rafmacalaba/datause-encoder-data huggingface dataset:
variables or LSDV in our case), (ii) are more robust to potential misspeci...cations, and (iii) can easily accommodate highly non-linear and ‡exible speci...cations that may be impractical in a multivariate SVAR context.

While we use simple non-linear speci...cations to estimate the di¤erent multipliers under expansionary or contractionary ...scal policy and along the economic cycle, regime transition in non-linear models can be problematic. By estimating the multipliers individually under each regime, the risk of bias in the coe¢ cients follows from the implicit assumption that the economy never changes regimes. In line with AG (2010), however, we sidestep the problems derived from assuming discrete changes in regimes by using a smooth (continuous) transition model. In that case, and while one regime (recession) has a relatively small number of observations, the smooth transition model allows us to e¤ectively pool more information taking advantage of the variation in the degree of a particular regime so that estimation and inference for each regime is based on a larger set of observations. Since we estimate properties of a given regime using in part information from another regime, our estimates may be biased. Nevertheless, it is important to note that the bias in our estimates will be toward not ...nding di¤erent ...scal multipliers across regimes.

In all of our regression analysis, we use robust Driscoll and Kraay (1998) standard errors to correct for potential heteroskedasticity, autocorrelation in the lags, and error correlation across panels.

In our basic linear speci...cation (Section 3.1), the response of output growth at the horizon h is estimated from the following regression: where i and t index countries and time, respectively, �i is the country ...xed e¤ect, and Y and G are the logarithms of real GDP and real government spending. Therefore, �Y and 8
**<u>Table 3: Descriptive statistics at village level</u>**

|||**Regressio**|**n Sample**||
|---|---|---|---|---|
||**All**|**North **|**Center **|**South **|
|**Availability of land**|||||
|Uncultivated land available|0.11|0.11|0.14|0.08|
|Unallocated land available|0.31|0.42|0.34|0.23|
|Households moved out due to land shortage|0.11|0.07|0.11|0.14|
|Outsiders get land without consulting village head|0.14|0.15|0.14|0.15|
|Sold land to outsiders in last 5 years|0.11|0.04|0.13|0.11|
|Gave land to outsiders in last 5 years|0.15|0.15|0.16|0.13|
|Has land owned by outsiders|0.37|0.33|0.34|0.41|
|Has land rented to outsiders|0.34|0.20|0.38|0.36|
|Has land taken by the government|0.14|0.19|0.12|0.14|
|**Incidence of land conflict**|||||
|Land conflicts by village families|0.29|0.28|0.28|0.32|
|Land conflict with neighboring villages|0.20|0.18|0.20|0.19|
|Estates within walking distance|0.24|0.26|0.26|0.22|
|Land conflict with neighbouring estates|0.05|0.04|0.06|0.04|
|**Inheritance practices**|||||
|Inheritance matrilineal and neolocal|0.04|0.00|0.03|0.07|
|Inheritance matrilineal and matrilocal|0.41|0.00|0.32|0.71|
|Inheritance matrilineal and patrilocal|0.22|0.02|0.44|0.06|
|Inheritance patrilineal and neolocal|0.02|0.01|0.02|0.02|
|Inheritance patrilineal and patrilocal|0.26|0.93|0.14|0.10|
|Inheritance do not know/other|0.05|0.04|0.06|0.04|
|**Infrastructure access**|||||
|All season road passes through the village|0.35|0.45|0.36|0.28|
|if no, distance to all season road (km)|10.15|6.26|10.35|11.32|
|Other road passes through the village|0.79|0.71|0.77|0.84|
|Distance to local produce market (km)|8.71|10.41|9.42|7.14|
|Number of villages|4107|729|1784|1594|

_Source:_ Own computation from 2006/07 NACAL.

17
internal mismanagement and those whose condition is related to economic outcomes. Thus up-to-date information on economic conditions could have a positive effect on financial sector development. This paper contends that better information on economic variables lowers risk and helps lenders and borrowers make better judgements about borrowing and lending decisions. Overall credit may rise or fall, but bank portfolios should improve.

This paper uses an indicator that reflects the timeliness of economic data reported by governments- called the transparency indicator (Islam, 2006) as an indicator of knowledge available to the general public on key economic variables. It links the timeliness of data as measured by this indicator to financial sector development. In the following section, the paper lays out in more detail why information on economic outcomes may lead to overall financial sector development, though it does not go so far as to build a theoretical model showing these links. This is followed by a description of the data and empirical strategy. The next section describes the results.

So far the impact of transparency on private credit and non-performing loans has been analyzed without reference to an additional potential source of improved performance. I have assumed that lenders only seek to improve the performance of their portfolios. But in the real world, lending decisions are not always based on a desire to enhance efficiency and profits of the bank. Bank officials or supervisors may be corrupt and may hinder an efficient or competitive allocation of credit. The impact of transparency on the behavior of the portfolio may come about because of institutional factors or improved governance: that is it may reduce corruption. When information is available that allows better monitoring and evaluation of the decisions that supervisors 10
disbursed markets. European FDI in Sub-Saharan Africa is probably motivated not by relative cost considerations but by the desire to gain first-mover advantage in emerging markets and to diversify risk. Furthermore, a very large share of the FDI in Sub-Saharan Africa is in either natural resources, which is quite isolated from any effects arising from the EMU, or concentrated in South Africa where there is a significant domestic market.

<!-- Start of picture text --> Chart 2<br>Foreign Direct Investments to SSA, net inflows (mill. US $) 1970-1997<br>~5000 l<br>l +4--:Sub-Saharan Africa --- South Africa Nigeria * Angola<br><!-- End of picture text --> _Soulrce:_ World Bank 1999c.

Mediterranean and Yeyati and Sturtznegger (1 999) for countries in Latin America.

11
- [18] General Agreement on Tariffs and Trade [1972], _Basic Instruments and Selected Documents,_ 18th supplement, Geneva.

- [19] General Agreement on Tariffs and Trade [1988], "Meeting on 8 March 1988," MTN.SB/5 (March 29), Geneva.

- [20] Goldstein, M. [2000], "IMF Structural Programs," NBER Conference on Economic and Financial Crises in Emerging Market Economies, October 19-21.

- (21] Greene, William H. [2000], _Econometrnc Analysis,_ 4th ed., Prentice-Hall.

- [22] Grossman, Gene M. [1982], "Import Competition from Developed and Developing Countries," _Review of Economwcs and Statzstzcs_ 64: 271-281.

- [23] Grossman, G. M. and E. Helpman [1994], "Protection for Sale," _Amertcan Economzc Revzew,_ vol. 84, p.<sup>8 33</sup> -<sup>85 0</sup> .

- [24] Hoekman, Bernard M., and Kostecki, Michel M. [2001], _The Polztwcal Economy of the World Tradzng System,_ 2nd ed., Oxford University Press.

- [25] Hudec, Robert E. [1987], _Developing Countries zn the GATT Legal System._ Gower.

- [26] Institute for International Economics. [2000], "Chronological Summary of Economic Sanctions for Foreign Policy Goals, 1914-99." Washington, DC: Institute for International Economics. http://www.iie.com/topics/sanctions/sanctions-overview.htm

- [27] International Monetary Fund [1998], _Trade Liberaltzatton in IMF-Supported Programs._ IMF.

- [28] Jackson, J. H. [1997], _The World Trading System: Law and Polzcy of Internatzonal Economzc Relat%ons,_ 2nd ed. MIT Press.

- [29] Johnson, Harry G. [1967], _Economzc Policzes Toward Less Developed Countries._ Washington, DC: Brookings.

- [30] Krishna, Pravin and Devashish Mitra [2000], "Reciprocated Unilateralism: A Political Economy Approach," _mzmeo_

- [31] Krueger, A. 0. [1995], _Trade Polzczes and Developing Nations,_ Brookings.

- [32] MacPhee, Craig R., and Oguledo, Victor Iwuagwu [1991], "The Trade Effects of the US Generalized System of Preferences," _Atlantic Economic Journal_ 19 (December): 19-26.

- [33] MacPhee, Craig R., and Rosenbaum, David I. [1989], "The Asymmetric Effects of Reversible Tariff Changes under the United States GSP," _Southern Economzc Journal_ 56 (July): 105-125.

24
consumption that are not correlated with the error term, the study applies two-step difference and system panel GMM estimation approaches to address endogeneity and reverse causality bias in the OLS and fixed-effects estimations. These two-step difference and system GMM estimators have two advantages. First, they use the lags of explanatory variables as instruments, assuming that they are not correlated with the error terms. Second, the two-step Difference Panel GMM uses the first-difference of variables in the regressions and applies their lagged level values as the instruments. In addition, the twostep system GMM augments the two-step difference equations with level regressions but applies the firstdifference of lagged endogenous regressors as instruments to eliminate the bias.

Table 1 shows the regression results of the fixed effects, two-step difference and system GMM approaches for the impact of electricity consumption on the value added in the agriculture sector. The first three columns of Table 1 demonstrate the results for all countries, while columns 4-6 show the coefficients only for non-high-income countries, using the same estimation methods. Excluding highincome countries from the estimations is motivated by Figure 2, which illustrates a stronger relationship between the sectoral value added and electricity consumption across all sectors using both level and growth variables in high-income countries compared to others. There are two possible explanations for these results. First, production technologies vary substantially across income groups and include more electricity-intensive processes in advanced economies, as opposed to poor countries, which rely on cheap and informal labor. For example, agriculture primarily consists of subsistence farmers who use their manual labor to perform tasks in developing countries, as opposed to developed countries with sophisticated technologies, including many automated processes that use electricity. Burgi et al. (forthcoming) show that the average share of informal employment, defined as self-employed and nonpaid workers with very low or no capital, accounts for 44 percent of total employment in agriculture in high-income countries as opposed to 57, 71, and 86 percent, respectively, in upper-middle-income, lower-middle-income, and low-income countries. The second explanation for the stronger linkages between the Gross Value Added (GVA) and electricity in high-income countries than others is the existence of many growth obstacles in developing countries in addition to electricity constraints. For example, Steinbuks (2012) shows that financial and electricity constraints often go hand in hand making addressing development challenges more difficult. Improving electricity access or supply would not necessarily lead to high growth without other reforms.

13
(1996)). This was a rather liberal application of the jurisdiction delegated to chiefs under the category of custom. Following the strengthening of chiefly rule after the Native Administration Ordinance of 1927, chiefs evidently felt confident enough to round up spinsters. Gocking‘s work in the Gold Coast for the early decades of colonial rule, on the other hand, documents the liberating effect of the dual legal structure in the Gold Coast for women, who took cases of divorce, maintenance and custody before colonial courts, aware that colonial administrators were more sympathetic towards cases of abandonment and neglect (Gocking (1990, 1993)). In the previously mentioned case of Maraka settlements that witnessed a mass exodus of slaves from 1906, Roberts (2010) shows large numbers of slave wives appearing before the new native courts established in the French Soudan from 1905 seeking divorce on the grounds of abandonment. This reflects the growth in slave agricultural villages in the 19<sup>th</sup> century, when slave owners found spouses for their slaves and settled them in agricultural villages in an era of cash crop cultivation. He notes the influence of Islamic law on native custom in non-Muslim parts of the French Soudan, and how indigenous women valued the Islamic understanding of the contractual, material nature of marriage that obligated husbands to provide materially for their wives and children. _Education in Colonial Africa: Women Outmanned_ The last aspect of colonial policy we want to examine deals with the sphere of education, where missionaries had enormous influence and used this to shape perceptions of gender and family in profound ways. Though Islamic education preceded Western education in Africa, the social and cultural expectations of females limited their access to Islamic education. So even though Islam requires basic religious education for all Muslims irrespective of gender, the amount of time it takes for the acquisition of the necessary knowledge and experiences for one to become an educated scholar did not accommodate the social status of females, who were expected to be married off by the end of their teenage years. Hence the itinerant Muslim scholar, the cleric, and the long-distance trader extensively documented in the history of pre-colonial Africa were all male (Goody (1968), Sanneh (1976), Owusu-Ansah (1991)). It is not surprising then that the African Muslim assessors attached to colonial courts in French West Africa and those assisting in the codification of customary law were all men (Ginio (2006), Jézéquel (2006)).

Education is a key area, instrumental in colonial understandings of the role of the colonial subject, and for independent governments the role of empowered citizenry in developing nations. Lord Hailey‘s magisterial African Survey, revised in 1956, provides an 25
# **_Current low efficiency of spending and incentives to expand rural roads network_**

Because this plan entails further massive investments in rural roads, it is crucial to know if the current investments are achieved according to road condition and agricultural potential. Indeed, if allocation to roads maintenance is assigned independently of road condition, future roads investments may face the same problem of spending efficiency. Based on reliable and extensive data per district, unfortunately, Table 12 (and Figure 4 at the district level) demonstrate that road condition, district area do not explain why some districts benefits from higher funding than others. Taking into account the extremely high correlation between network length and allocation for roads maintenance, one can assume that there is a formula based on network length to define the allocation per district. Finally, it is worth noting that this allocation is probably adjusted with some political factors; indeed, the number of constituents in the Parliament seems to have an impact on the amount allocated for roads maintenance.

Due to the current investment strategy in rural roads, it seems better for a local authority to expand its network than maintain it due to the fact that increased allocation probably mainly depends on the network length and can explain why local authorities now strive to upgrade many community roads to district roads.<sup>28</sup>

> 28 However, this incentive can have a negative impact on the network sustainability (which is developed below in this chapter).

33
constituencies, and minimizing the number of idle parcels. But land is “subject to multiple, overlapping claims and ongoing debate over these claims’ legitimacy and their implications for land use” (Berry, 2001). Consequently, disputes are common over both who has a right to a particular plot, and even who has the right to allocate that plot. Without a central registry, there are frequent instances, especially in urban and peri-urban areas, where land is rented, sold or allocated land multiple times to different people. Of course, such practices can create an array of conflicts, disputes and ownership insecurity, as for example shown in Kuntu-Mensah (2006), which have resulted in frequent litigation (Aryeetey & Udry, 2010). As noted by Jones-Casey & Knox (2011), the Ghanaian courts were clogged with 35,000 land disputes in 2006.

Given the lack of clear land rights, it is not surprising that investment in housing provision and mortgage markets has been inhibited. In 2010 for example when the housing stock deficit stood at 1,200,000 houses, only 199,000 units of houses were built (Afrane et al., 2016). In the wider international context, tenure insecurity particularly discourages investments by multinational companies in Ghana and thus the national economy forgoes potential positive effects from additional job creation and technology transfer (Barthel et al, 2011).

Amid this complexity, however, a cultivator’s rights over her growing crops, on the other hand, are quite secure. Plots are virtually never lost while under cultivation; the 2018 wave of the ISSERNorthwestern-Yale Ghana Panel Survey (GPRL, 2019) reveals only two instances among 5,366 plots in which disputed tenure caused interference with cultivation. Historian Ivor Wilks (1993) summarizes the principle as “the cultivated farm is my property, the land is the stool’s”. This feature of the property rights system creates an incentive for farmers to use (or appear to be using) their land. Goldstein & Udry (2008) show that individuals, especially women, forsake fallowing on insecure land. Since fallowing is a key input for soil fertility, this has significant costs for productivity. They estimate that for maize and cassava farms alone, this represents a loss equal to around 1 percent of GDP.

# **3. Model**

As described above, Besley & Ghatak (2010) provide a thoughtful overview of the pathways through which insecure land tenure might reduce agricultural productivity, distort household decisions and reduce welfare. The most basic of these is the fact that the expropriation risk generated by insecure tenure reduces incentives to invest. In addition, cultivators may divert resources to protect their property rights (“guard labor”). Insecure rights over land might also reduce its usefulness as collateral and thereby limit access to credit. And insecure property rights may be a source of friction that 6
# **3. Data**

_Data collection and survey rounds_ The pre-assistance baseline survey (PAB) was conducted with a stratified random sample of 8,690 applicant households, after the decision on their eligibility was made, but before any assistance was provided. The sample was drawn from over 268,000 assessed eligible and ineligible applications, representing 1.6 million individuals, nearly 50% of the refugee population during the first half of 2017 (Cuevas et al. 2019). The sample was stratified by region, which included Istanbul, Aegean, Anatolia/Thrace, Mediterranean, and South-East (Appendix Figure 1). Within each stratum, the households to be interviewed were drawn as a simple random sample from the list of all applications. The data collection for the PAB took place between February and May 2017 (Appendix Figure 2).

Three follow-up panel surveys, called post-distribution monitoring (PDM) surveys, were collected to reinterview all PAB households at 6-month intervals (Appendix Figure 2). The schedule of the first three followup surveys used in this paper (PDM2, 4, and 6) and the numbers of beneficiary and non-beneficiary households interviewed successfully are presented in Table 1 below.<sup>17</sup> The surveys were designed by the WFP with inputs from the World Bank and the data were collected remotely by the TRC monitoring and evaluation operators based in Gaziantep. This phone-based data collection modality allowed for a large sample size, while remaining cost efficient. However, the surveys had to remain shorter than face-to-face household surveys due to the higher likelihood of survey fatigue. <mark>Informed consent was obtained from each household in the study sample, according to guidance from the Turkish authorities.</mark>

**Table 1: Data collection rounds and sample sizes**

|**Date of data collection**|**Feb-May 2017**|**Nov 2017-Jan 2018**|**April-Jul 2018**|**Nov-Dec 2018**|
|---|---|---|---|---|
|Round Name|PAB|PDM2|PDM4|PDM6|
|Non-beneficiary households|4,193|2,801|2,530|1,924|
|Beneficiaryhouseholds|4,497|3,739|3,652|3,046|
|Total observations|8,690|6,540|6,182|4,970|

The reader will note the large and increasing attrition from the study sample, an issue for phone-based surveys, which is discussed in great detail in Section 4. Of the 8,690 households interviewed in the PAB, 24.7% could not be reached in the first follow-up surveys, with this figure reaching 28.9% and 42.8% at the 12- and 18month follow-ups, respectively (Table 1). Some of the main reasons for attrition include the households simply not picking up their phone (after a maximum of three attempts), phone numbers going out of service, or phone numbers belonging to someone else. At the 18-month follow-up, for the 8,690 households included in the original baseline sample, the most common reasons for being lost to follow-up included: households not

> 17 A fourth and final follow-up panel survey was started in December 2019, continuing until March 2020, but data collection was paused due to the outbreak of the COVID-19 pandemic. It has not resumed as of August 20, 2020.

9
10 per ton CO2 (Antinori and Sathaye, 2007; Antle et al 2003). As Antle et al. (2003) point out, however, larger projects can take advantage of economies of scale, and unique sampling designs, and drive these costs down. Waggoner (2009) reviewed the U.S. Forest Service Forest Inventory and Analysis (FIA) program and found that annual costs for that program are $72 million per year. This amounts to about US$0.24 per hectare, over 300 million hectares of forestland. The annual change in carbon stocks in the U.S. is around 635 million tons of CO2 sequestered per year, suggesting that measuring costs are in the range of $0.11 per ton CO2. The FIA program involves on-the-ground measurements and provides the key data used to estimate and officially report annual U.S. carbon sequestration in U.S. forests.

Currently, using satellite or optical data may or may not be more expensive than ground assessment. Remote sensing approaches are still in the research phase, and require substantial on-the-ground verification. Macauley and Sedjo (2011) review cost estimates for measuring and monitoring forests find that high resolution optical monitoring is an estimated US$25 per square kilometer, and more advanced LIDAR monitoring is significantly costlier at approximately US$465 per square kilometer. This implies a range of measuring and monitoring costs from US$0.25 to US$4.65 per hectare. The average hectare of forests in the U.S. has about 183 tons CO2, so the costs of measurement are less than $0.03 per ton CO2 using these types of data.

Both on-the-ground sampling and the use of satellite or optical techniques have some uncertainties (Waggoner 2009), and there are costs associated with reducing these uncertainties. Detecting change in forest carbon from year to year, or over a 5 year period, will require repeating measurements over time. One can control the uncertainty in measurement by increasing the sample size, but this is costly. Thus, there will be tradeoffs between the level of

> 10 These estimates involve on-the-ground sampling.

23