rafmacalaba/datause-encoder-data huggingface dataset:
# **V. MAIZE**

54. Maize is by far the most important food crop in Malawi and is grown on over 70% of land cultivated by small-scale farmers. Due to the dominance of the food self-sufficiency policy, coupled with weak and very thin maize markets, almost all smallholder farmers grow maize as a matter of precautionary principle. Moreover, maize is the dominant cereal in Malawi’s food basket, both in terms of the area under food crops (where it takes up over 90 percent) and in cereal- based calories where it comprises over 90 percent (see Table 8). However, it is important to note that in terms of total calories produced roots and tubers (cassava and potatoes) have also become increasingly important, more especially after economic liberalization in the 1990s (see Figure 12). This notwithstanding, maize is still an important staple crop and the primary supplier of calories in many parts of Malawi and any major fluctuations in its production have always had significant implications on the country’s food security.

**Table 8: Importance of maize in Malawi’s food basket**

|||**Area (000 h**|**a)**|**Yi**|**eld (kg/hect**|**are)**|**Kcal/capit**|**a/day (Pro**|**duction)**|
|---|---|---|---|---|---|---|---|---|---|
||**2006**|**2007**|**2008**|**2006**|**2007**|**2008**|**2006**|**2007**|**2008**|
|Wheat|2|2|3|1 211|2 297|1 981|2|4|4|
|Maize|1 620|1 688|1 647|1 590|2 040|1 790|1433|1883|1427|
|Millet|41|45|44|658|719|758|22|26|24|
|Sorghum|71|74|75|769|859|847|44|51|45|
|Rice (Paddy)|53|53|53|1 743|1 743|1 743|50|49|44|
|Rice (Mill.) - imports|53|53|53|1 129|1 129|1 129|50|49|43|
|**Total Cereals (coarse**<br>**Grains + milled rice)**|**1 733**|**1 807**|**1 766**|**1 534**|**1 959**|**1 724**|**1601**|**2062**|**1587**|
|Cassava|162|169|185|17,300|18,200|19,400|706|764|793|
|S. potatoes|130|142|159|13,400|14,500|15,900|370|428|475|
|I. potatoes|41|39|46|13,000|15,400|14,700|97|107|108|
|Total roots/tubers|333|350|390||||1173|1299|1376|
|**Total (Cereals + roots**<br>**and tubers)**|**2066**|**2157**|**2156**||||**2874**|**3361**|**2963**|
|**%**<br>**maize**<br>**in**<br>**total**<br>**cereals**|**93.5**|**93.4**|**93.3**||||**90**|**91**|**90**|
|**%**<br>**maize**<br>**in**<br>**total**<br>**Calories**|**78.4**|**78.3**|**76.4**||||**52**|**56**|**48**|

Note: Authors calculations based on FAO conversion factors (Kcal per kg): maize grain = 356; wheat=342; millet=342; sorghum=339; milled rice=350; cassava=140; s/potatoes=110; i/potatoes=90.

22
We test our theory empirically focusing on the case of Indonesian manufacturing, where our estimates suggest that the vast majority of plants exert some degree of labor market power. Specifically, we take advantage of quasi-exogenous variation in investment restrictions across 346 narrowly-defined manufacturing product markets in Indonesia. Such restrictions are implemented by the government through the publication of the Negative Investment List (NIL), a Presidential regulation which details the conditions that new investors have to fulfill to register a company in any Indonesian sector. After addressing concerns of potential endogeneity of changes to the NIL and product market trends, we exploit changes in restrictions across product markets in 2011 to estimate the causal impact of product market regulation on wage markdowns.

To estimate markdowns, we use granular information on prices and quantities of 9- digit products and intermediate inputs from a highly representative sample of Indonesian manufacturing plants.<sup>3</sup> This constitutes an advantage over previous studies, which often rely on industry-level price indices to deflate nominal quantities, incurring in several sources of bias (Bond et al., 2021; De Loecker et al., 2016; Foster et al., 2008). Importantly, we also disentangle wage markdowns from price markups, which are embedded in the “naive” comparison of marginal product of labor and the wage paid to employees.

Using this approach, we provide reduced-form estimates of the elasticity of wage markdowns to investment restrictions, which is around 0.25. This implies that NILrelated entry barriers in the product market have increased markdowns by around 4.3% in our sample. The results are robust to a battery of tests and are also consistent with an event study regression based on a change of the NIL as the “event”. The absence of pre-trends in the event study further relieves endogeneity concerns for the instrument. In addition, we probe the robustness of our results using an alternative identification strategy based on a Bartik instrument exploiting the differential exposure to regulated product markets across 274 commuting zones. Such design minimizes the potential endogeneity of changes to the NIL and labor market power in a given product market, thus providing further reassurance against possible endogeneity biasing our results.

> 3 Indonesian manufacturing data have been extensively used in previous work (e.g. Amiti and Konings, 2007; Javorcik, 2004; Arnold and Javorcik, 2009; Hallward-Driemeier and Rijkers, 2013).

3
-H-

# **Results**

The data on institutional ownership is based on the Spectrum 3 **reports compiled** by CDA Technologies. This data in turn is based on the 13F filings furnished by institutional investors with the SEC every quarter and includes all institutions with investment control over $100 million. The data used here refers to the last quarter of the year; i.e., as of December 31st. The data on all other variables are taken from Standard & Poor's COMPUSTAT database. Overall, the data refers to 557 U.S. manufacturing firms for the 1985-90 period. **Table 1 shows** the increasing institutionalization of the stock market during this time.

When the effect of institutional ownership on tangible and intangible investments is conducted in a univariate framework by dividing the sample on the basis of the median values of institutional ownership, capital expenditures and R and D expenditures are found to be higher for firms with high levels of institutional ownership than firms with low levels of institutional ownership (table 2). This is reversed in the case of advertising expenditures. On balance therefore, institutional ownership does not appear to be detrimental to the firm's long-term investments. Overall, institutional ownership seems to have a positive effect on the firm's investment behavior. It is also interesting to note that these results are consistent with the findings of Jones et al. (1990) in that institutional ownership does not seem to induce shorttermist behavior amongst corporate management.

Tables 3 and 4 show the details of institutional ownership in various industry groups. In general, the distribution of institutional ownership across industries appear random and is not significantly different across high tech and low tech industries, when the analysis is conducted 16
products at subsidized prices—as is in fact reported for a non-negligible share of the population (mostly middle and upper-income tranches) in Libya. However, in the absence of information on the share of households taking advantage of the quota system in their food purchases, it is more conservative to assume that households take the maximum advantage of the benefit available to them so as not to underestimate the impact of any reform on the population. This also compensate for the non-observable leakages due to “double dipping”.

Households allocate about 9.3 percent (1.2 billion LYD) of their total expenditure on subsidized food products if we consider the share bought under the quota system and the share bought at market prices (Table 6). About 22.2% of this amount is expenditure on quotas at subsidized prices, while the rest is on the same products bought on the free market. This may seem at odds with the fact that quotas provide generous quantities. However, richer households, as noted earlier, are unlikely to shop at cooperatives (which administer quotas), opting for better quality and more expensive products, while poorer households may also consume a share of better quality brands not available in the quota system. Indeed, for most of these food products, the market may offer several better quality options that may be preferred by the rich and poor alike. Also and more importantly, expenditure on quotas is low because prices are low under the quota system as compared to the market prices. For some products, like flour-bread and milk for children, the total expenditure is only on quotas, and there are no purchases of these products at non-subsidized prices. For other products like bread which is also sold outside cooperatives the quota system is not binding.

**Table 6: Household Expenditure on Subsidized Food Products (Million LYD)**

||**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|**Total**|**% at subsidized**<br>**prices**|
|---|---|---|---|---|---|---|---|
|Flour|11.2|11.6|11.8|11.7|10.5|56.9|15|
|Flour-bread|3.6|3.3|2.9|2.4|1.9|14.1|100|
|Semolina|7.6|7.9|7.2|6.3|5.1|34.1|3.7|
|Rice|18.6|20.6|20.2|20.2|19.3|98.8|15.7|
|Sugar|21.3|23.5|23.2|23.3|21.8|113.1|21.7|
|Tea|17.9|20.2|20.0|19.4|19.1|96.6|12.6|
|Macaroni|33.4|34.8|33.3|32.1|29.8|163.5|11.4|
|Vegetable Oil|58.0|60.9|61.4|59.1|55.4|294.8|18|
|Paste tomatoes|24.2|25.8|26.2|25.0|24.2|125.3|28.4|
|Milk for children|4.5|6.8|8.3|9.7|9.0|38.3|99.9|
|Milk<br>(concentrated)|26.5|29.1|27.9|24.3|23.0|130.8|28.3|
|Total|227.0|244.5|242.4|233.4|219.0|1,166.2|22.2|
|Total(% tot.exp.)|12.3|10.7|9.4|8.5|7.1|9.3|2.1|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

In terms of quantities, households consume approximately half of the food products via purchases made under the quota system at subsidized prices, while they buy the other half at market prices (Table 7). The first and second quintiles consume products at subsidized prices in higher quantities than the upper quintiles. This is natural given the larger size of households in lower quintiles and the larger reliance on 9
11 Figure A2.1 and Figure A2.2 in annex 2 reproduce Figure 2 for urban and rural areas, respectively. Unsurprisingly, far more urban residents use clean energy than their rural counterparts. Urban households on average are richer, have better access to infrastructure (electricity grid, paved roads for distribution of fuels), and enjoy lower prices for certain commercial fuels—for example, in countries with deregulated LPG pricing, bottled LPG would be cheaper in urban than rural areas because of greater economies of scale and close proximity to bottling plants and large depots. There are 11 countries in which more than half the urban population used clean cooking energy against 10 countries in which more than half used wood fuel or coal as the primary cooking fuel. By contrast, wood dominated cooking in rural areas irrespective of average per capita expenditures, with more than half the population using wood as their primary cooking fuel in 22 of 23 countries for which rural data were available. The only exception was South Africa, in which two-thirds of the rural population used clean cooking energy.

Comparison of cooking energy use between the rich and the poor in each country gives an indication of how rising income will likely shift the type of primary energy households use in that country. When the patterns of primary cooking energy use are compared between the top and bottom quintiles, predictably the top quintile cited LPG and electricity far more than the bottom quintile. In every country, a smaller proportion of the rich used wood as the primary cooking fuel than the poor. In several Southern African countries—Botswana, Eswatini, Namibia, Zambia, and, although not shown in the figure for a lack of data to compute quintiles, South Africa—rising income shifted households to cooking with electricity rather than LPG. In several low- and lower-middle-income countries, rising income shifted many households from wood to charcoal rather than to LPG or electricity. In Kenya and Nigeria, a greater proportion of the rich cooked with kerosene than the poor (Figure 3). The corresponding urban and rural results are shown in Figure A2.3 and Figure A2.4 in annex 2 with an associated commentary.

**Figure 3  Difference between the top and bottom quintile in the percentage of people citing different forms of primary energy for cooking**

<!-- Start of picture text --> Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Biogas+solar<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data _Note_ : For each energy category, the number shown is obtained by subtracting the share of the population in the bottom quintile from that in the top quintile. Others = mostly agricultural residues and dung.
Policy Research Working Paper 10894

# **Abstract**

Examining one of the world’s largest public business support programs, this paper studies how subsidized credit and partial credit guarantees shape access to finance for micro and small businesses in Indonesia. The analysis uses administrative data on more than 8.4 million borrowers and unique quantitative and qualitative data to show that subsidized credit can enable firms to access formal credit for the first time and boost financial inclusion. However, subsidized credit does not alleviate longer-term credit constraints by serving as a stepping stone to unsubsidized commercial credit in this context. The results highlight the challenge of reaching borrowers without collateral, even in programs that explicitly target them using instruments such as partial credit guarantees. The paper sheds light on how public credit schemes for small businesses can be designed to optimize inclusiveness and additionality.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Gender Innovation Lab, East Asia and Pacific Region. 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 authors may be contacted at at aalibhai@worldbank.org, hjohnson1@ worldbank.org, cniang@worldbank.org, and fstrobbe@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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 Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **2.1 The Multidimensional Poverty Index measures**

We follow Alkire and Santos (2010) and Alkire and Foster (2011) in the presentation of the multidimensional poverty index (MPI), with the distinction that we do not focus on the identification, censoring and aggregation steps. Additionally, we depart from the deprivation matrix notation and, instead, describe the index in terms of random variables in order to ease the transition to our discussion in the next section.

Let _i_ = 1 _,_ 2 _, . . . , n_ index individuals. Let **y** _i_ = ( _yi_ 1 _, yi_ 2 _, . . . , yiD_ ) be a vector of _achievements_ for individual _i_ in dimensions _d_ = 1 _,_ 2 _, . . . , D_ . Let _cd_ be the _deprivation cut-off_ of dimension _d_ . An individual _i_ is said to be _deprived_ in dimension _d_ if _yid < cd_ . Now let **w** = ( _w_ 1 _, w_ 2 _, . . . , wD_ ) be a vector of weights given to each dimension, such that _wd ≥_ 0 and<sup>�</sup><sup>_D_</sup> _d_ =1<sup>_wd_= 11</sup><sup>_._Individual</sup><sup>_i_is said to be</sup><sup>_multidimensionally poor_if</sup> where 1 is the indicator function; and _k_ is called the _cross-dimensional cutoff_ . Simply put, an individual is multi-dimensionally poor if a weighted sum of deprivation indicators falls below a pre-specified threshold. The amount<sup>�</sup> _d_<sup>_wd_1(</sup><sup>_yid < cd_)is</sup> called the _deprivation share._ From this individual measure of three wide measures are poverty, population built. The _multidimensional headcount ratio_ is the proportion of the population that is multi-dimensionally poor. It measures the incidence of multidimensional poverty over the population: Defining _p ≡_<sup>�</sup><sup>_n_</sup> _i_ =1<sup>_pi_, the</sup><sup>_average deprivation share among the poor_is</sup> 1Note that we define the weights as adding up to 1 and not to the number of dimensions.

5
the model shows that trade balance to GDP ratio has a negative correlation with output, and the magnitude of the correlation increases in the post-reform period, but it does not become strongly countercyclical after the reform. The correlation of consumption with output declines, whereas it increases in the data after the reform.

_Discussion of the Results_ The open economy framework, when calibrated to Indian data, supports the main prediction of rising relative consumption volatility with financial inclusion. Broadly, the model-simulated moments show similar patterns observed in the closed economic framework, except for a marginal rise in the output volatility in the post reform period.

One plausible reason for the open economy setup to show similar trends in the volatility and correlation of the key macroeconomic indicators, as in the closed economy scenario, is that financial deepening, in the present model, works through the household channel. Under strong permanent income shock, relative to transitory income fluctuations, Ricardian households behave in a similar manner in both closed and open economy setups. However, the extent of fluctuations is higher in an open economy. In response to permanent income shock, in an open economy, households can even raise current consumption more by using funds borrowed against future income. Hence fluctuation in consumption is even higher than the closed economy scenario. Financial inclusion, in this setup results in larger fluctuations in aggregate consumption. A sharp rise in consumption volatility with a relatively smaller decline in investment volatility causes a marginal rise in post-reform output fluctuations. Hence, the open and closed economy setups show qualitatively similar results.

In this open economy framework, consumers transact an internationally traded bond, which is the source of capital flows in the economy. A bulk of literature has explored macroeconomic effects of the interaction between financial 27
<!-- Start of picture text --> 50%<br>40%<br>30%<br>m 2008<br>20%(e) m 2012<br>10% m 2016<br>0%<br>-o aOi oO= Ss ra[@)'. cc<br>ca<br><!-- End of picture text --> A: Agriculture, forestry and fishing B-C-D-E: Mining and quarrying; manufacturing; electricity, gas, steam and air conditioning production and supply; water supply; sewerage, waste management and decontamination activities F: Construction G-H-I: Wholesale and retail; repair of motor vehicles and motorcycles; transport and storage; hotels and restaurants J: Information and communication K: Financial intermediation and insurance L: Real estate activities M-N: Professional, scientific and technical activities; activities of administrative services and support services O-P-Q: Public administration and defence; social insurance of public sector; education; health and social assistance R-S: Shows, culture and recreation activities; repair of households goods and other services
# <u>Table 2. Unit Root Tests</u>

The table shows the results of country-by-country and panel unit root tests performed for the main series used in the paper. The first and second halves of the table (Panels A and B) show the tests for the series in levels and differences, respectively. In each panel, sections I and II report the fraction of countries inthe sample in which a standard, country-by-country augmented Dickey Fuller and PhillipsPerron tests could not reject the null hypothesis of a unit root selecting the optimal number of lags on a country by country basis, or using the optimal number of 2 lags for the system as a whole. Section III shows the p-values of the Levin-Lin-Chu (2002) panel unit root test obtained for each variable. All the tests allow for a country-specific intercept and trend, and use the Newey-West bandwith selection with the Bartlett kernel for the estimation of the long run variance of the series.

|G|P it<br>T f Td|Real Exchange|
|---|---|---|
|Groups<br>|per capa<br>erms o rae<br>|Rate<br>|
||(1)<br>(2)|(3)|
|A. Tests for Series in levels<br> <br>I. Fraction of countries that cannot r|eject UR in ADF test||
|Automatic lag selection|85<br>73|69|
|All countries with 2 lags|88<br>90|88|
|II. Fraction of countries that cannot|reject UR in PP test||
|Automatic lag selection|96.00<br>77.00|66.00|
|III. P-values of Levin-Lin-Chu test|||
|Automatic lag selection|0.27<br>0|0|
|All countries with 2 lags|0.99<br>0.99|0.96|
|B. Tests for Series in Differences<br> <br>I. Fraction of countries that cannot r|eject UR in ADF test||
|Automatic lag selection|14<br>1|4|
|All countries with 2 lags|44<br>23|28|
|_II. Fraction of countries that cannot reje_|_ct UR in PP test_||
|Automatic lag selection|12.00<br>1.00|2.00|
|_III. P-values of Levin-Lin-Chu test_|||
|Automatic lag selection|0<br>0|0|
|All countries with 2 lags|0<br>0.01|0.21|

33