rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table 5: Determinants of Fraction Consistent or Fraction Present-biased</u>**

||Fraction c|onsistent<br>Dependent|Fraction pre<br>variable<br>:|sent-biased|
|---|---|---|---|---|
|Male|0.029*|0.026*|0.001|0.002|
||(0.015)|(0.014)|(0.013)|(0.013)|
|Age 35 or under|-0.029|-0.006|0.017|0.008|
||(0.021)|(0.020)|(0.018)|(0.018)|
|36-56 yrs old|-0.021|-0.020|0.009|0.008|
||(0.017)|(0.015)|(0.015)|(0.014)|
|Some primary school|-0.032*|-0.032*|0.031*|0.031**|
||(0.019)|(0.016)|(0.016)|(0.016)|
|Primary school|-0.036|-0.023|0.017|0.012|
||(0.027)|(0.024)|(0.023)|(0.022)|
|More than primary school|-0.068**|-0.069**|0.046|0.046|
||(0.034)|(0.030)|(0.031)|(0.030)|
|Have adequate maize|0.032*|0.027*|-0.007|-0.005|
||(0.018)|(0.016)|(0.015)|(0.015)|
|Baseline wealth (100s of MK)|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Words recalled|0.004|0.002|0.002|0.003|
||(0.006)|(0.005)|(0.005)|(0.005)|
|Raven's Tests Correct|-0.001|0.005|-0.006|-0.009|
||(0.008)|(0.007)|(0.007)|(0.007)|
|Financial Literacy Questions Correct|0.008|-0.003|-0.004|-0.000|
||(0.009)|(0.008)|(0.007)|(0.007)|
|Number of relatives in the village|0.001|0.000|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Adherence to law of demand ratio [0,1]||0.744***||-0.277***|
|||(0.038)||(0.033)|
|Constant|0.456***|-0.143***|0.252***|0.474***|
||(0.032)|(0.041)|(0.028)|(0.039)|
|N|2142|2142|2142|2142|
|Adjusted R-squared|0.00|0.18|-0.00|0.03|

Note: Dependent variable in columns 1 and 2 is the fraction of the five choices pairs that were dynamically consistent. Dependent variable in columns 3 and 4 is the fraction of choice pairs that exhibited present bias. Unit of observation is individuals included in the baseline sample. All allocations made in Jan-Feb 2010.
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assessment of poverty in 1987, relative to 1984. The sampla was stratified spatially. All our calculation have used the local expansion factors (inverse sampling rates) in aggregation.

(vi) The SUSENAS is the only available source of data on household consumption and income in Indonesia which provides national coverage. It is thus essential for constructing consumption or income distributions such as required for convincingly measuring inequality, poverty or undernutrition. There is another important data source for the national aggregates, such as consumption, notably the national accounts. However, the two sources do not generally agree on the aggregates; the SUSENAS typically yields lower estimates of mean consumption than the national accounts (see, for example, Dapice, 1980). The reason for this is unclear. It is thought to be plausible that the SUSENAS undereports consumption, though it can also be argued that this is more likely to be so for the rich than the poor, in which case poverty assessments may still be reasonably accurste.23 Also of concern here is that the SUSENAS implies a higher growth rate of mean consumption per person over the period 1984-87 than do the national accounts; the former source suggests a three year growth rate (at constant prices) of about 15 percent, while the latter implies a much lower figure of about 5 percent. There is no obvious explanation for this disparity. In view of this, it is also important to test how sensitive results are to possible overestimation of aggregate consvmption growth rates (see Section 5).
**Figure A.1. Number of Models with Predicted Estimates of (Log) of Consumption That Are Statistically Insignificantly Different from the True Estimates**

<!-- Start of picture text --> Bangladesh 2015-2018/19 Malawi 2016/17-2019/20<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75th 90th 95th<br>Percentiles Percentiles<br>Nigeria 2012/13-2018/19 Tanzania 2019/20-2020/21<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75th 90th 95th<br>Percentiles Percentiles<br>Vietnam 2014-2016<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>5th 10th 25th 50th 75th 90th 95th<br>Percentiles<br>Number of models Number of models<br>Number of models Number of models<br>Number of models<br><!-- End of picture text --> 58
# **3.1. BAU baseline over 2023-2050.**

Under a baseline or business-as-usual growth path, Peru’s potential GDP growth would slow down from 2.1 percent in 2024 to around 1.4 percent by 2050 (Figure 8). The BAU baseline measures the potential growth rate of the economy and is not a forecast of actual growth. The recent forecast of GDP growth and GDP per capita growth (dotted lines in Figure 8) are slightly above the potential growth as it incorporates the short run expectations about investment and trade balance effects due to starting operations of Quellaveco mine, one of the top ten copper mines in terms of proven reserves. However, short run deviations will not have much effect on the long-run growth potential that is the focus of this paper. This section explains the drivers of current growth, and the reasons for the declining trend. Under this baseline simulation, Peru will achieve HI status in 64 years.

Figure 8. Peru’s BAU Baseline GDP and GDP PC Growth <!-- Start of picture text --> 3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>HL GDP growth LTGM GDP PC growth LTGM<br>HL GDP growth WB projections GDP PC growth WB projections<br>Source: LTGM simulations, World Bank Staff’s calculations<br>2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050<br><!-- End of picture text -->

# **What explains current trend growth rates?**

Investment in the non-resource sector and non-resource TFP growth are the largest drivers of current economic growth, followed by a declining contribution of labor. Current growth can be decomposed using log-linear approximation of the production function. Investment in the non-resource sector is the most important growth driver in different periods of the simulation, with a contribution around 0.9ppts, which is almost half of total growth (Figure 9). The next largest contribution is from TFP growth of non-resource sector, with a contribution of 0.5ppts, with labor force growth of 0.3ppts.

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preferences, _how_ these changes translate into investment decisions is then likely to be influenced by the same set of economic, political and institutional factors.

We are primarily interested in identifying the factors that explain the increase in expenditures between pre- and post- _Pilkada_ periods in the districts that belong to the treatment groups most relevant for our analysis – districts that held direct elections in 2005. However, for the purpose of comparison, we also report the estimates of the same regression on the sample of the districts that did not have direct elections until 2008 and later, i.e. the districts used as a comparison group thus far. The explanatory variables used in the analysis are obtained from the Village Potential series (PODES) and the National Socio-Economic Survey (SUSENAS) both administered by Central Bureau of Statistics‘ (BPS). The PODES is a village census that collects detailed information every three years on a range of characteristics – ranging from infrastructure to village finance – for all of Indonesia‘s villages and neighborhoods (about 69,000). The survey is implemented by sub-district level statistical agents, who work for BPS, and information is typically provided by the village heads and neighborhood heads. The SUSENAS survey is an annual household survey covering around 205,000 households from all over Indonesia that is representative at the district (Kabupaten) level. The survey collects household level data, such as household age and gender composition, consumption expenditures in major food and non-food categories, as well as individual level data, including school attendance and educational attainment, use of health facilities, participation in the labor market, earnings and fertility-related questions for women.

The dependent variables in the regressions are the _difference between post- and pre- Pilkada_ expenditures (total, development and routine) for education, health and all sectors combined and budget deficit for all sectors combined.<sup>24</sup> Education and health sectors are considered separately given that these represent key public services that are likely to matter for human development outcomes. Even though no treatment effect (of direct elections) is found for health expenditures, these are still important to analyze separately since average health expenditure for all districts increased between pre- and post- _Pilkada_ periods (see Figure 4 and Table 1). We do not attempt to conduct a separate analysis for other sectors (e.g. transport, agriculture, housing) since information on the pre-decentralization status of public services in these sectors is not available.

> 24 Actually, the dependent variable is (expenditures in 2006 - average expenditures during the period 2001-05).

25
(i.e., the countries from which migrants send money) as instruments for the remittances flows received by the countries in our sample.

Economic conditions in the remittance-source countries are likely to affect the volume of remittance flows that migrants are able to send, but are not expected to affect financial development in the remittance receiving countries in ways other than through its impact on remittances or through the effect on other variables we already control for like exports or capital flows. Because bilateral remittance data are largely unavailable, we identify the top remittancesource countries for each country in our sample, using bilateral migration data from the OECD’s _Database on Immigrants and Expatriates._ This dataset identifies the top five OECD countries that receive the most migrants from each remittance-recipient country.<sup>12</sup> Here we assume that these OECD countries receive the bulk of the migrants from the countries in our sample and account for the majority of the remittance flows sent to the countries in our sample. We construct three instruments by multiplying, respectively, the GDP per capita, the real GDP growth, and the unemployment rate, in each of the top five remittance-source countries by the share of migration to each of these five OECD countries.<sup>13</sup>

# **III. Empirical Results**

Table 3 reports FE estimates of equation (1) for the share of deposits and credit to GDP, assuming that remittances are exogenous and adequately measured. In all regressions we control for the log of GDP, the level of GDP per capita, the inflation rate, the presence of dual exchange rates and for the extent of current and capital account openness. Because the variable capturing

> 12 http://www.oecd.org/document/51/0,2340,en_2825_494553_34063091_1_1_1_1,00.html.

> 13 Note that the bilateral migration data is only available for 2000, so the weights we use are constant. The time variation arises from the series on the GDP per capita, real growth rate, and unemployment rate in remittance-source countries.

14
**Table 7:  Consumption Outcomes; Poor Households Only Sample and Households Interviewed September-March**

||**Log To**|**tal Va**|**lue of Food**|**Log Tota**|**l Value of Food**|
|---|---|---|---|---|---|
||IHPS||FIAS|IHPS|FIAS|
|**Poorer HH Sample**||||||
|Current Shock|-0.018||-0.045|0.012|-0.112|
||(.054)||(.106)|(.097)|(.186)|
|Sequential Shock|-0.217|*|-0.348<br>**|-0.425|*<br>-0.433|
||(.112)||(.158)|(.258)|(.304)|
|Aid|-0.205||-0.216|-0.207|0.000|
||(.174)||(.221)|(.360)|(.442)|
|Aid * Current Shock|-0.029||-0.159|-0.780|*<br>-0.733|
||(.217)||(.263)|(.458)|(.546)|
|Aid * Sequential Shock|0.564|*|0.892<br>***|0.913|0.394|
||(.288)||(.307)|(.569)|(.503)|
|# of Obs.|2135||1158|2135|1158|
|R2 (within)|0.269||0.257|0.256|0.225|
|R2 (between)|0.434||0.404|0.388|0.364|
|R2 (overall)|0.389||0.370|0.340|0.320|
|**Sept-March Sample**||||||
|Current Shock|0.000||-0.090|-0.032|0.049|
||(.045)||(.098)|(.074)|(.156)|
|Seq. Shock|-0.171||-0.307<br>**|0.010|-0.096|
||(.113)||(.144)|(.148)|(.209)|
|Aid|0.105||-0.311|0.572|0.148|
||(.200)||(.267)|(.378)|(.463)|
|Aid * Current Shock|-0.485|**|0.147|-1.081|*<br>-0.423|
||(.243)||(.291)|(.554)|(.540)|
|Aid * Sequential Shock|0.413||0.874<br>***|-0.376|-0.002|
||(.288)||(.309)|(.450)|(.504)|
|# of Obs.|1968||1068|1968|1068|
|R2 (within)|0.323||0.215|0.310|0.262|
|R2 (between)|0.598||0.524|0.513|0.331|
|R2 (overall)<br>**Notes:*****/**/* denote statistica<br>**Current Shock**is a dichotomous va|0.549<br>l significance at 1/<br>riable that is equal|5/10 p<br>to 1 if|0.411<br>ercent respectively<br>the household has|0.495<br>. Standard Er<br>been expose|0.331<br>rors in parentheses.<br>d to a weather shock|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

40
effects of the interaction between _Rel_restrictiveness_ and either the importance of the product or the destination market for firms’ total agricultural exports are negative but insignificant. Therefore, firm size is the channel by which the relative restrictiveness of pesticide standards in the importing country affects the firms’ decision to stop exporting to a product-destination market.

# **6. Robustness Results**

In this section we describe a number of robustness checks that we performed to our results. We re-estimated the export participation, entry, and exit regressions including interactions with firm size for three variants of the sample. The first set of regressions whose results are shown in columns (1)-(3) of Table 11 restricts the sample of importing countries and exporting countries to be only those that draft specific MRL regulations, that is, the sample excludes the countries that use solely Codex standards as their own regulations. The effect of the importing country’s relative restrictiveness of standards on firms’ export participation and entry into new destinations remains negative and significant while the effect on firms’ exit is positive and significant. The coefficient on the interaction between _Rel_restrictiveness_ and firm size is significant in both the entry and exit specifications suggesting that the effect of pesticide standards on firms’ export decisions depends on firm size. Smaller firms are more negatively affected by the relative stringency of importing country standards than larger firms. The second set of regressions whose results are shown in columns (4)-(6) of Table 11 restricts the sample in terms of the years covered, keeping only years 2008-2012 and thus addressing the fact that in 2006 and 2007 there is missing standards data for a few destinations including the EU, as seen in Table 2. Again, the results show that the relative restrictiveness of standards of the importing country negatively impacts a firm’s decision to export and firm size determines how much the standards affect the firm’s decision. The third set of regressions whose results are shown in columns (7)-(9) of Table 11 restricts the sample of products to those belonging to HS chapters 7 and 8 (edible vegetable and edible fruits). The match on MRL standards obtained from Homologa database was better for these two HS chapters. Again all results are qualitatively maintained.

We conduct one final robustness check in which we include additional sets of stringent fixed effects in order to control for unobservables that might not be fully controlled for in our 25
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<!-- Start of picture text --> mmm Price mmm MedicalExp. mam YLL Net effect<br><!-- End of picture text -->
Different methodologies imply different aggregation levels. While assessing the finance and growth relationship on a more disaggregated level might allow better controlling for different biases – such as measurement error when considering a specific policy change on the sub-national level or simultaneity bias when using household data in a controlled randomized experiment – this has to be balanced with the limited extent to which we can draw policy conclusions from such a specification. Further, using firmlevel or household level data does not properly control for spill-over effects, are often very costly exercises, and do not lend themselves easily to compute the aggregate growth effect of financial development. While randomized experiments have the advantage that they are the cleanest exercise possible, as they are controlled by researchers, they might not properly mimic the real world, and might not allow inferences outside the geographic and institutional experiment area.

While a wide array of cross-country techniques has been applied to the finance and growth field, some techniques have not been used yet, such as identification through heterogeneity in structural shocks (Rigobon, 2003). Further, it is easy to predict that there will be further advances in GMM techniques that control better for country heterogeneity and in techniques to assess the finance and growth relationship at different frequencies. As before, the finance and growth literature will benefit in the years to come from methodological advances in neighboring fields, especially in growth econometrics. Merging VAR and cross-country techniques – two literatures which have moved mostly parallel to each other up to now – also promises further methodological insights.

More important than these advances at the aggregate level, however, will be advances at the micro-level, and specifically on two fronts. First, randomized experiments involving both households and micro- and small enterprises will shed light on the effect of access to finance on household welfare and firm growth. One of the challenges to overcome will be to include spill-over effects and thus move beyond partial equilibrium results to aggregate results. Second, further studies evaluating the effect of specific policy interventions can give insights into which policy reforms are most effective in enhancing financial development and positive real sector outcomes.<sup>37</sup>

> 37 One example assessing the effect of different legal reforms is Haselmann, Pistor and Vig (2005).

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**Table 4: Heterogeneous Effects by Absolute and Relative Earning Potential within Household**

|Dependent variable|1(Work)|Days<br>worked|Hours<br>worked<br>|1(Work)|Days<br>worked|Hours<br>worked|
|---|---|---|---|---|---|---|
|Other worker ill|0.052|0.078|1.557|0.068|0.225|2.533|
||[0.042]|[0.291]|[2.495]|[0.048]|[0.288]|[2.111]|
|Other worker ill X Female|‐0.058|0.039|‐2.632|‐0.122|‐0.477|‐3.433|
||[0.066]|[0.337]|[3.127]|[0.075]|[0.363]|[3.289]|
|Highest baseline earnings in household|0.083|0.760|7.549*||||
|X Other worker ill|[0.062]|[0.463]|[4.347]||||
|Highest baseline earnings in household|‐0.202*|‐1.389*|‐7.254||||
|X Other worker ill X Female|[0.104]|[0.719]|[7.377]||||
|Highest usual hours of work in household||||0.054|0.489|6.032|
|X Other worker ill||||[0.068]|[0.512]|[5.760]|
|Highest usual hours of work in household||||‐0.024|‐0.103|‐6.212|
|X Other worker ill X Female||||[0.113]|[0.657]|[7.469]|
|Net Effect on a Male w/ Highest Earn/Hours|0.135|0.838|9.106|0.122|0.713|8.565|
|P‐value|0.007|0.007|0.006|0.009|0.069|0.068|
|Net Effect on a Female w/ Highest Earn/Hours|‐0.124|‐0.511|‐0.780|‐0.024|0.134|‐1.079|
|P‐value|0.081|0.259|0.875|0.706|0.607|0.950|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,951|3,948|3,948|3,951|3,948|3,948|
|R‐squared|0.507|||0.507|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Usual earnings and usual hours of work taken from reports in baseline survey. All specifications include individual fixed effects. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
Sub-Hypothesis 3: Technology transfered by the private sector has a significant positive impact on producer and/or consumer incomes.

If research supports these hypotheses, it makes sense for donors and governments to promote regulatory reforms. Since regulatory reforms cost little to implement, this would not at all undermine other programs and strategies for agricultural growth, such as public sector research and extension.

# **1.2** Country Study to Test Hypotheses

Fortunately, research can be designed to test these hypotheses. Over the last several decades, governments of developing countries have demonstrated a wide range of regulatory strategies. Some countries, such as Thailand, have allowed private companies to introduce new agricultural technology with minimal interference. Others have extended input regulations beyond externalities into other issues, such as technology performance, price, and protection for domestic input industries.

Over the last 15 years, a small but growing number of countries have eased regulations to facilitate private introduction of new agricultural technology. A partial list of reforming countries includes Chile from the 1970s, Turkey from the early 1980s, and Bangladesh and India from the late 1980s. These countries present opportunities to test the hypothesis that easing regulations has an impact on technical change, productivity, and incomes. This country case study looks at Turkey, a country with extensive regulatory reformns for which impact can be observed over more than a decade.

# **1.3 Pre-Reform Agriculture in Turkey**

Turkey lies the same distance from the equator as Spain, northern China, Japan, Kansas in the US, and southern Argentina. The country offers a wide range of climates that supports a varied agriculture. Along the Mediterranean coast, hot dry summers and mild winters support citrus and other fruit production, while on the Anatolian Plateau in the center and east of the country, cold winters with little precipitation favors grain and pastures. With its relatively high latitude and wide range of climates, Turkey has been able over the years to adopt plant varieties, animal breeds, and other technologies from the US and Central and Western Europe as well as from Mediterranean and sub-tropical countries.

After WWII, Turkey shifted rapidly from an agriculture-dominated economy to a mixed economy. Around 1950, agriculture in Turkey employed 80 percent of the labor force, produced 50 percent of GNP, and supplied 90 percent of exports. By 1980, agriculture's share of labor was near 60 percent, while share of GNP had fallen below 25 percent; agriculture continued to produce two-thirds of total exports (MARA 1968, pp 11, 107; SIS 1977, p 166; SIS 1985, p 210; GATT 1994, vol 1, p 21).

Tractors and Fertilizers (Table 4.1): From WWII to 1980, despite government controls on technology transfer, government allowed and sponsored enough new technology to support agricultural growth, though technology and growth was uneven across crops and products. For

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