firm further renders CRS contracts ineffective in improving growth. A CRS contract usually lasts for three to five years, not long enough to internalize iong-term investment. The government, moreover, used the CRS as an experiment, not a permanent policy instrument. Finally, SOEs have a tendency to sacrifice their potential long-term benefits for myopic gains: (a) Firm-level pay sensitivity rewards current profits, which further reduces incentives to invest, and (b) Xu (1996) found that better firms were given lower base amounts of transfers including less investmnent subsidies. This may produce "ratchet effects", that is, SOEs under CRS may mimic inefficiency by not working hard so that they get easy performance targets.

_Hypothesis 5._ Except the effects of firm-level pay sensitivity, the adoption of CRS will, on average, improve neither productivity level nor the growth rate. The adoption of CRS may affect growth rate negatively.

# IV. EMPIRICAL **IMPLEMENTATION**

All variables used are constructed from _A Survey of State Enterprises:_ 1980-1989. Some variables have many missing observations, and each of them has a different set of missing observations. In the empirical analysis, we delete observations where: (1) the dependent variable is missing; (2) capital intensity, the key conventional explanatory variable, is missing; (3) and some key variables are unreasonable outliers.<sup>1 5</sup> The sample, now an unbalanced panel, consists of _716_ firms and a total of 6712 Observations. To avoid losing too much of the sample and to make full use of the information in the data, we impute the value of missing observations of a generic variable xi, by giving them the

> **'**<sup>**5**</sup> **We** consider an observation to be an outlier when its value seems unreasonable, for example, when the marginal retention rate is negative or larger than 1.

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reserve holdings. But the phenomenon is not confined to Asia; Latin American economies (with Chile at the top) and oil exporting countries have also accumulated large volumes of international reserves over the last decade.

Even if we accept the view that the accumulation of international reserves, or external assets more broadly, was a deliberate policy choice of emerging economies, the question remains whether it was driven by caution against the volatility of international capital flows, or by the pursuance of competitive exchange rates. Assessing the relative weight of these two factors is not an easy task. Moreover, their respective roles likely differ across countries and over time. Aizenman and Lee (2007) examine the question empirically using data from 49 countries in the 1980‐2000 period, and conclude that both motives were at work, but the precautionary saving motive was more important. Jeanne and Ranciere (2009), in turn, conclude that the accumulation of external assets after 2000 has been too large, particularly in Asia, to be justified by the precaution motive alone.

On the other hand, if deliberate policy choices of the economic authorities had been the main force behind the accumulation of assets on the United States, capital inflows to the U.S. should reflect a dominant role of official flows over private flows. Figure 7 shows that the picture is more mixed. Net purchases of U.S. assets by official entities (central banks and other government bodies) from emerging markets in Latin America, Asia and the Middle East have grown increasingly large in the 2000s. After the onset of the crisis in 2007, they became the sole source of inflows from these countries. However, over the decade as a whole the total volume of emerging‐market official inflows to the U.S. was roughly on par with that of private inflows.

Thus, the big role played by private capital in the total flows from emerging markets in the run up to the crisis seems to lend some support also to the first version of the equilibrium approach summarized earlier, which explains global imbalances primarily on the basis of asymmetries in the supply and/or investors’ demand for international assets. Forbes (2010) offers some corroborating evidence, based on an analysis of the geographic origin of private capital flows to the U.S. She finds that investors from countries with less developed financial markets tend to hold greater shares of their investment portfolios in the U.S. This fact in accordance with the equilibrium approach views of Caballero, Fahri and Gourinchas (2008a, b) and Mendoza, Quadrini and Ríos‐Rull (2009) discussed above.

# **4. Global imbalances and the origin of the crisis**

The role of global imbalances in the inception of the world crisis has been hotly debated. Some observers view the imbalances as one of the key causes of the crisis, and make their elimination an urgent priority to safeguard the stability of the world economy. Others think that the 10
characteristics of the data used. The fifth section introduces the private investment model tested and the results of the estimations. The sixth section uses this model to determine which factors would boost the level of MENA’s private investments in the future. The last section presents the conclusions.

# **2. Governance Institutions: An Attempt at Classification**

A first step in our analysis of the link between governance and private investment has been to differentiate and categorize the numerous different dimensions of governance, to better understand which institutions are investment-conducive. Existing literature on the classification of governance institutions provides some alternatives. Various authors have aggregated certain indices to better capture the common features of the existing data.

Kaufmann, Kraay and Mastruzzi (2003) categorize governance institutions in six broad groups _._ Their measures of governance indicators are based on 194 variables drawn from 17 different sources, in order to measure six different aspects of governance. “Government Effectiveness” and “Regulatory Quality” summarize the ability of the government to formulate and implement sound policies. The respect of citizens and the state for the institutions which govern their interactions is categorized as “Rule of Law” and “Control of Corruption”. "Political Stability and Absence of Violence" measure perceptions of likelihood that the government in power will not be destabilized and indicate the continuity of policies. “Voice and Accountability” captures the process by which citizens of a country are able to participate in the selection of their government (see _Annex. 2_ for more details on these indicators). The World Bank (2004) has used two 8
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87, In the SARs *nd sectoral gui4elines reviewed, distributional objectives were given prominent focus. These were naturally defined differently in the different sectors; and various approaches were developed to incorporate then into the pricing analysis. The main ones are discussed below.

# Explicit Distributional Weights

88. In the Bank's basic text on shadow pricing and the economic evaluation of projects, a technique is developed for the differential weighting of project benefits according to the income level of the beneficiaries. In the pricing context, this would imply inflating the willingress to pay of poorer consumers by that weighting factor to determine the intersection of demand and supply (and thus the allocation of the good), then setting differential prices to different consumer groups in order to achieve tnat allocation.

89. So far as we are aware, explicit distributional weights have not been used in any sector to incorporate distributional objectives into pricing analyses. Nor did we observe their use in the economic evaluation section of the SARs reviewed. The difficulty of 'guesstimating" distributional weights, and the somewhat spurious accuracy of the resulting prices or net present values have worked against their practical incorporation into project and pricing analysis.

# Percent of Proiect Beneficiaries Below Income Target

90. The closest approach to the use of distributional weights is probably in the irrigation sector, although the link with pricing is not a direct one. Cost recovery objectives take into account the proportion of project beneficiaries below certain poverty thresholds. The "Critical Consumption Level," below which the social value of a unit of extra consumption is greater than the social value of a unit of extra public revenue, is the most frequently used cut-off. The level of absolute poverty, defined in terms of a "basic needs" consomption basket of food, clothing and shelter, is also sometimes used. Nearly 80% of the SARs reviewed specified an index of ability to pay along with the proportion of project beneficiaries falling below the threshold. That proportion ranged from 10-100% of beneficiaries, so it is not surprising that cost recovery targets varied widely. As discussed below, irrigation is also a sector with one of the lowest levels of overall cost recovery. Its economic rates of return are quite high (relative to other sectors), so it is clearly the perception of distributional constraints that is holding down user charges. Because other sectors use different approaches to adjust for distributional concerns, it is not possible to directly compare the stringency of those constraints across sectors.

# Rules of Thumb on Prices as Percent of Income

91. The most cormonly used approach across sectors was to compare efficiency prices with income levels, using a rule of thumb for the proportion of income that a poor family could devote to that item. The two main variations on this approach are illustrated by the water and housing sectors. In water supply, the figure of 5% of income is frequently cited
**Table 1: Local Business Registration Procedures in Mexico**

||Average|Std. Dev.|Min|Max|
|---|---|---|---|---|
|Pre-reform|||||
|Days|30.1|16.7|2|60|
|Procedures|7.9|7.0|1|27|
|Office visits|4.2|2.0|1|10|
|Post-reform|||||
|Days|1.4|0.6|1|3|
|Procedures|2.7|2.1|1|9|
|Office visits|1.0|0.2|1|2|

_Note:_ These data come from the COFEMER website and are based on 32 municipalities from 17 different states. On top of these procedures, most firms need to comply with one or two federal procedures, which can often be done in the same location (this one-stop-shop solution is part of the reform).

**Table 2: Pre-Reform Income by Occupation**

||Non-registered<br>business owners|<sup>Wage workers</sup>|Registered<br>business owners|
|---|---|---|---|
|Avg. pre-reform income (in logs)|7.519|8.010|8.324|
|Avg.pre-reform income residual|-0.276|0.015|0.189|

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# **2 Methodology**

# Baseline specification

The equation of interest is: The dependent variable, IPV��� is an indicator for whether a woman i in country j in interview year t experienced intimate partner violence in the last twelve months. The variables of interest are ln U<sup>�</sup> ����<sup>and ln U�</sup> ����<sup>which are de-trended log female and male unemployment in country j in the</sup> previous year, respectively.<sup>23</sup> In a robustness check, the study investigates replacing the de-trended rates with the first-difference of the rate.

The control variables X��� in equation (1) include the education of the woman and her partner, her age, and an indicator for whether she resides in an urban location. For the sample for which it is available, religion is also controlled for.<sup>24</sup> Country (γ�) and year (γ�) fixed effects are included to purge country-level unobservables and common shocks respectively. The study does not include country specific trends in the main specification since unemployment is already de-trended. However, the study checks robustness of the results to the inclusion of country-specific trends. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. The study estimates linear probability models using OLS.

> 23 The study uses lags because surveys conducted in year t query violence in the twelve months up until t, which is contemporaneous with unemployment shocks in t-1.

> 24 Religion is missing for 34.4% of the sample, since information on religion is not collected in 13 of our surveys; including all surveys for Colombia, Jordan, Kyrgyz Republic, Pakistan, Peru, Tajikistan and Tanzania, and for one survey in the Dominican Republic (2007). Amongst surveys for which information on religion is recorded, it is missing for less than 1% of observations.  The study chooses to drop missing observations rather than, for example, choosing to code missing values under a new variable (i.e. an indicator for missing religion) due to concerns that inclusion of such a variable can bias estimates (Jones, 1996). The study shows robustness to the religion control for the subsample for which it is available.

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|||taken into account if they<br>were bought||
|---|---|---|---|
|_seed_value_USD_|Value of<br>seeds in USD|<br>Seed values<br>(_seed_value_LCU)_are<br>converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World<br>Bank databases (Azevedo et<br>al., 2011)||
|_improved_|Does this plot<br>contain<br>improved<br>seeds?<br>(default:<br>traditional)||NGA: absent in w1, w2<br>TZA: absent in w1, w2<br>UGA: absent in w1, w2, w3|
|_used_pesticides_|Were<br>pesticides<br>used on this<br>plot?|||
|_crop_shock_|Did a shock<br>affect crops in<br>the current<br>season?||UGA: crop shocks are missing in w7,<br>w5|
|_pests_shock_<br>_rain_shock_<br>_drought_shock_<br>_flood_shock_|Were crops<br>affected by<br>pests in the<br>current<br>agricultural<br>season?<br>Were crops<br>affected by<br>rains in the<br>current<br>agricultural<br>season?<br>Were crops<br>affected by<br>drought in the<br>current<br>agricultural<br>season?<br>Were crops<br>affected by<br>floods in the<br>current<br>agricultural<br>season?||Flood shock is absent in MWI w1,<br>MLI all waves,<br>Rain shock is absent in NER all<br>waves,, UGA all waves<br>Rain and flood shocks could not be<br>distinguished in TZA<br>Shocks only reported on plots with<br>full losses in NGA waves 1 to 3, and<br>UGA<br>UGA: crop shocks are missing in w7,<br>w5|

28
in private unadjusted disposable income is almost perfectly compensated by an increase (decline) in public unadjusted disposable income. As a consequence, changes in the national saving rate must be explained by movements in consumption and net capital gains.

# **5. Conclusions and Final Remarks**

This paper looked closely to the Colombian National Account data. In order to ensure a correct diagnosis of the recent behavior of private savings, several inadequacies of the data were corrected. In particular, the following problems were solved: (i) The under (over) estimation of private (public) caused by subtracting interests paid on total external debt from private disposable income and excluding interests received by the private sector from the public debt it holds; and (ii) the lack of consistency of the saving measure with consumption theory. In the National Accounts saving is a current account measure but consumption theory is based on a capital account measure of saving in which it is computed as the change in net wealth. This problem was solved adjusting the current account measure of savings by net capital gains from revaluations of assets and liabilities due to inflation and changes in the real exchange rate.

According to the adjusted measure of savings, the decline of private savings in the 1990s is less dramatic than the suggested by the National Accounts. The latter indicates that in 1992 the private saving rate reached the lowest level since 1950 (9.3% of GNP) and was 3.5% of GNP below the average private saving rate of the period 1951-1993. In contrast, in 1993 the adjusted measure was below the level attained in 1992 but it was 0.5% of GNP below the average of the period 1951-1993. However, the decline in the adjusted measure is more dramatic. In fact, between 1988 and 1993 the private saving rate fell 5.4% of GNP and 4.6% of GNP corresponding to the adjusted and traditional measures, respectively. Still it should be pointed out that the level attained in 1988 was the highest of the period 1950-1993.

In order to understand the recent behavior of Colombian private savings, the performance of its main components was analyzed. This was useful to reject and/or confirm some of the hypothesis that have been suggested as explanation of the decline of 17