rafmacalaba/data-use-mentions-extended huggingface dataset:
UL-2/2-1-88

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It is seen clearly in Table 7a that this policy was accompanied by a systematic fiscal support, with the originally small public sector deficit turning gradually into a surplus in 1979-81. Thus, the eventual failure to maintain exchange rate stabilization cannot be explained by the standard fiscal argument along the lines of the Krugman (1979) model. We shall therefore look for alternative explanations.

While quarterly data show that the policy **was** eventually successful in bringing down inflation to international levels (see figure 5) it is noteworthy that this took five years, with the decisive period (of pegging the exchange rate) lasting two years. Thus, under the ezchange rate stabilization strategy in Chile inflation exhibited the sam kind of stubbornness as in the fiscal-monetary phase.

The asynchronization between inflation and devaluation over the adjustment period led to a real appreciation of over 30Z with respect to 1975 and 20X with respect to 1976. Comparing 1981 with 1983, after the policy was abandoned, we obtain again an estimate of overvaluation of around 202.1/ During the adjustment period the external position deteriorated significantly, causing a reversal of the policy accompanied by a severe crisis which resulted in an astounding drop of 15.7Z in real CGP in 1982 [see Edwards (1987) and Ramos (1986) for a complete account].

One of the explanations offered for the sluggish reaction of domestic prices to exchange rate stabilization in Chile was the existence of backward looking wage indexation [Dornbusch (1985) and Edwards (1987)1. It is 1/ This casts some doubt on the argument that the appreciation of the exchange rate represented a long term adjustment.
is extensive evidence of tax collectors seeking to extract informal payments from taxpayers. There is some evidence that this extraction sometimes targets more vulnerable or less educated groups,<sup>104</sup> or areas in which tax collectors have greater leverage – owing, for example, to the ability to disrupt business operations<sup>105</sup> or access to key services.<sup>106</sup> But extraction is not limited to these groups. In trying to combat this source of mistrust in the tax system, one strategy has been to try to minimize face-toface interaction between tax officials and taxpayers, or to minimize the scope for negotiation of tax payments. Automation has been one strategy for doing so. Another has been simplification, to remove discretion in the administration of tax laws. An alternative option is to seek to offer

> 98 Afrobarometer Data, Round 6, 2014/15, available at http://www.afrobarometer.org.

> 99 Jibao, S., Prichard, W., & van den Boogaard, V. (2017). Informal taxation in post-conflict Sierra Leone: Taxpayers' experiences and perceptions. _ICTD Working Paper 66_ . Brighton: International Centre for Tax and Development.; Paler, L., Prichard, W., Sanchez de la Sierra. R., & Samii, C. (2017). Survey on total tax burden in the DRC, Final Report. Kinshasa: Department for International Development.

> 100 Prichard, W., & van den Boogaard, V. (2017). Norms, power and the socially embedded realities of market taxation in Northern Ghana. _African Studies Review_ , 60 (1):171-194.

> 101 Joshi, A., Prichard, W., & Heady, C. (2014). Taxing the informal economy: The current state of knowledge and agendas for future research. _Journal of Development Studies_ , 50 (10):1325-1347.

> 102 OECD. (2015)
|
|Constant|0.38***|0.12***|0.05***|0.10***|0.41***|
||[9.98]|[6.34]|[3.52]|[5.48]|[11.15]|
|Observations|40,977|40,961|40,870|40,977|40,977|
|R-squared|0.04|0.02|0.01|0.02|0.04|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ province and year of survey fixed effects. The weights used are person weights adjusted for the national populations. All regressions use data on rural households from 5 Southern African countries (Mozambique, Malawi, Tanzania, Zambia and Zimbabwe). All metrics are based on children being measured below -2 standard deviation thresholds. 41.0 percent of the children are stunted, 15.1 percent are underweight, 5.1 percent are wasted, 12.6 percent are stunted and underweight, and 45.2 percent are malnourished based on any of the three metrics.

26
- [5] Bhattacharyya, S. C., and Timilsina, G. R. (2010). Modelling Energy Demand of Developing Countries: Are the Specific Features Adequately Captured? _Energy Policy_ , 38(4), 1979-1990.

- [6] Bianco, V., Manca, O., and Nardini, S. (2009). Electricity Consumption Forecasting in Italy using Linear Regression Models. _Energy_ , 34(9), 14131421.

- [7] Calderón, C., and Servén, L. (2004). The Effects of Infrastructure Development on Growth and Income Distribution. _World Bank Policy Research Paper 3400._

- [8] Clements, M. P., and Hendry, D. F. (2001). _Forecasting Non-Stationary Economic Time Series._ MIT Press.

- [9] Diebold, F.X., and Mariano, R.S. (1995). Comparing Predictive Accuracy. _Journal of Business and Economic Statistics_ 13, 253-63.

- [10] Dilaver, Z., & Hunt, L. C. (2011). Modelling and Forecasting Turkish Residential Electricity Demand. _Energy Policy_ , 39(6), 3117-3127.

- [11] Elliott, G. R., Rothenberg, T. J. and Stock, J. H. (1996). Efficient Tests for an Autoregressive Unit Root. _Econometrica_ 64, 813–836.

- [12] El-Shazly, A. (2013). Electricity Demand Analysis and Forecasting: A Panel Cointegration Approach. _Energy Economics_ , 40, 251-258.

- [13] Enders, W. (2008). _Applied Econometric Time Series_ . John Wiley & Sons.

- [14] Feenstra, R.C., Inklaar, R. and Timmer, M. (2013). The Next Generation of the Penn World Table. _NBER Working Paper 19255_

- [15] Fisher-Vanden, K., Mansur, E. and Wang, Q. (2015)
# **4. Results**

The analysis of the IHPS data suggests that (long-term) rights to bequeath and sell -but not for decisionmaking- affect land-related investment. Women’s right to bequeath in matrilineal systems or to sell in nonmatrilineal ones affects organic manure use while men’s bequest rights affect take-up of cash-crop production. Using the full IHS4 data or the subset of self-reported (co-)owners yields completely different results that are often counterintuitive, suggesting that beyond measurement error due to respondents’ lack of knowledge, reliance on proxy respondents may also encourage strategic behavior.

# **4.1 Evidence from self-reported IHPS data**

The results from household fixed effects regressions for application of organic manure and planting of cash crops are presented in Table 3. The regressions presented in columns 1 and 3 do not control for parcel-level characteristics, while the opposite is true regarding the regressions presented in columns 2 and 4. The rights to make decisions or invest are consistently estimated to have insignificant effects regardless of the choice of outcome variable. By contrast, transfer rights are highly significant, similar to what was found in Ethiopia (Deininger and Jin 2006): the right to bequeath or sell land is estimated to significantly increase the likelihood of long-term investment via organic manure application. With 0.12 and 0.08 percentage points, the estimated effects are large; increasing bequest or sale rights by half a standard deviation (0.2) each would be expected to lead to an increase in organic manure application by 10 percent or 6 percent from the mean value of 0.23. The estimated effects are weaker for cash crop production where bequest rights are marginally significant at the 10% level if parcel characteristics are controlled for. An estimate of 0.06 points suggests that increasing bequest rights by half a standard deviation would increase cash crop production by 17 percent.

Exploring differences by right holders’ gender points towards marked differences between manure use and cash crop production (Table 4). Female bequest rights
# **I.  Introduction**

The economic performance of a firm is influenced by two types of factors. The first type comprises internal factors such as the technology embodied in the firm’s capital stock, its management practices and its marketing strategies. The second factor type may be referred to collectively as the investment climate: the policy and institutional environment in which the firm functions. Even the best-managed firms have difficulty flourishing in a poor investment climate.

In this paper, we conduct a two-step analysis of manufacturing firms surveyed in China and Brazil.<sup>1</sup> In the first step, we estimate a measure of total factor productivity (TFP) for each firm in the survey. In the second step, we test for a statistical relationship between the productivity measure and indicators of both types of factors: those internal to the firm and those related to the investment climate.

An economy may grow in the short run for many reasons – widespread exploitation of cheap labor, massive public expenditure, protectionist policies – but it will need its industries to increase productivity in order to sustain its growth. The choice of China and Brazil for our analysis is based in part on the relatively rich databases that the Investment Climate Surveys have produced for those two countries. But more importantly, they, along with Russia and India, make up a bloc of large developing countries (known recently as the BRICs) that by some estimates could come to dominate the world economy by the middle of the 21<sup>st</sup> century (Wilson and Purushothaman, 2003). As these economies grow, we expect to see significant variation in economic performance across firms and significant variation in investment climate across regions and sectors.

The remainder of the paper is organized as follows. Sections II and III explain how TFP is measured and suggest links between TFP and the investment climate. Next, Section IV reviews basic investment climate indicators for both countries. Sections V and VI report on the two stages of the
# **Acronyms and Abbreviations**

|ACER|Australian Council for Education Research|
|---|---|
|_bansos_|social assistance|
|Baperjakat|Advisory Board for Position and Rank|
|Bappenas|Agency for National Development Planning / National<br>Ministry of Development Planning|
|BERMUTU|Better Education through Reformed Management and<br>Universal Teacher Upgrading|
|BKD|_Badan Kepegawaian Daerah_, Regional Civil Service<br>Agency|
|BKDD|_Badan Kepegawaian Daerah dan Diklat_Regional Civil<br>Service and Training Agency|
|BOS|_Bantuan Operasional Sekolah,_School Operational<br>Assistance|
|BPK|State Financial Audit Agency|
|BPKP|State Finance and Development Supervision Agency|
|_bupati_|district head|
|CBT|computer-based competency test|
|CPNS|civil service candidate|
|DAK|Dana Alokasi Khusus, Special Allocation Funds|
|_dana bagi hasil_|Revenue Sharing Funds|
|_dana perimbangan_|Funds provided by the central government transfers to<br>regional governments to enable the latter to carry out their<br>functions under decentralization.|
|Dapodik|MoEC education database|
|DAU|General Allocation Funds|
|_dewan pendidikan_|District-level Education Boards|
|DFAT|Department of Foreign Affairs and Trade (Australia)|

3
|

Notes: The program costs reported in Lines (1) to (6) were provided by CAWTAR, the NGO in charge of implementation in 2018. IMF estimates of inflation are used to express values in 2021 Tunisian Dinars. In Line (7), we inflate the total costs to year 2 using a social discount rate of 5% per year. In Line (8), the Year 2 ITT effect on yearly consumption is obtained by estimating equation (1) with a measure of household yearly consumption. The ITT effect on yearly consumption in year 1 (Line 9) is assumed to be equal to the ITT effect on yearly consumption in year 2 (line 8). In Line (10), we assume a social discount rate of 5% to estimate the value of perpetuity of the ITT effect on yearly consumption (= _−_ 883 _/ln_ (0 _._ 95)). In Line (12), the ITT effect on the value of animals is taken from Table 1. In Line (13), the ITT effect on the value of household assets is estimated using equation (1) with a measure of the value of household assets. The unit-value of each asset is estimated using the median replacement value reported in the baseline survey, subject to data availability. In Line (14), the ITT effect on the total value of assets is estimated using equation (1) with a measure of the total value of assets, which is equal to the sum of the value of animals and household assets. In Line (15), the ITT effect on the total value of post-treatment consumption and assets is estimated using equation (1) with a measure of total benefits equal to the sum of the total value of post-treatment consumption and the value of assets.

51
Policy Research Working Paper 8927

# **Abstract**

This paper analyzes the long-term health impacts of Nepal’s 1996–2006 civil conflict. It exploits the heterogeneity in conflict intensity across villages and birth cohorts to document long-term health and intergenerational impacts. The analysis finds that childhood exposure to conflict and, in particular, exposure starting in infancy, negatively impacts attained adult height. Each additional month of exposure decreases a women’s adult height by 1.36 millimeters. The impacts are not limited to first-generation. The analysis also finds that a mother’s exposure to conflict in her childhood is detrimental to her child’s health. Mothers exposed to conflict during their childhood have more children and live in less wealthy households, likely reducing their ability to invest during their children’s critical period of physical development. The finding points to a potential trade-off between the quantity and quality of children. The paper uses information on monthly conflict incidents at the village level, which allows identifying identify the effects of exposure to conflict more accurately than prior studies.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at lphadera@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive
**Figure 11: Workers' remittances and compensation of employees, received, Togo, 1974‐2007 (LHS), and Workers' remittances and compensation of employees received, Togo and Benchmarks, 2007 (RHS) (% of GDP).**

<!-- Start of picture text --> 12<br>High income<br>Middle income<br>10 Low income<br>Cape Verde<br>Togo<br>8 Sierra Leone<br>Liberia<br>6 Senegal<br>Guinea‐Bissau<br>Gambia, The<br>4 Nigeria<br>Benin<br>2 Mali<br>Niger<br>Cote d'Ivoire<br>0 Ghana<br>Burkina Faso<br>0 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> _Source:_ DDP Data, World Bank.

# **4.3 Demand Distribution of Growth**

**An initial post‐independence decade of prudent macroeconomic policies in the 1960s was followed by a period of expansionary fiscal policies, debt growth and a questionable public investment program in the 1970s** . Government expenditures and (mainly public) capital formation increased in the period at rates close to 10% per year (see Table 1), but did not result in substantial growth as most of purchased goods were of foreign origin. The sudden oil price shock in the early 1980s put an end to an era of easy access to international capital markets at a time when Togo’s external debt had already become unsustainable. The 1980s were a time period of contracted government expenditures and public and private investments, with private consumption providing most of the contribution to growth in aggregate demand. The structural adjustment program included measures to reduce the budget deficit mainly through a control of government expenditure and a gradual disengagement of the government from some sectors of activity. However, it contributed to a
# **CONCLUSION**

This paper presents the view that the informal economy arises when excessive taxes and regulations are imposed by governments that lack the capability to enforce compliance.

The determinants and effects of the informal sector are studied in an endogenous growth model whose production technology depends essentially on congestable public services. The model concludes that in economies where the statutory tax burden is larger than optimal and where the enforcement system is too weak, the relative size of the informal sector is negatively correlated with the rate of economic growth; in other words, changes, both in policy paraneters and the quality of government institutions, that promote an increase in the relative size of the informal economy will also generate a reduction in the rate of economic growth.

Many Latin American countries have (or had until recently) a tradition of excessive regulations and weak government institutions. The paper uses data from Latin American countries in the early 1990's to test some of the implications of the model and to provide estimates for the size of the informal sector throughout these countries. The empirical approach consists of identifying the size of the informal sector to a latent variable for which multiple causes and multiple indicators exist. The size of the informal sector is found to depend positively on proxies for tax burden and labor-market restrictions, and negatively so on a proxy for the quality of government institutions. Furthermore, the empirical results indicate that an increase in the size of the informal sector negatively affects growth by, first, reducing the availability of public services for everyone in the economy, and, second, increasing the number of activities that use some of the existing

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/u>_

Table 5 shows the estimated effects (LATE for 2SLS and ATE for biprobit) from two-stage estimations of the full models from Table 3, Columns 2 and 4; the single variate probit estimates are re-presented here as well, for comparison. Each two-stage model uses only one additional, excluded instrument in addition to all co-variates (which are in both stages). Appendix A.VI includes several diagnostic statistics for both two-stage methods. From the 2SLS results, Olea and Pflueger’s (2013) weak instrument test —which can handle the use of non i.i.d. standard errors, such as the clustered standard errors required by the use of complex survey data—strongly rejects the null of a weak instrument with one endogenous regressor.<sup>39</sup> However, only the LM Kleibergen-Paap rank statistic for NOWAGE suggests at any level of confidence (and even then weakly with a p-value=0.099) that

> 38 Survey-weighted; age of business 25th to 75th percentile is 1 to 6 years. Years in “industry” 25th to 75th range is 2 to 10 years. The two variables have a correlation of 76.7% (weighted), significant at 99% confidence.

> 39 Implemented using the user-written command weakivtest in Stata, using a tau, threshold value of 5% with 95% confidence.

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# **1 Introduction**

In developing countries, staple foods frequently account for a significant share of poor households’ budgets. Many poor people have limited possibilities to insure against adverse price shocks. Price spikes are very problematic for poor households that are not self-sufficient, and often jeopardize their capacity to feed themselves. An important response of developing country governments to expressions of this concern is to implement food price stabilization policies. Yet, the study of these policies has been confined mainly to closed economy contexts, with an emphasis on the role of storage (see Wright, 2001, for a survey). From a theoretical standpoint, little is known about the role of trade policy in price stabilization programs, despite their widespread use. Recourse to trade policy to counter price volatility has been common in most Asian countries where stabilizing the domestic price of rice is a central objective (Timmer, 1989, Islam and Thomas, 1996, Dorosh, 2008), and also in Middle East and African countries in the case of wheat and to a lesser extent maize as well as rice (Dorosh, 2009, Wright and Cafiero, 2011). The question is probably less acute in Latin America, where most countries are net exporters of grains, but it is not irrelevant, as witnessed by the use by Chile of a price-band system for wheat and a few other food products (Bagwell and Sykes, 2004). More generally, based on a large-scale database on agricultural price distortions, Anderson and Nelgen (2012) show that countries tend to vary their nominal rate of assistance to agriculture so as to limit the effects of variations in world prices on domestic prices.

Trade and trade policies are key aspects that need to be taken into account in considerations of food security and price stabilization in developing countries. They raise numerous questions, the most important perhaps being: How should storage and trade policies be combined to achieve price stabilization? Increased reliance on national buffer stocks is frequently suggested as a remedy for developing countries faced with significant volatility
version of the VIIRS data (Elvidge et al. (2021)). Both data are constructed at an annual scale and are adjusted for clouds, flares, and other sources of noise. However, the two measures are not fully consistent with each other (Elvidge et al. (2013)). Therefore, we perform and report separate analysis for each sensor.<sup>16</sup>

# **3.1 Nighttime Light Luminosity and Sectoral Economic Activity**

Data on nighttime light luminosity is publicly available and reported to be a useful measure of urban economic activity. When one flies over a country by plane or views the many images available from satellites (e.g., Figure 4), it seems likely that these data capture the location of cities. Indeed, a large literature has confirmed this to be true across countries and time (e.g., Donaldson and Storeygard (2016)). What we are less confident about is whether changes in nighttime light luminosity are related to sub-national changes in economic activity.<sup>17</sup> More specifically, in our context, how do changes in nighttime lights correlate with changes in sectoral economic activity in Türkiye? This subsection shows that changes in nighttime lights are, in fact, correlated with changes in sectoral GVA. However, part of the association may come from capturing business cycle effects and other changes that are common to all provinces in a given year. When we remove the effect of these annual changes (by using year fixed effects), we find that (idiosyncratic) changes in construction and manufacturing are still associated with changes in nighttime lights, but the service and agriculture sectors are not.

Nighttime light emissions are more useful indicators of economic growth in urban areas. Therefore, we use the Global Human Settlement Layer (GHSL) constructed by Melchiorri et al. (2019) data to identify raster grid cells that are likely to be part of urban areas.<sup>18</