rafmacalaba/datause-encoder-data huggingface dataset:
notion. It has been found that intercropping cotton with pigeon pea (both susceptible crops) reduces the pest damage on both (Nigel Armes, pers. com.) but the reasons are not clear. Source crops can exert an important influence on the resistance of these pests. For example, the large unsprayed sorghum areas in southern Maharashtra provide a source of susceptible moths which lead to a dilution<sup>of</sup> resistance in Andhra Pradesh, through migration (Armes, et al., 1991).

Because of the gap in research knowledge, armigera population models have been understandably limited. For example, of the few that exist (El-Zik and Frisbie, 1990), the MOTHZV program is used to predict the seasonal dynamics of H. zea and H. virescens in Texas. These predictions require as inputs the size and timing of early season light trap catches. Similarly, pheromone trap catches are thought to be good predictors for the size of the early pest broods in the Lam farm area of Guntur (Dr. Venugopal Rao, pers. com., Metcalf and Luckmann, 1982) and indicate the timing of pesticide application. However, since population dynamics of the Heliothis group of species are less predictable elsewhere, these examples are too few to be generalized and therefore of<sup>limited</sup> value. one major limitation of the present modelling exercises is that they are typically within season models and do not look at the between season dynamics. Clearly, it is extremely important to develop models that will give longer term population predictions, so that appropriate pest control policies can be instituted well in 41
same over the years, from 2003 to 2007. However, a simple average of the rankings over the years is used to calculate the overall _Ease of Doing Business_ ( _EDB_ ) in each country.

Data on annual bilateral trade between all countries in the world are obtained from International Monetary Fund (IMF, 2007), for the period of 1960-2003. Using the sum of the volumes of bilateral trade and _GDP_ (in current US$) of the host and investor countries, two alternative trade variables, _Trade_ 1 and _Trade_ 2 ( _TRD_ 1 and _TRD_ 2) are constructed following Dinar et al (2007).<sup>8</sup> The first trade variable ( _TRD_ 1) expresses total trade (the sum of the volume of bilateral imports and exports) between the host and investor countries as a fraction of the sum of the countries’ GDPs. The second trade variable ( _TRD_ 2) measures the total trade (the sum of the volume of bilateral imports and exports) between the host and investor countries as a fraction of their trade with the rest of the world.<sup>9</sup> _TRD_ 1 is referred to as trade importance and _TRD_ 2 is referred to as trade dependency. For a group of _N_ ≥2 countries the _TRD_ 1 and _TRD_ 2 ( _Trade_ 1 and _Trade_ 2) are: Where _IMP_ and _EXP_ are import and export, respectively, _t_ is year, _w_ is the rest of the world, not including any country _j_ and _k_ in _N_ .

> 8 The definitions of these variables are available in Dinar et al (2007). Since the IMF trade data are in current $US; country-level GDP in current $US (collected from the WDI of the World Bank) are used to construct the trade variables.

> 9 For multiple investor countries, _TRD_ 1 expresses trade as the sum of total volume of exports and imports between the host country and each investor country as a fraction of the sum of GDP of each of the country involved in the CDM project. On the other hand, _TRD_ 2 measures the total trade (the sum of the volume of bilateral imports and exports) between the host and each investor country as a fraction of their trade with the rest of the world.

12
- 20 -

shipping.<sup>3 9</sup> Coastal shipping services in India can be provided by national flag carriers only. In 1952, the cabotage trade of general cargc represented about 50 percent of total coastal ship movements. Today, there is practically no general cargo moved in the coastal trades. Advances in the land transport networks and appalling port performance records account for much of these developments.

43. The Railways: When the need for effective inland distributicn of containers escalated into a serious issue, the Government's first reaction was to use India's extensive national railway network -the largest in Asia- as the main system. But the railways had too little time left over after organizing movements of essential bulks, to give the specialized attention needed for container traffic that is high in value. Aggravating this situation -and biasing the minds of railway managerswas the fact that container volumes make up less the 0.05 percent of Indian rail goods traffic. Thus arrangements for efficient container traffic management did really not materialize; this traffic was actually treated as a marginal, and often unwelcome activity by regional rail managers.

44. In the meantime, the Government instituted rules which made inland transport of containers by any mode other than railways extremely cumbersome. In support of the railway operations, the development of inland container depots (ICDs) was initiated. These ICDs were to serve as cargo clearance centers and as facilities for stuffing and stripping of containers outside the already congested ports. Further details on the lCD system are given below.

45. In the operating mode, Indian railways have introduced flatbed cars that are designed to carry Twenty-foot and Forty-foot IS0<sup>4 0</sup> containers, as well as Eight-foot domestic containers.<sup>4 1</sup> Under current arrangements, rakes with a capacity to transport 70 TEUs are in use. Since these rakes compete with other cargo and passenger trains for the same track in the heavily trafficked corridors between Bombay or Madras and Northern India, they are frequently delayed because they are shunted ontc sidings to give way for what is considered higher priority traffic. Thus there are the excessive transit times which were reported earlier. Apparently there are now new cars being designed to get 105 TEUs into the same train length, a productivity gain which will be critical with the expected throughput by the end of the century, requiring three container trains a day -rather than per weekon the busy lines between Bombay, Madras and New Delhi.

- 39/ **Income generated by coastal or inter-island shipping of general cargo could potentially mitigate** to **some extent their poor financial performance** in **the international trades.**

40/ **International Standards Organization**

**41/ Permission** to **use** ISC **sea** containers in domestic **trades was** ; ven in **1984 only.**
# **6 Conclusions**

This paper presents a model to examine the relationship between corruption in the public sector and concentration of the ownership structure of media firms. The main result of the paper shows that the relationship between corruption and ownership concentration is non-monotonic, with extreme cases, where the ownership is completely concentrated or widely held, likely to deliver the same levels of corruption. Thus, the paper shows that conventional regulatory prescriptions on ownership concentration may not be appropriate when it comes to industries such as the media. This is because the prescriptions must take into account not only economic welfare considerations, but also accountability effects. In this respect, ownership concentration may harm neither media freedom nor public accountability; this fact contradicts the conventional wisdom, and therefore constitutes overall the main contribution of this work.

# **References**

- [1] Ades, A. and R. di Tella, ”Rents, Competition and Corruption,” American Economic Review, 1999, 89(4), 982-993.

- [2] Agrawal, A. and G. Mandelker, ”Managerial Incentives and Corporate Investment and Financing Decisions,” Journal of Finance, 1987, 42(4), 823-37.

- [3] Bagdikian, B. _”The Media Monopoly”_ , Beacon Press, 1997.

- [4] Baron, D., ”Persistent Media Bias,” Journal of Public Economics, 2006, 90, pp. 1-36.

- [5] Bennedsen, M. and D. Wolfenzon, ”The Balance of Power in Closed Corporations,” Journal of Financial Economics, 2000, 58(1-2), 113-139.

- [6] Berle, A. and G. Means, ”The Modern Corporation and Private Property,” eds. MacMillan, New York, 1932.

16
# **Appendix 2: Descriptive statistics of the variables**

**Panel I: Product level data, based on the sample in column (1) of Table 1**

|Variable|Obs|Mean|Std. Dev|Min|Max|
|---|---|---|---|---|---|
|ind_sh_con|7,009|31.6587|25.4603|0|100|
|ind_sh_lib|7,009|32.4761|25.7032|0|100|
|(t-r)|7,009|4.7150|3.1034|0|17|
|prc_sh|7,009|0.2124|0.2899|0|1|
|collpriv_sh|7,009|0.3196|0.2532|0|1|
|for_sh|7,009|0.3605|0.3111|0|1|

Notes: ind_sh_con refers to the indirect export value share in percentage under the conservative measure. ind_sh_lib refers to the indirect export value share in percentage under the liberal measure. (t-r) refers to the unrefunded export VAT rate in percentage. prc_sh is the export value share of processing trade with imported materials (in percent). collpriv_sh is the export value share of private and collective firms. for_sh is the export value share of foreign wholly owned companies, equity joint venture, and contractual joint ventures (in percent).

**Panel II: Product-country level data, based on the sample in column (1) of Table 3**

|Variable|Obs|Mean|Std. Dev|Min|Max|
|---|---|---|---|---|---|
|ind_sh_con|60,952|41.2524|37.4467|0|100|
|(t-r)|60,952|4.1616|1.9537|0|17|
|prc_sh|60,952|0.1408|0.2822|0|1|
|collpriv_sh|60,952|0.4086|0.3753|-2.98e-08|1|
|for_sh|60,952|0.2510|0.3394|0|1|

Notes: See the notes of Panel I above.

**Panel III: Transaction level data, based on the sample used in Table 4**

|Variable|Obs|Mean|Std. Dev|Min|Max|
|---|---|---|---|---|---|
|ind_sh_con|13,716,691|0.3696|0.4827|0|1|
|(t-r)|13,716,691|0.0402|0.0151|0|0.17|
|log(value)|13,716,691|-5.1174|2.1422|-13.8155|6.3493|
|prc|13,716,691|0.1953|0.3964|0|1|
|oth_regime|13,716,691|0.0471|0.2119|0|1|
|collpriv|13,716,691|0.4130|0.4924|0|1|
|for|13,716,691|0.3095|0.4623|0|1|
|oth_owner|13,716,691|0.0012|0.0345|0|1|

Notes: ind_sh_con is a dummy variable for a trading firm based on the conservative definition. (t-r) refers to the unrefunded export VAT rate in percent, ranging from 0 to 0.17 (not in percentage, adjusted to avoid very small coefficient estimates). log(value) is the logarithm of the transaction value in million current dollars. prc equals one if the exporting firm involved in the transaction belongs to processing trade, and zero otherwise. oth_regime equals one if the exporter involved in the transaction belongs to any of the trade regimes other than normal trade and processing trade, and zero otherwise. collpriv equals one if the exporter involved in the transaction is either a collective or a private firm, and zero otherwise. for equals one if the exporter involved in the transaction is a foreign wholly owned company, or an equity joint venture, or a contractual joint venture, and zero otherwise. oth_owner equals one if the exporter involved in the transaction belongs to any of the ownership types other than SOEs, collective & private, and foreign firms, and zero otherwise.

43
# **4. The results**

## _(i) Whole sample_

In this section, we extended the Wilson, Mann and Otsuki (WMO) (2003a, 2003b, 2004)<sup>12</sup> sample to include 25 countries primarily from Africa. We have also captured the effect of African regional integration schemes. The gravity model was run using an ordinary least squares (OLS) with the Huber/White robust standard error.

The coefficients of ports efficiency of both the importer and the exporter are positively associated with trade. Comparing the effect of port efficiency on import vs. exports, we find, as did WMO, that the coefficient is higher for exporter than importer (see table 4). Customs environment of the importer is significant and positively related to trade in pooled crosssection and year 2003 fixed effect regressions, while the customs coefficient of the exporter is not significant. This implies that global trade flows get a bigger boost when the importer's customs environment improves. The regulatory environment of the exporter is significant and negatively associated to trade in all regressions. The regulatory environment of the importer is insignificant. The services infrastructure coefficient of the importer is significant and positively associated with trade in all regressions, while services infrastructure of the exporter is not significant.

African regional agreements, namely, CEMAC, COMESA, SADC, and UEMOA, have a positive and significant effect on trade. When we use the year 2003 and exporter fixed effects, coefficients of regional trade agreements become significant and positively related to trade. We applied the Tobit model (table 5) on the whole sample countries to take into account missing or zero trade. We also added a dummy to isolate the effect of intra-African trade. The results (see third column of table 5) show that when African countries trade among them, the world trade increases significantly.

In the pooled cross-section regression, tariff is negative and significant. In the fixed effects regressions, it becomes insignificant but remains negative. For the trade facilitation indicators, port efficiency of exporter is positive and significant whereas port efficiency of importer

> 12 The results obtained with WMO's sample are very similar with the previous one. These results are available on request to the authors.
might make them less salient for treated households. Third, there is much heterogeneity in the impacts across seasons. Electricity savings start in the months following the distribution of CFLs in the spring of 2013, and continue over the summer and fall. In the winter, impacts diverge greatly from the engineering predictions, but in the spring of 2014 the electricity savings return and are similar to those predicted. The regression analysis that follows looks more closely into these impacts of the CFL treatment.

# **5.2 Basic estimates**

To assess the impacts on household electricity consumption, we first estimate a simple difference-in-differences model using the electricity utility records: where _qit_ is household _i_ ’s electricity consumption (kWh) in month _t_ , _Treati_ is an indicator of treatment status, and _Xit_ is a vector of household-level variables.<sup>47</sup> Month-by-year fixed effects, _γt_ , and household fixed effects, _λi_ , address the large seasonal variations in consumption and potential heterogeneities across households in baseline electricity consumption, as identified in the event-study graph.

The _Treati ∗ Postt_ interaction denotes the household’s assignment to treatment and if that treatment occurred in that month or prior months. The coefficient on this interaction term, _τ_ , is therefore an estimate of the average change in household monthly electricity consumption (in kWh) that resulted from random assignment to treatment.

Table 1 reports intent-to-treat estimates of _τ_ . In Table 1, Column 1, the estimated impact is identified according to the above basic regression, from variation within households over time, controlling for month-by-year shocks. This basic estimate indicates that the CFL treatment reduces household electricity consumption by 16 kWh per month. The magnitude of this impact is only half the expected size based on engineering calculations. However, in Column 1, the omitted group is comprised of all control households, regardless of whether 47These include controls for number of days in monthly billing period and whether the household uses electricity for heating. We also control for heating degree days; however, we only have variation in temperature over time. The 7 villages in the study sample are all covered by one weather station and data are reported at that level. Nevertheless, we do not expect for there to be much spatial variation in temperatures across villages included in the study given their size and proximity.

18
These laws redistributed revenue within states according to an algorithm handed down by the federal government, and thus exogenous to any one municipality’s investment decisions. The rules changed slightly each year, but always identify two things: how much each municipality has to pay into its state’s education fund (always a set fraction of its revenue), and how much of the fund is given to each municipality (equal to its share of total public school students in the state, with students at different grade levels weighted differently each year). Given enrollment and initial revenue data for all municipalities, the algorithm delivers a “new” revenue to each municipality, which is lower for net losers and higher for net winners from the reform.

Of course, the reforms almost certainly induced endogenous revenue and enrollment responses (Hoxby 2001; Gordon 2004). A good instrument should encapsulate the credibly exogenous variation in revenue generated by the law, but _exclude_ variation due to municipalities’ own actions. To do this, I simulate the revenue each municipality would have each year if the current year algorithm were applied to 1997 (pre-reform) enrollment and revenue data. I then instrument for actual (endogenous) revenue with the simulated instrument, which allows me to test how different municipalities differentially spend an exogenous shock to revenue, all else equal. I instrument for the interaction of per capita revenue with covariates using the interaction of simulated per capita revenue with those covariates.<sup>31</sup> See Appendix D for further details.

The first stage equations state that per capita revenue and its interactions with per capita income and inequality are a function of simulated per capita revenue, _sit_ and its interactions with per capita income and inequality:

> 31In a similar vein, Mart ́ınez-Fritscher _et al._ (2010) use commodity price shocks in Brazil during 1889-1930 as an instrument for state capacity to spend on publicly-provided goods. They find that education expenditures increased during this period in response to positive shocks to the prices of commodities produced heavily in a given state.

19
apply with a SRS or unrestricted sample of the same size”. In formulas, _Deft_<sup>_2_</sup> ( _Deft_ in terms of standard errors), could be written as: It could be very useful to decompose _Deft_<sup>_2_</sup> in several multiplicative parts; the most important ones are: i) effect of stratification (usually, but not always,<sup>2</sup> smaller than 1); ii) effect of clustering (greater than 1); iii) effect of sample weighting adjustment (greater than 1; known as the _Kish effect_ ).

In Section 3, we provide a brief description of the theoretical background of poverty mapping and its implementation in Tunisia from 2014 to 2015.

# **3. Theory of poverty mapping and its application in Tunisia**

There are several methodologies for estimating poverty at a local level; one of the first was developed by Elbers _et al._ (2002, 2003), and it is often known as the ELL approach. In general, such model-based techniques are defined as Small Area Estimation (SAE) methods (Rao, 2003), and may fall in one of the broad categories: i) area based models (for examples Fay and Herriot, 1979) in which the model is estimated on the basis of areas as statistical units; and ii) unit-level models, where instead the unit of analysis in the model is the individual or the household. Within this second category, Molina and Rao (2010) have innovated with the Empirical Best (EB): in the general literature of SAE, both ELL and EB are nested error model used for small area estimation, which were originally proposed by Battese _et al._ (1988). Later, the World Bank improved the original ELL method by including EB predictors and incorporating heteroskedasticity and survey weights in order to take into account complex sampling designs (Van der Weide, 2014).

The present paper is based on Molina’s H3-CensusEB estimator found to be superior in terms of bias and mean square error (MSE) to ELL and Molina and

> 2 Not always in the case of designs for multi-topic household surveys. In these cases, in fact, generally rural/poor areas are oversampled to get enough observations for domain level analysis on these populations – which has the opposite effect (design greater than 1).

9
<!-- Start of picture text --> Effect of Mediator on Violence - Return Relationship<br>wo<br>2<br>oO<br>So<br>: ¢<br>a®—2w9° ’<br>=<br>Ww<br>se<br>ai 9<br>2 * Control<br>? ¢ Treatment<br>© Overall<br>oa<br>N<br>°<br>ACME ADE ATE<br>Effect Type<br><!-- End of picture text -->
Panel c. Kakwani Coefficient and Redistributive Effect <!-- Start of picture text --> Kakwani Marginal contribution to redistributive effect<br>0.25 0.005<br>0.20 0.004<br>0.15 0.003<br>0.10 0.002<br>0.05 0.001<br>0.00 0.000<br>-0.05 -0.001<br>-0.10 -0.002<br>Gas Direct Effects Electricity Indirect Gas Indirect Effects Electricity Direct<br>Effects Effects<br>Kakwani<br>Redistributive effect<br>unequalizing <-------> equalizing<br>Regresive <-----------------------> Progressive<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

**It is worth noting that the size of some fiscal interventions explains their ability to reduce inequality** . For example, indirect subsidies for electricity are less progressive but larger than all direct transfers. Gas subsidies are even less progressive and smaller than electricity subsidies. Due to disaggregation, family and child transfers are highly progressive, but their size is small, limiting their impact. Some transfers like GMI and non-contributory pensions have high progressivity but small size. The heating allowance is also very progressive and has more resources allocated to it. Unemployment and disability have a sizeable individual size and are progressive, so their impact on inequality reduction is significant.

# Incidence of Taxes and Transfers

**Finally, most components of the fiscal system in Bulgaria are progressive, with the poorest being net recipients of social benefits; beginning with the third decile, Bulgarians contribute more to the fiscal system than they receive in cash benefits, resulting in a negative net cash position.** The net cash position, which measures the difference between MIPP and consumable income as a percentage of MIPP, is favorable for the first and second deciles. However, starting from the third decile, Bulgarians become net contributors to the fiscal system, as their tax payments exceed their cash benefits. Therefore, the net cash position is negative for 80% of the population. However, when in-kind benefits such as health and education are included, the net position changes for the bottom half of the income distribution, as they receive more benefits than they pay in taxes. The tax burden is relatively higher for the first decile because direct transfers increase the disposable income, i.e., their consumption capacity. So, even though they pay less indirect taxes as a share of their disposable income than top deciles, the indirect taxes are higher than market income plus pensions. The total net position curve (see annex) shows a decreasing pattern, from the poorest receiving net benefits equivalent to 109.0% of their MYPP to the richest paying about 31% of their income (figure 9).

31