rafmacalaba/datause-encoder-data huggingface dataset:
with support from the World Bank’s Living Standards Measurement Study (LSMS) team.<sup>2</sup> These six countries are part of the LSMS-Integrated Surveys on Agriculture (LSMS-ISA) project that fields longitudinal, multi-topic household surveys with focus on agriculture. Thus, the households included in the HFPS were also interviewed as part of the LSMS-ISA panel survey conducted in these countries. A uniform methodology was adopted in sampling, weighting, and implementing the HFPS across the countries, making cross-country comparison feasible. In each country, the most recent face-to-face survey prior to the start of the pandemic formed the frame for the HFPS. The HFPS was designed to be nationally representative, with rural/urban stratification.

The HFPS collects information on various indicators, including demographic information, shocks and coping strategies, economic sentiments, employment and business operation, and agriculture, among others. The current study uses data primarily from the agriculture and price modules. Given the season-specific requirements of agriculture data, the HFPS rounds that collected agriculture information were implemented either at the end of the main agriculture season or split across the post-planting and post-harvest periods in the respective countries. The agriculture module collects information on crops planted and area; harvest; fertilizer access, use and challenges with access and coping strategies; among others. While the HFPS began after the onset of the COVID-19 pandemic, the timing of implementation and number of rounds completed to date varies across countries. Table A1 gives the sample distribution across countries and the rounds of the phone survey data used for the analysis. Figures A1-A6 visualize the timeline of data collection.

# _2.2. Variable Creation_

The information collected through the HFPS that is most relevant to the objectives of this study broadly falls into three categories, described below. Not all information was collected across all rounds of the HFPS such that data availability varies by country and over time. Unless explicitly specified otherwise, all information collected was with respect to the latest agricultural season.<sup>3</sup> First, we collected information on agricultural engagement, which comprises information on the incidence of crop farming among all households, the share of crop-farming households that

> 2 This survey is part of the World Bank’s effort to support the collection of monthly high-frequency phone surveys to monitor the impact of the COVID-19 pandemic and external shocks on households. <u>https://www.worldbank.org/en/programs/lsms/brief/lsms-launches-high-frequency-phone-surveys</u> 3 For interviews conducted after planting but before harvesting, this was the ongoing agricultural season. For interviews conducted after harvesting, this referred to the recently completed agricultural season.

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Table 5: Investments and productive assets

||(1)<br>Total start-up<br>capital<br>(all activities)|(2)<br>Value of<br>productive assets<br>(all activities)|
|---|---|---|
|Panel A. Pooled Estimates|||
|Treatment (ITT)|12462.86***<br>(2355.42)|9320.74***<br>(3097.54)|
|PDS Lasso selected controls|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|15260.21|39538.50|
|Observations|2,620|2,620|
|Panel B. Treatment Arm Estimates|||
|VSLA (T1) (ITT)|7259.34**<br>(2907.06)|9613.97***<br>(3674.09)|
|Cash Grant with repayment (T2) (ITT|)<br>14828.19***<br>(3066.31)|7788.24**<br>(3755.91)|
|Cash Grant (T3) (ITT)|17812.93***|12244.49*|
||(5341.51)|(6243.08)|
|PDS Lasso selected controls|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|15260.21|39538.50|
|p-value T1=T2|0.04|0.64|
|p-value T2=T3|0.60|0.50|
|p-value T1=T3|0.07|0.68|
|p-value T1=T2=T3|0.06|0.77|
|Observations|2,620|2,620|

Robust standard errors clustered at locality level. Start-up capital and value of assets are in CFA franc and winsorized at 99%.

The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
results to explore the spatial implications for countries’ 30x30 commitments.

Rather than presenting a detailed description of our methodology at the outset, we use our country cases to introduce its features sequentially. We begin with an econometric analysis that identifies the appropriate scale for envisioned protected areas in each country.

# **2.  Country Size and Protected Area Scale**

In each country, our sequential algorithm begins by overlaying the occurrence maps of all unprotected species on a spatial grid. It counts resident species for each grid cell and identifies the highest-count cell (P1) as the priority 1 candidate for protection coverage. In step 2, it sets aside the species resident in P1 and repeats the grid cell count for the remaining species. It identifies the highest-count cell as P2. The algorithm can repeat this exercise until all species have coverage in at least one grid cell.<sup>2</sup> A critical feature of our approach is the selection of grid scale for each country. We use a spatial grid whose cell area is the same as the average protected-area size, controlling for country size. Figure 1 displays the relationship (in logarithms) between country area and mean protected area (PA) size in sq km for 212 countries and political units.<sup>3</sup> The regression results in Table 1 confirm the strong visual relationship: Mean PA size increases by 0.614% with each 1% increase in country area, with very high significance (regression t-statistic of 15.47<sup>4</sup> ). Using this result, we can predict the mean PA size for any area and translate it to an appropriate grid cell resolution.<sup>5</sup>

> 2 When maximum resident populations are the same in more than one cell, the algorithm makes a random selection.

> 3 We compute average protected area size from WDPA (2024) and country area from the World Bank’s country shapefile (World Bank, 2024).

> 4 The t-statistic is the estimated regression coefficient divided by its standard error.  Classical significance with 95% confidence generally requires a t-statistic above 2.0.

> 5 We could compute mean PA size directly for individual countries, but across the 212 countries and political units we observe great variation in both the numbers and sizes of PAs.  We believe that our logarithmic regression provides more robust and stable results for the computation of appropriate grid scales.

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**Table 5:  Predicting various dimensions of GVC integration**

|Dependent variable is a<br>dummy for:|Any exports|Exports<br>beyond<br>Neighbors|Exports<br>beyond sub-<br>Saharan Africa|Any import|Export &<br>Import|
|---|---|---|---|---|---|
|(a)Agriculture|(1a)|<br>(2a)|(3a)|(4a)|(5a)|
|Sales missing or zero|0.019***|0.022***|0.011***|-0.022***|0.030***|
||(0.003)|(0.003)|(0.002)|(0.005)|(0.002)|
|Sales<sup>0.5</sup>|0.306***|0.267***|0.183***|0.332***|0.280***|
||(0.005)|(0.004)|(0.004)|(0.009)|(0.004)|
|Age (years in dataset)|<br>0.002**|<br>0.002**|<br>0.002***|<br>0.006***|<br>0.001**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Kigali|0.007|-0.005|-0.005|0.034***|0.005|
||(0.006)|(0.005)|(0.005)|(0.011)|(0.004)|
|Other Tax Centre|<br>-0.006|<br>-0.006|<br>-0.011***|<br>-0.030***|<br>0.005|
||(0.005)|(0.004)|(0.004)|(0.008)|(0.003)|
|Time|<br>-0.003***|<br>-0.004***|<br>-0.004***|<br>-0.010***|<br>-0.002***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|R-squared|0.28|0.29|0.19|0.16|0.37|
|Number of observations|11771|11771|11771|11771|11771|
|(b)Mining|(1b)|(2b)|(3b)|(4b)|(5b)|
|Sales missing or zero|-0.008*|-0.001|0.000|-0.025***|0.012***|
||(0.004)|(0.004)|(0.004)|(0.006)|(0.003)|
|Sales<sup>0.5</sup>|0.239***|0.240***|0.239***|0.166***|0.162***|
||(0.004)|(0.004)|(0.004)|(0.005)|(0.003)|
|Age (years in dataset)|<br>0.009***|<br>0.007***|<br>0.007***|<br>0.017***|<br>0.008***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|
|Kigali|-0.034**|-0.028**|-0.027**|-0.012|-0.025**|
||(0.015)|(0.014)|(0.014)|(0.021)|(0.011)|
|Other Tax Centre|-0.023***|-0.016***|-0.015***|-0.019***|-0.006|
||(0.006)|(0.006)|(0.006)|(0.008)|(0.006)|
|Time|<br>-0.006***|<br>-0.005***|<br>-0.006***|<br>-0.019***|<br>-0.009***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|
|R-squared|0.39|0.42|0.43|0.18|0.34|
|Number of observations|7864|7864|7864|7864|7864|
|(c)Manufacturing|(1c)|(2c)|(3c)|(4c)|(5c)|
|Sales missing or zero|-0.038***|-0.004|0.001|-0.189***|-0.024***|
||(0.006)|(0.004)|(0.003)|(0.009)|(0.005)|
|Sales<sup>0.5</sup>|0.170***|0.091***|0.058***|0.177***|0.171***|
||(0.004)|(0.002)|(0.002)|(0.006)|(0.003)|
|Age (years in dataset)|0.011***|0.002**|-0.001*|0.026***|0.012***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|
|Kigali|-0.070***|-0.023***|-0.005|-0.178***|-0.059***|
||(0.007)|(0.005)|(0.004)|(0.013)|(0.007)|
|Other Tax Centre|-0.055***|-0.015***|-0.003|-0.250***|-0.043***|
||(0.008)|(0.006)|(0.004)|(0.014)|(0.007)|
|Time|<br>-0.005***|<br>-0.002*|<br>0.001|<br>-0.036***|<br>-0.006***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|
|R-squared|0.36|0.21|0.15|0.32|0.39|
|Number of observations|6709|6709|6709|6709|6709|
|Meanof the dep. variable|0.032|0.020|0.015|0.085|0.023|

Note: All regressions include firm FE and a control for firm size. Robust standard errors in parentheses. Significance at the 10%, 5%, and 1% levels are indicated by *, **, and ***, respectively.

29
that ability and morality are not correlated, the endogeneity due to omitted heterogeneity in the propensity to pay bribe equation arises because The last inequality follows from the fact that Cov(Ai; A<sup>f</sup> i<sup>)>0duetogenetictransmissionof</sup> ability from parents to children, and �A; �A > 0; �M ; �M < 0: The available evidence from behavioral genetics shows that the correlation in the IQ of parents and children is about 0.50. See, for example, Plomin et al. (2008). This positive bias in the estimated e¤ects can easily mask a negative e¤ect of income that arises from better bargaining power of richer households. The results reported below seem to justify this worry; without credible identi...cation, one is likely to underestimate the regressive e¤ect of corruption and may even ...nd spurious progressive e¤ect.

We focus on household income as the indicator of a household’s economic status. An alternative is to use household consumption expenditure which is widely used in the existing studies, motivated by the observation that consumption is usually less subject to measurement error compared to income (see Deaton (1997)). However, an important problem with consumption expenditure as an indicator of economic status in our application is that consumption and bribe payments to teachers are simultaneously determined, given income (see equations (1) and (3) above). Simultaneity bias is a serious problem in addition to omitted heterogeneity and measurement error in the case of household consumption expenditure. We thus prefer income as the indicator of the economic status of a household.

If measurement error were the only source of bias, then one could utilize some indicators of household’s wealth such as housing characteristics as instruments for income under the assumption that measurement error in wealth is not correlated with the measurement error in income (Hunt and Lazslo (2012)). However, if preference and ability heterogeneity is important, then such instruments fail to satisfy the exclusion restrictions. Instead of relying on wealth indicators as instruments, we propose an alternative instrumentation strategy that exploits rainfall di¤erences across villages as a source of exogeneous variation.

Bangladesh is a deltaic plain at the con‡uence of the Ganges (Padma), Brahmaputra (Jamuna), and Meghna Rivers and their tributaries. Most of the country is low lying with an 15
_Fuel subsidy expenditures and international oil prices_ Higher international oil prices mean a wider gap with the domestic retail price, and larger subsidies as a result. In turn, increasing the subsidized retail price of fuel products lowers the government’s total subsidy payments. Here we present some estimates of the effects of some plausible increases in international oil prices on government expenditure. The effects are dramatic. This analysis also answers the policy-makers’ question of how much retail fuel prices may have to increase to hold fuel subsidy spending constant given increases in oil prices .

For 2008, the revised Indonesian government projects energy subsidies at US$ 17 billion (Rp. 160 trillion). This projection is based on an oil price assumption of US$95 per barrel and even oil prices, energy subsides would be higher than capital investments and social programs combined (see Figure 15). It is worth noting that the approach taken here to estimate fuel subsidies — in contrast to the government’s approach — accounts for a potential source of underestimation of the total cost of fuel subsidies insofar as it includes the cost of levying value added taxes on an artificially low price (Annex 3 describes this potential source of underestimation).

**Figure 15. Energy subsidies dominate 2008 central government expenditure** _Billions of US dollars_

<!-- Start of picture text --> 21<br>Electricity*<br>14<br>7 Fuel*<br>0<br>Subsidies Capital invest't Social programs<br>Source: Ministry of Finance, 2008 APBN-P, assuming oil at US$95;<br>social program expenditure data from 2008 APBN.<br><!-- End of picture text --> Energy subsides cover more than just artificially low prices for certain fuels. Subsidies to the state-owned electricity generator PLN are also highly significant – they are likely to approach US$ 10 billion in 2008. The government also subsidies the cost of low-volume LPG cylinders (typically bought by poorer households) but the difference between the subsidized price and cost is relatively small and volumes are low. The subsidy to PLN is highly complex, given the differential supply cost across the archipelago and the complex array of tariffs levied depending on the type of user and their connection’s capacity. For this exercise we reduce that complexity to a ratio between PLN’s generating costs, the USD price of oil and the rupiah exchange rate: a US$1-per-barrel increase in the price of crude oil increases the required subsidy by Rp 790 billion in 2008. Finally, the state-owned railway company receives subsidized fuel, however we do not consider these given their small order of magnitude relative to the other subsidies.

These estimates allow for international higher oil prices to affect the quantity of subsidized fuel consumed, hence the total cost of the fuel subsidy. Two factors are likely to drive the response of 18
increased steadily and achieved significant levels in Eastern Europe (52 percent), Latin America (34 percent), and Sub-Saharan Africa (50 percent), foreign bank participation has remained constant at very low levels in South Asia (7.5 percent). In East Asia and the Middle East, foreign bank entry has increased since the mid-1990s, but it still represents less than 20 percent of the system.

In terms of the drivers of foreign bank entry, the available empirical literature suggests that local profit opportunities, the absence of barriers to entry, and the presence of mechanisms to mitigate information problems have been the main factors driving the process of foreign bank entry in developing countries. Regarding the implications of foreign bank participation, with few exceptions, the overwhelming evidence from cross-country research and from a significant number of case studies focused on Eastern Europe and Latin America suggests that foreign banks are more efficient than domestic banks and, consequently, can exert competitive pressure. On the other hand, the evidence for Asia, a region that has been a latecomer to the process of foreign bank entry and where many barriers still exist, is more mixed. Research on the impact of foreign bank participation on banking stability suggests that for the most part foreign banks have played a stabilizing role in developing countries. As for the effects of foreign bank participation on access to finance, the evidence is mixed with many cross-country studies suggesting that foreign banks enhance access, but some case studies providing evidence to the contrary.

The rest of the paper is organized as follows. Section 2 discusses the trends and regional patterns of foreign bank participation. Section 3 summarizes the existing literature on the factors that have propelled this process, while Section 4 presents a survey of the literature on the implications of foreign bank participation for developing countries. Section 5 concludes and offers some suggestions for areas in need of further research.

# 2. **Foreign Bank Participation in Developing Countries: Trends and Regional Patterns**

While most developing countries have witnessed an increase in foreign bank participation since the mid-1990s, there are significant differences in the extent to which different regions have embraced this process. We look at the importance of foreign banks across regions in three ways. First, we examine the number of foreign banks relative to the total number of banks (see Figure 1). Second, we look into the share of assets held by foreign banks (Figure 2). Data on both of 3
6

# **Pension Investment**

At year-end 1994, aggregate pension investments were allocated among asset classes as shown in the chart below. Differences in asset allocation are small across pension categories. The aggregate picture thus captures the main characteristics.

<!-- Start of picture text --> Asset Allocation of the Pension System<br>(year-end 1994; in/o)<br>Deposhs<br>41%<br>Govt. papers<br>OterS/<br>1%<br>Direct in7v_<br>6% Poperties<br>I% Stocks Bonds<br>8% 11%<br><!-- End of picture text --> Source: Ministry of Finance.

A distinctive feature is the predominant share of short-term instruments or cash-equivalent assets, including short-term government papers (issued primarily by Bank Indonesia) and bank deposits. The share of capital market assets, such as stocks and bonds, which is about 19 percent of total investments, is very small. Among other countries, the portfolio share of capital market instruments is much higher, as shown in the table below.

||**Pe**|**nsion Funds Ass**<br>(in percent|**et Allocation A**<br>of 1994 total inve|**cross**Countrie<br>stments)|s|
|---|---|---|---|---|---|
||Indonesia|Philippines|Malaysia|U.S.|U.K.|
|Stocks|8|11|10|43|82|
|Bonds|11|46|78|36|12|
|Other<br>Assets|81|43|12|21|6|

Sources: Indonesia - Ministry of Finance.

Malaysia, Philippines - Institutional Investor, October 1995.

United States -"Flows of Funds", Board of Governor, Federal Reserve System. United Kingdom - Seki (1995)
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<!-- Start of picture text --> ©<br>A<br>+ oon nN,a pnt/hy\ \<br>oa \ j TM \<br>N ai) ‘ \ / \<br>7 t i \<br>o<br>=<br>‘offf \ e<br>N 1. of<br>' \ é<br>4<br>st<br>2006q3 2008q1 2009q3 201141 2012q3<br>(GN). Push Effect * Deviation World export growth<br>HE, Geographical Effect —--——- Export Growth<br>HE) Sectoral Effect<br><!-- End of picture text --> <!-- Start of picture text --> © oy<br>ram" \<br>~~ é \ woN<br>\. . / i \Nae oN<br>\ oot \ i<br>°<br>{<br>\<br>1' \\ ii<br>\ ra<br>17<br>7<br>2006q3 2008q1 200943 201141 20123<br>MEE) Push Effect . Deviation World export growth<br>MEE Geographical Effect. —--—~~- Export Growth<br>HE) Sectoral Effect<br><!-- End of picture text --> <!-- Start of picture text --> a. / a \<br>/ \ mh ions<br>n ya f \ / \ / .<br>y . / av \<br>No 1 “7 \<br>° \ \<br>\ 4<br>' \<br>\ !<br>r \ i<br>\ oF<br>\ /<br>© SU<br>200693 2008q1 200943 201191 201243<br>GE) Push Effect * Deviation World export growth<br>HE, Geographical Effect —--——- Export Growth<br>HE) Sectoral Effect<br><!-- End of picture text -->
# **_6.2: Robustness Considerations_**

As a first robustness test, we exclude the 2008 EA-level baseline measures of health and education services when modeling the respective 2011 values. Under these specifications, the model results only predict the 2011 levels across councils rather than the magnitude of change across councils from 2008 to 2011. In these results (not shown), three of our four relationshiprelated variables maintain their statistical significance at the 5 percent level under this specification. The expected effect of the fourth variable, the percentage of councilors related to chiefs, on the 2011 EA-level use of government health facilities only achieves statistical significance at the 10 percent level when excluding the 2008 baseline measure.<sup>14</sup> As a second robustness test, we run council-level interactions between the percentage of self-reported disputes and contact with chiefs for each of our three outcome variables of interest. These interaction terms, which potentially signal whether our contact variable is measuring frequent dispute-related contact, do not achieve statistical significance across any of our three outcome variables. We take these non-results to mean that councilors who report disputes are not the same councilors who report having frequent contact with chiefs – and, therefore, that our contact variable on its own does not signal frequent dispute-related contact. Rather councilors who report disputes with chiefs are distinct from councilors who report frequent contact with chiefs, which our bivariate regressions above accurately capture.

As a final robustness consideration we test whether councilor-chief relations interact with any of our EA-level variables to affect the provision of local public goods. We do this through a series of theoretically motivated cross-level interactions. Cross-level interaction effects are an extension of random slope models in that they allow the _effect size_ of councilor-chief relations (i.e. council-level slopes) to vary as a function of particular EA-levels variables. This serves both a robustness check to assess whether our council-level findings are consistent across EAs, as well as a way to reveal potential theoretically relevant EA-level moderators relating the effects of councilor-chief relations to the provision of local public goods. We run cross-level interactions between our six potential EA-level moderator variables of interest (distance to chiefdom/district headquarters, EA visited by chief/councilor, 2008 baseline use of public

> 14 We also note that our results our robust to measuring self-reported contact with chiefs as a mean rather than median value, although we do not include these model results here.

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