rafmacalaba/datause-encoder-data huggingface dataset:
# **3.6 Regional Taxes**

The regional taxes taken into consideration in our model are the following:

- PIT at regional and municipal level aggregated by regions for the tax year 2016. The former is equal to €11.8 billion, the former €4.5 billion. Note that, given the regional structure of our SAM, we attribute municipal PIT to regions, although this is actually a tax collected by municipal governments and not by the regional ones.

- IRAP (BT) minus IRAP paid by public agents. It is regionalized and includes IRAP calculated on the regional-plant breakdown table for the tax year 2016. The sum over regions brings to a total IRAP collected by regions equal to €13 billion;

- IMU (property tax) from F24 revenue data, for the tax year 2016. The result is obtained from the sum of the government share and the municipalities share. In addition, the taxes were partitioned between natural persons and legal persons. The percentage of natural persons has been incorporated into the real estate sector. The ATECO sector already broke down the share relating to legal persons in the original data. As for municipal PIT, even IMU should be attributed to municipalities instead of regions. However, here we attribute the total amount (€20 billion ) to regional governments.

# **3.7 Savings**

The savings account is the sum of households, the Government, and the rest of the world’s savings.

In the specific, these components are calculated as follows:

- The households’ savings are equal to €70.3 billion. As for capital income in Equation 6, they are obtained by applying the share of households’ income from public debt interest ( _σhh_<sup>_interest_</sup> ) to the households’ savings from the national macro SAM. The calculation is the following

- _Sr,hh_ = _σhh_<sup>_interest_</sup> _Shh_<sup>_macroSAM_</sup> (7)

- Enterprises’ savings are calculated as enterprises’ capital income net transfers from enterprises to households. The imbalances in the account enterprises’ landing/borrowing are considered savings. The total amount of enterprises’ savings is equal to €234 billion;

- Government savings is the sum of government savings from national SAM and imbalances so that the imbalances in the account central government account are considered as savings. The total government savings are equal to €46 billion;

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# **Table 4. Telecommunications reforms and labor market outcomes**

||log(employment)|share temporary|share part‐time|share working from hom|e<br>share self‐employed|share with two jobs|
|---|---|---|---|---|---|---|
|ICT x Reform (any)|0.186**<br>(0.0743)|‐0.000847<br>(0.0166)|0.0857***<br>(0.0139)|0.0127<br>(0.0335)|‐0.0626***<br>(0.0147)|‐0.00771<br>(0.00609)|
||6,653|6,613|6,614|6,081|6,653|6,653|
||0.001|0.000|0.006|0.000|0.003|0.000|

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Each cell shows the OLS estimate of � in equation (1). The reform variable is a dummy variable equal to one after the country introduced its first telecommunications reform.

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the Penn World Tables (Heston et al, 2006).

External macroeconomic management - we use the standard deviation of the current account-to-GDP ratio (CA/GDP) to approximate this indicator. It may appear that the actual exchange rate deviates from the equilibrium exchange rate that brings the economy to external balance. This is especially true in the case of less ‡exible exchange rate regimes present in our sample. If the external macroeconomic management (policies) are poor, i.e. there are frequent or large deviations of the actual exchange rate from its equilibrium, this will result in higher variability of external balances, measured here by CA/GDP. We use the quality of external macroeconomic policies as one of the indicators of the overall quality of general government policies and draw a link to the quality of public debt management - existence of a debt management strategy. To set an alternative hypothesis, one may argue that whatever the external policy its success, as measured by the standard deviation of CA/GDP here, depends on the magnitude and frequency of external shocks such as those to the terms of trade and capital ‡ows. Further, one may extend this argument and assume that the higher the importance (impact) of external shocks the more likely is a country to adopt better instruments for public debt management, such as a debt management strategy. The series of CA/GDP was retrieved from the GDF & WDI Central database of the World Bank.

Management of foreign reserves - we measure the quality of management of foreign reserves using the coe¢ cient of variation in the stock of FX reserves-to-imports ratio. Since management of foreign reserves is part of the ...nancial management of the consolidated government balance sheet we assume that its quality can be positively linked to the quality of public debt management. This is especially true if there exist a high degree of coordination between monetary policy and debt management. The series of FX reserves as a percentage of imports was taken from the GDF & WDI Central database of the World Bank.

Volatility of o¢ cial transfers - more speci...cally we use the coe¢ cient of variation for net o¢ cial current transfers. This indicator is employed to approximate the exogenous volatility (risk) in foreign aid that developing countries may face. This volatility may force countries to take some precautionary actions which may include 19
Figure 2: Karongi 12 hillside irrigation scheme

# **2.2 Data**

## **2.2.1 Spatial sampling**

To take advantage of the spatial discontinuity in access generated by the command area boundary, we randomly sampled plots in close proximity to this discontinuity. In practice, we constructed this sample of plots by dropping a uniform grid of points across the site at 2-meter resolution, and then randomly sampling points within the grid within 50m of the command area boundary.<sup>15,16</sup> After each point was sampled, georeferenced engineering layout plan that specifies the command area, in addition to the maps of the actually constructed command area. In results available upon request, our estimates are qualitatively similar when we use either the engineering layout plan or the actual map. This is consistent with the engineering layout plan closely aligning with the actual map, which in turn is consistent with the high costs of construction of these schemes.

> 15This procedure will produce a sample of plots that is more representative of land than of households. In Supplementary Appendix A, we reproduce our analysis in Section 3 but weighting households inversely proportionally to their number of plots, and find that our results are qualitatively similar with this alternative weighting.

> 16In all three irrigation sites, we additionally sampled some points further from the canal inside the command area. We use these points primarily to examine experimental treatments described

10
# **References**

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and satisfaction with services is especially low in rural areas. A recent World Bank impact evaluation also found that these factors lead to shortfalls in demand and under-utilization of public health services.

When cases of COVID-19 were first confirmed in Tajikistan in April 2020, there were few reliable tools to quickly convey urgent health information to the public. Survey data collected during that time show that television emerged as the primary source of official announcements, while relatively small numbers of people also got information from newspapers, the internet, and social media. A somewhat larger number of people received their information in-person from health care providers and other officials; however, these interactions were still limited, especially in areas with unreliable transportation and other infrastructure. The challenges of communication and service delivery were further complicated in an environment where in-person contact was unsafe due to the risk of spreading infection.

**Figure 1: Daily number of confirmed cases**

<!-- Start of picture text --> 250<br>May 20, 2020: Message<br>sending begins<br>200<br>150<br>100<br>50<br>0<br><!-- End of picture text --> _Source: Our World in Data_ To address the difficulty of quickly disseminating key public health messages, the World Bank launched the initial design phase of the Mobile Engage project in March 2020, with financial support from a Trust Fund financed by the Republic of Korea.<sup>6</sup> The project focused on providing crucial public health information about reducing the risks posed by COVID-19 via SMS to the general population.<sup>7</sup> The project received official endorsement from the Ministry of Health and Social Protection of Tajikistan in April 2020. Messages were first sent in May 2020 and managed in close

> 6 <u>https://www.worldbank.org/en/programs/korea-trust-fund</u>

> 7 In parallel, the Mobile Engage project financed development of a targeted messaging system to send messages to specific phone numbers in preparation for vaccination rollout for the purposes of notifying priority groups on local vaccine availability. This system was completed in early 2021.

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Therefore, we propose an instrument that better suits the Bangladesh export context. First, we focus on the U.S rather than all OECD countries. Our variable for the second stage of regressions consists of Bangladeshi exports per worker to the U.S.

Second, as mentioned above, most of these regions imports from Bangladesh consist of garments final goods. To ensure true exogeneity of our instrument, we need a variable that predicts imports from Bangladesh based solely on the United States internal demand growth, rather than supplyside determinants such as changes in production volumes from other garment export competing countries. Hence, we construct our instrument using time-series regressions of Bangladesh exports to the United States on the United States GDP by industry at the four-digit level, from 1991 to 2018 annually. Predicted values from these regressions proxy for Bangladesh exports to the U.S. explained exclusively by U.S domestic aggregate demand. This variable is, by construction, orthogonal to every supply-side factor in the international garments market, and to every Bangladeshi local market condition. Using this instrument helps us calculate the exogenous portion of the variation in exports from Bangladesh, preventing a bias in our regressions due to endogeneity. In other words, it is extremely unlikely that local economic outcomes in Bangladesh determine U.S. domestic demand for apparel.

Figure 10 shows the relationship between EU-US imports from Bangladesh and imports from the world. The strong positive correlation suggests that the instrument is appropriate.

**Figure 10. Relationship between OECD and US-EU imports from Bangladesh (X) and imports**

<!-- Start of picture text --> from all around the world (Z, instrument)<br><!-- End of picture text --> <!-- Start of picture text -->  2005-2010  2005-2013  2005-2016<br> 100 y = 0.0623x + 3.505 100 y = 0.0772x + 1.6889 100 y = 0.101x + 3.6958<br> 80 80 80<br> 60 60 60<br> 40 40 40<br> 20 20 20<br> - - -<br>-200 - 200 400 600 800 -200 - 200 400 600 800 -200 - 200 400 600 800<br>-20 -20 -20<br>-40 -40 -40<br>-60 -60 -60<br>-80 -80 -80<br>-100 -100 -100<br>Change in X Change in X Change in X<br>Change in Z Change in Z Change in Z<br><!-- End of picture text --> Source: Prepared by the authors using data from the Bangladesh LFS.

Each sub-district in Bangladesh constitutes an observation in our regressions. Given lack of data on international trade at the sub-district level in Bangladesh, we use the approach proposed by Bartik (1991) that takes advantage of a concentration of production and local labor markets to assess the relationship between globalization and local labor market outcomes. In other words, we can compute a measure for sub-district exports based on the sub-district employment concentration in each exporting industry. We call this new variable our “trade exposure index”.

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profitable returns to farmers, our estimate of nitrogen use efficiency implies that the net value of output produced from incremental fertilizer does not exceed the cost of the NAIVS program per farmer.

Goyal and Nash (2017) suggest that the potential for positive impacts of ISPs on fertilizer use is greatest when they are administered in areas where the private sector has been inactive and among households that cannot afford fertilizer at commercial prices (Jayne et al. 2013; Mason and Jayne 2013; Mather and Jayne 2015; Ricker-Gilbert et al. 2011). In contrast, we study the impact of NAIVS in a region where the private sector was already active and the program targeted households that were already purchasing fertilizers at commercial prices.

Corral et al. (in preparation) in Mexico and, in particular, Harou et al. (in preparation) using data from Tanzania provide another explanation for the low use of fertilizer. They provide plot-specific soil testing and find evidence of important within-village variation in soil nutrient deficiencies. As a result, the blanket NAIVS fertilizer recommendations may not be relevant for the majority of farmers in their sample given the diversity in soil nutrient limitations.

Columns 1 to 3 of Table 8 report welfare outcomes using the full sample. The dependent variable in column 1 is the share of the agricultural production sold to the market, column 2 reports the log of household income and column 3 reports the principal component analysis (PCA) of answers to five questions related to food security.

Column 1 shows that beneficiaries in lottery villages sell 19 percentage points more of their production compared to non-beneficiaries, and in non-lottery villages, beneficiaries sell approximately 25 percentage points more. Interestingly, non-beneficiaries in lottery villages report selling higher share of their output too (compared to non-beneficiaries in non-lottery villages) although the increase is only marginally significant (p-value is 0.128). While there are no differences in household income (column 2), beneficiaries in non-lottery villages report higher food security than non-beneficiaries, but there are no statistically significant differences between beneficiaries and non-beneficiaries in lottery villages (column 3). Due to the large number of missing observations for household income, we use the percentage of output sold and the first component of the PCA of food security to construct a standardized index analogous to that of columns 7 to 9 in Table 7. Column 4 of Table 8 reports the standardized index. Similar to results in columns 1 to 3, we find that beneficiaries in non-lottery 26
**Table 4.2 Health-protection control measures and associated pathogen reductions**

|**Control measure**|**Pathogen**<br>**reduction**<br>**(log units)**|**Notes**|
|---|---|---|
|**A. Wastewater treatment**|1−7|Pathogen reduction depends on type and degree of<br>treatment selected.|
|**B. On-farm options**|||
|Crop restriction (i.e., no<br>food crops eaten uncooked)|6−7|Depends on (a) effectiveness of local enforcement of crop<br>restriction, and (b) comparative profit margin of the<br>alternative crop(s).|
|_On-farm treatment:_|||
|(a) Three-tank system|1−2|System described in section 4.3.1.|
|(b) Simple sedimentation|0.5−1|Sedimentation for ~18 hours (section 4.3.1).|
|(c) Simple filtration|1−3|Value depends on filtration system used (section 4.3.2).|
|_Method of wastewater applica_|_tion:_||
|(a) Furrow irrigation|1−2|Crop density and yield may be reduced.|
|(b) Low-cost drip irrigation|2−4|2-log unit reduction for low-growing crops, and<br>4-log unit reduction for high-growing crops.|
|(c) Reduction of splashing|1−2|Farmers trained to reduce splashing when watering cans<br>used (splashing adds contaminated soil particles on to crop<br>surfaces which can be minimized).|
|Pathogen die-off|0.5−2<br>per day|Die-off between last irrigation and harvest (value depends<br>on climate, crop type, etc.).|
|**C. Post-harvest options at lo**|**cal markets**||
|Overnight storage in<br>baskets|0.5−1|Selling produce after overnight storage in baskets (rather<br>than overnight storage in sacks or selling fresh produce<br>without overnight storage).|
|Produce preparation prior<br>to sale|1−2|(a) Rinsing salad crops, vegetables and fruit with clean<br>water.|
||2−3|(b) Washing salad crops, vegetables and fruit with running<br>tap water.|
||1−3|(c) Removing the outer leaves on cabbages, lettuces, etc.|
|**D. In-kitchen produce-prepa**|**ration options**||
|Produce disinfection|2−3|Washing salad crops, vegetables and fruit with an<br>appropriate disinfectant solution and rinsing with clean<br>water.|
|Produce peeling|2|Fruits, root crops.|
|Produce cooking|5−6|Option depends on local diet and preference for cooked<br>food.|

_Sources:_ EPHC/NRMMC/AHMC, 2006; WHO, 2006; Amoah _et al_ ., 2007b; Abaidoo _et al_ ., 2010; and Keraita _et al_ ., 2010.

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Kee, H., Nicita, A., Olarreaga, M. (2009). Estimating Trade Restrictiveness Indices, _Economic Journal_ , 119, 172-199.

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