rafmacalaba/datause-encoder-data huggingface dataset:
_Annex 2 Table 6: Alternative estimates of relationship between food prices (ln) and program saturation in nationally representative data-- unit values derived from households in top quartile of per capita consumption-expenditures._

||_M_|_odel with cutoff at sat_<br>Saturation*Above|_uration of 65perc_|_ent_<br>Saturation +<br>Saturation*Above|_Linear Model_|
|---|---|---|---|---|---|
||Saturation|<br>cutoff|Above cutoff|<br>cutoff|Saturation|
|Rice, raw regular||||||
|milled|0.081 ***|-0.238 ***|0.231 ***|-0.158 *|0.096***|
||0.013|0.090|0.069|0.089<br>|<br>0.012|
|Other raw rice and||||||
|grains|-0.167 *|0.495|-0.462|0.328|-0.190**|
||0.100|0.652|0.505|0.639<br>|0.088|
|Roots and tubers|0.248 ***|0.686 *|-0.423|0.934 **|0.308***|
||0.058|0.405|0.311|0.397<br>|0.052|
|Eggs (fresh)|0.088 ***|0.128|-0.051|0.216 *|0.112***|
||0.017|0.113|0.087|0.111<br>|0.015|
|Eggs (processed)|0.099 ***|0.413|-0.277|0.511 *|0.108***|
||0.026|0.300|0.231|0.299<br>|0.026|
|Fish (fresh)|-0.012|0.271 ***|-0.212 ***|0.259 ***|-0.008|
||0.014|0.095|0.073|0.094<br>|0.013|
|Fish (processed)|-0.061|0.642 **|-0.507 **|0.581 **|-0.054|
||0.038|0.293|0.226|0.288<br>|<br>0.034|
|Meats (fresh)|0.015|0.016|0.015|0.030<br>|0.027***|
||0.012|0.083|0.064|0.082<br>|0.011|
|Meats (processed)|1.646 ***|-4.532 *|2.511|-2.887<br>|1.233***|
||0.330|2.689|2.062|2.654<br>|0.304|
|Fruits (fresh)|-0.178 ***|0.117|-0.020|-0.061<br>|<br>-0.144***|
||0.031|0.208|0.161|0.204<br>|0.028|
|Vegetables (fresh)|0.053 ***|0.190 *|-0.078|0.243 **|0.089***|
||0.017|0.110|0.085|0.107<br>|0.015|
|Rice and grains||||||
|processed|0.176 ***|-0.326|0.146|-0.150|0.119***|
||0.049|0.318|0.246|0.311<br>|<br>0.043|
|Coffee, cocoa, tea|0.128 ***|-0.337|0.253|-0.209<br>|0.116***|
||0.042|0.276|0.214|0.270<br>|<br>0.037|
|Sugar|-0.493 ***|0.564 ***|-0.346 **|0.071<br>|-0.436***|
||0.026|0.175|0.136|0.171<br>|0.023|
|Milk Products|0.087|0.394|-0.362|0.481<br>|0.072|
||0.059|0.434|0.331|0.427<br>|0.054|

note: *** p<0.01, ** p<0.05, * p<.1. Standard errors in parenthesis. Each row corresponds to a separate regression. Observations are at the village-food item-year level and pooled across the years 2009, 2012, and 2015. All models include year and village fixed effects. Prices are calculated as unit values, see text for details. Source: 2009, 2012, and 2015 Family Income and Expenditure Surveys and Department of Social Welfare Administrative Data on _Pantawid_ Program Enrollment 77
Expansion and Multi-Tiered Marketing System (1962 to 1974* At independence in 1962, a Southern Region Cashew Nut Board was established with a mandate to stabilize prices and to develop cooperatives in the region.<sup>1</sup> 3 The Board was given sole authority to purchase the cashew crop and to sell it to exporters at auction. The Board divided up the region Into 11 procurement zones, appolnting a 'main buying agent" for each, who in turn appointed "primary buying agents" to deal directly with farmers. Initially, most of these buying agents were the private merchants already active in the trade. However, by 1963, 80% of the crop in the Mtwara and Rumuva Regions was handled by primary cooperative societies at the local level and newly-formed cooperative unions at the secondary level. In that same year, the functions of the SRCB were taken over the Southern Agricultural Products Board. A year later, the latter would be superceded by the National Agricultural Products Board. Thus began the institutional roller coaster which has plagued the cashew nut industry up to the present.

The multi-tiered system operated satisfactorily through much of the mid-to-late 1960's, although the official producer prices set by the government were not adjusted upward. While available records suggest that both crop collection and payments to farmers took place on a timely basis, the cooperatives were apparently not effective in screening product quality. For example, in 1968 the NAPB lost large quantities of nuts due to improper drying and grading at the post-harvest stage. In response to this problem, the Board commandeered the agricultural staff assigned to the southern zone, using them as cashew nut collection observers and graders.

While the pace of new plantings declined, the maturation of existing shambas contributed to continued rapid growth in produdtion and trade. Graph 3 traces the growth in marketed production through to the early 1970s. This production peaked at over 145,000 tons in 1973/74, then accounting for nearly 30% of total world production.

Graph 3: Marketed Cashew Nut Production in Tanzania (Tons) <!-- Start of picture text --> 160_.<br>ISO_<br>'150<br>140 -<br>130 -<br>120-<br>110<br>100<br>so _<br>70 -<br>70 950<br>4019150Z | 1986iS4 | 19V880 |19065/8 1 1SV70 1 1974/72 197iV74<br>1962 63 196465 1966/67 196869 1970/71 197V73<br><!-- End of picture text -->

'<sup>3</sup> This and the subsequent pamgraph are based on Northwood and Kayumbo (1970), Ohler (1979), Annual Reports of Agriculture, and an interview with former cashew researcher A. Tsakaris.

13
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http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK =64165421&menuPK=64166093&entityID=000158349_20080929085154.

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- Harris, Richard (1084), ―Applied General Equilibrium Analysis of Small Open Economies with Scale Economies and Imperfect Comp,‖ _The American Economic Review_ , Vol. 74 (5), December, 1016-1032

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34
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23
_t_ to _t_ + 5. We find a positive and significant impact of entering the international market on adopting more sophisticated technologies for most business functions, with particularly large coefficients for Business Administration, Production Planning, Supply Chain Management, and Quality Control. For instance, after starting to export, establishments tend to have a 13.7% larger propensity of adopting specialized software or ERP for Business Administration, compared to those not exporting. Moreover, in the case of Quality Control, the export status is associated with an 8.9% larger probability of adopting statistical process control with software monitoring and data management or automated systems for inspection. It is also interesting to note that coefficients are positive for all business functions - although not statistically significant in some cases.

Table 3: Effect of exporting on the adoption of advanced technologies for business functions

||(1)<br>Business<br>Administration|(2)<br>Production<br>Planning|(3)<br>Supply<br>Chain|(4)<br>Marketing|(5)<br>Sales|(6)<br>Payment|(7)<br>Quality<br>Control|(8)<br>Fabrication|
|---|---|---|---|---|---|---|---|---|
|ATT|0.137***|0.065**|0.063**|0.035|0.043*|0.025|0.089***|0.008|
||(0.043)|(0.033)|(0.029)|(0.023)|(0.026)|(0.033)|(0.028)|(0.043)|
|N|19,916|19,916|19,916|19,916|19,916|19,916|19,916|2,183|

Note: For each business function, the dependent variable is a dummy equal to 1 from the year the firm *** ** * adopted an advanced technology and 0 otherwise. p _<_ 0.01, p _<_ 0.05, p _<_ 0.1.

Figure 4 panel (a) shows the disaggregated coefficient estimates for Business Administration from _t−_ 5 to _t_ +5 from the event study. The results indicate that during the years before treatment, coefficients are not statistically different from zero, which we interpret as an indication that the parallel trends assumption holds and that there is no anticipation effect. In contrast, following the treatment, we observe a clear positive effect, which increases over time. The results are consistent with a model in which export increases firms’ managerial layers (Caliendo and Rossi-Hansberg, 2012; Garicano and Rossi-Hansberg, 2014). To cope with more complex tasks induced by trade participation, firms raise the number of managers and adopt more sophisticated technologies for business administration. Results are also consistent with the scale effect channel, through which larger demand induces the adoption of new technologies (Bustos, 2011).

We also find similar results for Production Planning, Supply Chain Management, and Quality Control. Coefficients are positive from _t_ to _t_ + 5, without signs of preparation to export. The findings align with the literature showing that firms raise product quality as they enter international markets (Alvarez<sup> ́</sup> and Fuentes, 2011). Export markets carry higher quality requirements, and exporting firms produce higher-quality products by increasing the quality of their inputs and varying the quality of their products across destinations (Kugler 12
consider only the overall effect of all potential sources of distortion on the economy.<sup>7</sup> In contrast, other papers take what is referred to as the ”direct” approach, picking a particular policy or institution and using quantitative models to assess its contribution to the misallocation of resources and its impact on aggregate TFP.<sup>8</sup> This paper contributes to the latter line of research by focusing on the static and dynamic effects of one specific mechanism, bribing, that generates misallocation.

This paper is relevant to Argentina because the country’s weak institutional capacity, high perception of corruption and repeated high-profile corruption scandals in recent years have caused a loss of faith in institutions, which this paper shows to play a key role in enabling and perpetuating idiosyncratic distortions across establishments. More generally, because difficulties in doing business not only affect investment decisions but also generate incentives to obtain private rents, simplifying the regulations around starting and running businesses in order to reduce those incentives is of first-order importance.

In the following section, I present the facts on bribing and development that motivated this research. In section 3, I present the model that will try to account for these facts. In sections 4 and 5, respectively, I present the strategy for calibrating the model and the model’s quantitative results. Section 6 concludes and gives some policy recommendations.

# **2. Data**

The establishment-level data I use to calibrate this model are from the WBES. The data were collected through interviews, typically with managers, aimed at understanding the major obstacles establishments face in their day-to-day operations. A convenient feature of the surveys is that they were administered in a similar form within continents, which allows for cross-country comparisons. The initial rollout of the survey was conducted in 2003, with subsequent and more complete rollouts conducted within a three-to-four-year span. For this paper, I use the data set from 2006–2018 provided by the World Bank. This data set contains firm-level observations for 135 countries, and it includes sections on corruption and regulations, among many other variables traditionally used in firm-level analysis.<sup>9</sup> In particular, establishments are asked whether they have

> 7 The names of these approaches were first introduced by Restuccia and Rogerson (2013).

> 8 For a more exhaustive description of the resource misallocation literature, see Hopenhayn (2014), Jones (2011), Restuccia and Rogerson (2013), and references therein.

> 9 The WBES data on corruption and bribery have reliability problems, but they are the only source of such data available, to my knowledge. Due to their illegal nature, it is difficult to obtain data on corruption and bribery. Data on lobbying activity (a “legal

6
There is huge variation in terms of the changes in violence across municipalities from the start of the “War on Drugs”. Some municipalities with originally high homicide rates, saw a reduction or no change in the homicide rates (our primary proxy measure for violence, although some specifications also use drug-related homicide rates). Among those with originally low violence, some remained relatively peaceful while others saw the homicide rate explode.

**Figure 3: Mean Homicide Rate for Selected Municipalities**

<!-- Start of picture text --> Low Change in Violence High Violence Increase<br>2000 2005 20102000 2005 2010<br>Year<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: INEGI (2014).

Note: In this graph, municipalities are divided into “high violence increase” and “low change in violence” according to their homicide rate trend percentile from 2007 to 2011. Municipalities classified as violent-increasing (471 total) represent 24.9% of our sample while non-violent municipalities (945 totals) represent 51% of our sample.

This huge variation in the paths of homicide rates across municipalities allows us to have enough power to estimate very precisely the effect of violence (as proxied by homicide rates) on education enrollments. Given that these large municipality-specific changes are not likely to have been brought about by the small differences in the trends in educational attainment, it is unlikely that our fixed effects approach will suffer from reverse-causality bias.

11
They were purchased from Berkeley Air Monitoring Group of Berkeley, California. The recording intervals on the SUM are adjustable and the memory on the DS1922T model used can record stove temperature every ten minutes for approximately 60 days and can tolerate temperatures up to 120 degrees Celsius. Logging software is loaded onto a computer and after completing the monitoring period the SUM is inserted into an interface and the temperature, time and date are downloaded for analysis. We define a cooking event as occurring when temperature exceeds 40 degrees Celsius.

Figure 3 Electronic Stove Use Monitor Respondents were shown the SUM device and informed that should they agree to participate, these devices would be placed on the stoves using heat resistant tape and that they would periodically record the surface temperature of the stoves.<sup>3</sup> Fieldworkers also informed participants that stove use is not required to receive a stove, but they are encouraged to use them. Respondents were asked for their formal oral consent and all respondents agreed to participate in the study. There were no refusals and no attrition in the sample.

Source: <u>https://www.measurementsystems.co.uk/images/categories/main/mi niature-data-loggers.jpg</u> The SUM was placed exactly at the back of the stove, because it is the coolest location on the Mirt stove, using heat-resistant tape. It was initially believed that the front of the stove was the coolest and SUM devices were ruined, because their temperature tolerances were exceeded. Because of such malfunctions, the panel data used are not balanced, with the second monitoring period having the fewest observations. While over the four rounds a maximum of 1,440 observations (360 per round) are possible, in practice, because of stove use monitor failures, observations in each round average just over 300, with a total of 1,209 observations across all rounds.

> 3 Fieldworkers informed respondents that the SUM devices are safe at reasonable temperatures, but they are potentially unsafe if they are put in or very close to fires, because they have flammable components inside them.  If SUM fall in the fire, they should be removed immediately and after they are cool replaced on the top of the stove using the heat resistant tape.

**11 |** P a g e
- 50 -

Table 22 Marginal Rate ot Substitution between Growth and inequality Poverty Gap Ratio

|Stt|Growth<br>|Elasticity<br>|||Inequalit<br>|y Elasticity<br>|<br>||
|---|---|---|---|---|---|---|---|---|
|aes<br>|73-74<br>|77-78<br>|83<br>|86-87<br>|73-74|77-78|83|86-87|
|ANDHRA PRADES<br>|0.6<br>|0.8<br>|1<br>|1.1<br>|0.9<br>|1.1<br>|1.4|1.6|
|ASSAM<br>|0.4<br>|0.2<br>|0.4<br>|0.5<br>|0.6<br>|0.4|0.7|0.8|
|BIHAR<br>|0.4<br>|0.4<br>|0.4<br>|0.5<br>|0.6|0.6|0.6|0.7|
|GUJRAT<br>|0.5<br>|0.7<br>|0.8<br>|1<br>|0.8<br>|1|1.2|1.4|
|HARYANA<br>|0.9<br>|1<br>|1<br>|1.2|1.2|1.4|1.5|1.7|
|Jammu-Kashmir<br>|0.4<br>|0.5<br>|0.7<br>|0.9<br>|0.7<br>|0.7|1|1.2|
|KARNATAKA<br>|0.5<br>|0.8<br>|0.9<br>|0.8<br>|0.8|1.1|1.2|1.2|
|KERALA<br>|0.6<br>|0.8<br>|0.9<br>|1.1<br>|0.9<br>|1.2<br>|1.3|1.5|
|MADHYA PRADES<br>|0.5<br>|0.6<br>|0.7<br>|0.7<br>|0.7<br>|0.8|0.9|1|
|MAHARASHTRA<br>|0.5<br>|0.5<br>|0.7<br>|0.7<br>|0.7|0.7|0.9|1|
|ORISSA<br>|0.3<br>|0.4<br>|0.5<br>|0.5<br>|0.4<br>|0.6|0.8|0.8|
|Punjab<br>|0.9<br>|1.4<br>|1.3<br>|1.3<br>|1.4<br>|2.1<br>|1.7|1.9|
|RAJASTHAN<br>|0.7<br>|1<br>|1.1<br>|1<br>|1'|1.3|1.5|1.4|
|TAMIL NADU<br>|0.5<br>|0.6<br>|0.8<br>|0.8<br>|0.7|0.8|1|1.1|
|UTTAR PRADESH<br>|0.4<br>|0.7<br>|0.7<br>|0.8<br>|0.6<br>|0.9|1|1.2|
|WEST BENGAL|0.4|0.4|0.5|0.5|0.6|0.6|0.8|0.8|

two states. Fluctuations in poverty have become more sensitive to changes in growth and inequality in recent years.

The inequality elasticity has increased slightly faster than the growth elasticity in most states. This pattern is quite evident from the increasing values of che marginal proportionate rate of substitution (MPRS) presented in Table 22. This suggests that inequality-reducing policies have an important role to play to help the poor not to lose their share of consumption in the process of growth. It also suggests that greater, not lesser, growth orientation is also required to maintain, if not to improve, the consumption shares of the poor.
Development Indicators database. α j and γt represent country and year fixed effects, respectively. Country fixed effects are important in our estimations as they allow us to capture any other country-specific characteristics that can impact access to finance, such as the quality of the contract laws and their enforcement. Country fixed effects can also capture country specific differences in the demand for loans. Time effects control for the impact of common shocks across countries. Hence, country and time fixed effects help isolate the impact of collateral registry reform from all other country differences and time effects. Table 3 provides data sources and detailed definitions for each of the variables used in our estimations. Table 4 presents summary statistics.

Our primary variable of interest is _Registry Reform_ , which is equal to one for all countries that introduced a collateral registry for movable assets but only for the years after the reform. Thus, the _Registry Reform_ variable can be thought of as the interaction of a dummy for the set of countries that introduced a collateral registry for movable assets during our sample period (i.e. the treatment sample) with a country-specific dummy which identifies the years after reform. Thus, our empirical methodology is akin to a difference-in-difference format, comparing countries with reform and countries without reform, and years pre- and post-reform. Because of the country fixed effects, we do not need to include a separate time-invariant dummy for reform countries.

We conduct different estimations of equation (1) varying the sample of countries included in the regressions. The “treatment sample” (i.e., those 7 countries that introduced collateral registries for movable assets – i.e. the “reform” countries) does not change, but we vary the “control group”. First, we consider all non-reform countries as part of the control group. This includes 59 countries. Second, we consider a matched sample of countries, obtained by 10