rafmacalaba/datause-encoder-data huggingface dataset:
and the domestic value added ratio (DVAR) in exports is defined as the share of DVA in exports: However, the domestic materials used in the production of exports may contain foreign contents, which should not be included in DVA. In contrast, the imported materials used in the production of exports that may contain domestic contents, which should be included in DVA. We define the foreign content embedded in domestic materials used for exports, and the domestic content embedded in imported materials used for exports, as _δ_<sup>_F_</sup> and _δ_<sup>_D_</sup> , respectively, expressed in terms of the shares of exports for both. Thus, the DVAR definition is subject to the following adjustment: Wang et al. (2013) provides estimates for foreign content embedded in total domestic production and domestic content embedded in total imports, both estimates can be used to infer _δ_<sup>_F_</sup> and _δ_<sup>_D_</sup> .11 There are two trade regimes in China, processing trade and ordinary trade (non-processing trade).12 Legally, the processing firms are required to use all their imports as imported intermediate inputs for exports, implying that _κ_ = 1, and thus: In contrast, for the ordinary or non-processing exporters, only a fraction of their imports, 0 _≤ κ ≤_ 1, will be used as inputs for the export production, given that these firms also produce for the domestic markets. To construct the DVAR for these firms, proper estimates for _κ_ are needed. Kee and Tang (2016) relies on industry census data for total material and total revenue at firm level to infer _κ_ for each firm. However, while customs transaction data is often accessible, matching it with less accessible industrial census data can be challenging, which limit the broader applicability of their method.13 To address this difficulty, we estimate _κ_ based on (2), allowing for a more direct calculation without requiring matched census data:

> 11Given that not all domestic materials are used in the production of exports, to obtain the part of foreign content embedded in domestic materials used for export, we multiply the estimates with _κ_ , which is the share of imports used for exports. The assumption is that the share of foreign content embedded in domestic materials used for exports and the share of imports used for exports are the same. Likewise for domestic content embodied in imports, given that not all imports are used for the production of exports, to obtain the domestic content embodied in imports that are used for exports, we multiply the estimates with _κ_ .

> 12Please refer to Branstetter and Feenstra (2002) for detailed discussions about the trade regimes of China. 13Please refer to Brandt et al. (2017) for a discussion on merging difficulties, particularly for the post-2007 period.

9
significantly, suggesting that the latter effect dominates. While not statistically significant, the sign of this correlation calls into question the institutional emphasis placed by the World Bank on disbursement delays.

As noted above, the potential problem flag is raised only if three or more subsidiary flags are raised during the ISR process. In columns (2), (4), (6) and (8) we investigate in more detail which of these individual flags matter most for project outcomes Slightly more than half of the 52 reported coefficients (13 flags times four specifications) turn out to be negative, consistent with the idea that early adverse flags are associated with worse project outcomes. However, only in a few cases are these negative partial correlations statistically significant. One striking example is the flag indicating concerns with the monitoring and evaluation components of projects ( _FLAGMONEVALH1_ ). This finding highlights the crucial importance of strong monitoring mechanisms in the design and implementation of projects. Among IDA projects, the Country Record flag ( _FLAGCTRYRCRDH1_ ) is also significantly negative. This is a country-level flag that is triggered if disbursements on unsatisfactory projects represent more than 20 percent of total disbursements to the country over the previous five years. This suggests strong inertia in the quality of projects over time, even conditional on the contemporaneous overall policy environment as captured by the CPIA.

A somewhat puzzling finding from the disaggregated flags is that the flag indicating concerns about the overall country environment ( _FLAGCTRYENVH1_ ) is significantly positively correlated with project outcomes. This could be due to the fact that we have already controlled for the overall policy environment using CPIA. A final noteworthy observation about the disaggregated flags is that the “golden flag” ( _FLAGGOLDENH1_ ) is not significantly positively correlated with project outcomes. Recall that it can be used at the discretion of Bank management to override other flags. These results could reflect the balance of two opposing forces. On the one hand, if a golden flag is only deployed if there are additional concerns with project implementation, then this selection effect would lead to lower average outcome ratings. On the other hand, a golden flag may be used to signal that identified implementation problems are not sufficiently severe to lead to unsatisfactory project outcomes.

# **5.  Countries, Projects, and Task Managers**

# _Good Countries or Good Projects?_

A key contribution of this paper is its consideration of a broad set of project-level correlates of project success. This contrasts with much of the existing literature, which has primarily emphasized 18
head and tail outlets) is only 9 kilometers, which limits the extent of spatial variation in land quality. On (2), there is no theoretical or evidentiary basis for the returns to scale varying with land quality, although it is certainly a possibility. On (3), consolidation could be achieved in part by one brother jointly managing his other brothers’ land, keeping _family_ landholdings (and, hence, log _θ_ ) constant or, similarly, through tenancy markets (leasing or sharecropping), which are quite active in Pakistan. Note, as well, that according to the 2010 Agricultural Census, farmland purchases in Punjab over the previous 10 years amounted to just 1.4% of total landownership.

These doubts aside, the differential returns to scale argument implies that the number of cultivators _per acre_ must be lower where land is more productive. Our survey records how many cultivators (inclusive of pure tenant households) are at each outlet, from which we can compute, except for two channels with missing values, the head-tail _difference_ in the (log) number of cultivators per acre CCA, ∆logNC/acre. There is a strong negative — association between log _θ_ and ∆logNC/acre (see Appendix Figure C.4) not surprisingly, where average landholdings are larger there tend to be fewer cultivators per acre. However, when we include ∆logNC/acre in our land value regressions (Table 6), its coefficient is insignificant, which suggests that returns to scale are _not_ correlated with unobserved land productivity differentials. Moreover, the estimates of _β_ 1 barely change. In sum, we find support for our contention that the arrow of causation runs from differential influence to land values, not the other way around.

# **5.5 Implications for inequality**

Does variation in lobbying power exacerbate wealth inequality? We have already seen that such variation can either reinforce or dampen wealth differences along a _channel_ depending on whether head or tail outlets have greater influence. Moreover, as indicated in Figure 7, head and tail outlets have nearly equal lobbying power on the average channel (i.e., mean 37
The Journal of Economic Perspectives, 9 (4): 175-192.

Nadelmann, Ethan (2007). “Legalize It: Why it’s time to just say no to prohibition.” Foreign Policy, September/October, 24-30.

- Pacula, Rosalie Liccardo (2008).  “What research tells us about the reasonableness of the current priorities of national drug control.”  Testimony presented before the House Oversight and Government Reform Committee, Subcommittee on Domestic Policy (March 12). http://rand.org/pubs/testimonies/CT302/

- Pudney, Stephen (2003). “The Road to Ruin? Sequences of Initiation to Drugs and Crime in Britain.” Economic Journal, 113(486), C182-C198.

Reinarman, Craig (2000). “The Dutch Example Shows that Liberal Drug Laws can be Beneficial.” In: Scott Barbour (ed.). Drug Legalization: Current Controversies. San Diego, Greenhaven Press, 102-108.

- Reuter, Peter (2001). “The Limits to Supply Side Drug Control.” Milken Institute Review, First Quarter, 14-23.

- Reuter, Peter, K. Jack Riley, Justin L. Adams, Susan S. Everingham, Robert Klitgaard, J.T. Quinlivan, With Kamil Akramov, Scott Hiromoto, Sergej Mahnovski (2004). “Mitigating the Effects of Illicit Drugs on Development.” Rand, Public Safety and Justice, Project Memorandum (PM-1645-PSJ-2).

- Robinson, Terry E. and Kent C. Berridge (1993). “The Neural Basis of Drug Craving: An Incentive-sensitization Theory of Addiction.” Brain Research Review, 18(3), 247-291.

- Sevigny, Eric and Jonathan P. Caulkins (2004).  “Kingpins or Mules?  An Analysis of Drug Offenders Incarcerated in Federal and State Prisons.” _Criminology and Public Policy_ 3:3, 401-434.

- Shui, Haiyan and Lawrence M. Ausubel (2005). “Time Inconsistency in the Credit Card Market.” Unpublished manuscript, University of Maryland.

- Smith, Peter H. (2005). “The Political Economy of Drug Trafficking.” Unpublished presentation, University of California at San Diego.

- Soares, Rodrigo R. (2006). “The Welfare Cost of Violence across Countries.” Journal of Health Economics, 25(5), 821-846.

Substance Abuse and Mental Health Services Administration (2006). “2005 National Survey on Drug Use and Health.” Rockville, MD: Office of Applied Studies, US Department of Health and Human Services (September).

The Economist (2008a). “Lexington: The Hypocrites’ Club.” March 15th, 2008.

The Economist (2008b). “Marching as to War.” February 2nd, 2008.

23
higher (42.5 percent), its poverty gap is lower (25 percent) than Russia's. Hungary and Poland show the most favorable situation with respective poverty rates of 21 percent and 23 percent. The poverty gap is slightly higher in Hungary (14.1 percent) than in Poland (13.3 percent). It thus appears that poverty in Eastern Europe is much more shallow than in FSU, which is good news from the point of view of poverty alleviation in Eastern Europe. It suggests that as economic growth resumes, rising incomes may rapidly lift many people above the poverty line.

Table 1: Poverty and Locality

|_Locality_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
||||Headcoun|t (P0, in pe|rcent)||
|Capital|17.5|20.3|10.1|20.6|22.9|18.2|
|OtherCities|20.5|17.7|16.9|31.6|38.0|38.4|
|UrbanSubtotal|19.9|18.5|16.2|27.5|33.3|35.8|
|Rural|39.2|24.0|33.8|38.7|47.2|49.6|
|Total|26.1|20.6|23.0|30.5|42.5|39.4|
||Poverty|Gap (in pe|rcent) 1/||||
|Capital|19.9|13.9|13.4|19.7|24.0|20.7|
|Other Cities|17.8|13.5|12.7|19.2|26.2|28.7|
|UrbanSubtotal|18.1|13.6|12.7|19.4|25.7|28.1|
|Rural|21.7|14.6|13.8|21.9|24.7|33.2|
|Total|19.8|14.1|13.3|20.2|25.0|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _Notes: ._ 1/ The poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

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# <u>The role of the banking system</u>

In the past several years, the banking system underwent significant transformation with regards to its market and ownership structure, the volume and array of its activities, and its performance. These changes enriched the spectrum of products and services offered, expanded the banking network, and stimulated the development of business practices and managerial skills in the banking system. Thus, the banking system gradually but steadily has been strengthening its role as an economic life bloodstream, visibly developing its role and significance in the overall economy.

**<u>Table 2. Growth of banks' assets versus GDP growth</u>**

|**Indicator **|**2004 **|**2005**|**2006**|**2007 **|**2008**|**2009**|**2010**|
|---|---|---|---|---|---|---|---|
|Nominal annualgrowth rate of assets|12.5%|19.0%|24.0%|28.5%|12.1%|7.1%|13.7%|
|Nominal annualgrowth rate of GDP|5.5%|8.3%|8.5%|14.0%|12.8%|-0.6%|3.6%|
|Annual rate of inflation*|-0.4%|0.5%|3.2%|2.3%|8.3%|-0.8%|1.6%|
|Real annualgrowth rate of assets**|13.0%|18.4%|20.1%|25.6%|3.5%|8.0%|11.9%|
|Real annualgrowth rate of GDP|4.6%|4.4%|5.0%|6.1%|5.0%|-0.9%|0.7%|
|Absolute value of the ratio between<br>nominal annual growth rate of total assets<br>and GDP|2.29|2.30|2.83|2.03|0.94|11.15|3.79|
|Absolute value of the ratio between real<br>annualgrowth rate of total assets and GDP|2.82|4.19|4.03|4.19|0.70|8.87|17.04|

* Average, on cumulative basis (Consumer Price Index – CPI) ** Calculated using the annual rate of inflation

**Chart 3. Level of financial intermediation**

<!-- Start of picture text --> 80.0% 72.0%<br>65.6%<br>70.0% 61.3% 60.9%<br>60.0% 54.4%<br>50.3%<br>50.0% 43.3% 47.6% 43.9% 43.9% 45.9%<br>38.5%<br>40.0% 33.2%<br>30.2% 40.8% 42.5% 44.0%<br>30.0%<br>34.2%<br>20.0% 28.1%<br>23.3%<br>21.1%<br>10.0%<br>0.0%<br>2004 2005 2006 2007 2008 2009 2010<br>Total assets / GDP Total loans / GDP Total deposits / GDP<br><!-- End of picture text --> Source: NBRM The growth of the level of financial intermediation confirms the increased role of the banking system in the overall economy (Table 2 and Chart 3). The ratio of total banking system assets to GDP was 43.3 percent at the end of 2004 and rose to 72 percent at the end of 2010. The increased level of financial intermediation is also evident through the continuous growth of the ratios of total loans and total deposits to GDP. The ratio of total loans to GDP grew at a faster pace than did growth in deposits. Still, the ratio of total deposits to GDP is somewhat higher. At the end of 2010 it reached 50.3 percent as opposed to the ratio of total loans to GDP of 44 percent. Despite the dynamic growth over the past several years (with exception of 2008 and 2009, when the global financial crisis influenced the domestic economy the most), the banking system still belongs 5
Table 10. Macroeconomic and fiscal indicators during government debt booms <u>(completed vs. ongoing booms)</u>

||World<br>C|Advanced<br>economies<br>ompleted boo|EMDEs<br>ms|World<br>O|Advanced<br>economies<br>ngoing boom|EMDEs<br>s|
|---|---|---|---|---|---|---|
|**Macroeconomic outcomes**|||||||
|Output growth|2.4|2.2|2.6|1.3**|0.9***|2.1|
|(Percent)|[0.5, 4.0]|[0.4, 3.2]|[0.6, 4.1]|[0.1, 3.3]|[0.1, 1.4]|[0.0, 3.6]|
|Investment growth|1.8|1.0|2.7|0.8|0.1|1.0*|
|(Percent)|[-1.7, 5.3]|[-1.8, 2.6]|[-1.7, 6.9]|[-1.6, 3.2]|[-1.5, 1.4]|[-2.3, 4.1]|
|Consumption growth|2.3|1.9|2.8|1.8*|0.6***|2.9|
|(Percent)|[0.4, 4.2]|[0.5, 3.1]|[0.4, 4.8]|[0.3, 3.4]|[-0.9, 1.2]|[0.4, 3.9]|
|**Debt sustainability**|||||||
|Primary balance, initial|0.4|1.0|0.2|0.4|1.5|-0.2|
|(Percent of GDP)|[-1.4, 2.8]|[-0.7, 2.7]|[-1.9, 2.8]|[-0.9, 1.8]|[-0.1, 2.4]|[-1.0, 1.7]|
|Primary balance, changes|-0.2|-0.3|-0.2|-0.8***|-1.0***|-0.7***|
|(Percentage points of GDP)|[-0.8, 0.1]|[-0.9, 0.0]|[-0.8, 0.2]|[-1.4, -0.4]|[-2.4, -0.7]|[-1.3, -0.3]|
|Sustainability gap, initial|1.5|1.2|2.3|1.8|3.1|1.7|
|(Percent of GDP)|[0.1, 4.7]|[0.1, 3.4]|[0.1, 5.1]|[0.4, 3.7]|[0.8, 4.4]|[0.1, 3.0]|
|Sustainability gap, changes|-0.4|-0.4|-0.4|-1.6***|-1.4***|-1.7***|
|(Percentage points of GDP)|[-1.4, -0.2]|[-1.4, -0.2]|[-1.7, 0.0]|[-3.3, -0.9]|[-3.9, -1.0]|[-3.1, -0.9]|
|Interest rate-growth differential, initial|-1.8|-0.7|-5.1|-3.1|-2.3*|-4.1|
|(Percentage points)|[-6.8, 1.6]|[-2.5, 1.8]|[-17.2, 0.2]|[-6.4, -0.7]|[-3.8, -0.7]|[-8.5, -0.4]|
|Interest rate-growth differential, changes|0.3|0.2|0.6|1.4***|1.0***|1.8**|
|(Percentage points)|[-0.4, 1.5]|[-0.3, 0.9]|[-0.6, 2.6]|[0.6, 3.4]|[0.5, 1.8]|[1.0, 4.3]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), of macroeconomic and debt sustainability indicators across completed government debt booms and ongoing booms in respective country groups. Indicators on macroeconomic outcomes are annual averages during debt booms. In indicators on debt sustainability, initial and changes refer to, respectively, the level at the beginning, or trough, of debt booms and average annual changes during debt booms. *, **, and *** denote that medians are statistically significantly different between completed and ongoing booms in respective country groups at the 10 percent, 5 percent, and 1 percent levels, respectively.

30
climate change on African agriculture. The Structural Ricardian technique is used to explain farmers’ choice of different farming types and their subsequent conditional net revenues. Special care is taken to allow this choice to be endogenous and to remove sample selection biases. The model is estimated from the data obtained from individual farmers across 10 countries in Africa.

The results reveal that farmers currently choose farm types depending on climate and other exogenous variables such as soils, water flows, household characteristics, and prices. Higher temperatures encourage farmers to adopt mixed farming and avoid specialized farms such as crop-only farms or livestock-only farms. Increases in precipitation induce farmers to rely on rainfed versus irrigated crops.

The analysis of conditional net revenues supports the results from the choice model of farm types. As temperatures warm, farm net revenues from crop-only farms or livestockonly farms fall whereas net revenues from mixed farms increase. With precipitation increases, farm net revenues from irrigated farms fall and net revenues from rainfed farms increase. These changes in net revenues encourage farmers to change their choice of farm type.

The model is then used to simulate how climate might affect future farm type choices and conditional net revenues. Different climate scenarios are explored to reveal a plausible range of outcomes by 2060. With the mild wet PCM climate scenario, Africa is predicted to have more rainfed farms and less irrigated farms and livestock-only farms. Under the very hot and dry CCC climate scenario, farmers are expected to choose mixed farms more often and crop-only irrigated farms or livestock-only farms less often. The results from the CCSR scenario are similar to those from the PCM scenario except that there are smaller changes in mixed farms. The net revenues generated by each farm type would shift in a comparable fashion. Under the PCM scenario, the net revenues of rainfed crop-only farms would increase causing farmers to shift to crop-only rainfed farming. There is also a large shift from mixed irrigated to mixed rainfed farms in the PCM scenario. With the CCSR scenario, there is still an increase in crop-only rainfed farms but the changes in mixed farms are smaller. In contrast, under the CCC scenario, there is a decrease in the net revenues for all farm types except mixed rainfed farming causing 13
dressing the interplay between compliance and gender remain scarce (López-Luzuriaga and Scartascini, 2023; Awasthi et al., 2023). Our analysis aims to fill this gap by analyzing gender-specific responses to tax enforcement measures, thereby contributing to a more nuanced understanding of effective tax administration strategies.

# **5.2.1 Experimental design**

We estimate the effects of personalized tax letters on current and subsequent property tax payments for women and men by leveraging a large-scale field experiment conducted by Cruces et al. (2023) in _Tres de Febrero_ in October 2020. The intervention consisted of sending a one-page personalized letter to randomly selected residences with information on the October 2020 bill (amount due and due date), past due debt, and how to pay online or in person.<sup>7</sup> It was originally designed as a partial population experiment to detect the presence of social interactions (spillovers) in tax compliance.

Randomization took place in two stages—first at the street-block level, and then at the taxpayer (i.e., property) level.<sup>8</sup> In the first stage, Cruces et al. (2023) randomly divided the sample of 3,982 street-blocks (clusters) into four groups with increasing treatment intensity (with 0%, 20%, 50%, and 80% of the accounts treated). In the second stage, they randomly selected about 25,000 accounts within the last three saturation groups to receive the treatment letter. To keep things simple, in this paper, we ignore the saturation groups and simply compare property tax payments between taxpayers who received a letter (treated) and those who did not (untreated).

The timeline of the intervention is displayed below. The letters were delivered between September 28th and October 7th, 2020, corresponding to payments due on October 9th, 2020, and past due debt (if any). The intervention was run on residential properties present in the municipality in 2019. The sample size consists of 68,808 accounts distributed in 3,982 blocks. The frequency of payments is highly polarized. About 45 percent of the accounts paid the twelve 2019 monthly bills ( _always payers_ ), and about 35 percent did not pay any bill at all ( _never payers_ ). The proportion of always payers is relatively low and, therefore, leaves room for potential behavioral responses from non-compliant and partially-compliant neighbors, and this was compounded by the context of the pandemic, during which lockdown measures reduced payments even from highly compliant individuals.

> 7. Figure C.7 in the appendix provides an anonymized example of the intervention letter.

> 8. For details on the experiment (randomization, power calculations, pre-analysis plan) see Cruces et al., 2023. The experimental design was preregistered in the AEA RCT Registry (RCT ID: **AEARCTR-0006569** ).

11